Informe_análisis_estadístico
Jhon Hernández, Guisell Llanos, Juan Perdomo, Laura Rodríguez
30/8/2021
INTRODUCCIÓN
Según un artículo publicado por el Observatorio Terrestre de la NASA en 2018, los desprendimientos de tierra son considerados uno de las catástrofes naturales más comunes y devastadores del planeta. Por lo anterior mencionado, es pertinente resaltar la relevancia de trabajos de investigación y de recopilación de información científica sobre los eventos naturales que afectan directamente a países y naciones, a su población, y a su economía. Con lo anterior mencionado, se tendrán en cuenta datos de deslizamientos extraídos del Catálogo Global de Deslizamientos de Tierra (GLC), los cuales serán visualizados a lo largo de este informe por medio de diferentes herramientas estadísticas () construidas con la ayuda del software RStudio. La primera parte del informe constará del análisis general de los datos asociados a diferentes países de Latinoamérica (Colombia, Ecuador, Perú, Trinidad y Tobago y Venezuela), tales como la población, el tipo de deslizamiento, y los diferentes factores causantes de deslizamientos. Por otro lado, la segunda parte del informe se enfocará más específicamente en el estudio de los datos pertenecientes a Colombia, tanto como para los datos generales anteriormente mencionados, como para la longitud del deslizamiento y el número de víctimas de estos eventos (decesos).
Gráficos estadísticos para Suramérica.
2.1 ECUADOR
Gráfico de barras para población en Ecuador
library(readr)
library(knitr)
library(ggplot2)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncolnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[8] <- "Population"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_ECU <- subset (df, Country == "Ecuador")
knitr::kable(head(df_ECU))| id | date | time | continent_code | Country | country_code | State | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 105 | 6/27/07 | SA | Ecuador | EC | Zamora-Chinchipe | 15276 | Zamora | 0.47714 | -4.0650 | -78.9510 | (-4.0650000000000004, -78.950999999999993) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 10 | 106 | 6/27/07 | SA | Ecuador | EC | Loja | 117796 | Loja | 0.35649 | -3.9900 | -79.2050 | (-3.99, -79.204999999999998) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 11 | 107 | 6/27/07 | SA | Ecuador | EC | Pichincha | 5114 | Sangolquí | 33.94603 | -0.3560 | -78.1480 | (-0.35599999999999998, -78.147999999999996) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 78 | 468 | 2/28/08 | SA | Ecuador | EC | Napo | 7309 | Archidona | 50.21741 | -0.4635 | -77.8928 | (-0.46350000000000002, -77.892799999999994) | Landslide | Landslide | Medium | Rain | NA | 7 | http://english.aljazeera.net/NR/exeres/868843D7-B211-4DE5-AFBE-31C29CF79C5A.htm | ||||
| 87 | 489 | 4/1/08 | SA | Ecuador | EC | Pichincha | 1399814 | Quito | 1.56942 | -0.2196 | -78.5347 | (-0.21959999999999999, -78.534700000000001) | Landslide | Landslide | Medium | Rain | NA | NA | http://www.plenglish.com/Article.asp?ID=%7B39BA85A0-5900-4A4A-A329-818B3FC61EA1%7D&language=EN | ||||
| 218 | 1186 | 12/28/09 | Night | SA | Ecuador | EC | Santo Domingo de los Tsáchilas | 200421 | Santo Domingo de los Colorados | 1.16036 | Unknown | -0.3152 | -78.9724 | (-0.31519999999999998, -78.972399999999993) | Landslide | Mudslide | Medium | Downpour | 5 | 3 | Latin American Herald Tribune | http://www.laht.com/article.asp?ArticleId=327938&CategoryId=14089 |
df_ECU %>%
select(Country, State, City, Distance, Population)## Country State City
## 9 Ecuador Zamora-Chinchipe Zamora
## 10 Ecuador Loja Loja
## 11 Ecuador Pichincha Sangolquí
## 78 Ecuador Napo Archidona
## 87 Ecuador Pichincha Quito
## 218 Ecuador Santo Domingo de los Tsáchilas Santo Domingo de los Colorados
## 238 Ecuador Pichincha Machachi
## 253 Ecuador Tungurahua Baños
## 339 Ecuador Esmeraldas Esmeraldas
## 586 Ecuador Pichincha Machachi
## 587 Ecuador Cotopaxi Saquisilí
## 660 Ecuador Morona-Santiago Macas
## 697 Ecuador Pichincha Quito
## 751 Ecuador Carchi El Ángel
## 868 Ecuador Manabi Chone
## 872 Ecuador Manabi Tosagua
## 942 Ecuador Guayas Balao
## 956 Ecuador Esmeraldas Esmeraldas
## 984 Ecuador Pichincha Cayambe
## 1304 Ecuador Carchi San Gabriel
## 1332 Ecuador Pichincha Quito
## 1333 Ecuador Pichincha Quito
## 1335 Ecuador Sucumbios Gonzalo Pizarro
## 1365 Ecuador Azuay Cuenca
## 1368 Ecuador Zamora-Chinchipe Zamora
## 1451 Ecuador Loja Macará
## 1452 Ecuador Loja Loja
## 1453 Ecuador Loja Catamayo
## 1454 Ecuador Azuay Cuenca
## 1566 Ecuador Pichincha Quito
## Distance Population
## 9 0.47714 15276
## 10 0.35649 117796
## 11 33.94603 5114
## 78 50.21741 7309
## 87 1.56942 1399814
## 218 1.16036 200421
## 238 26.18676 25742
## 253 11.91442 9501
## 339 2.81891 165216
## 586 25.82923 25742
## 587 30.81169 3778
## 660 46.77007 23687
## 697 4.39517 1399814
## 751 28.29459 3983
## 868 19.85816 44751
## 872 7.67919 14680
## 942 16.34404 12205
## 956 21.26652 165216
## 984 45.69792 26582
## 1304 10.47204 15112
## 1332 26.72137 1399814
## 1333 23.97854 1399814
## 1335 11.55916 0
## 1365 13.21139 276964
## 1368 1.23724 15276
## 1451 18.88784 13035
## 1452 1.82885 117796
## 1453 17.57187 18565
## 1454 10.16196 276964
## 1566 4.25486 1399814ggplot(data=df_ECU, aes(fill=State, x="Ecuador", y=Population)) +
geom_bar(position="dodge", stat="identity")
ggplot(data=df_ECU, aes(fill=State, x="Ecuador", y=Population)) +
geom_bar(position="stack", stat="identity")
Medidas de Tendencia central para muertes en Ecuador
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"
library(readr)
library(knitr)
df_COL <- subset (df, Country == "Ecuador")
knitr::kable(head(df_COL))| id | date | time | continent_code | Country | country_code | state_province | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | Fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 105 | 6/27/07 | SA | Ecuador | EC | Zamora-Chinchipe | 15276 | Zamora | 0.47714 | -4.0650 | -78.9510 | (-4.0650000000000004, -78.950999999999993) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 10 | 106 | 6/27/07 | SA | Ecuador | EC | Loja | 117796 | Loja | 0.35649 | -3.9900 | -79.2050 | (-3.99, -79.204999999999998) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 11 | 107 | 6/27/07 | SA | Ecuador | EC | Pichincha | 5114 | Sangolquí | 33.94603 | -0.3560 | -78.1480 | (-0.35599999999999998, -78.147999999999996) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 78 | 468 | 2/28/08 | SA | Ecuador | EC | Napo | 7309 | Archidona | 50.21741 | -0.4635 | -77.8928 | (-0.46350000000000002, -77.892799999999994) | Landslide | Landslide | Medium | Rain | NA | 7 | http://english.aljazeera.net/NR/exeres/868843D7-B211-4DE5-AFBE-31C29CF79C5A.htm | ||||
| 87 | 489 | 4/1/08 | SA | Ecuador | EC | Pichincha | 1399814 | Quito | 1.56942 | -0.2196 | -78.5347 | (-0.21959999999999999, -78.534700000000001) | Landslide | Landslide | Medium | Rain | NA | NA | http://www.plenglish.com/Article.asp?ID=%7B39BA85A0-5900-4A4A-A329-818B3FC61EA1%7D&language=EN | ||||
| 218 | 1186 | 12/28/09 | Night | SA | Ecuador | EC | Santo Domingo de los Tsáchilas | 200421 | Santo Domingo de los Colorados | 1.16036 | Unknown | -0.3152 | -78.9724 | (-0.31519999999999998, -78.972399999999993) | Landslide | Mudslide | Medium | Downpour | 5 | 3 | Latin American Herald Tribune | http://www.laht.com/article.asp?ArticleId=327938&CategoryId=14089 |
df_COL[is.na(df_COL)] <- 0
df_COL %>%
select(Country,state_province,Fatalities) ## Country state_province Fatalities
## 9 Ecuador Zamora-Chinchipe 0
## 10 Ecuador Loja 0
## 11 Ecuador Pichincha 0
## 78 Ecuador Napo 7
## 87 Ecuador Pichincha 0
## 218 Ecuador Santo Domingo de los Tsáchilas 3
## 238 Ecuador Pichincha 3
## 253 Ecuador Tungurahua 4
## 339 Ecuador Esmeraldas 3
## 586 Ecuador Pichincha 0
## 587 Ecuador Cotopaxi 2
## 660 Ecuador Morona-Santiago 7
## 697 Ecuador Pichincha 5
## 751 Ecuador Carchi 0
## 868 Ecuador Manabi 20
## 872 Ecuador Manabi 0
## 942 Ecuador Guayas 10
## 956 Ecuador Esmeraldas 14
## 984 Ecuador Pichincha 0
## 1304 Ecuador Carchi 0
## 1332 Ecuador Pichincha 2
## 1333 Ecuador Pichincha 0
## 1335 Ecuador Sucumbios 13
## 1365 Ecuador Azuay 0
## 1368 Ecuador Zamora-Chinchipe 3
## 1451 Ecuador Loja 0
## 1452 Ecuador Loja 0
## 1453 Ecuador Loja 0
## 1454 Ecuador Azuay 0
## 1566 Ecuador Pichincha 0library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ tibble 3.1.4 ✓ stringr 1.4.0
## ✓ tidyr 1.1.3 ✓ forcats 0.5.1
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()tablafinal<-(df_COL %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.250 5.500 6.857 10.000 20.000 knitr::kable(tablafinal)| state_province | fatalities |
|---|---|
| Azuay | 0 |
| Carchi | 0 |
| Cotopaxi | 2 |
| Esmeraldas | 17 |
| Guayas | 10 |
| Loja | 0 |
| Manabi | 20 |
| Morona-Santiago | 7 |
| Napo | 7 |
| Pichincha | 10 |
| Santo Domingo de los Tsáchilas | 3 |
| Sucumbios | 13 |
| Tungurahua | 4 |
| Zamora-Chinchipe | 3 |
Tabla de frecuencia simple de las fatalidades en Ecuador
dataEcu <- c("Zamora-Chinchipié" , "Zamora-Chinchipié" , "Zamora-Chinchipié", "Pichincha", "Pichincha", "Pichincha", "Pichincha", "Pichincha", "Pichincha", "Pichincha", "Pichincha", "Pichincha", "Pichincha", "Napo", "Napo", "Napo", "Napo", "Napo", "Napo", "Napo", "Santo Domingo de los Tsáchilas", "Santo Domingo de los Tsáchilas", "Santo Domingo de los Tsáchilas", "Tungurahua" , "Tungurahua" , "Tungurahua" , "Tungurahua" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Esmeraldas" , "Cotopaxi" , "Cotopaxi" , "Morona-Santiago" , "Morona-Santiago" , "Morona-Santiago" , "Morona-Santiago" , "Morona-Santiago" , "Morona-Santiago" , "Morona-Santiago" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Manabi" , "Guayas" ,"Guayas" ,"Guayas" ,"Guayas" ,"Guayas" ,"Guayas" ,"Guayas", "Guayas" ,"Guayas" ,"Guayas" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios" , "Sucumbios")
knitr::kable(head(dataEcu))| x |
|---|
| Zamora-Chinchipié |
| Zamora-Chinchipié |
| Zamora-Chinchipié |
| Pichincha |
| Pichincha |
| Pichincha |
library(questionr)
table <- questionr::freq(dataEcu, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Manabi | 20 | 20.8 | 20.8 | 20.8 | 20.8 |
| Esmeraldas | 17 | 17.7 | 17.7 | 38.5 | 38.5 |
| Sucumbios | 13 | 13.5 | 13.5 | 52.1 | 52.1 |
| Guayas | 10 | 10.4 | 10.4 | 62.5 | 62.5 |
| Pichincha | 10 | 10.4 | 10.4 | 72.9 | 72.9 |
| Morona-Santiago | 7 | 7.3 | 7.3 | 80.2 | 80.2 |
| Napo | 7 | 7.3 | 7.3 | 87.5 | 87.5 |
| Tungurahua | 4 | 4.2 | 4.2 | 91.7 | 91.7 |
| Santo Domingo de los Tsáchilas | 3 | 3.1 | 3.1 | 94.8 | 94.8 |
| Zamora-Chinchipié | 3 | 3.1 | 3.1 | 97.9 | 97.9 |
| Cotopaxi | 2 | 2.1 | 2.1 | 100.0 | 100.0 |
| Total | 96 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Manabi | 20 |
| Esmeraldas | 17 |
| Sucumbios | 13 |
| Guayas | 10 |
| Pichincha | 10 |
| Morona-Santiago | 7 |
| Napo | 7 |
| Tungurahua | 4 |
| Santo Domingo de los Tsáchilas | 3 |
| Zamora-Chinchipié | 3 |
| Cotopaxi | 2 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Diagrama de Pareto para tipo de deslizamientos en Ecuador
df <- data.frame(tipo=
c("mudslide","landslide","complex","rockfall","Lahar"),
Frecuencia = c( 9,17,0,4,0))
knitr::kable(df)| tipo | Frecuencia |
|---|---|
| mudslide | 9 |
| landslide | 17 |
| complex | 0 |
| rockfall | 4 |
| Lahar | 0 |
head(df)## tipo Frecuencia
## 1 mudslide 9
## 2 landslide 17
## 3 complex 0
## 4 rockfall 4
## 5 Lahar 0library(qcc)## Package 'qcc' version 2.7## Type 'citation("qcc")' for citing this R package in publications.Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$tipo
pareto.chart(Frecuencia,
ylab="Frecuencia",
col = heat.colors(length(Frecuencia)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "Gráfico de Pareto para tipos de deslizamientos en Ecuador")
##
## Pareto chart analysis for Frecuencia
## Frequency Cum.Freq. Percentage Cum.Percent.
## landslide 17.00000 17.00000 56.66667 56.66667
## mudslide 9.00000 26.00000 30.00000 86.66667
## rockfall 4.00000 30.00000 13.33333 100.00000
## complex 0.00000 30.00000 0.00000 100.00000
## Lahar 0.00000 30.00000 0.00000 100.00000Gráficos y tablas de frecuencia acerca de los causales de deslizamientos en Ecuador
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
library(dplyr)
library(readr)
library(knitr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_cr <- subset (df, Country == "Ecuador")
knitr::kable(head(df_cr))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 105 | 6/27/07 | SA | Ecuador | EC | Zamora-Chinchipe | 15276 | Zamora | 0.47714 | -4.0650 | -78.9510 | (-4.0650000000000004, -78.950999999999993) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 10 | 106 | 6/27/07 | SA | Ecuador | EC | Loja | 117796 | Loja | 0.35649 | -3.9900 | -79.2050 | (-3.99, -79.204999999999998) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 11 | 107 | 6/27/07 | SA | Ecuador | EC | Pichincha | 5114 | Sangolquí | 33.94603 | -0.3560 | -78.1480 | (-0.35599999999999998, -78.147999999999996) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 78 | 468 | 2/28/08 | SA | Ecuador | EC | Napo | 7309 | Archidona | 50.21741 | -0.4635 | -77.8928 | (-0.46350000000000002, -77.892799999999994) | Landslide | Landslide | Medium | Rain | NA | 7 | http://english.aljazeera.net/NR/exeres/868843D7-B211-4DE5-AFBE-31C29CF79C5A.htm | ||||
| 87 | 489 | 4/1/08 | SA | Ecuador | EC | Pichincha | 1399814 | Quito | 1.56942 | -0.2196 | -78.5347 | (-0.21959999999999999, -78.534700000000001) | Landslide | Landslide | Medium | Rain | NA | NA | http://www.plenglish.com/Article.asp?ID=%7B39BA85A0-5900-4A4A-A329-818B3FC61EA1%7D&language=EN | ||||
| 218 | 1186 | 12/28/09 | Night | SA | Ecuador | EC | Santo Domingo de los Tsáchilas | 200421 | Santo Domingo de los Colorados | 1.16036 | Unknown | -0.3152 | -78.9724 | (-0.31519999999999998, -78.972399999999993) | Landslide | Mudslide | Medium | Downpour | 5 | 3 | Latin American Herald Tribune | http://www.laht.com/article.asp?ArticleId=327938&CategoryId=14089 |
df_ECU %>%
select(Country, State, City, Distance) ## Country State City
## 9 Ecuador Zamora-Chinchipe Zamora
## 10 Ecuador Loja Loja
## 11 Ecuador Pichincha Sangolquí
## 78 Ecuador Napo Archidona
## 87 Ecuador Pichincha Quito
## 218 Ecuador Santo Domingo de los Tsáchilas Santo Domingo de los Colorados
## 238 Ecuador Pichincha Machachi
## 253 Ecuador Tungurahua Baños
## 339 Ecuador Esmeraldas Esmeraldas
## 586 Ecuador Pichincha Machachi
## 587 Ecuador Cotopaxi Saquisilí
## 660 Ecuador Morona-Santiago Macas
## 697 Ecuador Pichincha Quito
## 751 Ecuador Carchi El Ángel
## 868 Ecuador Manabi Chone
## 872 Ecuador Manabi Tosagua
## 942 Ecuador Guayas Balao
## 956 Ecuador Esmeraldas Esmeraldas
## 984 Ecuador Pichincha Cayambe
## 1304 Ecuador Carchi San Gabriel
## 1332 Ecuador Pichincha Quito
## 1333 Ecuador Pichincha Quito
## 1335 Ecuador Sucumbios Gonzalo Pizarro
## 1365 Ecuador Azuay Cuenca
## 1368 Ecuador Zamora-Chinchipe Zamora
## 1451 Ecuador Loja Macará
## 1452 Ecuador Loja Loja
## 1453 Ecuador Loja Catamayo
## 1454 Ecuador Azuay Cuenca
## 1566 Ecuador Pichincha Quito
## Distance
## 9 0.47714
## 10 0.35649
## 11 33.94603
## 78 50.21741
## 87 1.56942
## 218 1.16036
## 238 26.18676
## 253 11.91442
## 339 2.81891
## 586 25.82923
## 587 30.81169
## 660 46.77007
## 697 4.39517
## 751 28.29459
## 868 19.85816
## 872 7.67919
## 942 16.34404
## 956 21.26652
## 984 45.69792
## 1304 10.47204
## 1332 26.72137
## 1333 23.97854
## 1335 11.55916
## 1365 13.21139
## 1368 1.23724
## 1451 18.88784
## 1452 1.82885
## 1453 17.57187
## 1454 10.16196
## 1566 4.25486library(ggplot2)
library(dplyr)
data <- data.frame(Desatadores=
c("Rain",
"Downpour",
"Continuous rain",
"Earthquake", "Unknown"),
Porcentaje = c(6, 14, 4, 3, 3))
ggplot(data, aes(x = "", y = Porcentaje, fill=Desatadores)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
library(ggplot2)
library(dplyr)
data <- data %>%
arrange(desc(Desatadores)) %>%
mutate(prop = Porcentaje / sum(data$Porcentaje) *100) %>%
mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)## Loading required package: scales##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorggplot(data, aes(x="", y = prop, fill=Desatadores)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0) +
theme_void() +
theme(legend.position="none") +
geom_text(aes(y = ypos, label = percent(Porcentaje/30)), color = "white", size=3) +
scale_fill_brewer(palette="Set1")
knitr::kable(data)| Desatadores | Porcentaje | prop | ypos |
|---|---|---|---|
| Unknown | 3 | 10.00000 | 5.00000 |
| Rain | 6 | 20.00000 | 20.00000 |
| Earthquake | 3 | 10.00000 | 35.00000 |
| Downpour | 14 | 46.66667 | 63.33333 |
| Continuous rain | 4 | 13.33333 | 93.33333 |
data <- c("Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Continuous rain", "Continuous rain", "Continuous rain", "Continuous rain", "Earthquake", "Earthquake", "Earthquake", "Unknown", "Unknown", "Unknown")
knitr::kable(head(data))| x |
|---|
| Rain |
| Rain |
| Rain |
| Rain |
| Rain |
| Rain |
library(questionr)
table <- questionr::freq(data, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Downpour | 14 | 46.7 | 46.7 | 46.7 | 46.7 |
| Rain | 6 | 20.0 | 20.0 | 66.7 | 66.7 |
| Continuous rain | 4 | 13.3 | 13.3 | 80.0 | 80.0 |
| Earthquake | 3 | 10.0 | 10.0 | 90.0 | 90.0 |
| Unknown | 3 | 10.0 | 10.0 | 100.0 | 100.0 |
| Total | 30 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]
df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Downpour | 14 |
| Rain | 6 |
| Continuous rain | 4 |
| Earthquake | 3 |
| Unknown | 3 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="red") +
xlab("Eventos causantes de deslizamientos en Ecuador") +
ylab("Frecuencia")
2.2 PERÚ
Gráfico de barras para población en Perú
library(readr)
library(knitr)
library(ggplot2)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[8] <- "Population"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_PER <- subset (df, Country == "Peru")
knitr::kable(head(df_PER))| id | date | time | continent_code | Country | country_code | State | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 80 | 472 | 3/5/08 | SA | Peru | PE | Huanuco | 53177 | Tingo María | 17.34318 | -9.2114 | -76.1311 | (-9.2113999999999994, -76.131100000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | http://news.xinhuanet.com/english/2008-03/06/content_7727748.htm | ||||
| 90 | 497 | 4/11/08 | SA | Peru | PE | Ancash | 5044 | Carhuaz | 2.43089 | -9.2965 | -77.6613 | (-9.2965, -77.661299999999997) | Landslide | Landslide | Medium | Rain | NA | NA | http://news.xinhuanet.com/english/2008-04/14/content_7972049.htm | ||||
| 184 | 1004 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Parcoy | 9.64894 | -7.9589 | -77.5239 | (-7.9588999999999999, -77.523899999999998) | Landslide | Mudslide | Medium | Downpour | NA | 12 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 185 | 1005 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Aricapampa | 0.00442 | -7.8058 | -77.7172 | (-7.8057999999999996, -77.717200000000005) | Landslide | Mudslide | Medium | Downpour | NA | 0 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 252 | 1459 | 1/28/10 | 15:00:00 | SA | Peru | PE | Ancash | 0 | Anta | 0.13147 | -9.3584 | -77.5984 | (-9.3583999999999996, -77.598399999999998) | Landslide | Complex | Medium | Downpour | NA | 0 | http://daveslandslideblog.blogspot.com/2010/01/new-images-of-level-of-destruction-in.html | |||
| 294 | 1612 | 4/1/10 | Early morning | SA | Peru | PE | Huanuco | 0 | Acomayo | 3.49890 | -9.7996 | -76.1038 | (-9.7995999999999999, -76.103800000000007) | Landslide | Lahar | Large | Downpour | NA | 12 | http://www.thejakartapost.com/news/2010/03/18/mudslide-isolates-1500-residents-solok.html |
df_PER %>%
select(Country, State, City, Distance, Population)## Country State City Distance Population
## 80 Peru Huanuco Tingo María 17.34318 53177
## 90 Peru Ancash Carhuaz 2.43089 5044
## 184 Peru La Libertad Parcoy 9.64894 0
## 185 Peru La Libertad Aricapampa 0.00442 0
## 252 Peru Ancash Anta 0.13147 0
## 294 Peru Huanuco Acomayo 3.49890 0
## 295 Peru Huanuco Ambo 0.55865 6865
## 516 Peru Huanuco Huánuco 1.01932 147959
## 578 Peru Ancash Yanac 3.70678 0
## 864 Peru Ancash Tauca 6.77672 0
## 912 Peru San Martín Yuracyacu 8.89799 0
## 1378 Peru Ancash Huachis 3.26788 0
## 1424 Peru San Martín Naranjos 12.70296 0
## 1425 Peru San Martín Tarapoto 2.89809 0ggplot(data=df_PER, aes(fill=City, x="Peru", y=Population)) +
geom_bar(position="dodge", stat="identity")
ggplot(data=df_PER, aes(fill=City, x="Peru", y=Population)) +
geom_bar(position="stack", stat="identity")
Medidas de Tendencia central para muertes en Perú
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"
library(readr)
library(knitr)
df_COL <- subset (df, Country == "Peru")
knitr::kable(head(df_COL))| id | date | time | continent_code | Country | country_code | state_province | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | Fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 80 | 472 | 3/5/08 | SA | Peru | PE | Huanuco | 53177 | Tingo María | 17.34318 | -9.2114 | -76.1311 | (-9.2113999999999994, -76.131100000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | http://news.xinhuanet.com/english/2008-03/06/content_7727748.htm | ||||
| 90 | 497 | 4/11/08 | SA | Peru | PE | Ancash | 5044 | Carhuaz | 2.43089 | -9.2965 | -77.6613 | (-9.2965, -77.661299999999997) | Landslide | Landslide | Medium | Rain | NA | NA | http://news.xinhuanet.com/english/2008-04/14/content_7972049.htm | ||||
| 184 | 1004 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Parcoy | 9.64894 | -7.9589 | -77.5239 | (-7.9588999999999999, -77.523899999999998) | Landslide | Mudslide | Medium | Downpour | NA | 12 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 185 | 1005 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Aricapampa | 0.00442 | -7.8058 | -77.7172 | (-7.8057999999999996, -77.717200000000005) | Landslide | Mudslide | Medium | Downpour | NA | 0 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 252 | 1459 | 1/28/10 | 15:00:00 | SA | Peru | PE | Ancash | 0 | Anta | 0.13147 | -9.3584 | -77.5984 | (-9.3583999999999996, -77.598399999999998) | Landslide | Complex | Medium | Downpour | NA | 0 | http://daveslandslideblog.blogspot.com/2010/01/new-images-of-level-of-destruction-in.html | |||
| 294 | 1612 | 4/1/10 | Early morning | SA | Peru | PE | Huanuco | 0 | Acomayo | 3.49890 | -9.7996 | -76.1038 | (-9.7995999999999999, -76.103800000000007) | Landslide | Lahar | Large | Downpour | NA | 12 | http://www.thejakartapost.com/news/2010/03/18/mudslide-isolates-1500-residents-solok.html |
df_COL[is.na(df_COL)] <- 0
df_COL %>%
select(Country,state_province,Fatalities) ## Country state_province Fatalities
## 80 Peru Huanuco 7
## 90 Peru Ancash 0
## 184 Peru La Libertad 12
## 185 Peru La Libertad 0
## 252 Peru Ancash 0
## 294 Peru Huanuco 12
## 295 Peru Huanuco 28
## 516 Peru Huanuco 2
## 578 Peru Ancash 0
## 864 Peru Ancash 3
## 912 Peru San Martín 13
## 1378 Peru Ancash 0
## 1424 Peru San Martín 0
## 1425 Peru San Martín 0library(tidyverse)
tablafinal<-(df_COL %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.00 9.75 12.50 19.25 22.00 49.00 knitr::kable(tablafinal)| state_province | fatalities |
|---|---|
| Ancash | 3 |
| Huanuco | 49 |
| La Libertad | 12 |
| San Martín | 13 |
Tabla de frecuencia simple de las fatalidades en Peru
dataPer <- c( "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Huanuco", "Ancash", "Ancash", "Ancash", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "La Libertad", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin", "San Martin")
knitr::kable(head(dataPer))| x |
|---|
| Huanuco |
| Huanuco |
| Huanuco |
| Huanuco |
| Huanuco |
| Huanuco |
library(questionr)
table <- questionr::freq(dataPer, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Huanuco | 49 | 63.6 | 63.6 | 63.6 | 63.6 |
| San Martin | 13 | 16.9 | 16.9 | 80.5 | 80.5 |
| La Libertad | 12 | 15.6 | 15.6 | 96.1 | 96.1 |
| Ancash | 3 | 3.9 | 3.9 | 100.0 | 100.0 |
| Total | 77 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Huanuco | 49 |
| San Martin | 13 |
| La Libertad | 12 |
| Ancash | 3 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Diagrama de Pareto para tipo de deslizamientos en Perú
df <- data.frame(tipo=
c("mudslide","landslide","complex","rockfall","Lahar"),
Frecuencia = c(5,8,0,0,1))
knitr::kable(df)| tipo | Frecuencia |
|---|---|
| mudslide | 5 |
| landslide | 8 |
| complex | 0 |
| rockfall | 0 |
| Lahar | 1 |
head(df)## tipo Frecuencia
## 1 mudslide 5
## 2 landslide 8
## 3 complex 0
## 4 rockfall 0
## 5 Lahar 1library(qcc)
Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$tipo
pareto.chart(Frecuencia,
ylab="Frecuencia",
col = heat.colors(length(Frecuencia)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "Gráfico de Pareto para tipos de deslizamientos en Perú")
##
## Pareto chart analysis for Frecuencia
## Frequency Cum.Freq. Percentage Cum.Percent.
## landslide 8.000000 8.000000 57.142857 57.142857
## mudslide 5.000000 13.000000 35.714286 92.857143
## Lahar 1.000000 14.000000 7.142857 100.000000
## complex 0.000000 14.000000 0.000000 100.000000
## rockfall 0.000000 14.000000 0.000000 100.000000Gráficos y tablas de frecuencia acerca de los causales de deslizamientos en Perú
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
library(dplyr)
library(readr)
library(knitr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_PER <- subset (df, Country == "Perú")
knitr::kable(head(df_PER))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link |
|---|
df_PER %>%
select(Country, State, City, Distance) ## [1] Country State City Distance
## <0 rows> (or 0-length row.names)library(ggplot2)
library(dplyr)
data <- data.frame(Desatadores=
c("Rain",
"Downpour"),
Porcentaje = c(3, 12))
ggplot(data, aes(x = "", y = Porcentaje, fill=Desatadores)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
library(ggplot2)
library(dplyr)
data <- data %>%
arrange(desc(Desatadores)) %>%
mutate(prop = Porcentaje / sum(data$Porcentaje) *100) %>%
mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=Desatadores)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0) +
theme_void() +
theme(legend.position="none") +
geom_text(aes(y = ypos, label = percent(Porcentaje/15)), color = "white", size=3) +
scale_fill_brewer(palette="Set1")
knitr::kable(data)| Desatadores | Porcentaje | prop | ypos |
|---|---|---|---|
| Rain | 3 | 20 | 10 |
| Downpour | 12 | 80 | 60 |
data <- c("Rain", "Rain", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour")
knitr::kable(head(data))| x |
|---|
| Rain |
| Rain |
| Downpour |
| Downpour |
| Downpour |
| Downpour |
library(questionr)
table <- questionr::freq(data, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Downpour | 14 | 87.5 | 87.5 | 87.5 | 87.5 |
| Rain | 2 | 12.5 | 12.5 | 100.0 | 100.0 |
| Total | 16 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]
df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Downpour | 14 |
| Rain | 2 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="red") +
xlab("Eventos causantes de deslizamientos en Perú") +
ylab("Frecuencia")
2.3 VENEZUELA
Gráfico de barras para población en Venezuela
library(readr)
library(knitr)
library(ggplot2)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[8] <- "Population"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_VEN <- subset (df, Country == "Venezuela")
knitr::kable(head(df_VEN))| id | date | time | continent_code | Country | country_code | State | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | 896 | 11/20/08 | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.92493 | 10.4660 | -66.8940 | (10.465999999999999, -66.894000000000005) | Landslide | Mudslide | Medium | Downpour | NA | 8 | http://www.foxnews.com/story/0,2933,456304,00.html | ||||
| 448 | 2473 | 9/20/10 | SA | Venezuela | VE | Vargas | 52564 | Maiquetía | 6.65506 | 10.5363 | -66.9492 | (10.536300000000001, -66.949200000000005) | Landslide | Landslide | Medium | Downpour | NA | 7 | http://www.laht.com/article.asp?ArticleId=367610&CategoryId=14091 | ||||
| 530 | 2749 | 11/25/10 | SA | Venezuela | VE | Miranda | 278890 | Santa Teresa | 8.45736 | 10.2452 | -66.5867 | (10.245200000000001, -66.586699999999993) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | ||||
| 531 | 2753 | 11/26/10 | Before dawn | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.95706 | 10.4913 | -66.9060 | (10.491300000000001, -66.906000000000006) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | |||
| 538 | 2761 | 11/27/10 | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 7.90754 | 10.5030 | -66.9995 | (10.503, -66.999499999999998) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=380021&CategoryId=10717 | ||||
| 539 | 2762 | 11/27/10 | Night | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 5.64050 | 10.4267 | -67.0342 | (10.4267, -67.034199999999998) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://english.eluniversal.com/2010/11/30/en_pol_esp_landslides-hit-sever_30A4792571.shtml |
df_VEN %>%
select(Country, State, City, Distance, Population)## Country State City Distance Population
## 165 Venezuela Distrito Federal Caracas 2.92493 3000000
## 448 Venezuela Vargas Maiquetía 6.65506 52564
## 530 Venezuela Miranda Santa Teresa 8.45736 278890
## 531 Venezuela Distrito Federal Caracas 2.95706 3000000
## 538 Venezuela Distrito Federal Caricuao 7.90754 0
## 539 Venezuela Distrito Federal Caricuao 5.64050 0
## 540 Venezuela Distrito Federal Caricuao 5.74106 0
## 541 Venezuela Distrito Federal Caracas 4.58994 3000000
## 542 Venezuela Miranda Baruta 2.69644 244216
## 543 Venezuela Miranda El Hatillo 1.04263 57591
## 544 Venezuela Distrito Federal Caracas 3.14060 3000000
## 545 Venezuela Distrito Federal Caracas 6.04235 3000000
## 546 Venezuela Miranda Guatire 15.84114 191903
## 547 Venezuela Distrito Federal Caracas 3.87793 3000000
## 548 Venezuela Distrito Federal Caracas 3.65044 3000000
## 549 Venezuela Distrito Federal Caracas 8.11953 3000000
## 599 Venezuela Vargas Maiquetía 7.89319 52564
## 768 Venezuela Aragua El Consejo 14.28266 0
## 769 Venezuela Falcón Santo Domingo 19.81345 0
## 847 Venezuela Distrito Federal Caracas 2.55507 3000000ggplot(data=df_VEN, aes(fill=State, x="Venezuela", y=Population)) +
geom_bar(position="dodge", stat="identity")
ggplot(data=df_VEN, aes(fill=State, x="Venezuela", y=Population)) +
geom_bar(position="stack", stat="identity")
Medidas de Tendencia central para muertes en Venezuela
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"
library(readr)
library(knitr)
df_COL <- subset (df, Country == "Venezuela")
knitr::kable(head(df_COL))| id | date | time | continent_code | Country | country_code | state_province | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | Fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | 896 | 11/20/08 | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.92493 | 10.4660 | -66.8940 | (10.465999999999999, -66.894000000000005) | Landslide | Mudslide | Medium | Downpour | NA | 8 | http://www.foxnews.com/story/0,2933,456304,00.html | ||||
| 448 | 2473 | 9/20/10 | SA | Venezuela | VE | Vargas | 52564 | Maiquetía | 6.65506 | 10.5363 | -66.9492 | (10.536300000000001, -66.949200000000005) | Landslide | Landslide | Medium | Downpour | NA | 7 | http://www.laht.com/article.asp?ArticleId=367610&CategoryId=14091 | ||||
| 530 | 2749 | 11/25/10 | SA | Venezuela | VE | Miranda | 278890 | Santa Teresa | 8.45736 | 10.2452 | -66.5867 | (10.245200000000001, -66.586699999999993) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | ||||
| 531 | 2753 | 11/26/10 | Before dawn | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.95706 | 10.4913 | -66.9060 | (10.491300000000001, -66.906000000000006) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | |||
| 538 | 2761 | 11/27/10 | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 7.90754 | 10.5030 | -66.9995 | (10.503, -66.999499999999998) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=380021&CategoryId=10717 | ||||
| 539 | 2762 | 11/27/10 | Night | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 5.64050 | 10.4267 | -67.0342 | (10.4267, -67.034199999999998) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://english.eluniversal.com/2010/11/30/en_pol_esp_landslides-hit-sever_30A4792571.shtml |
df_COL[is.na(df_COL)] <- 0
df_COL %>%
select(Country,state_province,Fatalities) ## Country state_province Fatalities
## 165 Venezuela Distrito Federal 8
## 448 Venezuela Vargas 7
## 530 Venezuela Miranda 1
## 531 Venezuela Distrito Federal 3
## 538 Venezuela Distrito Federal 1
## 539 Venezuela Distrito Federal 0
## 540 Venezuela Distrito Federal 2
## 541 Venezuela Distrito Federal 0
## 542 Venezuela Miranda 0
## 543 Venezuela Miranda 0
## 544 Venezuela Distrito Federal 1
## 545 Venezuela Distrito Federal 2
## 546 Venezuela Miranda 0
## 547 Venezuela Distrito Federal 0
## 548 Venezuela Distrito Federal 0
## 549 Venezuela Distrito Federal 1
## 599 Venezuela Vargas 0
## 768 Venezuela Aragua 0
## 769 Venezuela Falcón 0
## 847 Venezuela Distrito Federal 2library(tidyverse)
tablafinal<-(df_COL %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 1.0 5.6 7.0 20.0 knitr::kable(tablafinal)| state_province | fatalities |
|---|---|
| Aragua | 0 |
| Distrito Federal | 20 |
| Falcón | 0 |
| Miranda | 1 |
| Vargas | 7 |
Tabla de frecuencia simple de las fatalidades en Venezuela
dataVen <- c("Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Distrito Federal" , "Vargas" , "Vargas" , "Vargas" , "Vargas" , "Vargas" , "Vargas" , "Vargas" , "Miranda")
knitr::kable(head(dataVen))| x |
|---|
| Distrito Federal |
| Distrito Federal |
| Distrito Federal |
| Distrito Federal |
| Distrito Federal |
| Distrito Federal |
library(questionr)
table <- questionr::freq(dataVen, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Distrito Federal | 20 | 71.4 | 71.4 | 71.4 | 71.4 |
| Vargas | 7 | 25.0 | 25.0 | 96.4 | 96.4 |
| Miranda | 1 | 3.6 | 3.6 | 100.0 | 100.0 |
| Total | 28 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Distrito Federal | 20 |
| Vargas | 7 |
| Miranda | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Diagrama de Pareto para tipo de deslizamientos en Venezuela
df <- data.frame(tipo=
c("mudslide","landslide","complex","rockfall","Lahar"),
Frecuencia = c(6,14,0,0,0))
knitr::kable(df)| tipo | Frecuencia |
|---|---|
| mudslide | 6 |
| landslide | 14 |
| complex | 0 |
| rockfall | 0 |
| Lahar | 0 |
head(df)## tipo Frecuencia
## 1 mudslide 6
## 2 landslide 14
## 3 complex 0
## 4 rockfall 0
## 5 Lahar 0library(qcc)
Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$tipo
pareto.chart(Frecuencia,
ylab="Frecuencia",
col = heat.colors(length(Frecuencia)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "Gráfico de Pareto para tipos de deslizamientos en Venezuela")
##
## Pareto chart analysis for Frecuencia
## Frequency Cum.Freq. Percentage Cum.Percent.
## landslide 14 14 70 70
## mudslide 6 20 30 100
## complex 0 20 0 100
## rockfall 0 20 0 100
## Lahar 0 20 0 100Gráficos y tablas de frecuencia acerca de los causales de deslizamientos en Venezuela
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
library(dplyr)
library(readr)
library(knitr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_VEN <- subset (df, Country == "Venezuela")
knitr::kable(head(df_VEN))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | 896 | 11/20/08 | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.92493 | 10.4660 | -66.8940 | (10.465999999999999, -66.894000000000005) | Landslide | Mudslide | Medium | Downpour | NA | 8 | http://www.foxnews.com/story/0,2933,456304,00.html | ||||
| 448 | 2473 | 9/20/10 | SA | Venezuela | VE | Vargas | 52564 | Maiquetía | 6.65506 | 10.5363 | -66.9492 | (10.536300000000001, -66.949200000000005) | Landslide | Landslide | Medium | Downpour | NA | 7 | http://www.laht.com/article.asp?ArticleId=367610&CategoryId=14091 | ||||
| 530 | 2749 | 11/25/10 | SA | Venezuela | VE | Miranda | 278890 | Santa Teresa | 8.45736 | 10.2452 | -66.5867 | (10.245200000000001, -66.586699999999993) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | ||||
| 531 | 2753 | 11/26/10 | Before dawn | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.95706 | 10.4913 | -66.9060 | (10.491300000000001, -66.906000000000006) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | |||
| 538 | 2761 | 11/27/10 | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 7.90754 | 10.5030 | -66.9995 | (10.503, -66.999499999999998) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=380021&CategoryId=10717 | ||||
| 539 | 2762 | 11/27/10 | Night | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 5.64050 | 10.4267 | -67.0342 | (10.4267, -67.034199999999998) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://english.eluniversal.com/2010/11/30/en_pol_esp_landslides-hit-sever_30A4792571.shtml |
df_VEN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 165 Venezuela Distrito Federal Caracas 2.92493
## 448 Venezuela Vargas Maiquetía 6.65506
## 530 Venezuela Miranda Santa Teresa 8.45736
## 531 Venezuela Distrito Federal Caracas 2.95706
## 538 Venezuela Distrito Federal Caricuao 7.90754
## 539 Venezuela Distrito Federal Caricuao 5.64050
## 540 Venezuela Distrito Federal Caricuao 5.74106
## 541 Venezuela Distrito Federal Caracas 4.58994
## 542 Venezuela Miranda Baruta 2.69644
## 543 Venezuela Miranda El Hatillo 1.04263
## 544 Venezuela Distrito Federal Caracas 3.14060
## 545 Venezuela Distrito Federal Caracas 6.04235
## 546 Venezuela Miranda Guatire 15.84114
## 547 Venezuela Distrito Federal Caracas 3.87793
## 548 Venezuela Distrito Federal Caracas 3.65044
## 549 Venezuela Distrito Federal Caracas 8.11953
## 599 Venezuela Vargas Maiquetía 7.89319
## 768 Venezuela Aragua El Consejo 14.28266
## 769 Venezuela Falcón Santo Domingo 19.81345
## 847 Venezuela Distrito Federal Caracas 2.55507library(ggplot2)
library(dplyr)
data <- data.frame(Desatadores=
c("Rain",
"Downpour"),
Porcentaje = c(2, 18))
ggplot(data, aes(x = "", y = Porcentaje, fill=Desatadores)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
library(ggplot2)
library(dplyr)
data <- data %>%
arrange(desc(Desatadores)) %>%
mutate(prop = Porcentaje / sum(data$Porcentaje) *100) %>%
mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=Desatadores)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0) +
theme_void() +
theme(legend.position="none") +
geom_text(aes(y = ypos, label = percent(Porcentaje/20)), color = "white", size=3) +
scale_fill_brewer(palette="Set1")
knitr::kable(data)| Desatadores | Porcentaje | prop | ypos |
|---|---|---|---|
| Rain | 2 | 10 | 5 |
| Downpour | 18 | 90 | 55 |
data <- c("Rain", "Rain", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour")
knitr::kable(head(data))| x |
|---|
| Rain |
| Rain |
| Downpour |
| Downpour |
| Downpour |
| Downpour |
library(questionr)
table <- questionr::freq(data, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Downpour | 18 | 90 | 90 | 90 | 90 |
| Rain | 2 | 10 | 10 | 100 | 100 |
| Total | 20 | 100 | 100 | 100 | 100 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]
df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Downpour | 18 |
| Rain | 2 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="red") +
xlab("Eventos causantes de deslizamientos en Venezuela") +
ylab("Frecuencia")
2.4 TRINIDAD Y TOBAGO
Gráfico de barras para población en Trinidad y Tobago
library(readr)
library(knitr)
library(ggplot2)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[8] <- "Population"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_TB <- subset (df, Country == "Trinidad and Tobago")
knitr::kable(head(df_TB))| id | date | time | continent_code | Country | country_code | State | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 224 | 9/1/07 | NA | Trinidad and Tobago | TT | Tobago | 17000 | Scarborough | 9.11607 | 11.2415 | -60.6742 | (11.2415, -60.674199999999999) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Felix | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161197580 | ||
| 61 | 357 | 11/17/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 7.33295 | 11.2965 | -60.6312 | (11.2965, -60.6312) | Landslide | Landslide | Medium | Rain | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161237574 | |||
| 65 | 390 | 12/11/07 | NA | Trinidad and Tobago | TT | Sangre Grande | 15968 | Sangre Grande | 29.28864 | 10.8410 | -61.0550 | (10.840999999999999, -61.055) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | 3 | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 66 | 391 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 8.62938 | 11.3000 | -60.6440 | (11.3, -60.643999999999998) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 67 | 392 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 2.66802 | 11.2670 | -60.5660 | (11.266999999999999, -60.566000000000003) | Landslide | Landslide | Small | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 149 | 780 | 9/7/08 | NA | Trinidad and Tobago | TT | Diego Martin | 8140 | Petit Valley | 10.61854 | 10.7603 | -61.4578 | (10.760300000000001, -61.457799999999999) | Landslide | Landslide | Medium | Downpour | NA | NA | http://www.newsday.co.tt/news/0,85847.html |
df_TB %>%
select(Country, State, City, Distance, Population)## Country State City Distance
## 30 Trinidad and Tobago Tobago Scarborough 9.11607
## 61 Trinidad and Tobago Eastern Tobago Roxborough 7.33295
## 65 Trinidad and Tobago Sangre Grande Sangre Grande 29.28864
## 66 Trinidad and Tobago Eastern Tobago Roxborough 8.62938
## 67 Trinidad and Tobago Eastern Tobago Roxborough 2.66802
## 149 Trinidad and Tobago Diego Martin Petit Valley 10.61854
## 164 Trinidad and Tobago City of Port of Spain Port-of-Spain 1.07831
## 171 Trinidad and Tobago Diego Martin Petit Valley 5.91101
## 172 Trinidad and Tobago Diego Martin Petit Valley 2.24772
## 173 Trinidad and Tobago Penal/Debe Peñal, 2.57071
## 206 Trinidad and Tobago City of San Fernando Marabella 9.42494
## 392 Trinidad and Tobago Diego Martin Petit Valley 4.00979
## 393 Trinidad and Tobago City of Port of Spain Mucurapo 7.43310
## 394 Trinidad and Tobago City of Port of Spain Port-of-Spain 2.15046
## 400 Trinidad and Tobago Diego Martin Petit Valley 4.33904
## 402 Trinidad and Tobago Diego Martin Petit Valley 6.95807
## 404 Trinidad and Tobago City of Port of Spain Mucurapo 7.24469
## 405 Trinidad and Tobago San Juan/Laventille Laventille 7.37181
## 421 Trinidad and Tobago City of San Fernando San Fernando 0.92162
## 446 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 6.91642
## 494 Trinidad and Tobago Sangre Grande Sangre Grande 33.51924
## 495 Trinidad and Tobago Eastern Tobago Roxborough 8.44112
## 496 Trinidad and Tobago Eastern Tobago Roxborough 3.36240
## 497 Trinidad and Tobago Tunapuna/Piarco Paradise 2.64003
## 498 Trinidad and Tobago Eastern Tobago Roxborough 0.91163
## 499 Trinidad and Tobago Tobago Scarborough 4.68038
## 500 Trinidad and Tobago Tobago Scarborough 6.81393
## 532 Trinidad and Tobago Diego Martin Petit Valley 3.33629
## 533 Trinidad and Tobago Diego Martin Petit Valley 3.08955
## 534 Trinidad and Tobago Diego Martin Petit Valley 4.66234
## 535 Trinidad and Tobago Diego Martin Petit Valley 4.50278
## 536 Trinidad and Tobago Diego Martin Petit Valley 6.58396
## 537 Trinidad and Tobago Diego Martin Petit Valley 1.83626
## 554 Trinidad and Tobago Princes Town Princes Town 8.41931
## 555 Trinidad and Tobago Sangre Grande Sangre Grande 33.10893
## 556 Trinidad and Tobago Sangre Grande Sangre Grande 33.10893
## 827 Trinidad and Tobago San Juan/Laventille Laventille 0.38521
## 829 Trinidad and Tobago Diego Martin Petit Valley 11.75674
## 839 Trinidad and Tobago Penal/Debe Peñal, 4.21321
## 840 Trinidad and Tobago Point Fortin Point Fortin 22.47289
## 841 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 8.55164
## 842 Trinidad and Tobago Diego Martin Petit Valley 5.73985
## 843 Trinidad and Tobago Sangre Grande Sangre Grande 2.00931
## 844 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 0.61975
## 845 Trinidad and Tobago Borough of Arima Arima 13.34116
## 846 Trinidad and Tobago Sangre Grande Sangre Grande 30.29383
## 858 Trinidad and Tobago Tunapuna/Piarco Paradise 16.73194
## 877 Trinidad and Tobago Eastern Tobago Roxborough 5.62092
## 878 Trinidad and Tobago Tobago Scarborough 3.48176
## 887 Trinidad and Tobago Tunapuna/Piarco Paradise 13.75900
## 897 Trinidad and Tobago Diego Martin Petit Valley 6.39375
## 898 Trinidad and Tobago Diego Martin Petit Valley 4.64073
## 906 Trinidad and Tobago Petit Valley 1.84331
## 907 Trinidad and Tobago City of Port of Spain Port-of-Spain 2.54016
## 925 Trinidad and Tobago Eastern Tobago Roxborough 7.87263
## 926 Trinidad and Tobago Eastern Tobago Roxborough 7.47816
## 927 Trinidad and Tobago Eastern Tobago Roxborough 8.24676
## 958 Trinidad and Tobago Tobago Scarborough 6.35974
## 990 Trinidad and Tobago Tunapuna/Piarco Tunapuna 2.63186
## 1174 Trinidad and Tobago Tobago Scarborough 3.22335
## 1175 Trinidad and Tobago Tobago Scarborough 3.88123
## 1183 Trinidad and Tobago Sangre Grande Sangre Grande 26.68822
## 1302 Trinidad and Tobago Siparia Siparia 3.75200
## Population
## 30 17000
## 61 0
## 65 15968
## 66 0
## 67 0
## 149 8140
## 164 49031
## 171 8140
## 172 8140
## 173 12281
## 206 26700
## 392 8140
## 393 4342
## 394 49031
## 400 8140
## 402 8140
## 404 4342
## 405 157258
## 421 55419
## 446 3314
## 494 15968
## 495 0
## 496 0
## 497 15067
## 498 0
## 499 17000
## 500 17000
## 532 8140
## 533 8140
## 534 8140
## 535 8140
## 536 8140
## 537 8140
## 554 10000
## 555 15968
## 556 15968
## 827 157258
## 829 8140
## 839 12281
## 840 19056
## 841 3314
## 842 8140
## 843 15968
## 844 3314
## 845 35000
## 846 15968
## 858 15067
## 877 0
## 878 17000
## 887 15067
## 897 8140
## 898 8140
## 906 8140
## 907 49031
## 925 0
## 926 0
## 927 0
## 958 17000
## 990 17758
## 1174 17000
## 1175 17000
## 1183 15968
## 1302 8568ggplot(data=df_TB, aes(fill=State, x="Trinidad and Tobago", y=Population)) +
geom_bar(position="dodge", stat="identity")
ggplot(data=df_TB, aes(fill=State, x="Trinidad and Tobago", y=Population)) +
geom_bar(position="stack", stat="identity")
Medidas de Tendencia central para muertes en Trinidad y Tobago
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"
library(readr)
library(knitr)
df_COL <- subset (df, Country == "Trinidad and Tobago")
knitr::kable(head(df_COL))| id | date | time | continent_code | Country | country_code | state_province | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | Fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 224 | 9/1/07 | NA | Trinidad and Tobago | TT | Tobago | 17000 | Scarborough | 9.11607 | 11.2415 | -60.6742 | (11.2415, -60.674199999999999) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Felix | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161197580 | ||
| 61 | 357 | 11/17/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 7.33295 | 11.2965 | -60.6312 | (11.2965, -60.6312) | Landslide | Landslide | Medium | Rain | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161237574 | |||
| 65 | 390 | 12/11/07 | NA | Trinidad and Tobago | TT | Sangre Grande | 15968 | Sangre Grande | 29.28864 | 10.8410 | -61.0550 | (10.840999999999999, -61.055) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | 3 | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 66 | 391 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 8.62938 | 11.3000 | -60.6440 | (11.3, -60.643999999999998) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 67 | 392 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 2.66802 | 11.2670 | -60.5660 | (11.266999999999999, -60.566000000000003) | Landslide | Landslide | Small | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 149 | 780 | 9/7/08 | NA | Trinidad and Tobago | TT | Diego Martin | 8140 | Petit Valley | 10.61854 | 10.7603 | -61.4578 | (10.760300000000001, -61.457799999999999) | Landslide | Landslide | Medium | Downpour | NA | NA | http://www.newsday.co.tt/news/0,85847.html |
df_COL[is.na(df_COL)] <- 0
df_COL %>%
select(Country,state_province,Fatalities) ## Country state_province Fatalities
## 30 Trinidad and Tobago Tobago 0
## 61 Trinidad and Tobago Eastern Tobago 0
## 65 Trinidad and Tobago Sangre Grande 3
## 66 Trinidad and Tobago Eastern Tobago 0
## 67 Trinidad and Tobago Eastern Tobago 0
## 149 Trinidad and Tobago Diego Martin 0
## 164 Trinidad and Tobago City of Port of Spain 1
## 171 Trinidad and Tobago Diego Martin 0
## 172 Trinidad and Tobago Diego Martin 0
## 173 Trinidad and Tobago Penal/Debe 0
## 206 Trinidad and Tobago City of San Fernando 0
## 392 Trinidad and Tobago Diego Martin 0
## 393 Trinidad and Tobago City of Port of Spain 0
## 394 Trinidad and Tobago City of Port of Spain 0
## 400 Trinidad and Tobago Diego Martin 0
## 402 Trinidad and Tobago Diego Martin 0
## 404 Trinidad and Tobago City of Port of Spain 0
## 405 Trinidad and Tobago San Juan/Laventille 1
## 421 Trinidad and Tobago City of San Fernando 0
## 446 Trinidad and Tobago Couva-Tabaquite-Talparo 0
## 494 Trinidad and Tobago Sangre Grande 0
## 495 Trinidad and Tobago Eastern Tobago 0
## 496 Trinidad and Tobago Eastern Tobago 0
## 497 Trinidad and Tobago Tunapuna/Piarco 0
## 498 Trinidad and Tobago Eastern Tobago 0
## 499 Trinidad and Tobago Tobago 0
## 500 Trinidad and Tobago Tobago 0
## 532 Trinidad and Tobago Diego Martin 0
## 533 Trinidad and Tobago Diego Martin 0
## 534 Trinidad and Tobago Diego Martin 0
## 535 Trinidad and Tobago Diego Martin 0
## 536 Trinidad and Tobago Diego Martin 0
## 537 Trinidad and Tobago Diego Martin 0
## 554 Trinidad and Tobago Princes Town 0
## 555 Trinidad and Tobago Sangre Grande 0
## 556 Trinidad and Tobago Sangre Grande 0
## 827 Trinidad and Tobago San Juan/Laventille 0
## 829 Trinidad and Tobago Diego Martin 0
## 839 Trinidad and Tobago Penal/Debe 0
## 840 Trinidad and Tobago Point Fortin 0
## 841 Trinidad and Tobago Couva-Tabaquite-Talparo 0
## 842 Trinidad and Tobago Diego Martin 0
## 843 Trinidad and Tobago Sangre Grande 0
## 844 Trinidad and Tobago Couva-Tabaquite-Talparo 0
## 845 Trinidad and Tobago Borough of Arima 0
## 846 Trinidad and Tobago Sangre Grande 1
## 858 Trinidad and Tobago Tunapuna/Piarco 0
## 877 Trinidad and Tobago Eastern Tobago 0
## 878 Trinidad and Tobago Tobago 0
## 887 Trinidad and Tobago Tunapuna/Piarco 0
## 897 Trinidad and Tobago Diego Martin 0
## 898 Trinidad and Tobago Diego Martin 0
## 906 Trinidad and Tobago 0
## 907 Trinidad and Tobago City of Port of Spain 0
## 925 Trinidad and Tobago Eastern Tobago 0
## 926 Trinidad and Tobago Eastern Tobago 0
## 927 Trinidad and Tobago Eastern Tobago 0
## 958 Trinidad and Tobago Tobago 0
## 990 Trinidad and Tobago Tunapuna/Piarco 0
## 1174 Trinidad and Tobago Tobago 0
## 1175 Trinidad and Tobago Tobago 0
## 1183 Trinidad and Tobago Sangre Grande 0
## 1302 Trinidad and Tobago Siparia 0library(tidyverse)
tablafinal<-(df_COL %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 0.0 0.0 0.4 0.0 4.0 knitr::kable(tablafinal)| state_province | fatalities |
|---|---|
| 0 | |
| Borough of Arima | 0 |
| City of Port of Spain | 1 |
| City of San Fernando | 0 |
| Couva-Tabaquite-Talparo | 0 |
| Diego Martin | 0 |
| Eastern Tobago | 0 |
| Penal/Debe | 0 |
| Point Fortin | 0 |
| Princes Town | 0 |
| San Juan/Laventille | 1 |
| Sangre Grande | 4 |
| Siparia | 0 |
| Tobago | 0 |
| Tunapuna/Piarco | 0 |
Tabla de frecuencia simple de las fatalidades en Trinidad y Togabo
dataTri <- c("Sangre Grande" , "Sangre Grande" , "Sangre Grande" , "Sangre Grande" , "City of Port of Spain" , "San Juan/Laventille")
knitr::kable(head(dataTri))| x |
|---|
| Sangre Grande |
| Sangre Grande |
| Sangre Grande |
| Sangre Grande |
| City of Port of Spain |
| San Juan/Laventille |
library(questionr)
table <- questionr::freq(dataTri, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Sangre Grande | 4 | 66.7 | 66.7 | 66.7 | 66.7 |
| City of Port of Spain | 1 | 16.7 | 16.7 | 83.3 | 83.3 |
| San Juan/Laventille | 1 | 16.7 | 16.7 | 100.0 | 100.0 |
| Total | 6 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Sangre Grande | 4 |
| City of Port of Spain | 1 |
| San Juan/Laventille | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Diagrama de Pareto para tipo de deslizamientos en Trinidad y Tobago
df <- data.frame(tipo=
c("mudslide","landslide","complex","rockfall","Lahar"),
Frecuencia = c(14,47,1,0,0))
knitr::kable(df)| tipo | Frecuencia |
|---|---|
| mudslide | 14 |
| landslide | 47 |
| complex | 1 |
| rockfall | 0 |
| Lahar | 0 |
head(df)## tipo Frecuencia
## 1 mudslide 14
## 2 landslide 47
## 3 complex 1
## 4 rockfall 0
## 5 Lahar 0library(qcc)
Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$tipo
pareto.chart(Frecuencia,
ylab="Frecuencia",
col = heat.colors(length(Frecuencia)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "Gráfico de Pareto para tipos de deslizamientos en Trinidad y Tobago")
##
## Pareto chart analysis for Frecuencia
## Frequency Cum.Freq. Percentage Cum.Percent.
## landslide 47.000000 47.000000 75.806452 75.806452
## mudslide 14.000000 61.000000 22.580645 98.387097
## complex 1.000000 62.000000 1.612903 100.000000
## rockfall 0.000000 62.000000 0.000000 100.000000
## Lahar 0.000000 62.000000 0.000000 100.000000COLOMBIA
Gráfico de barras para población en Colombia
library(readr)
library(knitr)
library(ggplot2)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[8] <- "Population"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_COL <- subset (df, Country == "Colombia")
knitr::kable(head(df_COL))| id | date | time | continent_code | Country | country_code | State | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | 77 | 5/21/07 | SA | Colombia | CO | Risaralda | 440118 | Pereira | 0.62022 | 4.8081 | -75.6941 | (4.8080999999999996, -75.694100000000006) | Landslide | Mudslide | Large | Rain | NA | 13 | Reuters - AlertNet.org | http://www.reuters.com/news/video/videoStory?videoId=53594&feedType=RSS&rpc=23 | |||
| 49 | 307 | 10/13/07 | SA | Colombia | CO | Cauca | 9985 | Suárez | 8.46579 | 2.9437 | -76.7719 | (2.9437000000000002, -76.771900000000002) | Landslide | Mudslide | Large | Continuous rain | NA | 24 | Reuters - AlertNet.org | http://www.reuters.com/article/newsOne/idUSN1329387220071013 | |||
| 70 | 397 | 12/19/07 | SA | Colombia | CO | Tolima | 4892 | Ambalema | 6.96130 | 4.8470 | -74.7631 | (4.8470000000000004, -74.763099999999994) | Landslide | Landslide | Large | Rain | NA | NA | Indiamuslims.info | http://www.indiamuslims.info/news/2007/dec/20/eight_people_rescued_colombian_landslide.html | |||
| 103 | 562 | 5/31/08 | SA | Colombia | CO | Antioquia | 1999979 | Medellín | 5.12170 | 6.2746 | -75.6039 | (6.2746000000000004, -75.603899999999996) | Landslide | Complex | Large | Downpour | NA | 27 | http://english.people.com.cn/90001/90777/90852/6422291.html | ||||
| 110 | 605 | 6/24/08 | SA | Colombia | CO | Norte de Santander | 1502 | Hacarí | 0.38844 | 8.3200 | -73.1500 | (8.32, -73.150000000000006) | Landslide | Landslide | Medium | Downpour | NA | 10 | http://news.xinhuanet.com/english/2008-06/25/content_8434589.htm | ||||
| 117 | 644 | 7/14/08 | SA | Colombia | CO | Cundinamarca | 1374 | Quetame | 8.58891 | 4.4100 | -73.8600 | (4.41, -73.86) | Landslide | Landslide | Medium | Downpour | NA | 4 | http://news.xinhuanet.com/english/2008-07/15/content_8548107.htm |
df_COL %>%
select(Country, State, City, Distance, Population)## Country State City Distance Population
## 8 Colombia Risaralda Pereira 0.62022 440118
## 49 Colombia Cauca Suárez 8.46579 9985
## 70 Colombia Tolima Ambalema 6.96130 4892
## 103 Colombia Antioquia Medellín 5.12170 1999979
## 110 Colombia Norte de Santander Hacarí 0.38844 1502
## 117 Colombia Cundinamarca Quetame 8.58891 1374
## 119 Colombia Antioquia Briceño 6.44532 2214
## 133 Colombia Antioquia Caldas 1.27637 65565
## 134 Colombia Córdoba Cereté 0.68544 55513
## 152 Colombia Córdoba Moñitos 23.49217 5385
## 163 Colombia Antioquia Medellín 4.09028 1999979
## 166 Colombia Cauca Miranda 0.70558 13223
## 180 Colombia Huila Hobo 10.18040 4444
## 191 Colombia Córdoba Sahagún 0.14776 59188
## 303 Colombia Cundinamarca Girardot 0.30587 130289
## 340 Colombia Valle del Cauca Cali 1.84941 2392877
## 349 Colombia Meta Puerto López 22.53724 16678
## 352 Colombia Cundinamarca Soacha 10.36239 313945
## 396 Colombia Magdalena Ciénaga 51.84125 88311
## 403 Colombia Norte de Santander San Calixto 7.85369 2080
## 425 Colombia Santander Málaga 2.99929 18739
## 434 Colombia Santander Málaga 2.99929 18739
## 454 Colombia Risaralda Apía 8.18229 6940
## 455 Colombia Antioquia Giraldo 2.55282 1464
## 479 Colombia Boyacá San Pablo de Borbur 1.53032 1519
## 517 Colombia Risaralda Quinchía 0.11421 10895
## 518 Colombia Antioquia Medellín 2.18776 1999979
## 519 Colombia Sucre San Marcos 25.51411 36549
## 520 Colombia Cundinamarca La Calera 7.28959 10175
## 523 Colombia Caldas Marquetalia 17.48659 6224
## 524 Colombia Caldas Marquetalia 3.72195 6224
## 525 Colombia Caldas Marulanda 0.72830 1256
## 526 Colombia Norte de Santander Arboledas 14.62503 2702
## 527 Colombia Santander San Andrés 3.06383 3032
## 528 Colombia Antioquia Cáceres 4.29197 4987
## 553 Colombia Antioquia Bello 2.04898 392939
## 574 Colombia Nariño San Pablo 4.95353 6522
## 594 Colombia Santander Matanza 6.16385 1669
## 619 Colombia Caldas Villamaría 2.36822 35302
## 621 Colombia Norte de Santander Cúcuta 3.60520 721398
## 623 Colombia Caldas Manizales 1.70850 357814
## 629 Colombia Antioquia Medellín 4.19867 1999979
## 631 Colombia Nariño El Tambo 0.60599 7279
## 632 Colombia Nariño El Tambo 3.81445 7279
## 637 Colombia Caldas Manizales 1.07765 357814
## 638 Colombia Caldas Villamaría 15.82404 35302
## 640 Colombia Antioquia Santa Rosa de Osos 18.91189 10191
## 643 Colombia Santander Florián 0.44753 1227
## 644 Colombia Cundinamarca San Antonio del Tequendama 6.08628 1577
## 658 Colombia Antioquia Municipio de Copacabana 7.98838 49169
## 659 Colombia Antioquia Frontino 5.06960 8603
## 690 Colombia Boyacá Samacá 0.85976 3689
## 701 Colombia Boyacá Belén 2.28425 5411
## 714 Colombia Valle del Cauca Cali 8.30406 2392877
## 715 Colombia Santander Lebrija 9.21217 8949
## 716 Colombia Santander Piedecuesta 10.55986 86387
## 718 Colombia Santander San Vicente de Chucurí 0.80432 11265
## 753 Colombia Bolívar Santa Rosa del Sur 26.89879 8904
## 821 Colombia Antioquia Medellín 1.73101 1999979
## 822 Colombia Valle del Cauca Buenaventura 20.25692 240387
## 826 Colombia Antioquia Ciudad Bolívar 24.48479 16225
## 830 Colombia Quindío Calarcá 1.35196 62170
## 831 Colombia Caldas Manizales 0.64094 357814
## 832 Colombia Tolima Cajamarca 8.56086 9309
## 835 Colombia Tolima Cajamarca 7.87303 9309
## 836 Colombia Cundinamarca Funza 9.23778 54421
## 838 Colombia Tolima Carmen de Apicalá 9.65157 5640
## 848 Colombia Santander Los Santos 12.61362 1310
## 849 Colombia Cundinamarca Soacha 5.17650 313945
## 850 Colombia Cauca La Cruz 0.64469 8751
## 851 Colombia Caldas Norcasia 16.94642 5976
## 852 Colombia Nariño San Lorenzo 0.37809 2394
## 853 Colombia Magdalena Santa Marta 0.79694 431781
## 854 Colombia Caldas Aranzazu 2.53047 9327
## 855 Colombia Risaralda Dos Quebradas 0.74201 179583
## 859 Colombia Antioquia Envigado 2.07081 163007
## 876 Colombia Antioquia Amalfi 11.11685 9733
## 879 Colombia Caquetá Florencia 15.42607 130337
## 911 Colombia Huila San Agustín 6.84683 9481
## 921 Colombia Norte de Santander San Calixto 11.19714 2080
## 928 Colombia Huila Gigante 0.93184 9829
## 1054 Colombia Caquetá Florencia 15.16116 130337
## 1071 Colombia Huila Áquira 3.14201 3942
## 1207 Colombia Nariño Cumbitara 16.97776 1091
## 1220 Colombia Valle del Cauca Palmira 7.70237 247986
## 1221 Colombia Huila Palermo 0.00003 9896
## 1222 Colombia Santander Bucaramanga 1.33829 571820
## 1290 Colombia Magdalena Ariguaní 28.50569 26246
## 1305 Colombia Santander San Vicente de Chucurí 1.08964 11265
## 1344 Colombia Cauca Almaguer 17.31514 3120
## 1362 Colombia Antioquia Barbosa 7.78677 16707
## 1456 Colombia Putumayo San Francisco 1.47940 4350
## 1457 Colombia Antioquia Salgar 3.09014 6428
## 1471 Colombia Cauca Jambaló 8.81287 1972
## 1565 Colombia Huila Rivera 20.31227 8044
## 1669 Colombia Antioquia Mutatá 15.04256 4811ggplot(data=df_COL, aes(fill=State, x="Colombia", y=Population)) +
geom_bar(position="dodge", stat="identity")
ggplot(data=df_COL, aes(fill=State, x="Colombia", y=Population)) +
geom_bar(position="stack", stat="identity")
Medidas de Tendencia central para muertes en Colombia
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"
library(readr)
library(knitr)
df_COL <- subset (df, Country == "Colombia")
knitr::kable(head(df_COL))| id | date | time | continent_code | Country | country_code | state_province | Population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | Fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | 77 | 5/21/07 | SA | Colombia | CO | Risaralda | 440118 | Pereira | 0.62022 | 4.8081 | -75.6941 | (4.8080999999999996, -75.694100000000006) | Landslide | Mudslide | Large | Rain | NA | 13 | Reuters - AlertNet.org | http://www.reuters.com/news/video/videoStory?videoId=53594&feedType=RSS&rpc=23 | |||
| 49 | 307 | 10/13/07 | SA | Colombia | CO | Cauca | 9985 | Suárez | 8.46579 | 2.9437 | -76.7719 | (2.9437000000000002, -76.771900000000002) | Landslide | Mudslide | Large | Continuous rain | NA | 24 | Reuters - AlertNet.org | http://www.reuters.com/article/newsOne/idUSN1329387220071013 | |||
| 70 | 397 | 12/19/07 | SA | Colombia | CO | Tolima | 4892 | Ambalema | 6.96130 | 4.8470 | -74.7631 | (4.8470000000000004, -74.763099999999994) | Landslide | Landslide | Large | Rain | NA | NA | Indiamuslims.info | http://www.indiamuslims.info/news/2007/dec/20/eight_people_rescued_colombian_landslide.html | |||
| 103 | 562 | 5/31/08 | SA | Colombia | CO | Antioquia | 1999979 | Medellín | 5.12170 | 6.2746 | -75.6039 | (6.2746000000000004, -75.603899999999996) | Landslide | Complex | Large | Downpour | NA | 27 | http://english.people.com.cn/90001/90777/90852/6422291.html | ||||
| 110 | 605 | 6/24/08 | SA | Colombia | CO | Norte de Santander | 1502 | Hacarí | 0.38844 | 8.3200 | -73.1500 | (8.32, -73.150000000000006) | Landslide | Landslide | Medium | Downpour | NA | 10 | http://news.xinhuanet.com/english/2008-06/25/content_8434589.htm | ||||
| 117 | 644 | 7/14/08 | SA | Colombia | CO | Cundinamarca | 1374 | Quetame | 8.58891 | 4.4100 | -73.8600 | (4.41, -73.86) | Landslide | Landslide | Medium | Downpour | NA | 4 | http://news.xinhuanet.com/english/2008-07/15/content_8548107.htm |
df_COL[is.na(df_COL)] <- 0
df_COL %>%
select(Country,state_province,Fatalities) ## Country state_province Fatalities
## 8 Colombia Risaralda 13
## 49 Colombia Cauca 24
## 70 Colombia Tolima 0
## 103 Colombia Antioquia 27
## 110 Colombia Norte de Santander 10
## 117 Colombia Cundinamarca 4
## 119 Colombia Antioquia 8
## 133 Colombia Antioquia 0
## 134 Colombia Córdoba 0
## 152 Colombia Córdoba 0
## 163 Colombia Antioquia 8
## 166 Colombia Cauca 10
## 180 Colombia Huila 5
## 191 Colombia Córdoba 1
## 303 Colombia Cundinamarca 2
## 340 Colombia Valle del Cauca 0
## 349 Colombia Meta 0
## 352 Colombia Cundinamarca 0
## 396 Colombia Magdalena 0
## 403 Colombia Norte de Santander 4
## 425 Colombia Santander 0
## 434 Colombia Santander 0
## 454 Colombia Risaralda 4
## 455 Colombia Antioquia 9
## 479 Colombia Boyacá 2
## 517 Colombia Risaralda 4
## 518 Colombia Antioquia 2
## 519 Colombia Sucre 0
## 520 Colombia Cundinamarca 4
## 523 Colombia Caldas 5
## 524 Colombia Caldas 1
## 525 Colombia Caldas 2
## 526 Colombia Norte de Santander 0
## 527 Colombia Santander 0
## 528 Colombia Antioquia 0
## 553 Colombia Antioquia 91
## 574 Colombia Nariño 13
## 594 Colombia Santander 3
## 619 Colombia Caldas 0
## 621 Colombia Norte de Santander 0
## 623 Colombia Caldas 0
## 629 Colombia Antioquia 4
## 631 Colombia Nariño 0
## 632 Colombia Nariño 5
## 637 Colombia Caldas 0
## 638 Colombia Caldas 20
## 640 Colombia Antioquia 4
## 643 Colombia Santander 11
## 644 Colombia Cundinamarca 0
## 658 Colombia Antioquia 2
## 659 Colombia Antioquia 0
## 690 Colombia Boyacá 2
## 701 Colombia Boyacá 0
## 714 Colombia Valle del Cauca 0
## 715 Colombia Santander 2
## 716 Colombia Santander 0
## 718 Colombia Santander 1
## 753 Colombia Bolívar 2
## 821 Colombia Antioquia 6
## 822 Colombia Valle del Cauca 3
## 826 Colombia Antioquia 5
## 830 Colombia Quindío 4
## 831 Colombia Caldas 48
## 832 Colombia Tolima 0
## 835 Colombia Tolima 0
## 836 Colombia Cundinamarca 3
## 838 Colombia Tolima 7
## 848 Colombia Santander 2
## 849 Colombia Cundinamarca 6
## 850 Colombia Cauca 1
## 851 Colombia Caldas 5
## 852 Colombia Nariño 9
## 853 Colombia Magdalena 1
## 854 Colombia Caldas 9
## 855 Colombia Risaralda 0
## 859 Colombia Antioquia 3
## 876 Colombia Antioquia 3
## 879 Colombia Caquetá 4
## 911 Colombia Huila 10
## 921 Colombia Norte de Santander 0
## 928 Colombia Huila 5
## 1054 Colombia Caquetá 6
## 1071 Colombia Huila 5
## 1207 Colombia Nariño 5
## 1220 Colombia Valle del Cauca 2
## 1221 Colombia Huila 1
## 1222 Colombia Santander 2
## 1290 Colombia Magdalena 6
## 1305 Colombia Santander 0
## 1344 Colombia Cauca 6
## 1362 Colombia Antioquia 2
## 1456 Colombia Putumayo 0
## 1457 Colombia Antioquia 92
## 1471 Colombia Cauca 0
## 1565 Colombia Huila 0
## 1669 Colombia Antioquia 0library(tidyverse)
tablafinal<-(df_COL %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 3.50 8.50 28.50 22.25 266.00 knitr::kable(tablafinal)| state_province | fatalities |
|---|---|
| Antioquia | 266 |
| Bolívar | 2 |
| Boyacá | 4 |
| Caldas | 90 |
| Caquetá | 10 |
| Cauca | 41 |
| Córdoba | 1 |
| Cundinamarca | 19 |
| Huila | 26 |
| Magdalena | 7 |
| Meta | 0 |
| Nariño | 32 |
| Norte de Santander | 14 |
| Putumayo | 0 |
| Quindío | 4 |
| Risaralda | 21 |
| Santander | 21 |
| Sucre | 0 |
| Tolima | 7 |
| Valle del Cauca | 5 |
Tabla de frecuencia simple de las fatalidades en Colombia
data <- c("Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Risaralda" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca" , "Cauca","Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Antioquia", "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander" , "Norte de Santander", "Córdoba", "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Huila" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Cundinamarca" , "Valle del Cauca" , "Valle del Cauca" , "Valle del Cauca" , "Valle del Cauca" , "Valle del Cauca" , "Magdalena" , "Magdalena" , "Magdalena" , "Magdalena" , "Magdalena" , "Magdalena" , "Magdalena" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Santander" , "Boyacá" , "Boyacá" , "Boyacá" , "Boyacá" , "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Caldas", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Nariño", "Caquetá" , "Caquetá" , "Caquetá" , "Caquetá" , "Caquetá" , "Caquetá" , "Caquetá" , "Caquetá" , "Caquetá" , "Caquetá" , "Bolívar", "Bolívar", "Quindío", "Quindío", "Quindío", "Quindío","Tolima" , "Tolima" , "Tolima" , "Tolima" , "Tolima" , "Tolima" , "Tolima" )
knitr::kable(head(data))| x |
|---|
| Risaralda |
| Risaralda |
| Risaralda |
| Risaralda |
| Risaralda |
| Risaralda |
library(questionr)
table <- questionr::freq(data, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Antioquia | 266 | 46.7 | 46.7 | 46.7 | 46.7 |
| Caldas | 90 | 15.8 | 15.8 | 62.5 | 62.5 |
| Cauca | 41 | 7.2 | 7.2 | 69.6 | 69.6 |
| Nariño | 32 | 5.6 | 5.6 | 75.3 | 75.3 |
| Huila | 26 | 4.6 | 4.6 | 79.8 | 79.8 |
| Risaralda | 21 | 3.7 | 3.7 | 83.5 | 83.5 |
| Santander | 21 | 3.7 | 3.7 | 87.2 | 87.2 |
| Cundinamarca | 19 | 3.3 | 3.3 | 90.5 | 90.5 |
| Norte de Santander | 14 | 2.5 | 2.5 | 93.0 | 93.0 |
| Caquetá | 10 | 1.8 | 1.8 | 94.7 | 94.7 |
| Magdalena | 7 | 1.2 | 1.2 | 96.0 | 96.0 |
| Tolima | 7 | 1.2 | 1.2 | 97.2 | 97.2 |
| Valle del Cauca | 5 | 0.9 | 0.9 | 98.1 | 98.1 |
| Boyacá | 4 | 0.7 | 0.7 | 98.8 | 98.8 |
| Quindío | 4 | 0.7 | 0.7 | 99.5 | 99.5 |
| Bolívar | 2 | 0.4 | 0.4 | 99.8 | 99.8 |
| Córdoba | 1 | 0.2 | 0.2 | 100.0 | 100.0 |
| Total | 570 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Antioquia | 266 |
| Caldas | 90 |
| Cauca | 41 |
| Nariño | 32 |
| Huila | 26 |
| Risaralda | 21 |
| Santander | 21 |
| Cundinamarca | 19 |
| Norte de Santander | 14 |
| Caquetá | 10 |
| Magdalena | 7 |
| Tolima | 7 |
| Valle del Cauca | 5 |
| Boyacá | 4 |
| Quindío | 4 |
| Bolívar | 2 |
| Córdoba | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Diagrama de pareto para tipo de deslizamientos en Colombia
df <- data.frame(tipo=
c("mudslide","landslide","complex","rockfall","Lahar"),
Frecuencia = c(24,66,4,3,0))
knitr::kable(df)| tipo | Frecuencia |
|---|---|
| mudslide | 24 |
| landslide | 66 |
| complex | 4 |
| rockfall | 3 |
| Lahar | 0 |
head(df)## tipo Frecuencia
## 1 mudslide 24
## 2 landslide 66
## 3 complex 4
## 4 rockfall 3
## 5 Lahar 0library(qcc)
Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$tipo
pareto.chart(Frecuencia,
ylab="Frecuencia",
col = heat.colors(length(Frecuencia)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "Gráfico de Pareto para tipos de deslizamientos en Colombia")
##
## Pareto chart analysis for Frecuencia
## Frequency Cum.Freq. Percentage Cum.Percent.
## landslide 66.000000 66.000000 68.041237 68.041237
## mudslide 24.000000 90.000000 24.742268 92.783505
## complex 4.000000 94.000000 4.123711 96.907216
## rockfall 3.000000 97.000000 3.092784 100.000000
## Lahar 0.000000 97.000000 0.000000 100.000000Diagrama de caja y extensión para distancia de los deslizamientos en Colombia
library(readr)
library(ggplot2)
library(dplyr)
library(knitr)
data <- c(0.62022, 8.46579, 6.9613, 5.1217, 0.38844, 8.58891, 6.44532, 1.27637, 0.68544, 23.49217, 23.49217, 0.70558, 10.1804, 0.14776, 0.30587, 1.84941, 22.53724, 10.36239, 51.84125, 7.85369, 2.99929, 2.99929, 8.18229, 2.55282, 1.53032, 0.11421, 2.18776, 25.51411, 7.28959, 17.48659, 3.72195, 0.7283, 14.62503, 3.06383, 4.29197, 2.04898, 4.95353, 6.16385, 2.36822, 3.6052, 1.7085, 4.19867, 0.60599, 3.81445, 1.07765, 15.82404, 18.91189, 0.44753, 6.08628, 7.98838, 5.0696, 0.85976, 2.28425, 8.30406, 9.21217, 10.55986, 0.80432, 26.89879, 1.73101, 20.25692, 24.48479, 1.35196, 0.64094, 8.56086, 7.87303, 9.23778, 9.65157, 12.61362, 5.1765, 0.64469, 16.94642, 0.37809, 0.79694, 2.53047, 0.74201, 2.07081, 11.11685, 15.42607, 6.84683, 11.19714, 0.93184, 15.16116, 3.14201, 16.97776, 7.70237, 0.00003, 1.33829, 28.50569, 10.47204, 1.08964, 17.31514, 7.78677, 1.4794, 3.09014, 8.81287, 20.31227, 15.04256)
boxplot(data, horizontal=TRUE, col='steelblue')
Gráficos y tablas de frecuencia acerca de los causales de deslizamientos en Colombia
library(ggplot2)
library(dplyr)
data <- data.frame(Desatadores=
c("Rain",
"Downpour",
"Mining digging",
"Continuous rain",
"Earthquake", "Unknown"),
Porcentaje = c(15, 79, 1, 5, 1, 2))
ggplot(data, aes(x = "", y = Porcentaje, fill=Desatadores)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
library(ggplot2)
library(dplyr)
data <- data %>%
arrange(desc(Desatadores)) %>%
mutate(prop = Porcentaje / sum(data$Porcentaje) *100) %>%
mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=Desatadores)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0) +
theme_void() +
theme(legend.position="none") +
geom_text(aes(y = ypos, label = percent(Porcentaje/103)), color = "white", size=3) +
scale_fill_brewer(palette="Set1")
knitr::kable(data)| Desatadores | Porcentaje | prop | ypos |
|---|---|---|---|
| Unknown | 2 | 1.9417476 | 0.9708738 |
| Rain | 15 | 14.5631068 | 9.2233010 |
| Mining digging | 1 | 0.9708738 | 16.9902913 |
| Earthquake | 1 | 0.9708738 | 17.9611650 |
| Downpour | 79 | 76.6990291 | 56.7961165 |
| Continuous rain | 5 | 4.8543689 | 97.5728155 |
data <- c("Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Rain", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Downpour", "Mining digging", "Continuous rain", "Continuous rain", "Continuous rain", "Continuous rain", "Continuous rain", "Continuous rain", "Earthquake", "Unknown", "Unknown" )
knitr::kable(head(data))| x |
|---|
| Rain |
| Rain |
| Rain |
| Rain |
| Rain |
| Rain |
library(questionr)
table <- questionr::freq(data, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Downpour | 79 | 76.0 | 76.0 | 76.0 | 76.0 |
| Rain | 15 | 14.4 | 14.4 | 90.4 | 90.4 |
| Continuous rain | 6 | 5.8 | 5.8 | 96.2 | 96.2 |
| Unknown | 2 | 1.9 | 1.9 | 98.1 | 98.1 |
| Earthquake | 1 | 1.0 | 1.0 | 99.0 | 99.0 |
| Mining digging | 1 | 1.0 | 1.0 | 100.0 | 100.0 |
| Total | 104 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]
df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Downpour | 79 |
| Rain | 15 |
| Continuous rain | 6 |
| Unknown | 2 |
| Earthquake | 1 |
| Mining digging | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="red") +
xlab("Eventos causantes de deslizamientos en Colombia") +
ylab("Frecuencia")
Brasil
Medidas de Tendencia central para muertes en Brasil
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"library(readr)
library(knitr)
df_BRA <- subset (df, Country == "Brazil")## Country state_province Fatalities
## 193 Brazil Pará 19
## 194 Brazil Maranhão 3
## 947 Brazil Amapá 6
## 1348 Brazil Pará 0library(tidyverse)
tablafinal <- (df_BRA %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 4.500 6.000 9.333 12.500 19.000knitr::kable(tablafinal)| state_province | fatalities |
|---|---|
| Amapá | 6 |
| Maranhão | 3 |
| Pará | 19 |
Gráfico de barras para población en Brasil
dataBra <- c("Pará" , "Pará" , "Pará" , "Pará", "Pará", "Pará", "Pará", "Pará", "Pará", "Pará","Pará" , "Pará" , "Pará" , "Pará", "Pará", "Pará", "Pará", "Pará", "Pará", "Maranhão","Maranhão","Maranhão","Amapá", "Amapá","Amapá","Amapá","Amapá","Amapá" )
knitr::kable(head(dataBra))| x |
|---|
| Pará |
| Pará |
| Pará |
| Pará |
| Pará |
| Pará |
library(questionr)
table <- questionr::freq(dataBra, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Pará | 19 | 67.9 | 67.9 | 67.9 | 67.9 |
| Amapá | 6 | 21.4 | 21.4 | 89.3 | 89.3 |
| Maranhão | 3 | 10.7 | 10.7 | 100.0 | 100.0 |
| Total | 28 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Pará | 19 |
| Amapá | 6 |
| Maranhão | 3 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Gráficos estadísticos para centroamérica.
Tabla de frecuencia simple de las fatalidades en El Salvador
dataSal <- c("San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "San Vicente" , "La Libertad", "La Libertad", "La Libertad", "La Libertad" , "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "San Salvador", "Cabañas", "Santa Ana", "Santa Ana", "Santa Ana")
knitr::kable(head(dataSal))| x |
|---|
| San Vicente |
| San Vicente |
| San Vicente |
| San Vicente |
| San Vicente |
| San Vicente |
library(questionr)
table <- questionr::freq(dataSal, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| San Salvador | 32 | 50.8 | 50.8 | 50.8 | 50.8 |
| San Vicente | 23 | 36.5 | 36.5 | 87.3 | 87.3 |
| La Libertad | 4 | 6.3 | 6.3 | 93.7 | 93.7 |
| Santa Ana | 3 | 4.8 | 4.8 | 98.4 | 98.4 |
| Cabañas | 1 | 1.6 | 1.6 | 100.0 | 100.0 |
| Total | 63 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| San Salvador | 32 |
| San Vicente | 23 |
| La Libertad | 4 |
| Santa Ana | 3 |
| Cabañas | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Tabla de frecuencia simple de las fatalidades en Panamá
dataPan <- c("Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá" , "Panamá")
knitr::kable(head(dataPan))| x |
|---|
| Panamá |
| Panamá |
| Panamá |
| Panamá |
| Panamá |
| Panamá |
library(questionr)
table <- questionr::freq(dataPan, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Panamá | 45 | 100 | 100 | 100 | 100 |
| Total | 45 | 100 | 100 | 100 | 100 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Panamá | 45 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Tabla de frecuencia simple de las fatalidades en Nicaragua
dataNic <- c("Masaya" , "Masaya" , "Masaya" , "Masaya" , "Masaya" , "Masaya" , "Masaya" , "Masaya" , "Masaya" , "Masaya" , "Managua" , "Managua" , "Managua" , "Managua" , "Managua" , "Managua" , "Managua" , "Managua" , "Managua" , "Ogun State" , "Ogun State" , "Ogun State" , "Ogun State" , "Ogun State" , "Ogun State" , "Ogun State" , "Rivas")
knitr::kable(head(dataNic))| x |
|---|
| Masaya |
| Masaya |
| Masaya |
| Masaya |
| Masaya |
| Masaya |
library(questionr)
table <- questionr::freq(dataNic, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Masaya | 10 | 37.0 | 37.0 | 37.0 | 37.0 |
| Managua | 9 | 33.3 | 33.3 | 70.4 | 70.4 |
| Ogun State | 7 | 25.9 | 25.9 | 96.3 | 96.3 |
| Rivas | 1 | 3.7 | 3.7 | 100.0 | 100.0 |
| Total | 27 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Masaya | 10 |
| Managua | 9 |
| Ogun State | 7 |
| Rivas | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Tabla de frecuencia simple de las fatalidades en Honduras
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"library(readr)
library(knitr)
df_Hon <- subset (df, Country == "Honduras")## Country state_province Fatalities
## 159 Honduras Copán 23
## 160 Honduras Francisco Morazán 29
## 376 Honduras Francisco Morazán 1
## 381 Honduras Francisco Morazán 0
## 406 Honduras Francisco Morazán 3
## 435 Honduras Francisco Morazán 5
## 474 Honduras Comayagua 0
## 485 Honduras Colón 0
## 820 Honduras Francisco Morazán 2
## 1100 Honduras Cortés 6
## 1279 Honduras Choluteca 8
## 1288 Honduras Yoro 0
## 1363 Honduras Ocotepeque 0
## 1377 Honduras Cortés 0
## 1379 Honduras Santa Bárbara 1
## 1599 Honduras El Paraíso 0
## 1602 Honduras Francisco Morazán 0
## 1603 Honduras Francisco Morazán 0
## 1604 Honduras Choluteca 0
## 1605 Honduras Choluteca 1
## 1610 Honduras Comayagua 0
## 1611 Honduras Comayagua 1
## 1612 Honduras Comayagua 5
## 1613 Honduras Copán 0
## 1614 Honduras Copán 0
## 1615 Honduras Copán 1
## 1616 Honduras La Paz 0
## 1617 Honduras Copán 0
## 1618 Honduras Ocotepeque 0
## 1619 Honduras Francisco Morazán 0
## 1620 Honduras Santa Bárbara 0
## 1622 Honduras Francisco Morazán 0
## 1623 Honduras Yoro 0
## 1624 Honduras Francisco Morazán 0
## 1638 Honduras Francisco Morazán 0
## 1639 Honduras Francisco Morazán 0
## 1640 Honduras Francisco Morazán 0
## 1641 Honduras Francisco Morazán 0library(tidyverse)
tablafinal <- (df_Hon %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 7.818 7.500 40.000knitr::kable(tablafinal) | state_province | fatalities |
|---|---|
| Choluteca | 9 |
| Colón | 0 |
| Comayagua | 6 |
| Copán | 24 |
| Cortés | 6 |
| El Paraíso | 0 |
| Francisco Morazán | 40 |
| La Paz | 0 |
| Ocotepeque | 0 |
| Santa Bárbara | 1 |
| Yoro | 0 |
dataHon <- c("Choluteca" ,"Choluteca" ,"Choluteca" ,"Choluteca" ,"Choluteca" ,"Choluteca" ,"Choluteca" ,"Choluteca" ,"Choluteca", "Comayagua", "Comayagua","Comayagua","Comayagua","Comayagua","Comayagua","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Copán","Francisco Morazán", "Francisco Morazán", "Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán", "Francisco Morazán", "Francisco Morazán", "Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán", "Francisco Morazán", "Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán", "Francisco Morazán", "Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Francisco Morazán","Santa Bárbara", "Cortés", "Cortés","Cortés","Cortés","Cortés","Cortés" )
knitr::kable(head(dataHon))| x |
|---|
| Choluteca |
| Choluteca |
| Choluteca |
| Choluteca |
| Choluteca |
| Choluteca |
library(questionr)
table <- questionr::freq(dataHon, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Francisco Morazán | 40 | 46.5 | 46.5 | 46.5 | 46.5 |
| Copán | 24 | 27.9 | 27.9 | 74.4 | 74.4 |
| Choluteca | 9 | 10.5 | 10.5 | 84.9 | 84.9 |
| Comayagua | 6 | 7.0 | 7.0 | 91.9 | 91.9 |
| Cortés | 6 | 7.0 | 7.0 | 98.8 | 98.8 |
| Santa Bárbara | 1 | 1.2 | 1.2 | 100.0 | 100.0 |
| Total | 86 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Francisco Morazán | 40 |
| Copán | 24 |
| Choluteca | 9 |
| Comayagua | 6 |
| Cortés | 6 |
| Santa Bárbara | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Tabla de frecuencia simple de las fatalidades en Costa Rica
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"library(readr)
library(knitr)
df_CRic <- subset (df, Country == "Costa Rica")## Country state_province Fatalities
## 38 Costa Rica Heredia 0
## 44 Costa Rica San José 4
## 45 Costa Rica Alajuela 14
## 46 Costa Rica San José 10
## 51 Costa Rica Puntarenas 0
## 102 Costa Rica Guanacaste 0
## 147 Costa Rica San José 0
## 153 Costa Rica San José 0
## 154 Costa Rica San José 0
## 156 Costa Rica Puntarenas 0
## 157 Costa Rica Puntarenas 0
## 229 Costa Rica Puntarenas 1
## 302 Costa Rica Alajuela 0
## 311 Costa Rica Heredia 0
## 347 Costa Rica Alajuela 0
## 395 Costa Rica Alajuela 0
## 459 Costa Rica Alajuela 0
## 469 Costa Rica San José 0
## 470 Costa Rica San José 0
## 480 Costa Rica Heredia 2
## 501 Costa Rica San José 23
## 502 Costa Rica San José 3
## 503 Costa Rica Alajuela 0
## 504 Costa Rica Guanacaste 0
## 505 Costa Rica Cartago 0
## 506 Costa Rica Puntarenas 0
## 507 Costa Rica San José 0
## 508 Costa Rica San José 0
## 509 Costa Rica Puntarenas 0
## 510 Costa Rica Puntarenas 0
## 511 Costa Rica Puntarenas 0
## 512 Costa Rica San José 0
## 513 Costa Rica San José 0
## 514 Costa Rica Alajuela 0
## 529 Costa Rica Heredia 0
## 579 Costa Rica Limón 0
## 702 Costa Rica Heredia 0
## 780 Costa Rica Alajuela 0
## 819 Costa Rica San José 0
## 828 Costa Rica Cartago 0
## 884 Costa Rica Heredia 0
## 888 Costa Rica Guanacaste 0
## 889 Costa Rica Limón 0
## 913 Costa Rica San José 0
## 1098 Costa Rica Alajuela 0
## 1156 Costa Rica Alajuela 0
## 1157 Costa Rica Heredia 0
## 1169 Costa Rica Guanacaste 0
## 1173 Costa Rica Guanacaste 2
## 1289 Costa Rica Alajuela 0
## 1301 Costa Rica Alajuela 0
## 1308 Costa Rica Alajuela 0
## 1342 Costa Rica Alajuela 0
## 1364 Costa Rica Alajuela 0
## 1383 Costa Rica Cartago 0
## 1384 Costa Rica Heredia 0
## 1385 Costa Rica San José 0
## 1386 Costa Rica San José 0
## 1404 Costa Rica San José 0
## 1406 Costa Rica San José 0
## 1461 Costa Rica Alajuela 0
## 1475 Costa Rica Alajuela 0
## 1528 Costa Rica San José 0
## 1529 Costa Rica San José 0
## 1600 Costa Rica San José 0
## 1642 Costa Rica Alajuela 0
## 1643 Costa Rica Alajuela 1
## 1644 Costa Rica Alajuela 1
## 1646 Costa Rica Cartago 0
## 1647 Costa Rica Cartago 0
## 1648 Costa Rica Cartago 0
## 1649 Costa Rica Puntarenas 0
## 1650 Costa Rica San José 0
## 1651 Costa Rica San José 0
## 1652 Costa Rica San José 0library(tidyverse)
tablafinal <- (df_CRic %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.500 2.000 8.714 9.000 40.000knitr::kable(tablafinal) | state_province | fatalities |
|---|---|
| Alajuela | 16 |
| Cartago | 0 |
| Guanacaste | 2 |
| Heredia | 2 |
| Limón | 0 |
| Puntarenas | 1 |
| San José | 40 |
dataCRic <- c("Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela","Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela", "Alajuela", "Guanacaste" ,"Guanacaste" ,"Heredias" , "Heredias" , "Puntarenas", "San José", "San José","San José","San José","San José","San José","San José","San José","San José","San José", "San José", "San José","San José","San José","San José","San José","San José","San José","San José","San José","San José", "San José","San José","San José","San José","San José","San José","San José","San José","San José","San José", "San José","San José","San José","San José","San José","San José","San José","San José","San José")
knitr::kable(head(dataCRic))| x |
|---|
| Alajuela |
| Alajuela |
| Alajuela |
| Alajuela |
| Alajuela |
| Alajuela |
library(questionr)
table <- questionr::freq(dataCRic, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| San José | 40 | 65.6 | 65.6 | 65.6 | 65.6 |
| Alajuela | 16 | 26.2 | 26.2 | 91.8 | 91.8 |
| Guanacaste | 2 | 3.3 | 3.3 | 95.1 | 95.1 |
| Heredias | 2 | 3.3 | 3.3 | 98.4 | 98.4 |
| Puntarenas | 1 | 1.6 | 1.6 | 100.0 | 100.0 |
| Total | 61 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| San José | 40 |
| Alajuela | 16 |
| Guanacaste | 2 |
| Heredias | 2 |
| Puntarenas | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Tabla de frecuencia simple de las fatalidades en Guatemala
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"library(readr)
library(knitr)
df_Guat <- subset (df, Country == "Guatemala")## Country state_province Fatalities
## 17 Guatemala Guatemala 5
## 27 Guatemala Alta Verapaz 0
## 28 Guatemala Izabal 0
## 41 Guatemala Guatemala 3
## 104 Guatemala Escuintla 1
## 108 Guatemala Guatemala 8
## 120 Guatemala Zacapa 21
## 158 Guatemala Chimaltenango 0
## 162 Guatemala Huehuetenango 0
## 169 Guatemala Izabal 2
## 351 Guatemala Quetzaltenango 4
## 353 Guatemala Escuintla 0
## 354 Guatemala Guatemala 0
## 355 Guatemala Guatemala 11
## 356 Guatemala Sacatepéquez 0
## 357 Guatemala Guatemala 17
## 358 Guatemala Sololá 14
## 359 Guatemala Chimaltenango 11
## 360 Guatemala Sololá 1
## 361 Guatemala Guatemala 4
## 362 Guatemala Quiché 3
## 363 Guatemala Totonicapán 3
## 372 Guatemala Sololá 2
## 383 Guatemala Guatemala 5
## 427 Guatemala Guatemala 0
## 428 Guatemala Guatemala 0
## 429 Guatemala Guatemala 0
## 430 Guatemala Chiquimula 0
## 431 Guatemala Huehuetenango 0
## 432 Guatemala Sololá 0
## 433 Guatemala Alta Verapaz 2
## 437 Guatemala Sololá 25
## 438 Guatemala Quetzaltenango 4
## 439 Guatemala Quiché 1
## 440 Guatemala Sacatepéquez 1
## 441 Guatemala Chimaltenango 10
## 442 Guatemala Quiché 5
## 818 Guatemala Huehuetenango 15
## 885 Guatemala Quiché 1
## 1112 Guatemala San Marcos 0
## 1244 Guatemala Huehuetenango 6
## 1347 Guatemala Sololá 6
## 1352 Guatemala Baja Verapaz 0
## 1353 Guatemala Guatemala 3
## 1354 Guatemala Guatemala 0
## 1356 Guatemala Quiché 0
## 1357 Guatemala Jutiapa 0
## 1358 Guatemala Huehuetenango 0
## 1359 Guatemala Guatemala 1
## 1360 Guatemala Quiché 2
## 1361 Guatemala San Marcos 2
## 1557 Guatemala Guatemala 280
## 1559 Guatemala Guatemala 0
## 1560 Guatemala Santa Rosa 0
## 1561 Guatemala Suchitepeque 0
## 1568 Guatemala Guatemala 0
## 1569 Guatemala Chimaltenango 0
## 1570 Guatemala Guatemala 0
## 1571 Guatemala Guatemala 0
## 1572 Guatemala Sololá 0
## 1573 Guatemala Sololá 0
## 1574 Guatemala Guatemala 0
## 1575 Guatemala Guatemala 0
## 1576 Guatemala Guatemala 0
## 1577 Guatemala Guatemala 0
## 1578 Guatemala Guatemala 0
## 1579 Guatemala Guatemala 0
## 1580 Guatemala Guatemala 0
## 1581 Guatemala Guatemala 0
## 1582 Guatemala Guatemala 0
## 1583 Guatemala Quiché 2
## 1584 Guatemala Guatemala 0
## 1585 Guatemala Guatemala 0
## 1588 Guatemala Sacatepéquez 0
## 1589 Guatemala Alta Verapaz 1
## 1590 Guatemala Escuintla 0
## 1591 Guatemala Guatemala 0
## 1592 Guatemala Sololá 0
## 1595 Guatemala Quetzaltenango 0library(tidyverse)
tablafinal <- (df_Guat %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.25 2.50 26.78 19.25 337.00knitr::kable(tablafinal) | state_province | fatalities |
|---|---|
| Alta Verapaz | 3 |
| Baja Verapaz | 0 |
| Chimaltenango | 21 |
| Chiquimula | 0 |
| Escuintla | 1 |
| Guatemala | 337 |
| Huehuetenango | 21 |
| Izabal | 2 |
| Jutiapa | 0 |
| Quetzaltenango | 8 |
| Quiché | 14 |
| Sacatepéquez | 1 |
| San Marcos | 2 |
| Santa Rosa | 0 |
| Sololá | 48 |
| Suchitepeque | 0 |
| Totonicapán | 3 |
| Zacapa | 21 |
Tabla de frecuencia simple y medidas de tendencia central de las fatalidades en Guatemala
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"library(readr)
library(knitr)
df_Guat <- subset (df, Country == "Guatemala")## Country state_province Fatalities
## 17 Guatemala Guatemala 5
## 27 Guatemala Alta Verapaz 0
## 28 Guatemala Izabal 0
## 41 Guatemala Guatemala 3
## 104 Guatemala Escuintla 1
## 108 Guatemala Guatemala 8
## 120 Guatemala Zacapa 21
## 158 Guatemala Chimaltenango 0
## 162 Guatemala Huehuetenango 0
## 169 Guatemala Izabal 2
## 351 Guatemala Quetzaltenango 4
## 353 Guatemala Escuintla 0
## 354 Guatemala Guatemala 0
## 355 Guatemala Guatemala 11
## 356 Guatemala Sacatepéquez 0
## 357 Guatemala Guatemala 17
## 358 Guatemala Sololá 14
## 359 Guatemala Chimaltenango 11
## 360 Guatemala Sololá 1
## 361 Guatemala Guatemala 4
## 362 Guatemala Quiché 3
## 363 Guatemala Totonicapán 3
## 372 Guatemala Sololá 2
## 383 Guatemala Guatemala 5
## 427 Guatemala Guatemala 0
## 428 Guatemala Guatemala 0
## 429 Guatemala Guatemala 0
## 430 Guatemala Chiquimula 0
## 431 Guatemala Huehuetenango 0
## 432 Guatemala Sololá 0
## 433 Guatemala Alta Verapaz 2
## 437 Guatemala Sololá 25
## 438 Guatemala Quetzaltenango 4
## 439 Guatemala Quiché 1
## 440 Guatemala Sacatepéquez 1
## 441 Guatemala Chimaltenango 10
## 442 Guatemala Quiché 5
## 818 Guatemala Huehuetenango 15
## 885 Guatemala Quiché 1
## 1112 Guatemala San Marcos 0
## 1244 Guatemala Huehuetenango 6
## 1347 Guatemala Sololá 6
## 1352 Guatemala Baja Verapaz 0
## 1353 Guatemala Guatemala 3
## 1354 Guatemala Guatemala 0
## 1356 Guatemala Quiché 0
## 1357 Guatemala Jutiapa 0
## 1358 Guatemala Huehuetenango 0
## 1359 Guatemala Guatemala 1
## 1360 Guatemala Quiché 2
## 1361 Guatemala San Marcos 2
## 1557 Guatemala Guatemala 280
## 1559 Guatemala Guatemala 0
## 1560 Guatemala Santa Rosa 0
## 1561 Guatemala Suchitepeque 0
## 1568 Guatemala Guatemala 0
## 1569 Guatemala Chimaltenango 0
## 1570 Guatemala Guatemala 0
## 1571 Guatemala Guatemala 0
## 1572 Guatemala Sololá 0
## 1573 Guatemala Sololá 0
## 1574 Guatemala Guatemala 0
## 1575 Guatemala Guatemala 0
## 1576 Guatemala Guatemala 0
## 1577 Guatemala Guatemala 0
## 1578 Guatemala Guatemala 0
## 1579 Guatemala Guatemala 0
## 1580 Guatemala Guatemala 0
## 1581 Guatemala Guatemala 0
## 1582 Guatemala Guatemala 0
## 1583 Guatemala Quiché 2
## 1584 Guatemala Guatemala 0
## 1585 Guatemala Guatemala 0
## 1588 Guatemala Sacatepéquez 0
## 1589 Guatemala Alta Verapaz 1
## 1590 Guatemala Escuintla 0
## 1591 Guatemala Guatemala 0
## 1592 Guatemala Sololá 0
## 1595 Guatemala Quetzaltenango 0Tabla de medidas de tendencia central
library(tidyverse)
tablafinal <- (df_Guat %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.25 2.50 26.78 19.25 337.00knitr::kable(tablafinal) | state_province | fatalities |
|---|---|
| Alta Verapaz | 3 |
| Baja Verapaz | 0 |
| Chimaltenango | 21 |
| Chiquimula | 0 |
| Escuintla | 1 |
| Guatemala | 337 |
| Huehuetenango | 21 |
| Izabal | 2 |
| Jutiapa | 0 |
| Quetzaltenango | 8 |
| Quiché | 14 |
| Sacatepéquez | 1 |
| San Marcos | 2 |
| Santa Rosa | 0 |
| Sololá | 48 |
| Suchitepeque | 0 |
| Totonicapán | 3 |
| Zacapa | 21 |
dataGuat <- c("Alta VeraPaz", "Alta VeraPaz", "Alta VeraPaz","Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango","Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango","Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Chimaltenango", "Escuintla", "Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala","Guatemala", "Huehuetenango", "Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango", "Huehuetenango", "Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango", "Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango","Huehuetenango", "Izabal", "Izabal", "Quetzaltenango", "Quetzaltenango","Quetzaltenango","Quetzaltenango","Quetzaltenango","Quetzaltenango","Quetzaltenango","Quetzaltenango", "Quiché", "Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Quiché","Sacatepéquez", "San Marcos", "San Marcos", "Sololá", "Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá","Sololá", "Sololá","Sololá","Sololá","Sololá","Sololá", "Totonicapán","Totonicapán","Totonicapán","Zacapa", "Zacapa","Zacapa","Zacapa","Zacapa","Zacapa","Zacapa","Zacapa", "Zacapa","Zacapa","Zacapa","Zacapa","Zacapa","Zacapa","Zacapa", "Zacapa","Zacapa","Zacapa","Zacapa","Zacapa","Zacapa")
knitr::kable(head(dataGuat))| x |
|---|
| Alta VeraPaz |
| Alta VeraPaz |
| Alta VeraPaz |
| Chimaltenango |
| Chimaltenango |
| Chimaltenango |
Tabla de frecuencia
library(questionr)
table <- questionr::freq(dataGuat, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Guatemala | 337 | 69.9 | 69.9 | 69.9 | 69.9 |
| Sololá | 48 | 10.0 | 10.0 | 79.9 | 79.9 |
| Chimaltenango | 21 | 4.4 | 4.4 | 84.2 | 84.2 |
| Huehuetenango | 21 | 4.4 | 4.4 | 88.6 | 88.6 |
| Zacapa | 21 | 4.4 | 4.4 | 92.9 | 92.9 |
| Quiché | 14 | 2.9 | 2.9 | 95.9 | 95.9 |
| Quetzaltenango | 8 | 1.7 | 1.7 | 97.5 | 97.5 |
| Alta VeraPaz | 3 | 0.6 | 0.6 | 98.1 | 98.1 |
| Totonicapán | 3 | 0.6 | 0.6 | 98.8 | 98.8 |
| Izabal | 2 | 0.4 | 0.4 | 99.2 | 99.2 |
| San Marcos | 2 | 0.4 | 0.4 | 99.6 | 99.6 |
| Escuintla | 1 | 0.2 | 0.2 | 99.8 | 99.8 |
| Sacatepéquez | 1 | 0.2 | 0.2 | 100.0 | 100.0 |
| Total | 482 | 100.0 | 100.0 | 100.0 | 100.0 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Guatemala | 337 |
| Sololá | 48 |
| Chimaltenango | 21 |
| Huehuetenango | 21 |
| Zacapa | 21 |
| Quiché | 14 |
| Quetzaltenango | 8 |
| Alta VeraPaz | 3 |
| Totonicapán | 3 |
| Izabal | 2 |
| San Marcos | 2 |
| Escuintla | 1 |
| Sacatepéquez | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Tabla de frecuencia simple y medidas de tendencia central de las fatalidades en Jamaica
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"library(readr)
library(knitr)
df_Jam <- subset (df, Country == "Jamaica")## Country state_province Fatalities
## 18 Jamaica Portland 0
## 48 Jamaica St. Elizabeth 0
## 50 Jamaica Saint Thomas 0
## 54 Jamaica Saint Thomas 1
## 55 Jamaica Saint Catherine 0
## 56 Jamaica Saint Andrew 0
## 60 Jamaica Saint Ann 0
## 93 Jamaica Saint Ann 0
## 143 Jamaica Saint Andrew 7
## 146 Jamaica Saint Thomas 0
## 237 Jamaica Portland 0
## 305 Jamaica Saint Thomas 0
## 306 Jamaica Clarendon 0
## 307 Jamaica Clarendon 0
## 308 Jamaica Portland 0
## 309 Jamaica Portland 0
## 408 Jamaica Saint Andrew 0
## 444 Jamaica Portland 0
## 456 Jamaica Portland 0
## 460 Jamaica Saint Mary 0
## 461 Jamaica Saint Andrew 0
## 462 Jamaica Saint Catherine 0
## 466 Jamaica Saint Andrew 2
## 486 Jamaica Saint Mary 0
## 522 Jamaica Portland 0
## 588 Jamaica Saint Mary 0
## 589 Jamaica Portland 0
## 900 Jamaica Portland 0
## 920 Jamaica Portland 0
## 1310 Jamaica Portland 0
## 1628 Jamaica Saint Catherine 0
## 1629 Jamaica Saint Ann 0
## 1630 Jamaica Portland 0Tabla de medidas de tendencia central
library(tidyverse)
tablafinal <- (df_Jam %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 0.00 0.00 1.25 0.25 9.00knitr::kable(tablafinal) | state_province | fatalities |
|---|---|
| Clarendon | 0 |
| Portland | 0 |
| Saint Andrew | 9 |
| Saint Ann | 0 |
| Saint Catherine | 0 |
| Saint Mary | 0 |
| Saint Thomas | 1 |
| St. Elizabeth | 0 |
dataJam <- c("Saint Andrew", "Saint Andrew","Saint Andrew","Saint Andrew","Saint Andrew","Saint Andrew","Saint Andrew","Saint Andrew","Saint Andrew","Saint Thomas")
knitr::kable(head(dataJam))| x |
|---|
| Saint Andrew |
| Saint Andrew |
| Saint Andrew |
| Saint Andrew |
| Saint Andrew |
| Saint Andrew |
Tabla de frecuencia
library(questionr)
table <- questionr::freq(dataJam, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| Saint Andrew | 9 | 90 | 90 | 90 | 90 |
| Saint Thomas | 1 | 10 | 10 | 100 | 100 |
| Total | 10 | 100 | 100 | 100 | 100 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| Saint Andrew | 9 |
| Saint Thomas | 1 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Tabla de frecuencia simple y medidas de tendencia central de las fatalidades en República Dominicana
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "state_province"
colnames(df)[21] <- "Fatalities"library(readr)
library(knitr)
df_Rep <- subset (df, Country == "Dominican Republic")## Country state_province Fatalities
## 15 Dominican Republic Distrito Nacional 0
## 52 Dominican Republic San Cristóbal 3
## 58 Dominican Republic La Vega 68
## 64 Dominican Republic Santiago 17
## 132 Dominican Republic Hato Mayor 0
## 138 Dominican Republic Distrito Nacional 8
## 178 Dominican Republic Santiago 0
## 211 Dominican Republic Puerto Plata 2
## 212 Dominican Republic Santiago 0
## 750 Dominican Republic Santiago 1
## 774 Dominican Republic Distrito Nacional 1
## 833 Dominican Republic La Vega 0
## 923 Dominican Republic Puerto Plata 0
## 1394 Dominican Republic Santo Domingo 0
## 1395 Dominican Republic Puerto Plata 0Tabla de medidas de tendencia central
library(tidyverse)
tablafinal <- (df_Rep %>%
group_by(state_province) %>%
summarise(fatalities = sum(Fatalities)))
summary(tablafinal$fatalities)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 1.00 3.00 14.29 13.50 68.00knitr::kable(tablafinal) | state_province | fatalities |
|---|---|
| Distrito Nacional | 9 |
| Hato Mayor | 0 |
| La Vega | 68 |
| Puerto Plata | 2 |
| San Cristóbal | 3 |
| Santiago | 18 |
| Santo Domingo | 0 |
dataRep <- c("Distrito Nacional", "Distrito Nacional","Distrito Nacional","Distrito Nacional","Distrito Nacional","Distrito Nacional","Distrito Nacional","Distrito Nacional","Distrito Nacional","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega", "La Vega","La Vega","La Vega","La Vega","La Vega","La Vega","La Vega","Puerto Plata", "Puerto Plata", "San Cristóbal", "San Cristóbal", "San Cristóbal","Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago", "Santiago")
knitr::kable(head(dataRep))| x |
|---|
| Distrito Nacional |
| Distrito Nacional |
| Distrito Nacional |
| Distrito Nacional |
| Distrito Nacional |
| Distrito Nacional |
Tabla de frecuencia
library(questionr)
table <- questionr::freq(dataRep, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| La Vega | 68 | 68 | 68 | 68 | 68 |
| Santiago | 18 | 18 | 18 | 86 | 86 |
| Distrito Nacional | 9 | 9 | 9 | 95 | 95 |
| San Cristóbal | 3 | 3 | 3 | 98 | 98 |
| Puerto Plata | 2 | 2 | 2 | 100 | 100 |
| Total | 100 | 100 | 100 | 100 | 100 |
x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]df <- data.frame(x = names, y = freqs)
knitr::kable(df)| x | y |
|---|---|
| La Vega | 68 |
| Santiago | 18 |
| Distrito Nacional | 9 |
| San Cristóbal | 3 |
| Puerto Plata | 2 |
library(ggplot2)
ggplot(data=df, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="black") + coord_flip() +
xlab("Departamentos") +
ylab("Frecuencia")
Gráfico circular y de barras de las distancias de desplazamientos para el país de Brasil
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_BRA <- subset (df, Country == "Brazil")
knitr::kable(head(df_BRA))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 193 | 1019 | 5/6/09 | SA | Brazil | BR | Pará | 70888 | Altamira | 1.01848 | -3.1946 | -52.2093 | (-3.1945999999999999, -52.209299999999999) | Landslide | Mudslide | Medium | Rain | NA | 19 | http://en.wikipedia.org/wiki/2009_Brazilian_floods_and_mudslides#cite_note-186k_homeless_from_flood-0 | ||||
| 194 | 1020 | 5/6/09 | SA | Brazil | BR | Maranhão | 48901 | Barra do Corda | 61.75306 | -5.0804 | -45.6010 | (-5.0804, -45.600999999999999) | Landslide | Mudslide | Medium | Rain | NA | 3 | http://www.3news.co.nz/Heavy-rains-leave-186K-homeless-in-northern-Brazil/tabid/417/articleID/102883/cat/782/Default.aspx | ||||
| 947 | 4789 | 3/28/13 | 0:30:00 | SA | Brazil | BR | Amapá | 83927 | Santana | 0.17020 | -0.0568 | -51.1818 | (-0.056800000000000003, -51.181800000000003) | Landslide | Landslide | Medium | Other | NA | 6 | bestonlinebroker.co | http://bestonlinebroker.co/p/3999693490/2013/04/10/beadell-resources-why-the-silence-after-a-major-mining-disaster | ||
| 1348 | 6591 | 1/4/14 | SA | Brazil | BR | Pará | 78050 | Abaetetuba | 1.04062 | Urban area | -1.7225 | -48.8907 | (-1.7224999999999999, -48.890700000000002) | Landslide | Riverbank collapse | Medium | Unknown | 0 | 0 | G1 | http://g1.globo.com/pa/para/noticia/2014/01/area-que-desabou-em-abaetetuba-tinha-aterro-irregular-diz-engenheira.html |
df_BRA %>%
select(Country, State, City, Distance)## Country State City Distance
## 193 Brazil Pará Altamira 1.01848
## 194 Brazil Maranhão Barra do Corda 61.75306
## 947 Brazil Amapá Santana 0.17020
## 1348 Brazil Pará Abaetetuba 1.04062library(ggplot2)
ggplot(data=df_BRA, aes(x = "Brasil", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_BRA, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para la entidad federal de Amapá
library(readr)
library(knitr)
df_BRA <- subset (df, State == "Amapá")
df_BRA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 947 Brazil Amapá Santana 0.1702ggplot(data=df_BRA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_BRA,aes(x="Amapá",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Amapá")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para la entidad federal de Maranhão
library(readr)
library(knitr)
df_BRA <- subset (df, State == "Maranhão")
df_BRA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 194 Brazil Maranhão Barra do Corda 61.75306ggplot(data=df_BRA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_BRA,aes(x="Maranhão",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Maranhão")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para la entidad federal de Pará
library(readr)
library(knitr)
df_BRA <- subset (df, State == "Pará")
df_BRA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 193 Brazil Pará Altamira 1.01848
## 1348 Brazil Pará Abaetetuba 1.04062ggplot(data=df_BRA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_BRA,aes(x="Pará",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Pará")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_BRA <- subset (df, Country == "Brazil")
knitr::kable(head(df_BRA))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 193 | 1019 | 5/6/09 | SA | Brazil | BR | Pará | 70888 | Altamira | 1.01848 | -3.1946 | -52.2093 | (-3.1945999999999999, -52.209299999999999) | Landslide | Mudslide | Medium | Rain | NA | 19 | http://en.wikipedia.org/wiki/2009_Brazilian_floods_and_mudslides#cite_note-186k_homeless_from_flood-0 | ||||
| 194 | 1020 | 5/6/09 | SA | Brazil | BR | Maranhão | 48901 | Barra do Corda | 61.75306 | -5.0804 | -45.6010 | (-5.0804, -45.600999999999999) | Landslide | Mudslide | Medium | Rain | NA | 3 | http://www.3news.co.nz/Heavy-rains-leave-186K-homeless-in-northern-Brazil/tabid/417/articleID/102883/cat/782/Default.aspx | ||||
| 947 | 4789 | 3/28/13 | 0:30:00 | SA | Brazil | BR | Amapá | 83927 | Santana | 0.17020 | -0.0568 | -51.1818 | (-0.056800000000000003, -51.181800000000003) | Landslide | Landslide | Medium | Other | NA | 6 | bestonlinebroker.co | http://bestonlinebroker.co/p/3999693490/2013/04/10/beadell-resources-why-the-silence-after-a-major-mining-disaster | ||
| 1348 | 6591 | 1/4/14 | SA | Brazil | BR | Pará | 78050 | Abaetetuba | 1.04062 | Urban area | -1.7225 | -48.8907 | (-1.7224999999999999, -48.890700000000002) | Landslide | Riverbank collapse | Medium | Unknown | 0 | 0 | G1 | http://g1.globo.com/pa/para/noticia/2014/01/area-que-desabou-em-abaetetuba-tinha-aterro-irregular-diz-engenheira.html |
Distance <- df_BRA$Distance
summary(df_BRA$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1702 0.8064 1.0295 15.9956 16.2187 61.7531library(pastecs)##
## Attaching package: 'pastecs'## The following object is masked from 'package:tidyr':
##
## extract## The following objects are masked from 'package:dplyr':
##
## first, laststat.desc(df_BRA)## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced## id date time continent_code Country country_code State
## nbr.val 4.000000e+00 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 1.019000e+03 NA NA NA NA NA NA
## max 6.591000e+03 NA NA NA NA NA NA
## range 5.572000e+03 NA NA NA NA NA NA
## sum 1.341900e+04 NA NA NA NA NA NA
## median 2.904500e+03 NA NA NA NA NA NA
## mean 3.354750e+03 NA NA NA NA NA NA
## SE.mean 1.397533e+03 NA NA NA NA NA NA
## CI.mean.0.95 4.447573e+03 NA NA NA NA NA NA
## var 7.812391e+06 NA NA NA NA NA NA
## std.dev 2.795065e+03 NA NA NA NA NA NA
## coef.var 8.331665e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 4.000000e+00 NA 4.000000 NA 4.0000000
## nbr.null 0.000000e+00 NA 0.000000 NA 0.0000000
## nbr.na 0.000000e+00 NA 0.000000 NA 0.0000000
## min 4.890100e+04 NA 0.170200 NA -5.0804000
## max 8.392700e+04 NA 61.753060 NA -0.0568000
## range 3.502600e+04 NA 61.582860 NA 5.0236000
## sum 2.817660e+05 NA 63.982360 NA -10.0543000
## median 7.446900e+04 NA 1.029550 NA -2.4585500
## mean 7.044150e+04 NA 15.995590 NA -2.5135750
## SE.mean 7.659093e+03 NA 15.253836 NA 1.0690311
## CI.mean.0.95 2.437465e+04 NA 48.544513 NA 3.4021339
## var 2.346468e+08 NA 930.717994 NA 4.5713096
## std.dev 1.531819e+04 NA 30.507671 NA 2.1380621
## coef.var 2.174597e-01 NA 1.907255 NA -0.8506061
## longitude geolocation hazard_type landslide_type
## nbr.val 4.00000000 NA NA NA
## nbr.null 0.00000000 NA NA NA
## nbr.na 0.00000000 NA NA NA
## min -52.20930000 NA NA NA
## max -45.60100000 NA NA NA
## range 6.60830000 NA NA NA
## sum -197.88280000 NA NA NA
## median -50.03625000 NA NA NA
## mean -49.47070000 NA NA NA
## SE.mean 1.46454689 NA NA NA
## CI.mean.0.95 4.66084185 NA NA NA
## var 8.57959042 NA NA NA
## std.dev 2.92909379 NA NA NA
## coef.var -0.05920866 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 1 4.000000 NA
## nbr.null NA NA NA 1 1.000000 NA
## nbr.na NA NA NA 3 0.000000 NA
## min NA NA NA 0 0.000000 NA
## max NA NA NA 0 19.000000 NA
## range NA NA NA 0 19.000000 NA
## sum NA NA NA 0 28.000000 NA
## median NA NA NA 0 4.500000 NA
## mean NA NA NA 0 7.000000 NA
## SE.mean NA NA NA NA 4.183300 NA
## CI.mean.0.95 NA NA NA NaN 13.313128 NA
## var NA NA NA NA 70.000000 NA
## std.dev NA NA NA NA 8.366600 NA
## coef.var NA NA NA NA 1.195229 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)## NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.## Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and## if Arial Narrow is not on your system, please see https://bit.ly/arialnarrowlibrary(viridis)## Loading required package: viridisLite##
## Attaching package: 'viridis'## The following object is masked from 'package:scales':
##
## viridis_paldf <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="red", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Brasil presentan un claro sesgo hacia la derecha.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Colombia
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[4] <- "Continent"
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_COL <- subset (df, Country == "Colombia")
knitr::kable(head(df_COL))| id | date | time | Continent | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | 77 | 5/21/07 | SA | Colombia | CO | Risaralda | 440118 | Pereira | 0.62022 | 4.8081 | -75.6941 | (4.8080999999999996, -75.694100000000006) | Landslide | Mudslide | Large | Rain | NA | 13 | Reuters - AlertNet.org | http://www.reuters.com/news/video/videoStory?videoId=53594&feedType=RSS&rpc=23 | |||
| 49 | 307 | 10/13/07 | SA | Colombia | CO | Cauca | 9985 | Suárez | 8.46579 | 2.9437 | -76.7719 | (2.9437000000000002, -76.771900000000002) | Landslide | Mudslide | Large | Continuous rain | NA | 24 | Reuters - AlertNet.org | http://www.reuters.com/article/newsOne/idUSN1329387220071013 | |||
| 70 | 397 | 12/19/07 | SA | Colombia | CO | Tolima | 4892 | Ambalema | 6.96130 | 4.8470 | -74.7631 | (4.8470000000000004, -74.763099999999994) | Landslide | Landslide | Large | Rain | NA | NA | Indiamuslims.info | http://www.indiamuslims.info/news/2007/dec/20/eight_people_rescued_colombian_landslide.html | |||
| 103 | 562 | 5/31/08 | SA | Colombia | CO | Antioquia | 1999979 | Medellín | 5.12170 | 6.2746 | -75.6039 | (6.2746000000000004, -75.603899999999996) | Landslide | Complex | Large | Downpour | NA | 27 | http://english.people.com.cn/90001/90777/90852/6422291.html | ||||
| 110 | 605 | 6/24/08 | SA | Colombia | CO | Norte de Santander | 1502 | Hacarí | 0.38844 | 8.3200 | -73.1500 | (8.32, -73.150000000000006) | Landslide | Landslide | Medium | Downpour | NA | 10 | http://news.xinhuanet.com/english/2008-06/25/content_8434589.htm | ||||
| 117 | 644 | 7/14/08 | SA | Colombia | CO | Cundinamarca | 1374 | Quetame | 8.58891 | 4.4100 | -73.8600 | (4.41, -73.86) | Landslide | Landslide | Medium | Downpour | NA | 4 | http://news.xinhuanet.com/english/2008-07/15/content_8548107.htm |
df_COL %>%
select(Country, State, City, Distance)## Country State City Distance
## 8 Colombia Risaralda Pereira 0.62022
## 49 Colombia Cauca Suárez 8.46579
## 70 Colombia Tolima Ambalema 6.96130
## 103 Colombia Antioquia Medellín 5.12170
## 110 Colombia Norte de Santander Hacarí 0.38844
## 117 Colombia Cundinamarca Quetame 8.58891
## 119 Colombia Antioquia Briceño 6.44532
## 133 Colombia Antioquia Caldas 1.27637
## 134 Colombia Córdoba Cereté 0.68544
## 152 Colombia Córdoba Moñitos 23.49217
## 163 Colombia Antioquia Medellín 4.09028
## 166 Colombia Cauca Miranda 0.70558
## 180 Colombia Huila Hobo 10.18040
## 191 Colombia Córdoba Sahagún 0.14776
## 303 Colombia Cundinamarca Girardot 0.30587
## 340 Colombia Valle del Cauca Cali 1.84941
## 349 Colombia Meta Puerto López 22.53724
## 352 Colombia Cundinamarca Soacha 10.36239
## 396 Colombia Magdalena Ciénaga 51.84125
## 403 Colombia Norte de Santander San Calixto 7.85369
## 425 Colombia Santander Málaga 2.99929
## 434 Colombia Santander Málaga 2.99929
## 454 Colombia Risaralda Apía 8.18229
## 455 Colombia Antioquia Giraldo 2.55282
## 479 Colombia Boyacá San Pablo de Borbur 1.53032
## 517 Colombia Risaralda Quinchía 0.11421
## 518 Colombia Antioquia Medellín 2.18776
## 519 Colombia Sucre San Marcos 25.51411
## 520 Colombia Cundinamarca La Calera 7.28959
## 523 Colombia Caldas Marquetalia 17.48659
## 524 Colombia Caldas Marquetalia 3.72195
## 525 Colombia Caldas Marulanda 0.72830
## 526 Colombia Norte de Santander Arboledas 14.62503
## 527 Colombia Santander San Andrés 3.06383
## 528 Colombia Antioquia Cáceres 4.29197
## 553 Colombia Antioquia Bello 2.04898
## 574 Colombia Nariño San Pablo 4.95353
## 594 Colombia Santander Matanza 6.16385
## 619 Colombia Caldas Villamaría 2.36822
## 621 Colombia Norte de Santander Cúcuta 3.60520
## 623 Colombia Caldas Manizales 1.70850
## 629 Colombia Antioquia Medellín 4.19867
## 631 Colombia Nariño El Tambo 0.60599
## 632 Colombia Nariño El Tambo 3.81445
## 637 Colombia Caldas Manizales 1.07765
## 638 Colombia Caldas Villamaría 15.82404
## 640 Colombia Antioquia Santa Rosa de Osos 18.91189
## 643 Colombia Santander Florián 0.44753
## 644 Colombia Cundinamarca San Antonio del Tequendama 6.08628
## 658 Colombia Antioquia Municipio de Copacabana 7.98838
## 659 Colombia Antioquia Frontino 5.06960
## 690 Colombia Boyacá Samacá 0.85976
## 701 Colombia Boyacá Belén 2.28425
## 714 Colombia Valle del Cauca Cali 8.30406
## 715 Colombia Santander Lebrija 9.21217
## 716 Colombia Santander Piedecuesta 10.55986
## 718 Colombia Santander San Vicente de Chucurí 0.80432
## 753 Colombia Bolívar Santa Rosa del Sur 26.89879
## 821 Colombia Antioquia Medellín 1.73101
## 822 Colombia Valle del Cauca Buenaventura 20.25692
## 826 Colombia Antioquia Ciudad Bolívar 24.48479
## 830 Colombia Quindío Calarcá 1.35196
## 831 Colombia Caldas Manizales 0.64094
## 832 Colombia Tolima Cajamarca 8.56086
## 835 Colombia Tolima Cajamarca 7.87303
## 836 Colombia Cundinamarca Funza 9.23778
## 838 Colombia Tolima Carmen de Apicalá 9.65157
## 848 Colombia Santander Los Santos 12.61362
## 849 Colombia Cundinamarca Soacha 5.17650
## 850 Colombia Cauca La Cruz 0.64469
## 851 Colombia Caldas Norcasia 16.94642
## 852 Colombia Nariño San Lorenzo 0.37809
## 853 Colombia Magdalena Santa Marta 0.79694
## 854 Colombia Caldas Aranzazu 2.53047
## 855 Colombia Risaralda Dos Quebradas 0.74201
## 859 Colombia Antioquia Envigado 2.07081
## 876 Colombia Antioquia Amalfi 11.11685
## 879 Colombia Caquetá Florencia 15.42607
## 911 Colombia Huila San Agustín 6.84683
## 921 Colombia Norte de Santander San Calixto 11.19714
## 928 Colombia Huila Gigante 0.93184
## 1054 Colombia Caquetá Florencia 15.16116
## 1071 Colombia Huila Áquira 3.14201
## 1207 Colombia Nariño Cumbitara 16.97776
## 1220 Colombia Valle del Cauca Palmira 7.70237
## 1221 Colombia Huila Palermo 0.00003
## 1222 Colombia Santander Bucaramanga 1.33829
## 1290 Colombia Magdalena Ariguaní 28.50569
## 1305 Colombia Santander San Vicente de Chucurí 1.08964
## 1344 Colombia Cauca Almaguer 17.31514
## 1362 Colombia Antioquia Barbosa 7.78677
## 1456 Colombia Putumayo San Francisco 1.47940
## 1457 Colombia Antioquia Salgar 3.09014
## 1471 Colombia Cauca Jambaló 8.81287
## 1565 Colombia Huila Rivera 20.31227
## 1669 Colombia Antioquia Mutatá 15.04256library(ggplot2)
ggplot(data=df_COL, aes(x = "Colombia", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_COL, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Antioquia
library(readr)
library(knitr)
df_COL <- subset (df, State == "Antioquia")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 103 Colombia Antioquia Medellín 5.12170
## 119 Colombia Antioquia Briceño 6.44532
## 133 Colombia Antioquia Caldas 1.27637
## 163 Colombia Antioquia Medellín 4.09028
## 455 Colombia Antioquia Giraldo 2.55282
## 518 Colombia Antioquia Medellín 2.18776
## 528 Colombia Antioquia Cáceres 4.29197
## 553 Colombia Antioquia Bello 2.04898
## 629 Colombia Antioquia Medellín 4.19867
## 640 Colombia Antioquia Santa Rosa de Osos 18.91189
## 658 Colombia Antioquia Municipio de Copacabana 7.98838
## 659 Colombia Antioquia Frontino 5.06960
## 821 Colombia Antioquia Medellín 1.73101
## 826 Colombia Antioquia Ciudad Bolívar 24.48479
## 859 Colombia Antioquia Envigado 2.07081
## 876 Colombia Antioquia Amalfi 11.11685
## 1362 Colombia Antioquia Barbosa 7.78677
## 1457 Colombia Antioquia Salgar 3.09014
## 1669 Colombia Antioquia Mutatá 15.04256ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Antioquia",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="black",size=1.2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Antioquia")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Bolívar
library(readr)
library(knitr)
df_COL <- subset (df, State == "Bolívar")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 753 Colombia Bolívar Santa Rosa del Sur 26.89879ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Bolívar",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="black",size=4)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Bolívar")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Boyacá
library(readr)
library(knitr)
df_COL <- subset (df, State == "Boyacá")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 479 Colombia Boyacá San Pablo de Borbur 1.53032
## 690 Colombia Boyacá Samacá 0.85976
## 701 Colombia Boyacá Belén 2.28425ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Boyacá",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="black",size=4)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Boyacá")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Caldas
library(readr)
library(knitr)
df_COL <- subset (df, State == "Caldas")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 523 Colombia Caldas Marquetalia 17.48659
## 524 Colombia Caldas Marquetalia 3.72195
## 525 Colombia Caldas Marulanda 0.72830
## 619 Colombia Caldas Villamaría 2.36822
## 623 Colombia Caldas Manizales 1.70850
## 637 Colombia Caldas Manizales 1.07765
## 638 Colombia Caldas Villamaría 15.82404
## 831 Colombia Caldas Manizales 0.64094
## 851 Colombia Caldas Norcasia 16.94642
## 854 Colombia Caldas Aranzazu 2.53047ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Caldas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Caldas")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Caquetá
library(readr)
library(knitr)
df_COL <- subset (df, State == "Caquetá")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 879 Colombia Caquetá Florencia 15.42607
## 1054 Colombia Caquetá Florencia 15.16116ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Caquetá",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Caquetá")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Cauca
library(readr)
library(knitr)
df_COL <- subset (df, State == "Cauca")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 49 Colombia Cauca Suárez 8.46579
## 166 Colombia Cauca Miranda 0.70558
## 850 Colombia Cauca La Cruz 0.64469
## 1344 Colombia Cauca Almaguer 17.31514
## 1471 Colombia Cauca Jambaló 8.81287ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Cauca",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Cauca")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Córdoba
library(readr)
library(knitr)
df_COL <- subset (df, State == "Córdoba")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 134 Colombia Córdoba Cereté 0.68544
## 152 Colombia Córdoba Moñitos 23.49217
## 191 Colombia Córdoba Sahagún 0.14776ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Córdoba",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Córdoba")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Cundinamarca
library(readr)
library(knitr)
df_COL <- subset (df, State == "Cundinamarca")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 117 Colombia Cundinamarca Quetame 8.58891
## 303 Colombia Cundinamarca Girardot 0.30587
## 352 Colombia Cundinamarca Soacha 10.36239
## 520 Colombia Cundinamarca La Calera 7.28959
## 644 Colombia Cundinamarca San Antonio del Tequendama 6.08628
## 836 Colombia Cundinamarca Funza 9.23778
## 849 Colombia Cundinamarca Soacha 5.17650ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Cundinamarca",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Cundinamarca")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Huila
library(readr)
library(knitr)
df_COL <- subset (df, State == "Huila")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 180 Colombia Huila Hobo 10.18040
## 911 Colombia Huila San Agustín 6.84683
## 928 Colombia Huila Gigante 0.93184
## 1071 Colombia Huila Áquira 3.14201
## 1221 Colombia Huila Palermo 0.00003
## 1565 Colombia Huila Rivera 20.31227ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Huila",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Huila")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Magdalena
library(readr)
library(knitr)
df_COL <- subset (df, State == "Magdalena")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 396 Colombia Magdalena Ciénaga 51.84125
## 853 Colombia Magdalena Santa Marta 0.79694
## 1290 Colombia Magdalena Ariguaní 28.50569ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Magdalena",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Magdalena")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Meta
library(readr)
library(knitr)
df_COL <- subset (df, State == "Meta")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 349 Colombia Meta Puerto López 22.53724ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Meta",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Meta")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Nariño
library(readr)
library(knitr)
df_COL <- subset (df, State == "Nariño")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 574 Colombia Nariño San Pablo 4.95353
## 631 Colombia Nariño El Tambo 0.60599
## 632 Colombia Nariño El Tambo 3.81445
## 852 Colombia Nariño San Lorenzo 0.37809
## 1207 Colombia Nariño Cumbitara 16.97776ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Nariño",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Nariño")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Norte de Santander
library(readr)
library(knitr)
df_COL <- subset (df, State == "Norte de Santander")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 110 Colombia Norte de Santander Hacarí 0.38844
## 403 Colombia Norte de Santander San Calixto 7.85369
## 526 Colombia Norte de Santander Arboledas 14.62503
## 621 Colombia Norte de Santander Cúcuta 3.60520
## 921 Colombia Norte de Santander San Calixto 11.19714ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Norte de Santander",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Norte de Santander")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Putumayo
library(readr)
library(knitr)
df_COL <- subset (df, State == "Putumayo")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1456 Colombia Putumayo San Francisco 1.4794ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Putumayo",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Putumayo")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Quindío
library(readr)
library(knitr)
df_COL <- subset (df, State == "Quindío")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 830 Colombia Quindío Calarcá 1.35196ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Quindío",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Quindío")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Risaralda
library(readr)
library(knitr)
df_COL <- subset (df, State == "Risaralda")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 8 Colombia Risaralda Pereira 0.62022
## 454 Colombia Risaralda Apía 8.18229
## 517 Colombia Risaralda Quinchía 0.11421
## 855 Colombia Risaralda Dos Quebradas 0.74201ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Risaralda",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Risaralda")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Santander
library(readr)
library(knitr)
df_COL <- subset (df, State == "Santander")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 425 Colombia Santander Málaga 2.99929
## 434 Colombia Santander Málaga 2.99929
## 527 Colombia Santander San Andrés 3.06383
## 594 Colombia Santander Matanza 6.16385
## 643 Colombia Santander Florián 0.44753
## 715 Colombia Santander Lebrija 9.21217
## 716 Colombia Santander Piedecuesta 10.55986
## 718 Colombia Santander San Vicente de Chucurí 0.80432
## 848 Colombia Santander Los Santos 12.61362
## 1222 Colombia Santander Bucaramanga 1.33829
## 1305 Colombia Santander San Vicente de Chucurí 1.08964ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Santander",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Santander")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Sucre
library(readr)
library(knitr)
df_COL <- subset (df, State == "Sucre")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 519 Colombia Sucre San Marcos 25.51411ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Sucre",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Sucre")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Tolima
library(readr)
library(knitr)
df_COL <- subset (df, State == "Tolima")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 70 Colombia Tolima Ambalema 6.96130
## 832 Colombia Tolima Cajamarca 8.56086
## 835 Colombia Tolima Cajamarca 7.87303
## 838 Colombia Tolima Carmen de Apicalá 9.65157ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Tolima",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Tolima")
Gráfico circular y de barras apiladas de las distancias de desplazamientos para el departamento de Valle del Cauca
library(readr)
library(knitr)
df_COL <- subset (df, State == "Valle del Cauca")
df_COL %>%
select(Country, State, City, Distance) ## Country State City Distance
## 340 Colombia Valle del Cauca Cali 1.84941
## 714 Colombia Valle del Cauca Cali 8.30406
## 822 Colombia Valle del Cauca Buenaventura 20.25692
## 1220 Colombia Valle del Cauca Palmira 7.70237ggplot(data=df_COL, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_COL,aes(x="Valle del Cauca",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Valle del Cauca")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_COL <- subset (df, Country == "Colombia")
knitr::kable(head(df_COL))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8 | 77 | 5/21/07 | SA | Colombia | CO | Risaralda | 440118 | Pereira | 0.62022 | 4.8081 | -75.6941 | (4.8080999999999996, -75.694100000000006) | Landslide | Mudslide | Large | Rain | NA | 13 | Reuters - AlertNet.org | http://www.reuters.com/news/video/videoStory?videoId=53594&feedType=RSS&rpc=23 | |||
| 49 | 307 | 10/13/07 | SA | Colombia | CO | Cauca | 9985 | Suárez | 8.46579 | 2.9437 | -76.7719 | (2.9437000000000002, -76.771900000000002) | Landslide | Mudslide | Large | Continuous rain | NA | 24 | Reuters - AlertNet.org | http://www.reuters.com/article/newsOne/idUSN1329387220071013 | |||
| 70 | 397 | 12/19/07 | SA | Colombia | CO | Tolima | 4892 | Ambalema | 6.96130 | 4.8470 | -74.7631 | (4.8470000000000004, -74.763099999999994) | Landslide | Landslide | Large | Rain | NA | NA | Indiamuslims.info | http://www.indiamuslims.info/news/2007/dec/20/eight_people_rescued_colombian_landslide.html | |||
| 103 | 562 | 5/31/08 | SA | Colombia | CO | Antioquia | 1999979 | Medellín | 5.12170 | 6.2746 | -75.6039 | (6.2746000000000004, -75.603899999999996) | Landslide | Complex | Large | Downpour | NA | 27 | http://english.people.com.cn/90001/90777/90852/6422291.html | ||||
| 110 | 605 | 6/24/08 | SA | Colombia | CO | Norte de Santander | 1502 | Hacarí | 0.38844 | 8.3200 | -73.1500 | (8.32, -73.150000000000006) | Landslide | Landslide | Medium | Downpour | NA | 10 | http://news.xinhuanet.com/english/2008-06/25/content_8434589.htm | ||||
| 117 | 644 | 7/14/08 | SA | Colombia | CO | Cundinamarca | 1374 | Quetame | 8.58891 | 4.4100 | -73.8600 | (4.41, -73.86) | Landslide | Landslide | Medium | Downpour | NA | 4 | http://news.xinhuanet.com/english/2008-07/15/content_8548107.htm |
Distance <- df_COL$Distance
summary(df_COL$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00003 1.44754 5.01156 7.62465 10.22590 51.84125library(pastecs)
stat.desc(df_COL)## id date time continent_code Country country_code State
## nbr.val 9.600000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 7.700000e+01 NA NA NA NA NA NA
## max 7.513000e+03 NA NA NA NA NA NA
## range 7.436000e+03 NA NA NA NA NA NA
## sum 3.289040e+05 NA NA NA NA NA NA
## median 3.366000e+03 NA NA NA NA NA NA
## mean 3.426083e+03 NA NA NA NA NA NA
## SE.mean 1.757988e+02 NA NA NA NA NA NA
## CI.mean.0.95 3.490047e+02 NA NA NA NA NA NA
## var 2.966901e+06 NA NA NA NA NA NA
## std.dev 1.722469e+03 NA NA NA NA NA NA
## coef.var 5.027518e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 9.600000e+01 NA 96.0000000 NA 96.0000000
## nbr.null 0.000000e+00 NA 0.0000000 NA 0.0000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.0000000
## min 1.091000e+03 NA 0.0000300 NA 1.1656000
## max 2.392877e+06 NA 51.8412500 NA 11.2475000
## range 2.391786e+06 NA 51.8412200 NA 10.0819000
## sum 2.132931e+07 NA 731.9660900 NA 516.6296000
## median 9.940500e+03 NA 5.0115650 NA 5.3945500
## mean 2.221804e+05 NA 7.6246468 NA 5.3815583
## SE.mean 5.633984e+04 NA 0.8623174 NA 0.2267416
## CI.mean.0.95 1.118487e+05 NA 1.7119165 NA 0.4501390
## var 3.047210e+11 NA 71.3847624 NA 4.9355287
## std.dev 5.520154e+05 NA 8.4489504 NA 2.2216050
## coef.var 2.484538e+00 NA 1.1081104 NA 0.4128182
## longitude geolocation hazard_type landslide_type
## nbr.val 9.600000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -7.773190e+01 NA NA NA
## max -7.249080e+01 NA NA NA
## range 5.241100e+00 NA NA NA
## sum -7.207062e+03 NA NA NA
## median -7.544180e+01 NA NA NA
## mean -7.507356e+01 NA NA NA
## SE.mean 1.301323e-01 NA NA NA
## CI.mean.0.95 2.583453e-01 NA NA NA
## var 1.625705e+00 NA NA NA
## std.dev 1.275031e+00 NA NA NA
## coef.var -1.698376e-02 NA NA NA
## landslide_size trigger storm_name injuries fatalities
## nbr.val NA NA NA 16.000000 91.000000
## nbr.null NA NA NA 9.000000 28.000000
## nbr.na NA NA NA 80.000000 5.000000
## min NA NA NA 0.000000 0.000000
## max NA NA NA 40.000000 92.000000
## range NA NA NA 40.000000 92.000000
## sum NA NA NA 73.000000 570.000000
## median NA NA NA 0.000000 2.000000
## mean NA NA NA 4.562500 6.263736
## SE.mean NA NA NA 2.554357 1.522548
## CI.mean.0.95 NA NA NA 5.444483 3.024808
## var NA NA NA 104.395833 210.951893
## std.dev NA NA NA 10.217428 14.524183
## coef.var NA NA NA 2.239436 2.318773
## source_name source_link
## nbr.val NA NA
## nbr.null NA NA
## nbr.na NA NA
## min NA NA
## max NA NA
## range NA NA
## sum NA NA
## median NA NA
## mean NA NA
## SE.mean NA NA
## CI.mean.0.95 NA NA
## var NA NA
## std.dev NA NA
## coef.var NA NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="yellow", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=11)) +
ggtitle("Datos de deslizamientos para Colombia") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Colombia están medianamente bien distribuidos con un ligero sesgo hacia la derecha y se encuentran presentes algunos datos aberrantes.
Gráfico circular y de barras de las distancias de desplazamientos para el país del Ecuador
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_ECU <- subset (df, Country == "Ecuador")
knitr::kable(head(df_ECU))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 105 | 6/27/07 | SA | Ecuador | EC | Zamora-Chinchipe | 15276 | Zamora | 0.47714 | -4.0650 | -78.9510 | (-4.0650000000000004, -78.950999999999993) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 10 | 106 | 6/27/07 | SA | Ecuador | EC | Loja | 117796 | Loja | 0.35649 | -3.9900 | -79.2050 | (-3.99, -79.204999999999998) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 11 | 107 | 6/27/07 | SA | Ecuador | EC | Pichincha | 5114 | Sangolquí | 33.94603 | -0.3560 | -78.1480 | (-0.35599999999999998, -78.147999999999996) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 78 | 468 | 2/28/08 | SA | Ecuador | EC | Napo | 7309 | Archidona | 50.21741 | -0.4635 | -77.8928 | (-0.46350000000000002, -77.892799999999994) | Landslide | Landslide | Medium | Rain | NA | 7 | http://english.aljazeera.net/NR/exeres/868843D7-B211-4DE5-AFBE-31C29CF79C5A.htm | ||||
| 87 | 489 | 4/1/08 | SA | Ecuador | EC | Pichincha | 1399814 | Quito | 1.56942 | -0.2196 | -78.5347 | (-0.21959999999999999, -78.534700000000001) | Landslide | Landslide | Medium | Rain | NA | NA | http://www.plenglish.com/Article.asp?ID=%7B39BA85A0-5900-4A4A-A329-818B3FC61EA1%7D&language=EN | ||||
| 218 | 1186 | 12/28/09 | Night | SA | Ecuador | EC | Santo Domingo de los Tsáchilas | 200421 | Santo Domingo de los Colorados | 1.16036 | Unknown | -0.3152 | -78.9724 | (-0.31519999999999998, -78.972399999999993) | Landslide | Mudslide | Medium | Downpour | 5 | 3 | Latin American Herald Tribune | http://www.laht.com/article.asp?ArticleId=327938&CategoryId=14089 |
df_ECU %>%
select(Country, State, City, Distance)## Country State City
## 9 Ecuador Zamora-Chinchipe Zamora
## 10 Ecuador Loja Loja
## 11 Ecuador Pichincha Sangolquí
## 78 Ecuador Napo Archidona
## 87 Ecuador Pichincha Quito
## 218 Ecuador Santo Domingo de los Tsáchilas Santo Domingo de los Colorados
## 238 Ecuador Pichincha Machachi
## 253 Ecuador Tungurahua Baños
## 339 Ecuador Esmeraldas Esmeraldas
## 586 Ecuador Pichincha Machachi
## 587 Ecuador Cotopaxi Saquisilí
## 660 Ecuador Morona-Santiago Macas
## 697 Ecuador Pichincha Quito
## 751 Ecuador Carchi El Ángel
## 868 Ecuador Manabi Chone
## 872 Ecuador Manabi Tosagua
## 942 Ecuador Guayas Balao
## 956 Ecuador Esmeraldas Esmeraldas
## 984 Ecuador Pichincha Cayambe
## 1304 Ecuador Carchi San Gabriel
## 1332 Ecuador Pichincha Quito
## 1333 Ecuador Pichincha Quito
## 1335 Ecuador Sucumbios Gonzalo Pizarro
## 1365 Ecuador Azuay Cuenca
## 1368 Ecuador Zamora-Chinchipe Zamora
## 1451 Ecuador Loja Macará
## 1452 Ecuador Loja Loja
## 1453 Ecuador Loja Catamayo
## 1454 Ecuador Azuay Cuenca
## 1566 Ecuador Pichincha Quito
## Distance
## 9 0.47714
## 10 0.35649
## 11 33.94603
## 78 50.21741
## 87 1.56942
## 218 1.16036
## 238 26.18676
## 253 11.91442
## 339 2.81891
## 586 25.82923
## 587 30.81169
## 660 46.77007
## 697 4.39517
## 751 28.29459
## 868 19.85816
## 872 7.67919
## 942 16.34404
## 956 21.26652
## 984 45.69792
## 1304 10.47204
## 1332 26.72137
## 1333 23.97854
## 1335 11.55916
## 1365 13.21139
## 1368 1.23724
## 1451 18.88784
## 1452 1.82885
## 1453 17.57187
## 1454 10.16196
## 1566 4.25486library(ggplot2)
ggplot(data=df_ECU, aes(x = "Ecuador", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_ECU, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Azuay
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Azuay")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1365 Ecuador Azuay Cuenca 13.21139
## 1454 Ecuador Azuay Cuenca 10.16196ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Azuay",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="black",size=1.2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Azuay")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Carchi
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Carchi")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 751 Ecuador Carchi El Ángel 28.29459
## 1304 Ecuador Carchi San Gabriel 10.47204ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Carchi",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="black",size=1.2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Carchi")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Cotopaxi
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Cotopaxi")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 587 Ecuador Cotopaxi Saquisilí 30.81169ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Cotopaxi",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="black",size=1.2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Cotopaxi")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Esmeraldas
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Esmeraldas")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 339 Ecuador Esmeraldas Esmeraldas 2.81891
## 956 Ecuador Esmeraldas Esmeraldas 21.26652ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Esmeraldas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Esmeraldas")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Guayas
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Guayas")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 942 Ecuador Guayas Balao 16.34404ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Guayas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Guayas")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Loja
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Loja")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 10 Ecuador Loja Loja 0.35649
## 1451 Ecuador Loja Macará 18.88784
## 1452 Ecuador Loja Loja 1.82885
## 1453 Ecuador Loja Catamayo 17.57187ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Loja",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Loja")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Morona-Santiago
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Morona-Santiago")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 660 Ecuador Morona-Santiago Macas 46.77007ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Morona-Santiago",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Morona-Santiago")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Napo
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Napo")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 78 Ecuador Napo Archidona 50.21741ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Napo",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Napo")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Pichincha
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Pichincha")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 11 Ecuador Pichincha Sangolquí 33.94603
## 87 Ecuador Pichincha Quito 1.56942
## 238 Ecuador Pichincha Machachi 26.18676
## 586 Ecuador Pichincha Machachi 25.82923
## 697 Ecuador Pichincha Quito 4.39517
## 984 Ecuador Pichincha Cayambe 45.69792
## 1332 Ecuador Pichincha Quito 26.72137
## 1333 Ecuador Pichincha Quito 23.97854
## 1566 Ecuador Pichincha Quito 4.25486ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Pichincha",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Pichincha")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Santo Domingo de los Tsáchilas
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Santo Domingo de los Tsáchilas")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City
## 218 Ecuador Santo Domingo de los Tsáchilas Santo Domingo de los Colorados
## Distance
## 218 1.16036ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Santo Domingo de los Tsáchilas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Santo Domingo de los Tsáchilas")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Sucumbios
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Sucumbios")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1335 Ecuador Sucumbios Gonzalo Pizarro 11.55916ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Sucumbios",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Sucumbios")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Tungurahua
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Tungurahua")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 253 Ecuador Tungurahua Baños 11.91442ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Tungurahua",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Tungurahua")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Zamora-Chinchipe
library(readr)
library(knitr)
df_ECU <- subset (df, State == "Zamora-Chinchipe")
df_ECU %>%
select(Country, State, City, Distance) ## Country State City Distance
## 9 Ecuador Zamora-Chinchipe Zamora 0.47714
## 1368 Ecuador Zamora-Chinchipe Zamora 1.23724ggplot(data=df_ECU, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ECU,aes(x="Zamora-Chinchipe",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Zamora-Chinchipe")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_ECU <- subset (df, Country == "Ecuador")
knitr::kable(head(df_ECU))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 105 | 6/27/07 | SA | Ecuador | EC | Zamora-Chinchipe | 15276 | Zamora | 0.47714 | -4.0650 | -78.9510 | (-4.0650000000000004, -78.950999999999993) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 10 | 106 | 6/27/07 | SA | Ecuador | EC | Loja | 117796 | Loja | 0.35649 | -3.9900 | -79.2050 | (-3.99, -79.204999999999998) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 11 | 107 | 6/27/07 | SA | Ecuador | EC | Pichincha | 5114 | Sangolquí | 33.94603 | -0.3560 | -78.1480 | (-0.35599999999999998, -78.147999999999996) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 78 | 468 | 2/28/08 | SA | Ecuador | EC | Napo | 7309 | Archidona | 50.21741 | -0.4635 | -77.8928 | (-0.46350000000000002, -77.892799999999994) | Landslide | Landslide | Medium | Rain | NA | 7 | http://english.aljazeera.net/NR/exeres/868843D7-B211-4DE5-AFBE-31C29CF79C5A.htm | ||||
| 87 | 489 | 4/1/08 | SA | Ecuador | EC | Pichincha | 1399814 | Quito | 1.56942 | -0.2196 | -78.5347 | (-0.21959999999999999, -78.534700000000001) | Landslide | Landslide | Medium | Rain | NA | NA | http://www.plenglish.com/Article.asp?ID=%7B39BA85A0-5900-4A4A-A329-818B3FC61EA1%7D&language=EN | ||||
| 218 | 1186 | 12/28/09 | Night | SA | Ecuador | EC | Santo Domingo de los Tsáchilas | 200421 | Santo Domingo de los Colorados | 1.16036 | Unknown | -0.3152 | -78.9724 | (-0.31519999999999998, -78.972399999999993) | Landslide | Mudslide | Medium | Downpour | 5 | 3 | Latin American Herald Tribune | http://www.laht.com/article.asp?ArticleId=327938&CategoryId=14089 |
Distance <- df_ECU$Distance
summary(df_ECU$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3565 4.2899 14.7777 17.1826 26.0974 50.2174library(pastecs)
stat.desc(df_ECU)## id date time continent_code Country country_code State
## nbr.val 3.000000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 1.050000e+02 NA NA NA NA NA NA
## max 7.403000e+03 NA NA NA NA NA NA
## range 7.298000e+03 NA NA NA NA NA NA
## sum 1.209440e+05 NA NA NA NA NA NA
## median 4.269500e+03 NA NA NA NA NA NA
## mean 4.031467e+03 NA NA NA NA NA NA
## SE.mean 4.600208e+02 NA NA NA NA NA NA
## CI.mean.0.95 9.408482e+02 NA NA NA NA NA NA
## var 6.348574e+06 NA NA NA NA NA NA
## std.dev 2.519638e+03 NA NA NA NA NA NA
## coef.var 6.249928e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 3.000000e+01 NA 30.0000000 NA 30.0000000
## nbr.null 1.000000e+00 NA 0.0000000 NA 0.0000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.0000000
## min 0.000000e+00 NA 0.3564900 NA -4.3313000
## max 1.399814e+06 NA 50.2174100 NA 0.9374000
## range 1.399814e+06 NA 49.8609200 NA 5.2687000
## sum 8.599781e+06 NA 515.4786400 NA -38.9118000
## median 2.574200e+04 NA 14.7777150 NA -0.5454000
## mean 2.866594e+05 NA 17.1826213 NA -1.2970600
## SE.mean 9.355366e+04 NA 2.6348903 NA 0.3161467
## CI.mean.0.95 1.913387e+05 NA 5.3889557 NA 0.6465926
## var 2.625686e+11 NA 208.2794055 NA 2.9984619
## std.dev 5.124145e+05 NA 14.4318885 NA 1.7316067
## coef.var 1.787538e+00 NA 0.8399119 NA -1.3350244
## longitude geolocation hazard_type landslide_type
## nbr.val 3.000000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -8.030080e+01 NA NA NA
## max -7.746130e+01 NA NA NA
## range 2.839500e+00 NA NA NA
## sum -2.364088e+03 NA NA NA
## median -7.882865e+01 NA NA NA
## mean -7.880293e+01 NA NA NA
## SE.mean 1.287692e-01 NA NA NA
## CI.mean.0.95 2.633627e-01 NA NA NA
## var 4.974455e-01 NA NA NA
## std.dev 7.052982e-01 NA NA NA
## coef.var -8.950152e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 15.000000 26.000000 NA
## nbr.null NA NA NA 8.000000 12.000000 NA
## nbr.na NA NA NA 15.000000 4.000000 NA
## min NA NA NA 0.000000 0.000000 NA
## max NA NA NA 17.000000 20.000000 NA
## range NA NA NA 17.000000 20.000000 NA
## sum NA NA NA 52.000000 96.000000 NA
## median NA NA NA 0.000000 2.000000 NA
## mean NA NA NA 3.466667 3.692308 NA
## SE.mean NA NA NA 1.372751 1.032948 NA
## CI.mean.0.95 NA NA NA 2.944257 2.127397 NA
## var NA NA NA 28.266667 27.741538 NA
## std.dev NA NA NA 5.316641 5.267024 NA
## coef.var NA NA NA 1.533646 1.426486 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión para Ecuador
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="blue", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=11)) +
ggtitle("Deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Ecuador se encuentran bien distribuidos.
Gráfico circular y de barras de las distancias de desplazamientos para el país del Peru
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_PE <- subset (df, Country == "Peru")
knitr::kable(head(df_PE))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 80 | 472 | 3/5/08 | SA | Peru | PE | Huanuco | 53177 | Tingo María | 17.34318 | -9.2114 | -76.1311 | (-9.2113999999999994, -76.131100000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | http://news.xinhuanet.com/english/2008-03/06/content_7727748.htm | ||||
| 90 | 497 | 4/11/08 | SA | Peru | PE | Ancash | 5044 | Carhuaz | 2.43089 | -9.2965 | -77.6613 | (-9.2965, -77.661299999999997) | Landslide | Landslide | Medium | Rain | NA | NA | http://news.xinhuanet.com/english/2008-04/14/content_7972049.htm | ||||
| 184 | 1004 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Parcoy | 9.64894 | -7.9589 | -77.5239 | (-7.9588999999999999, -77.523899999999998) | Landslide | Mudslide | Medium | Downpour | NA | 12 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 185 | 1005 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Aricapampa | 0.00442 | -7.8058 | -77.7172 | (-7.8057999999999996, -77.717200000000005) | Landslide | Mudslide | Medium | Downpour | NA | 0 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 252 | 1459 | 1/28/10 | 15:00:00 | SA | Peru | PE | Ancash | 0 | Anta | 0.13147 | -9.3584 | -77.5984 | (-9.3583999999999996, -77.598399999999998) | Landslide | Complex | Medium | Downpour | NA | 0 | http://daveslandslideblog.blogspot.com/2010/01/new-images-of-level-of-destruction-in.html | |||
| 294 | 1612 | 4/1/10 | Early morning | SA | Peru | PE | Huanuco | 0 | Acomayo | 3.49890 | -9.7996 | -76.1038 | (-9.7995999999999999, -76.103800000000007) | Landslide | Lahar | Large | Downpour | NA | 12 | http://www.thejakartapost.com/news/2010/03/18/mudslide-isolates-1500-residents-solok.html |
df_PE %>%
select(Country, State, City, Distance)## Country State City Distance
## 80 Peru Huanuco Tingo María 17.34318
## 90 Peru Ancash Carhuaz 2.43089
## 184 Peru La Libertad Parcoy 9.64894
## 185 Peru La Libertad Aricapampa 0.00442
## 252 Peru Ancash Anta 0.13147
## 294 Peru Huanuco Acomayo 3.49890
## 295 Peru Huanuco Ambo 0.55865
## 516 Peru Huanuco Huánuco 1.01932
## 578 Peru Ancash Yanac 3.70678
## 864 Peru Ancash Tauca 6.77672
## 912 Peru San Martín Yuracyacu 8.89799
## 1378 Peru Ancash Huachis 3.26788
## 1424 Peru San Martín Naranjos 12.70296
## 1425 Peru San Martín Tarapoto 2.89809library(ggplot2)
ggplot(data=df_PE, aes(x = "Peru", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_PE, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Ancash
library(readr)
library(knitr)
df_PE <- subset (df, State == "Ancash")
df_PE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 90 Peru Ancash Carhuaz 2.43089
## 252 Peru Ancash Anta 0.13147
## 578 Peru Ancash Yanac 3.70678
## 864 Peru Ancash Tauca 6.77672
## 1378 Peru Ancash Huachis 3.26788ggplot(data=df_PE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PE,aes(x="Ancash",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Ancash")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Huanuco
library(readr)
library(knitr)
df_PE <- subset (df, State == "Huanuco")
df_PE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 80 Peru Huanuco Tingo María 17.34318
## 294 Peru Huanuco Acomayo 3.49890
## 295 Peru Huanuco Ambo 0.55865
## 516 Peru Huanuco Huánuco 1.01932ggplot(data=df_PE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PE,aes(x="Huanuco",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Huanuco")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de La Libertad
library(readr)
library(knitr)
df_PE <- subset (df, State == "La Libertad")
df_PE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 105 El Salvador La Libertad Santa Tecla 4.96416
## 184 Peru La Libertad Parcoy 9.64894
## 185 Peru La Libertad Aricapampa 0.00442
## 225 El Salvador La Libertad Antiguo Cuscatlán 4.86219
## 1374 El Salvador La Libertad Santa Tecla 4.60655
## 1596 El Salvador La Libertad Santa Tecla 4.67722
## 1597 El Salvador La Libertad Santa Tecla 9.87553ggplot(data=df_PE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PE,aes(x="La Libertad",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - La Libertad")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de San Martín
library(readr)
library(knitr)
df_PE <- subset (df, State == "San Martín")
df_PE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 912 Peru San Martín Yuracyacu 8.89799
## 1424 Peru San Martín Naranjos 12.70296
## 1425 Peru San Martín Tarapoto 2.89809ggplot(data=df_PE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PE,aes(x="San Martín",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San Martín")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_PE <- subset (df, Country == "Peru")
knitr::kable(head(df_PE))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 80 | 472 | 3/5/08 | SA | Peru | PE | Huanuco | 53177 | Tingo María | 17.34318 | -9.2114 | -76.1311 | (-9.2113999999999994, -76.131100000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | http://news.xinhuanet.com/english/2008-03/06/content_7727748.htm | ||||
| 90 | 497 | 4/11/08 | SA | Peru | PE | Ancash | 5044 | Carhuaz | 2.43089 | -9.2965 | -77.6613 | (-9.2965, -77.661299999999997) | Landslide | Landslide | Medium | Rain | NA | NA | http://news.xinhuanet.com/english/2008-04/14/content_7972049.htm | ||||
| 184 | 1004 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Parcoy | 9.64894 | -7.9589 | -77.5239 | (-7.9588999999999999, -77.523899999999998) | Landslide | Mudslide | Medium | Downpour | NA | 12 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 185 | 1005 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Aricapampa | 0.00442 | -7.8058 | -77.7172 | (-7.8057999999999996, -77.717200000000005) | Landslide | Mudslide | Medium | Downpour | NA | 0 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 252 | 1459 | 1/28/10 | 15:00:00 | SA | Peru | PE | Ancash | 0 | Anta | 0.13147 | -9.3584 | -77.5984 | (-9.3583999999999996, -77.598399999999998) | Landslide | Complex | Medium | Downpour | NA | 0 | http://daveslandslideblog.blogspot.com/2010/01/new-images-of-level-of-destruction-in.html | |||
| 294 | 1612 | 4/1/10 | Early morning | SA | Peru | PE | Huanuco | 0 | Acomayo | 3.49890 | -9.7996 | -76.1038 | (-9.7995999999999999, -76.103800000000007) | Landslide | Lahar | Large | Downpour | NA | 12 | http://www.thejakartapost.com/news/2010/03/18/mudslide-isolates-1500-residents-solok.html |
Distance <- df_PE$Distance
summary(df_PE$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00442 1.37221 3.38339 5.20616 8.36767 17.34318library(pastecs)
stat.desc(df_PE)## id date time continent_code Country country_code State
## nbr.val 1.400000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 4.720000e+02 NA NA NA NA NA NA
## max 6.829000e+03 NA NA NA NA NA NA
## range 6.357000e+03 NA NA NA NA NA NA
## sum 4.245300e+04 NA NA NA NA NA NA
## median 2.160500e+03 NA NA NA NA NA NA
## mean 3.032357e+03 NA NA NA NA NA NA
## SE.mean 6.368217e+02 NA NA NA NA NA NA
## CI.mean.0.95 1.375770e+03 NA NA NA NA NA NA
## var 5.677586e+06 NA NA NA NA NA NA
## std.dev 2.382769e+03 NA NA NA NA NA NA
## coef.var 7.857810e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 1.400000e+01 NA 14.000000 NA 14.0000000
## nbr.null 1.000000e+01 NA 0.000000 NA 0.0000000
## nbr.na 0.000000e+00 NA 0.000000 NA 0.0000000
## min 0.000000e+00 NA 0.004420 NA -10.1258000
## max 1.479590e+05 NA 17.343180 NA -5.7135000
## range 1.479590e+05 NA 17.338760 NA 4.4123000
## sum 2.130450e+05 NA 72.886190 NA -118.2365000
## median 0.000000e+00 NA 3.383390 NA -8.9183000
## mean 1.521750e+04 NA 5.206156 NA -8.4454643
## SE.mean 1.088153e+04 NA 1.393140 NA 0.3908785
## CI.mean.0.95 2.350813e+04 NA 3.009695 NA 0.8444417
## var 1.657709e+09 NA 27.171731 NA 2.1390041
## std.dev 4.071497e+04 NA 5.212651 NA 1.4625334
## coef.var 2.675536e+00 NA 1.001247 NA -0.1731738
## longitude geolocation hazard_type landslide_type
## nbr.val 1.400000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -7.808340e+01 NA NA NA
## max -7.610380e+01 NA NA NA
## range 1.979600e+00 NA NA NA
## sum -1.079477e+03 NA NA NA
## median -7.738725e+01 NA NA NA
## mean -7.710551e+01 NA NA NA
## SE.mean 1.943987e-01 NA NA NA
## CI.mean.0.95 4.199728e-01 NA NA NA
## var 5.290719e-01 NA NA NA
## std.dev 7.273733e-01 NA NA NA
## coef.var -9.433479e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 3 13.000000 NA
## nbr.null NA NA NA 3 6.000000 NA
## nbr.na NA NA NA 11 1.000000 NA
## min NA NA NA 0 0.000000 NA
## max NA NA NA 0 28.000000 NA
## range NA NA NA 0 28.000000 NA
## sum NA NA NA 0 77.000000 NA
## median NA NA NA 0 2.000000 NA
## mean NA NA NA 0 5.923077 NA
## SE.mean NA NA NA 0 2.330020 NA
## CI.mean.0.95 NA NA NA 0 5.076678 NA
## var NA NA NA 0 70.576923 NA
## std.dev NA NA NA 0 8.401007 NA
## coef.var NA NA NA NaN 1.418352 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="red", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=11)) +
ggtitle("Deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Perú presentan una distribución con un sesgo hacia la derecha.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Trinidad y Tobago
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_TAT <- subset (df, Country == "Trinidad and Tobago")
knitr::kable(head(df_TAT))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 224 | 9/1/07 | NA | Trinidad and Tobago | TT | Tobago | 17000 | Scarborough | 9.11607 | 11.2415 | -60.6742 | (11.2415, -60.674199999999999) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Felix | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161197580 | ||
| 61 | 357 | 11/17/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 7.33295 | 11.2965 | -60.6312 | (11.2965, -60.6312) | Landslide | Landslide | Medium | Rain | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161237574 | |||
| 65 | 390 | 12/11/07 | NA | Trinidad and Tobago | TT | Sangre Grande | 15968 | Sangre Grande | 29.28864 | 10.8410 | -61.0550 | (10.840999999999999, -61.055) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | 3 | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 66 | 391 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 8.62938 | 11.3000 | -60.6440 | (11.3, -60.643999999999998) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 67 | 392 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 2.66802 | 11.2670 | -60.5660 | (11.266999999999999, -60.566000000000003) | Landslide | Landslide | Small | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 149 | 780 | 9/7/08 | NA | Trinidad and Tobago | TT | Diego Martin | 8140 | Petit Valley | 10.61854 | 10.7603 | -61.4578 | (10.760300000000001, -61.457799999999999) | Landslide | Landslide | Medium | Downpour | NA | NA | http://www.newsday.co.tt/news/0,85847.html |
df_TAT %>%
select(Country, State, City, Distance)## Country State City Distance
## 30 Trinidad and Tobago Tobago Scarborough 9.11607
## 61 Trinidad and Tobago Eastern Tobago Roxborough 7.33295
## 65 Trinidad and Tobago Sangre Grande Sangre Grande 29.28864
## 66 Trinidad and Tobago Eastern Tobago Roxborough 8.62938
## 67 Trinidad and Tobago Eastern Tobago Roxborough 2.66802
## 149 Trinidad and Tobago Diego Martin Petit Valley 10.61854
## 164 Trinidad and Tobago City of Port of Spain Port-of-Spain 1.07831
## 171 Trinidad and Tobago Diego Martin Petit Valley 5.91101
## 172 Trinidad and Tobago Diego Martin Petit Valley 2.24772
## 173 Trinidad and Tobago Penal/Debe Peñal, 2.57071
## 206 Trinidad and Tobago City of San Fernando Marabella 9.42494
## 392 Trinidad and Tobago Diego Martin Petit Valley 4.00979
## 393 Trinidad and Tobago City of Port of Spain Mucurapo 7.43310
## 394 Trinidad and Tobago City of Port of Spain Port-of-Spain 2.15046
## 400 Trinidad and Tobago Diego Martin Petit Valley 4.33904
## 402 Trinidad and Tobago Diego Martin Petit Valley 6.95807
## 404 Trinidad and Tobago City of Port of Spain Mucurapo 7.24469
## 405 Trinidad and Tobago San Juan/Laventille Laventille 7.37181
## 421 Trinidad and Tobago City of San Fernando San Fernando 0.92162
## 446 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 6.91642
## 494 Trinidad and Tobago Sangre Grande Sangre Grande 33.51924
## 495 Trinidad and Tobago Eastern Tobago Roxborough 8.44112
## 496 Trinidad and Tobago Eastern Tobago Roxborough 3.36240
## 497 Trinidad and Tobago Tunapuna/Piarco Paradise 2.64003
## 498 Trinidad and Tobago Eastern Tobago Roxborough 0.91163
## 499 Trinidad and Tobago Tobago Scarborough 4.68038
## 500 Trinidad and Tobago Tobago Scarborough 6.81393
## 532 Trinidad and Tobago Diego Martin Petit Valley 3.33629
## 533 Trinidad and Tobago Diego Martin Petit Valley 3.08955
## 534 Trinidad and Tobago Diego Martin Petit Valley 4.66234
## 535 Trinidad and Tobago Diego Martin Petit Valley 4.50278
## 536 Trinidad and Tobago Diego Martin Petit Valley 6.58396
## 537 Trinidad and Tobago Diego Martin Petit Valley 1.83626
## 554 Trinidad and Tobago Princes Town Princes Town 8.41931
## 555 Trinidad and Tobago Sangre Grande Sangre Grande 33.10893
## 556 Trinidad and Tobago Sangre Grande Sangre Grande 33.10893
## 827 Trinidad and Tobago San Juan/Laventille Laventille 0.38521
## 829 Trinidad and Tobago Diego Martin Petit Valley 11.75674
## 839 Trinidad and Tobago Penal/Debe Peñal, 4.21321
## 840 Trinidad and Tobago Point Fortin Point Fortin 22.47289
## 841 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 8.55164
## 842 Trinidad and Tobago Diego Martin Petit Valley 5.73985
## 843 Trinidad and Tobago Sangre Grande Sangre Grande 2.00931
## 844 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 0.61975
## 845 Trinidad and Tobago Borough of Arima Arima 13.34116
## 846 Trinidad and Tobago Sangre Grande Sangre Grande 30.29383
## 858 Trinidad and Tobago Tunapuna/Piarco Paradise 16.73194
## 877 Trinidad and Tobago Eastern Tobago Roxborough 5.62092
## 878 Trinidad and Tobago Tobago Scarborough 3.48176
## 887 Trinidad and Tobago Tunapuna/Piarco Paradise 13.75900
## 897 Trinidad and Tobago Diego Martin Petit Valley 6.39375
## 898 Trinidad and Tobago Diego Martin Petit Valley 4.64073
## 906 Trinidad and Tobago Petit Valley 1.84331
## 907 Trinidad and Tobago City of Port of Spain Port-of-Spain 2.54016
## 925 Trinidad and Tobago Eastern Tobago Roxborough 7.87263
## 926 Trinidad and Tobago Eastern Tobago Roxborough 7.47816
## 927 Trinidad and Tobago Eastern Tobago Roxborough 8.24676
## 958 Trinidad and Tobago Tobago Scarborough 6.35974
## 990 Trinidad and Tobago Tunapuna/Piarco Tunapuna 2.63186
## 1174 Trinidad and Tobago Tobago Scarborough 3.22335
## 1175 Trinidad and Tobago Tobago Scarborough 3.88123
## 1183 Trinidad and Tobago Sangre Grande Sangre Grande 26.68822
## 1302 Trinidad and Tobago Siparia Siparia 3.75200library(ggplot2)
ggplot(data=df_TAT, aes(x = "Trinidad and Tobago", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_TAT, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Borough of Arima
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Borough of Arima")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 845 Trinidad and Tobago Borough of Arima Arima 13.34116ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Borough of Arima",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Borough of Arima")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de City of Port of Spain
library(readr)
library(knitr)
df_TAT <- subset (df, State == "City of Port of Spain")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 164 Trinidad and Tobago City of Port of Spain Port-of-Spain 1.07831
## 393 Trinidad and Tobago City of Port of Spain Mucurapo 7.43310
## 394 Trinidad and Tobago City of Port of Spain Port-of-Spain 2.15046
## 404 Trinidad and Tobago City of Port of Spain Mucurapo 7.24469
## 907 Trinidad and Tobago City of Port of Spain Port-of-Spain 2.54016ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="City of Port of Spain",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - City of Port of Spain")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de City of San Fernando
library(readr)
library(knitr)
df_TAT <- subset (df, State == "City of San Fernando")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 206 Trinidad and Tobago City of San Fernando Marabella 9.42494
## 421 Trinidad and Tobago City of San Fernando San Fernando 0.92162ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="City of San Fernando",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - City of San Fernando")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Couva-Tabaquite-Talparo
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Couva-Tabaquite-Talparo")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 446 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 6.91642
## 841 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 8.55164
## 844 Trinidad and Tobago Couva-Tabaquite-Talparo Tabaquite 0.61975ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Couva-Tabaquite-Talparo",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Couva-Tabaquite-Talparo")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Diego Martin
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Diego Martin")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 149 Trinidad and Tobago Diego Martin Petit Valley 10.61854
## 171 Trinidad and Tobago Diego Martin Petit Valley 5.91101
## 172 Trinidad and Tobago Diego Martin Petit Valley 2.24772
## 392 Trinidad and Tobago Diego Martin Petit Valley 4.00979
## 400 Trinidad and Tobago Diego Martin Petit Valley 4.33904
## 402 Trinidad and Tobago Diego Martin Petit Valley 6.95807
## 532 Trinidad and Tobago Diego Martin Petit Valley 3.33629
## 533 Trinidad and Tobago Diego Martin Petit Valley 3.08955
## 534 Trinidad and Tobago Diego Martin Petit Valley 4.66234
## 535 Trinidad and Tobago Diego Martin Petit Valley 4.50278
## 536 Trinidad and Tobago Diego Martin Petit Valley 6.58396
## 537 Trinidad and Tobago Diego Martin Petit Valley 1.83626
## 829 Trinidad and Tobago Diego Martin Petit Valley 11.75674
## 842 Trinidad and Tobago Diego Martin Petit Valley 5.73985
## 897 Trinidad and Tobago Diego Martin Petit Valley 6.39375
## 898 Trinidad and Tobago Diego Martin Petit Valley 4.64073ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Diego Martin",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Diego Martin")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Eastern Tobago
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Eastern Tobago")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 61 Trinidad and Tobago Eastern Tobago Roxborough 7.33295
## 66 Trinidad and Tobago Eastern Tobago Roxborough 8.62938
## 67 Trinidad and Tobago Eastern Tobago Roxborough 2.66802
## 495 Trinidad and Tobago Eastern Tobago Roxborough 8.44112
## 496 Trinidad and Tobago Eastern Tobago Roxborough 3.36240
## 498 Trinidad and Tobago Eastern Tobago Roxborough 0.91163
## 877 Trinidad and Tobago Eastern Tobago Roxborough 5.62092
## 925 Trinidad and Tobago Eastern Tobago Roxborough 7.87263
## 926 Trinidad and Tobago Eastern Tobago Roxborough 7.47816
## 927 Trinidad and Tobago Eastern Tobago Roxborough 8.24676ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Eastern Tobago",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Eastern Tobago")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Penal/Debe
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Penal/Debe")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 173 Trinidad and Tobago Penal/Debe Peñal, 2.57071
## 839 Trinidad and Tobago Penal/Debe Peñal, 4.21321ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Penal/Debe",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Penal/Debe")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Point Fortin
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Point Fortin")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 840 Trinidad and Tobago Point Fortin Point Fortin 22.47289ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Point Fortin",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Point Fortin")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Princes Town
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Princes Town")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 554 Trinidad and Tobago Princes Town Princes Town 8.41931ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Princes Town",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Princes Town")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de San Juan/Laventille
library(readr)
library(knitr)
df_TAT <- subset (df, State == "San Juan/Laventille")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 405 Trinidad and Tobago San Juan/Laventille Laventille 7.37181
## 827 Trinidad and Tobago San Juan/Laventille Laventille 0.38521ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="San Juan/Laventille",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San Juan/Laventille")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Sangre Grande
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Sangre Grande")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 65 Trinidad and Tobago Sangre Grande Sangre Grande 29.28864
## 494 Trinidad and Tobago Sangre Grande Sangre Grande 33.51924
## 555 Trinidad and Tobago Sangre Grande Sangre Grande 33.10893
## 556 Trinidad and Tobago Sangre Grande Sangre Grande 33.10893
## 843 Trinidad and Tobago Sangre Grande Sangre Grande 2.00931
## 846 Trinidad and Tobago Sangre Grande Sangre Grande 30.29383
## 1183 Trinidad and Tobago Sangre Grande Sangre Grande 26.68822ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Sangre Grande",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Sangre Grande")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Siparia
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Siparia")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1302 Trinidad and Tobago Siparia Siparia 3.752ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Siparia",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Siparia")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Tobago
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Tobago")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 30 Trinidad and Tobago Tobago Scarborough 9.11607
## 499 Trinidad and Tobago Tobago Scarborough 4.68038
## 500 Trinidad and Tobago Tobago Scarborough 6.81393
## 878 Trinidad and Tobago Tobago Scarborough 3.48176
## 958 Trinidad and Tobago Tobago Scarborough 6.35974
## 1174 Trinidad and Tobago Tobago Scarborough 3.22335
## 1175 Trinidad and Tobago Tobago Scarborough 3.88123ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Tobago",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Tobago")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Tunapuna/Piarco
library(readr)
library(knitr)
df_TAT <- subset (df, State == "Tunapuna/Piarco")
df_TAT %>%
select(Country, State, City, Distance) ## Country State City Distance
## 497 Trinidad and Tobago Tunapuna/Piarco Paradise 2.64003
## 858 Trinidad and Tobago Tunapuna/Piarco Paradise 16.73194
## 887 Trinidad and Tobago Tunapuna/Piarco Paradise 13.75900
## 990 Trinidad and Tobago Tunapuna/Piarco Tunapuna 2.63186ggplot(data=df_TAT, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_TAT,aes(x="Tunapuna/Piarco",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Tunapuna/Piarco")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_TAT <- subset (df, Country == "Trinidad and Tobago")
knitr::kable(head(df_TAT))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 30 | 224 | 9/1/07 | NA | Trinidad and Tobago | TT | Tobago | 17000 | Scarborough | 9.11607 | 11.2415 | -60.6742 | (11.2415, -60.674199999999999) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Felix | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161197580 | ||
| 61 | 357 | 11/17/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 7.33295 | 11.2965 | -60.6312 | (11.2965, -60.6312) | Landslide | Landslide | Medium | Rain | NA | NA | Trinadad Express | http://www.trinidadexpress.com/index.pl/article_news?id=161237574 | |||
| 65 | 390 | 12/11/07 | NA | Trinidad and Tobago | TT | Sangre Grande | 15968 | Sangre Grande | 29.28864 | 10.8410 | -61.0550 | (10.840999999999999, -61.055) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | 3 | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 66 | 391 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 8.62938 | 11.3000 | -60.6440 | (11.3, -60.643999999999998) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 67 | 392 | 12/11/07 | NA | Trinidad and Tobago | TT | Eastern Tobago | 0 | Roxborough | 2.66802 | 11.2670 | -60.5660 | (11.266999999999999, -60.566000000000003) | Landslide | Landslide | Small | Tropical cyclone | Tropical Storm Olga | NA | NA | Trinidad and Tobago’s Newsday | http://www.newsday.co.tt/news/0,69681.html | ||
| 149 | 780 | 9/7/08 | NA | Trinidad and Tobago | TT | Diego Martin | 8140 | Petit Valley | 10.61854 | 10.7603 | -61.4578 | (10.760300000000001, -61.457799999999999) | Landslide | Landslide | Medium | Downpour | NA | NA | http://www.newsday.co.tt/news/0,85847.html |
Distance <- df_TAT$Distance
summary(df_TAT$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3852 3.1564 5.9110 8.2504 8.4964 33.5192library(pastecs)
stat.desc(df_TAT)## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced## id date time continent_code Country country_code State
## nbr.val 6.300000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 2.240000e+02 NA NA NA NA NA NA
## max 6.301000e+03 NA NA NA NA NA NA
## range 6.077000e+03 NA NA NA NA NA NA
## sum 1.939570e+05 NA NA NA NA NA NA
## median 2.759000e+03 NA NA NA NA NA NA
## mean 3.078683e+03 NA NA NA NA NA NA
## SE.mean 1.907885e+02 NA NA NA NA NA NA
## CI.mean.0.95 3.813809e+02 NA NA NA NA NA NA
## var 2.293217e+06 NA NA NA NA NA NA
## std.dev 1.514337e+03 NA NA NA NA NA NA
## coef.var 4.918783e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 6.300000e+01 NA 63.000000 NA 63.00000000
## nbr.null 1.000000e+01 NA 0.000000 NA 0.00000000
## nbr.na 0.000000e+00 NA 0.000000 NA 0.00000000
## min 0.000000e+00 NA 0.385210 NA 10.08760000
## max 1.572580e+05 NA 33.519240 NA 11.31760000
## range 1.572580e+05 NA 33.134030 NA 1.23000000
## sum 1.091655e+06 NA 519.777480 NA 679.61780000
## median 8.140000e+03 NA 5.911010 NA 10.71420000
## mean 1.732786e+04 NA 8.250436 NA 10.78758413
## SE.mean 3.563658e+03 NA 1.064793 NA 0.04203106
## CI.mean.0.95 7.123651e+03 NA 2.128491 NA 0.08401889
## var 8.000786e+08 NA 71.428394 NA 0.11129642
## std.dev 2.828566e+04 NA 8.451532 NA 0.33361118
## coef.var 1.632381e+00 NA 1.024374 NA 0.03092548
## longitude geolocation hazard_type landslide_type
## nbr.val 6.300000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -6.186420e+01 NA NA NA
## max -6.054170e+01 NA NA NA
## range 1.322500e+00 NA NA NA
## sum -3.856206e+03 NA NA NA
## median -6.139170e+01 NA NA NA
## mean -6.120962e+01 NA NA NA
## SE.mean 4.867603e-02 NA NA NA
## CI.mean.0.95 9.730199e-02 NA NA NA
## var 1.492694e-01 NA NA NA
## std.dev 3.863540e-01 NA NA NA
## coef.var -6.311982e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 1 44.00000000 NA
## nbr.null NA NA NA 1 40.00000000 NA
## nbr.na NA NA NA 62 19.00000000 NA
## min NA NA NA 0 0.00000000 NA
## max NA NA NA 0 3.00000000 NA
## range NA NA NA 0 3.00000000 NA
## sum NA NA NA 0 6.00000000 NA
## median NA NA NA 0 0.00000000 NA
## mean NA NA NA 0 0.13636364 NA
## SE.mean NA NA NA NA 0.07687686 NA
## CI.mean.0.95 NA NA NA NaN 0.15503697 NA
## var NA NA NA NA 0.26004228 NA
## std.dev NA NA NA NA 0.50994341 NA
## coef.var NA NA NA NA 3.73958502 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="brown", alpha=0.7) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=11)) +
ggtitle("Deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Trinidad y Tobago están medianamente bien distribuidos con un ligero sesgo hacia la izquierda y se considera la presencia de algunos datos aberrantes.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Venezuela
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_VEN <- subset (df, Country == "Venezuela")
knitr::kable(head(df_VEN))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | 896 | 11/20/08 | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.92493 | 10.4660 | -66.8940 | (10.465999999999999, -66.894000000000005) | Landslide | Mudslide | Medium | Downpour | NA | 8 | http://www.foxnews.com/story/0,2933,456304,00.html | ||||
| 448 | 2473 | 9/20/10 | SA | Venezuela | VE | Vargas | 52564 | Maiquetía | 6.65506 | 10.5363 | -66.9492 | (10.536300000000001, -66.949200000000005) | Landslide | Landslide | Medium | Downpour | NA | 7 | http://www.laht.com/article.asp?ArticleId=367610&CategoryId=14091 | ||||
| 530 | 2749 | 11/25/10 | SA | Venezuela | VE | Miranda | 278890 | Santa Teresa | 8.45736 | 10.2452 | -66.5867 | (10.245200000000001, -66.586699999999993) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | ||||
| 531 | 2753 | 11/26/10 | Before dawn | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.95706 | 10.4913 | -66.9060 | (10.491300000000001, -66.906000000000006) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | |||
| 538 | 2761 | 11/27/10 | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 7.90754 | 10.5030 | -66.9995 | (10.503, -66.999499999999998) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=380021&CategoryId=10717 | ||||
| 539 | 2762 | 11/27/10 | Night | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 5.64050 | 10.4267 | -67.0342 | (10.4267, -67.034199999999998) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://english.eluniversal.com/2010/11/30/en_pol_esp_landslides-hit-sever_30A4792571.shtml |
df_VEN %>%
select(Country, State, City, Distance)## Country State City Distance
## 165 Venezuela Distrito Federal Caracas 2.92493
## 448 Venezuela Vargas Maiquetía 6.65506
## 530 Venezuela Miranda Santa Teresa 8.45736
## 531 Venezuela Distrito Federal Caracas 2.95706
## 538 Venezuela Distrito Federal Caricuao 7.90754
## 539 Venezuela Distrito Federal Caricuao 5.64050
## 540 Venezuela Distrito Federal Caricuao 5.74106
## 541 Venezuela Distrito Federal Caracas 4.58994
## 542 Venezuela Miranda Baruta 2.69644
## 543 Venezuela Miranda El Hatillo 1.04263
## 544 Venezuela Distrito Federal Caracas 3.14060
## 545 Venezuela Distrito Federal Caracas 6.04235
## 546 Venezuela Miranda Guatire 15.84114
## 547 Venezuela Distrito Federal Caracas 3.87793
## 548 Venezuela Distrito Federal Caracas 3.65044
## 549 Venezuela Distrito Federal Caracas 8.11953
## 599 Venezuela Vargas Maiquetía 7.89319
## 768 Venezuela Aragua El Consejo 14.28266
## 769 Venezuela Falcón Santo Domingo 19.81345
## 847 Venezuela Distrito Federal Caracas 2.55507library(ggplot2)
ggplot(data=df_VEN, aes(x = "Venezuela", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_VEN, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el estado de Aragua
library(readr)
library(knitr)
df_VEN <- subset (df, State == "Aragua")
df_VEN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 768 Venezuela Aragua El Consejo 14.28266ggplot(data=df_VEN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_VEN,aes(x="Aragua",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Aragua")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el estado del Distrito Federal
library(readr)
library(knitr)
df_VEN <- subset (df, State == "Distrito Federal")
df_VEN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 165 Venezuela Distrito Federal Caracas 2.92493
## 531 Venezuela Distrito Federal Caracas 2.95706
## 538 Venezuela Distrito Federal Caricuao 7.90754
## 539 Venezuela Distrito Federal Caricuao 5.64050
## 540 Venezuela Distrito Federal Caricuao 5.74106
## 541 Venezuela Distrito Federal Caracas 4.58994
## 544 Venezuela Distrito Federal Caracas 3.14060
## 545 Venezuela Distrito Federal Caracas 6.04235
## 547 Venezuela Distrito Federal Caracas 3.87793
## 548 Venezuela Distrito Federal Caracas 3.65044
## 549 Venezuela Distrito Federal Caracas 8.11953
## 847 Venezuela Distrito Federal Caracas 2.55507ggplot(data=df_VEN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_VEN,aes(x="Distrito Federal",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Distrito Federal")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el estado de Falcón
library(readr)
library(knitr)
df_VEN <- subset (df, State == "Falcón")
df_VEN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 769 Venezuela Falcón Santo Domingo 19.81345ggplot(data=df_VEN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_VEN,aes(x="Falcón",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Falcón")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el estado de Miranda
library(readr)
library(knitr)
df_VEN <- subset (df, State == "Miranda")
df_VEN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 530 Venezuela Miranda Santa Teresa 8.45736
## 542 Venezuela Miranda Baruta 2.69644
## 543 Venezuela Miranda El Hatillo 1.04263
## 546 Venezuela Miranda Guatire 15.84114ggplot(data=df_VEN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_VEN,aes(x="Miranda",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Miranda")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el estado de Vargas
library(readr)
library(knitr)
df_VEN <- subset (df, State == "Vargas")
df_VEN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 448 Venezuela Vargas Maiquetía 6.65506
## 599 Venezuela Vargas Maiquetía 7.89319ggplot(data=df_VEN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_VEN,aes(x="Vargas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Vargas")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_VEN <- subset (df, Country == "Venezuela")
knitr::kable(head(df_VEN))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 165 | 896 | 11/20/08 | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.92493 | 10.4660 | -66.8940 | (10.465999999999999, -66.894000000000005) | Landslide | Mudslide | Medium | Downpour | NA | 8 | http://www.foxnews.com/story/0,2933,456304,00.html | ||||
| 448 | 2473 | 9/20/10 | SA | Venezuela | VE | Vargas | 52564 | Maiquetía | 6.65506 | 10.5363 | -66.9492 | (10.536300000000001, -66.949200000000005) | Landslide | Landslide | Medium | Downpour | NA | 7 | http://www.laht.com/article.asp?ArticleId=367610&CategoryId=14091 | ||||
| 530 | 2749 | 11/25/10 | SA | Venezuela | VE | Miranda | 278890 | Santa Teresa | 8.45736 | 10.2452 | -66.5867 | (10.245200000000001, -66.586699999999993) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | ||||
| 531 | 2753 | 11/26/10 | Before dawn | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.95706 | 10.4913 | -66.9060 | (10.491300000000001, -66.906000000000006) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | |||
| 538 | 2761 | 11/27/10 | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 7.90754 | 10.5030 | -66.9995 | (10.503, -66.999499999999998) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=380021&CategoryId=10717 | ||||
| 539 | 2762 | 11/27/10 | Night | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 5.64050 | 10.4267 | -67.0342 | (10.4267, -67.034199999999998) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://english.eluniversal.com/2010/11/30/en_pol_esp_landslides-hit-sever_30A4792571.shtml |
Distance <- df_VEN$Distance
summary(df_VEN$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.043 3.095 5.691 6.691 7.961 19.813library(pastecs)
stat.desc(df_VEN)## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced## id date time continent_code Country country_code State
## nbr.val 2.000000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 8.960000e+02 NA NA NA NA NA NA
## max 4.087000e+03 NA NA NA NA NA NA
## range 3.191000e+03 NA NA NA NA NA NA
## sum 5.679300e+04 NA NA NA NA NA NA
## median 2.767500e+03 NA NA NA NA NA NA
## mean 2.839650e+03 NA NA NA NA NA NA
## SE.mean 1.378519e+02 NA NA NA NA NA NA
## CI.mean.0.95 2.885274e+02 NA NA NA NA NA NA
## var 3.800630e+05 NA NA NA NA NA NA
## std.dev 6.164925e+02 NA NA NA NA NA NA
## coef.var 2.171016e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 2.000000e+01 NA 20.0000000 NA 20.00000000
## nbr.null 5.000000e+00 NA 0.0000000 NA 0.00000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.00000000
## min 0.000000e+00 NA 1.0426300 NA 8.68150000
## max 3.000000e+06 NA 19.8134500 NA 10.61440000
## range 3.000000e+06 NA 18.7708200 NA 1.93290000
## sum 2.787773e+07 NA 133.8288800 NA 207.66930000
## median 2.615530e+05 NA 5.6907800 NA 10.48790000
## mean 1.393886e+06 NA 6.6914440 NA 10.38346500
## SE.mean 3.337376e+05 NA 1.0898349 NA 0.09105869
## CI.mean.0.95 6.985209e+05 NA 2.2810507 NA 0.19058803
## var 2.227616e+12 NA 23.7548025 NA 0.16583371
## std.dev 1.492520e+06 NA 4.8738899 NA 0.40722685
## coef.var 1.070762e+00 NA 0.7283764 NA 0.03921878
## longitude geolocation hazard_type landslide_type
## nbr.val 2.000000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -7.070160e+01 NA NA NA
## max -6.658060e+01 NA NA NA
## range 4.121000e+00 NA NA NA
## sum -1.341936e+03 NA NA NA
## median -6.691785e+01 NA NA NA
## mean -6.709678e+01 NA NA NA
## SE.mean 1.925813e-01 NA NA NA
## CI.mean.0.95 4.030773e-01 NA NA NA
## var 7.417512e-01 NA NA NA
## std.dev 8.612498e-01 NA NA NA
## coef.var -1.283593e-02 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 0 20.000000 NA
## nbr.null NA NA NA 0 10.000000 NA
## nbr.na NA NA NA 20 0.000000 NA
## min NA NA NA Inf 0.000000 NA
## max NA NA NA -Inf 8.000000 NA
## range NA NA NA -Inf 8.000000 NA
## sum NA NA NA 0 28.000000 NA
## median NA NA NA NA 0.500000 NA
## mean NA NA NA NaN 1.400000 NA
## SE.mean NA NA NA NA 0.509902 NA
## CI.mean.0.95 NA NA NA NaN 1.067237 NA
## var NA NA NA NA 5.200000 NA
## std.dev NA NA NA NA 2.280351 NA
## coef.var NA NA NA NA 1.628822 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="red", fill="orange", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=11)) +
ggtitle("Deslizamientos") +
coord_flip() +
xlab("") +
ylab("") +
xlab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Venezuela están medianamente bien distribuidos con un ligero sesgo hacia la izquierda y se considera la presencia de algunos datos aberrantes.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Costa Rica
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_CR <- subset (df, Country == "Costa Rica")
knitr::kable(head(df_CR))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 249 | 9/9/07 | NA | Costa Rica | CR | Heredia | 21947 | Heredia | 0.26208 | 10.0000 | -84.1167 | (10, -84.116699999999994) | Landslide | Landslide | Medium | Rain | NA | NA | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_09/0911072.htm | |||
| 44 | 299 | 10/9/07 | NA | Costa Rica | CR | San José | 3072 | San Ignacio | 4.57763 | 9.7789 | -84.1250 | (9.7789000000000001, -84.125) | Landslide | Complex | Medium | Rain | NA | 4 | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_10/1010071.htm | |||
| 45 | 301 | 10/11/07 | NA | Costa Rica | CR | Alajuela | 7014 | Atenas | 3.08459 | 9.9869 | -84.4070 | (9.9869000000000003, -84.406999999999996) | Landslide | Mudslide | Large | Rain | NA | 14 | Agence France-Presse, afp.google.com | http://afp.google.com/article/ALeqM5hu6a8oyAM1ycq9nU_6Zyj_l7F0AA | |||
| 46 | 302 | 10/11/07 | NA | Costa Rica | CR | San José | 26669 | 9.56251 | 10.0214 | -83.9451 | (10.0214, -83.945099999999996) | Landslide | Landslide | Large | Rain | NA | 10 | International Herald | http://www.iht.com/articles/ap/2007/10/12/america/LA-GEN-Costa-Rica-Mudslide.php | ||||
| 51 | 323 | 10/24/07 | NA | Costa Rica | CR | Puntarenas | 6540 | Miramar | 3.82425 | Mine construction | 10.0715 | -84.7575 | (10.0715, -84.757499999999993) | Landslide | Mudslide | Medium | Downpour | NA | NA | Reuters - AlertNet.org | http://www.reuters.com/article/companyNewsAndPR/idUSN2435152820071025 | ||
| 102 | 556 | 5/29/08 | NA | Costa Rica | CR | Guanacaste | 4108 | Bagaces | 17.65521 | 10.4024 | -85.3555 | (10.4024, -85.355500000000006) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Alma | NA | NA | http://www.reliefweb.int/rw/RWB.NSF/db900SID/ASAZ-7FHCHL?OpenDocument |
df_CR %>%
select(Country, State, City, Distance)## Country State City Distance
## 38 Costa Rica Heredia Heredia 0.26208
## 44 Costa Rica San José San Ignacio 4.57763
## 45 Costa Rica Alajuela Atenas 3.08459
## 46 Costa Rica San José 9.56251
## 51 Costa Rica Puntarenas Miramar 3.82425
## 102 Costa Rica Guanacaste Bagaces 17.65521
## 147 Costa Rica San José Daniel Flores 1.85787
## 153 Costa Rica San José San Isidro 16.24937
## 154 Costa Rica San José Santiago 12.85801
## 156 Costa Rica Puntarenas Golfito 11.74074
## 157 Costa Rica Puntarenas Miramar 8.92048
## 229 Costa Rica Puntarenas San Vito 18.00524
## 302 Costa Rica Alajuela Desamparados 6.88715
## 311 Costa Rica Heredia Ángeles 19.51432
## 347 Costa Rica Alajuela Desamparados 6.92174
## 395 Costa Rica Alajuela Desamparados 4.24199
## 459 Costa Rica Alajuela San Rafael 1.47396
## 469 Costa Rica San José Salitral 0.25254
## 470 Costa Rica San José Salitral 0.25254
## 480 Costa Rica Heredia Ángeles 14.81614
## 501 Costa Rica San José Escazú 3.67691
## 502 Costa Rica San José San Marcos 0.55804
## 503 Costa Rica Alajuela San Rafael 9.61692
## 504 Costa Rica Guanacaste Tilarán 10.21631
## 505 Costa Rica Cartago Orosí 19.28722
## 506 Costa Rica Puntarenas Golfito 7.87044
## 507 Costa Rica San José Tejar 6.49523
## 508 Costa Rica San José San Isidro 15.64997
## 509 Costa Rica Puntarenas Corredor 4.93053
## 510 Costa Rica Puntarenas Parrita 13.48919
## 511 Costa Rica Puntarenas Ciudad Cortés 20.06633
## 512 Costa Rica San José San Isidro 11.31047
## 513 Costa Rica San José Mercedes 8.21372
## 514 Costa Rica Alajuela Santiago 5.43516
## 529 Costa Rica Heredia Ángeles 19.54581
## 579 Costa Rica Limón Guápiles 17.23264
## 702 Costa Rica Heredia Ángeles 15.05161
## 780 Costa Rica Alajuela Upala 0.70048
## 819 Costa Rica San José San Isidro 21.67452
## 828 Costa Rica Cartago Cot 9.63616
## 884 Costa Rica Heredia Santo Domingo 21.95470
## 888 Costa Rica Guanacaste Tilarán 12.33807
## 889 Costa Rica Limón Siquirres 5.36500
## 913 Costa Rica San José Daniel Flores 4.89954
## 1098 Costa Rica Alajuela Sabanilla 4.87432
## 1156 Costa Rica Alajuela Sabanilla 10.32968
## 1157 Costa Rica Heredia Santo Domingo 9.85736
## 1169 Costa Rica Guanacaste Tilarán 12.21952
## 1173 Costa Rica Guanacaste Tilarán 12.18115
## 1289 Costa Rica Alajuela La Fortuna 9.84213
## 1301 Costa Rica Alajuela 5.57523
## 1308 Costa Rica Alajuela Desamparados 5.95519
## 1342 Costa Rica Alajuela Rio Segundo 11.96524
## 1364 Costa Rica Alajuela Desamparados 5.12667
## 1383 Costa Rica Cartago Cartago 3.07297
## 1384 Costa Rica Heredia Dulce Nombre de Jesus 10.01310
## 1385 Costa Rica San José Dulce Nombre de Jesus 2.92605
## 1386 Costa Rica San José San Isidro 10.73752
## 1404 Costa Rica San José San Isidro 22.32368
## 1406 Costa Rica San José Dulce Nombre de Jesus 8.39161
## 1461 Costa Rica Alajuela La Fortuna 5.96634
## 1475 Costa Rica Alajuela Atenas 6.80061
## 1528 Costa Rica San José Ángeles 9.53611
## 1529 Costa Rica San José Dulce Nombre de Jesus 3.71407
## 1600 Costa Rica San José San Juan 0.72957
## 1642 Costa Rica Alajuela Santo Domingo 3.21979
## 1643 Costa Rica Alajuela Alajuela 3.08916
## 1644 Costa Rica Alajuela Naranjo 2.08469
## 1646 Costa Rica Cartago 5.15142
## 1647 Costa Rica Cartago Cot 9.53493
## 1648 Costa Rica Cartago Cartago 2.94804
## 1649 Costa Rica Puntarenas Buenos Aires 0.35225
## 1650 Costa Rica San José San José 1.16705
## 1651 Costa Rica San José Mercedes 10.01198
## 1652 Costa Rica San José Santiago 8.27042library(ggplot2)
ggplot(data=df_CR, aes(x = "Costa Rica", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_CR, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Alajuela
library(readr)
library(knitr)
df_CR <- subset (df, State == "Alajuela")
df_CR %>%
select(Country, State, City, Distance) ## Country State City Distance
## 45 Costa Rica Alajuela Atenas 3.08459
## 302 Costa Rica Alajuela Desamparados 6.88715
## 347 Costa Rica Alajuela Desamparados 6.92174
## 395 Costa Rica Alajuela Desamparados 4.24199
## 459 Costa Rica Alajuela San Rafael 1.47396
## 503 Costa Rica Alajuela San Rafael 9.61692
## 514 Costa Rica Alajuela Santiago 5.43516
## 780 Costa Rica Alajuela Upala 0.70048
## 1098 Costa Rica Alajuela Sabanilla 4.87432
## 1156 Costa Rica Alajuela Sabanilla 10.32968
## 1289 Costa Rica Alajuela La Fortuna 9.84213
## 1301 Costa Rica Alajuela 5.57523
## 1308 Costa Rica Alajuela Desamparados 5.95519
## 1342 Costa Rica Alajuela Rio Segundo 11.96524
## 1364 Costa Rica Alajuela Desamparados 5.12667
## 1461 Costa Rica Alajuela La Fortuna 5.96634
## 1475 Costa Rica Alajuela Atenas 6.80061
## 1642 Costa Rica Alajuela Santo Domingo 3.21979
## 1643 Costa Rica Alajuela Alajuela 3.08916
## 1644 Costa Rica Alajuela Naranjo 2.08469ggplot(data=df_CR, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CR,aes(x="Alajuela",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Alajuela")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Cartago
library(readr)
library(knitr)
df_CR <- subset (df, State == "Cartago")
df_CR %>%
select(Country, State, City, Distance) ## Country State City Distance
## 505 Costa Rica Cartago Orosí 19.28722
## 828 Costa Rica Cartago Cot 9.63616
## 1383 Costa Rica Cartago Cartago 3.07297
## 1646 Costa Rica Cartago 5.15142
## 1647 Costa Rica Cartago Cot 9.53493
## 1648 Costa Rica Cartago Cartago 2.94804ggplot(data=df_CR, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CR,aes(x="Cartago",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Cartago")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Guanacaste
library(readr)
library(knitr)
df_CR <- subset (df, State == "Guanacaste")
df_CR %>%
select(Country, State, City, Distance) ## Country State City Distance
## 102 Costa Rica Guanacaste Bagaces 17.65521
## 504 Costa Rica Guanacaste Tilarán 10.21631
## 888 Costa Rica Guanacaste Tilarán 12.33807
## 1169 Costa Rica Guanacaste Tilarán 12.21952
## 1173 Costa Rica Guanacaste Tilarán 12.18115ggplot(data=df_CR, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CR,aes(x="Guanacaste",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Guanacaste")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Heredia
library(readr)
library(knitr)
df_CR <- subset (df, State == "Heredia")
df_CR %>%
select(Country, State, City, Distance) ## Country State City Distance
## 38 Costa Rica Heredia Heredia 0.26208
## 311 Costa Rica Heredia Ángeles 19.51432
## 480 Costa Rica Heredia Ángeles 14.81614
## 529 Costa Rica Heredia Ángeles 19.54581
## 702 Costa Rica Heredia Ángeles 15.05161
## 884 Costa Rica Heredia Santo Domingo 21.95470
## 1157 Costa Rica Heredia Santo Domingo 9.85736
## 1384 Costa Rica Heredia Dulce Nombre de Jesus 10.01310ggplot(data=df_CR, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CR,aes(x="Heredia",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Heredia")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Limón
library(readr)
library(knitr)
df_CR <- subset (df, State == "Limón")
df_CR %>%
select(Country, State, City, Distance) ## Country State City Distance
## 579 Costa Rica Limón Guápiles 17.23264
## 889 Costa Rica Limón Siquirres 5.36500ggplot(data=df_CR, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CR,aes(x="Limón",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Limón")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de Puntarenas
library(readr)
library(knitr)
df_CR <- subset (df, State == "Puntarenas")
df_CR %>%
select(Country, State, City, Distance) ## Country State City Distance
## 51 Costa Rica Puntarenas Miramar 3.82425
## 156 Costa Rica Puntarenas Golfito 11.74074
## 157 Costa Rica Puntarenas Miramar 8.92048
## 229 Costa Rica Puntarenas San Vito 18.00524
## 506 Costa Rica Puntarenas Golfito 7.87044
## 509 Costa Rica Puntarenas Corredor 4.93053
## 510 Costa Rica Puntarenas Parrita 13.48919
## 511 Costa Rica Puntarenas Ciudad Cortés 20.06633
## 1649 Costa Rica Puntarenas Buenos Aires 0.35225ggplot(data=df_CR, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CR,aes(x="Puntarenas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Puntarenas")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para la provincia de San José
library(readr)
library(knitr)
df_CR <- subset (df, State == "San José")
df_CR %>%
select(Country, State, City, Distance) ## Country State City Distance
## 44 Costa Rica San José San Ignacio 4.57763
## 46 Costa Rica San José 9.56251
## 147 Costa Rica San José Daniel Flores 1.85787
## 153 Costa Rica San José San Isidro 16.24937
## 154 Costa Rica San José Santiago 12.85801
## 469 Costa Rica San José Salitral 0.25254
## 470 Costa Rica San José Salitral 0.25254
## 501 Costa Rica San José Escazú 3.67691
## 502 Costa Rica San José San Marcos 0.55804
## 507 Costa Rica San José Tejar 6.49523
## 508 Costa Rica San José San Isidro 15.64997
## 512 Costa Rica San José San Isidro 11.31047
## 513 Costa Rica San José Mercedes 8.21372
## 819 Costa Rica San José San Isidro 21.67452
## 913 Costa Rica San José Daniel Flores 4.89954
## 1385 Costa Rica San José Dulce Nombre de Jesus 2.92605
## 1386 Costa Rica San José San Isidro 10.73752
## 1404 Costa Rica San José San Isidro 22.32368
## 1406 Costa Rica San José Dulce Nombre de Jesus 8.39161
## 1528 Costa Rica San José Ángeles 9.53611
## 1529 Costa Rica San José Dulce Nombre de Jesus 3.71407
## 1600 Costa Rica San José San Juan 0.72957
## 1650 Costa Rica San José San José 1.16705
## 1651 Costa Rica San José Mercedes 10.01198
## 1652 Costa Rica San José Santiago 8.27042ggplot(data=df_CR, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CR,aes(x="San José",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San José")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_CRC <- subset (df, Country == "Costa Rica")
knitr::kable(head(df_CRC))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 249 | 9/9/07 | NA | Costa Rica | CR | Heredia | 21947 | Heredia | 0.26208 | 10.0000 | -84.1167 | (10, -84.116699999999994) | Landslide | Landslide | Medium | Rain | NA | NA | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_09/0911072.htm | |||
| 44 | 299 | 10/9/07 | NA | Costa Rica | CR | San José | 3072 | San Ignacio | 4.57763 | 9.7789 | -84.1250 | (9.7789000000000001, -84.125) | Landslide | Complex | Medium | Rain | NA | 4 | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_10/1010071.htm | |||
| 45 | 301 | 10/11/07 | NA | Costa Rica | CR | Alajuela | 7014 | Atenas | 3.08459 | 9.9869 | -84.4070 | (9.9869000000000003, -84.406999999999996) | Landslide | Mudslide | Large | Rain | NA | 14 | Agence France-Presse, afp.google.com | http://afp.google.com/article/ALeqM5hu6a8oyAM1ycq9nU_6Zyj_l7F0AA | |||
| 46 | 302 | 10/11/07 | NA | Costa Rica | CR | San José | 26669 | 9.56251 | 10.0214 | -83.9451 | (10.0214, -83.945099999999996) | Landslide | Landslide | Large | Rain | NA | 10 | International Herald | http://www.iht.com/articles/ap/2007/10/12/america/LA-GEN-Costa-Rica-Mudslide.php | ||||
| 51 | 323 | 10/24/07 | NA | Costa Rica | CR | Puntarenas | 6540 | Miramar | 3.82425 | Mine construction | 10.0715 | -84.7575 | (10.0715, -84.757499999999993) | Landslide | Mudslide | Medium | Downpour | NA | NA | Reuters - AlertNet.org | http://www.reuters.com/article/companyNewsAndPR/idUSN2435152820071025 | ||
| 102 | 556 | 5/29/08 | NA | Costa Rica | CR | Guanacaste | 4108 | Bagaces | 17.65521 | 10.4024 | -85.3555 | (10.4024, -85.355500000000006) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Alma | NA | NA | http://www.reliefweb.int/rw/RWB.NSF/db900SID/ASAZ-7FHCHL?OpenDocument |
Distance <- df_CRC$Distance
summary(df_CRC$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2525 3.7692 8.2137 8.6152 12.0732 22.3237library(pastecs)
stat.desc(df_CRC)## id date time continent_code Country country_code State
## nbr.val 7.500000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 2.490000e+02 NA NA NA NA NA NA
## max 7.496000e+03 NA NA NA NA NA NA
## range 7.247000e+03 NA NA NA NA NA NA
## sum 3.121850e+05 NA NA NA NA NA NA
## median 3.762000e+03 NA NA NA NA NA NA
## mean 4.162467e+03 NA NA NA NA NA NA
## SE.mean 2.838021e+02 NA NA NA NA NA NA
## CI.mean.0.95 5.654880e+02 NA NA NA NA NA NA
## var 6.040771e+06 NA NA NA NA NA NA
## std.dev 2.457798e+03 NA NA NA NA NA NA
## coef.var 5.904667e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 7.500000e+01 NA 75.0000000 NA 75.00000000
## nbr.null 4.000000e+00 NA 0.0000000 NA 0.00000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.00000000
## min 0.000000e+00 NA 0.2525400 NA 8.61170000
## max 3.350070e+05 NA 22.3236800 NA 10.89160000
## range 3.350070e+05 NA 22.0711400 NA 2.27990000
## sum 1.127522e+06 NA 646.1411800 NA 740.80410000
## median 6.784000e+03 NA 8.2137200 NA 9.96430000
## mean 1.503363e+04 NA 8.6152157 NA 9.87738800
## SE.mean 4.498709e+03 NA 0.6924979 NA 0.05198610
## CI.mean.0.95 8.963873e+03 NA 1.3798322 NA 0.10358456
## var 1.517878e+09 NA 35.9665004 NA 0.20269158
## std.dev 3.895996e+04 NA 5.9972077 NA 0.45021281
## coef.var 2.591521e+00 NA 0.6961181 NA 0.04558015
## longitude geolocation hazard_type landslide_type
## nbr.val 7.500000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -8.535550e+01 NA NA NA
## max -8.294180e+01 NA NA NA
## range 2.413700e+00 NA NA NA
## sum -6.307925e+03 NA NA NA
## median -8.408790e+01 NA NA NA
## mean -8.410567e+01 NA NA NA
## SE.mean 5.429493e-02 NA NA NA
## CI.mean.0.95 1.081850e-01 NA NA NA
## var 2.210955e-01 NA NA NA
## std.dev 4.702079e-01 NA NA NA
## coef.var -5.590680e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities
## nbr.val NA NA NA 27.0000000 60.0000000
## nbr.null NA NA NA 26.0000000 50.0000000
## nbr.na NA NA NA 48.0000000 15.0000000
## min NA NA NA 0.0000000 0.0000000
## max NA NA NA 3.0000000 23.0000000
## range NA NA NA 3.0000000 23.0000000
## sum NA NA NA 3.0000000 61.0000000
## median NA NA NA 0.0000000 0.0000000
## mean NA NA NA 0.1111111 1.0166667
## SE.mean NA NA NA 0.1111111 0.4750805
## CI.mean.0.95 NA NA NA 0.2283922 0.9506339
## var NA NA NA 0.3333333 13.5420904
## std.dev NA NA NA 0.5773503 3.6799579
## coef.var NA NA NA 5.1961524 3.6196308
## source_name source_link
## nbr.val NA NA
## nbr.null NA NA
## nbr.na NA NA
## min NA NA
## max NA NA
## range NA NA
## sum NA NA
## median NA NA
## mean NA NA
## SE.mean NA NA
## CI.mean.0.95 NA NA
## var NA NA
## std.dev NA NA
## coef.var NA NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="red", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Costa Rica están medianamente bien distribuidos con un ligero sesgo hacia la izquierda.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Cuba
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_CUB <- subset (df, Country == "Cuba")
knitr::kable(head(df_CUB))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 483 | 2611 | 10/18/10 | NA | Cuba | CU | Provincia de La Habana | 132351 | Cerro | 0.89865 | 23.1098 | -82.3691 | (23.1098, -82.369100000000003) | Landslide | Complex | Medium | Tropical cyclone | Tropical Storm Paula | NA | 0 | http://www.reliefweb.int/rw/RWFiles2010.nsf/FilesByRWDocUnidFilename/VDUX-8ADM53-full_report.pdf/$File/full_report.pdf | |||
| 515 | 2706 | 11/7/10 | NA | Cuba | CU | Guantanamo | 48362 | Baracoa | 10.45795 | 20.2526 | -74.4867 | (20.252600000000001, -74.486699999999999) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Tomas | NA | 0 | http://www.solvision.co.cu/english/index.php?option=com_content&view=article&id=1631:viaduct-la-farola-in-baracoa-traffic-restored&catid=34:portada&Itemid=171 | |||
| 1031 | 5067 | 7/9/13 | NA | Cuba | CU | Artemisa Province | 7205 | Soroa | 11.87914 | 22.7943 | -83.1322 | (22.7943, -83.132199999999997) | Landslide | Landslide | Medium | Downpour | NA | 0 | www.havanatimes.org | http://www.havanatimes.org/?p=96131 |
df_CUB %>%
select(Country, State, City, Distance)## Country State City Distance
## 483 Cuba Provincia de La Habana Cerro 0.89865
## 515 Cuba Guantanamo Baracoa 10.45795
## 1031 Cuba Artemisa Province Soroa 11.87914library(ggplot2)
ggplot(data=df_CUB, aes(x = "Cuba", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_CUB, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Artemisa Province.
library(readr)
library(knitr)
df_CUB <- subset (df, State == "Artemisa Province")
df_CUB %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1031 Cuba Artemisa Province Soroa 11.87914ggplot(data=df_CUB, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CUB,aes(x="Artemisa Province",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Artemisa Province")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Guantanamo.
library(readr)
library(knitr)
df_CUB <- subset (df, State == "Guantanamo")
df_CUB %>%
select(Country, State, City, Distance) ## Country State City Distance
## 515 Cuba Guantanamo Baracoa 10.45795ggplot(data=df_CUB, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CUB,aes(x="Guantanamo",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Guantanamo")
### Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Provincia de La Habana.
library(readr)
library(knitr)
df_CUB <- subset (df, State == "Provincia de La Habana")
df_CUB %>%
select(Country, State, City, Distance) ## Country State City Distance
## 483 Cuba Provincia de La Habana Cerro 0.89865ggplot(data=df_CUB, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_CUB,aes(x="Provincia de La Habana",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Provincia de La Habana")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_CUB <- subset (df, Country == "Cuba")
knitr::kable(head(df_CUB))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 483 | 2611 | 10/18/10 | NA | Cuba | CU | Provincia de La Habana | 132351 | Cerro | 0.89865 | 23.1098 | -82.3691 | (23.1098, -82.369100000000003) | Landslide | Complex | Medium | Tropical cyclone | Tropical Storm Paula | NA | 0 | http://www.reliefweb.int/rw/RWFiles2010.nsf/FilesByRWDocUnidFilename/VDUX-8ADM53-full_report.pdf/$File/full_report.pdf | |||
| 515 | 2706 | 11/7/10 | NA | Cuba | CU | Guantanamo | 48362 | Baracoa | 10.45795 | 20.2526 | -74.4867 | (20.252600000000001, -74.486699999999999) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Tomas | NA | 0 | http://www.solvision.co.cu/english/index.php?option=com_content&view=article&id=1631:viaduct-la-farola-in-baracoa-traffic-restored&catid=34:portada&Itemid=171 | |||
| 1031 | 5067 | 7/9/13 | NA | Cuba | CU | Artemisa Province | 7205 | Soroa | 11.87914 | 22.7943 | -83.1322 | (22.7943, -83.132199999999997) | Landslide | Landslide | Medium | Downpour | NA | 0 | www.havanatimes.org | http://www.havanatimes.org/?p=96131 |
Distance <- df_CUB$Distance
summary(df_CUB$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.8986 5.6783 10.4580 7.7452 11.1685 11.8791library(pastecs)
stat.desc(df_CUB)## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced## id date time continent_code Country country_code State
## nbr.val 3.000000e+00 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 2.611000e+03 NA NA NA NA NA NA
## max 5.067000e+03 NA NA NA NA NA NA
## range 2.456000e+03 NA NA NA NA NA NA
## sum 1.038400e+04 NA NA NA NA NA NA
## median 2.706000e+03 NA NA NA NA NA NA
## mean 3.461333e+03 NA NA NA NA NA NA
## SE.mean 8.033016e+02 NA NA NA NA NA NA
## CI.mean.0.95 3.456328e+03 NA NA NA NA NA NA
## var 1.935880e+06 NA NA NA NA NA NA
## std.dev 1.391359e+03 NA NA NA NA NA NA
## coef.var 4.019720e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 3.000000e+00 NA 3.0000000 NA 3.0000000
## nbr.null 0.000000e+00 NA 0.0000000 NA 0.0000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.0000000
## min 7.205000e+03 NA 0.8986500 NA 20.2526000
## max 1.323510e+05 NA 11.8791400 NA 23.1098000
## range 1.251460e+05 NA 10.9804900 NA 2.8572000
## sum 1.879180e+05 NA 23.2357400 NA 66.1567000
## median 4.836200e+04 NA 10.4579500 NA 22.7943000
## mean 6.263933e+04 NA 7.7452467 NA 22.0522333
## SE.mean 3.682509e+04 NA 3.4477944 NA 0.9044142
## CI.mean.0.95 1.584456e+05 NA 14.8346622 NA 3.8913802
## var 4.068262e+09 NA 35.6618597 NA 2.4538952
## std.dev 6.378293e+04 NA 5.9717552 NA 1.5664914
## coef.var 1.018257e+00 NA 0.7710219 NA 0.0710355
## longitude geolocation hazard_type landslide_type
## nbr.val 3.00000000 NA NA NA
## nbr.null 0.00000000 NA NA NA
## nbr.na 0.00000000 NA NA NA
## min -83.13220000 NA NA NA
## max -74.48670000 NA NA NA
## range 8.64550000 NA NA NA
## sum -239.98800000 NA NA NA
## median -82.36910000 NA NA NA
## mean -79.99600000 NA NA NA
## SE.mean 2.76344412 NA NA NA
## CI.mean.0.95 11.89014040 NA NA NA
## var 22.90987027 NA NA NA
## std.dev 4.78642563 NA NA NA
## coef.var -0.05983331 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 0 3 NA
## nbr.null NA NA NA 0 3 NA
## nbr.na NA NA NA 3 0 NA
## min NA NA NA Inf 0 NA
## max NA NA NA -Inf 0 NA
## range NA NA NA -Inf 0 NA
## sum NA NA NA 0 0 NA
## median NA NA NA NA 0 NA
## mean NA NA NA NaN 0 NA
## SE.mean NA NA NA NA 0 NA
## CI.mean.0.95 NA NA NA NaN 0 NA
## var NA NA NA NA 0 NA
## std.dev NA NA NA NA 0 NA
## coef.var NA NA NA NA NaN NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="red", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Cuba presentan un claro sesgo hacia la izquierda.
Gráfico circular y de barras de las distancias de desplazamientos para el país de El Salvador
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_ES <- subset (df, Country == "El Salvador")
knitr::kable(head(df_ES))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | 230 | 9/5/07 | NA | El Salvador | SV | Ahuachapán | 7797 | Concepción de Ataco | 0.00273 | 13.8703 | -89.8486 | (13.8703, -89.848600000000005) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Felix | NA | NA | Azcentral.com | http://www.azcentral.com/news/articles/1108sr-fhsistercity1109-ON.html | ||
| 105 | 564 | 6/2/08 | NA | El Salvador | SV | La Libertad | 124694 | Santa Tecla | 4.96416 | 13.7205 | -89.2687 | (13.720499999999999, -89.268699999999995) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Arthur | NA | NA | http://news.xinhuanet.com/english/2008-06/04/content_8310737.htm | |||
| 224 | 1285 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 7.60946 | 13.6409 | -88.8699 | (13.6409, -88.869900000000001) | Landslide | Complex | Very_large | Tropical cyclone | Tropical Cyclone Ida | NA | 23 | http://www.google.com/hostednews/ap/article/ALeqM5j0XCCb1n12DyhoBoDzGj_hTyEtrAD9BRKPRG0 | |||
| 225 | 1286 | 11/8/09 | NA | El Salvador | SV | La Libertad | 33767 | Antiguo Cuscatlán | 4.86219 | 13.7156 | -89.2521 | (13.7156, -89.252099999999999) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | 4 | http://www.google.com/hostednews/ap/article/ALeqM5j0XCCb1n12DyhoBoDzGj_hTyEtrAD9BRKPRG0 | |||
| 226 | 1287 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 5.90726 | 13.6094 | -88.8488 | (13.609400000000001, -88.848799999999997) | Landslide | Rockfall | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | NA | http://news.bbc.co.uk/2/hi/in_depth/8349333.stm | |||
| 227 | 1288 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 4.03125 | 13.6466 | -88.8347 | (13.646599999999999, -88.834699999999998) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | NA | http://news.yahoo.com/s/afp/20091109/wl_afp/salvadorweatherstorm_20091109100952 |
df_ES %>%
select(Country, State, City, Distance)## Country State City Distance
## 34 El Salvador Ahuachapán Concepción de Ataco 0.00273
## 105 El Salvador La Libertad Santa Tecla 4.96416
## 224 El Salvador San Vicente San Vicente 7.60946
## 225 El Salvador La Libertad Antiguo Cuscatlán 4.86219
## 226 El Salvador San Vicente San Vicente 5.90726
## 227 El Salvador San Vicente San Vicente 4.03125
## 453 El Salvador Ahuachapán Tacuba 5.29901
## 824 El Salvador San Salvador Apopa 3.01739
## 1294 El Salvador San Miguel Chirilagua 6.94536
## 1366 El Salvador San Miguel San Rafael Oriente 10.06695
## 1367 El Salvador Cabañas San Martín 8.82525
## 1369 El Salvador Sonsonate Nahuizalco 4.23875
## 1370 El Salvador Sonsonate Sonzacate 3.22235
## 1371 El Salvador La Paz San Pedro Masahuat 0.31933
## 1372 El Salvador San Miguel Chirilagua 9.97227
## 1373 El Salvador Santa Ana Coatepeque 8.83210
## 1374 El Salvador La Libertad Santa Tecla 4.60655
## 1375 El Salvador San Salvador Antiguo Cuscatlán 3.25227
## 1594 El Salvador Santa Ana Ciudad Arce 1.15810
## 1596 El Salvador La Libertad Santa Tecla 4.67722
## 1597 El Salvador La Libertad Santa Tecla 9.87553
## 1598 El Salvador Sonsonate Juayúa 0.49346library(ggplot2)
ggplot(data=df_ES, aes(x = "El Salvador", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_ES, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Ahuachapán
library(readr)
library(knitr)
df_ES <- subset (df, State == "Ahuachapán")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 34 El Salvador Ahuachapán Concepción de Ataco 0.00273
## 453 El Salvador Ahuachapán Tacuba 5.29901ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="Ahuachapán",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Ahuachapán")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Cabañas
library(readr)
library(knitr)
df_ES <- subset (df, State == "Cabañas")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1367 El Salvador Cabañas San Martín 8.82525ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="Cabañas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Cabañas")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de La Libertad
library(readr)
library(knitr)
df_ES <- subset (df, State == "La Libertad")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 105 El Salvador La Libertad Santa Tecla 4.96416
## 184 Peru La Libertad Parcoy 9.64894
## 185 Peru La Libertad Aricapampa 0.00442
## 225 El Salvador La Libertad Antiguo Cuscatlán 4.86219
## 1374 El Salvador La Libertad Santa Tecla 4.60655
## 1596 El Salvador La Libertad Santa Tecla 4.67722
## 1597 El Salvador La Libertad Santa Tecla 9.87553ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="La Libertad",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - La Libertad")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de La Paz
library(readr)
library(knitr)
df_ES <- subset (df, State == "La Paz")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1371 El Salvador La Paz San Pedro Masahuat 0.31933
## 1616 Honduras La Paz San José 4.69133ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="La Paz",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - La Paz")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de San Miguel
library(readr)
library(knitr)
df_ES <- subset (df, State == "San Miguel")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1294 El Salvador San Miguel Chirilagua 6.94536
## 1366 El Salvador San Miguel San Rafael Oriente 10.06695
## 1372 El Salvador San Miguel Chirilagua 9.97227ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="San Miguel",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San Miguel")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de San Salvador
library(readr)
library(knitr)
df_ES <- subset (df, State == "San Salvador")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 824 El Salvador San Salvador Apopa 3.01739
## 1375 El Salvador San Salvador Antiguo Cuscatlán 3.25227ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="San Salvador",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San Salvador")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de San Vicente
library(readr)
library(knitr)
df_ES <- subset (df, State == "San Vicente")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 224 El Salvador San Vicente San Vicente 7.60946
## 226 El Salvador San Vicente San Vicente 5.90726
## 227 El Salvador San Vicente San Vicente 4.03125ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="San Vicente",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San Vicente")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Santa Ana
library(readr)
library(knitr)
df_ES <- subset (df, State == "Santa Ana")
df_ES %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1373 El Salvador Santa Ana Coatepeque 8.8321
## 1594 El Salvador Santa Ana Ciudad Arce 1.1581ggplot(data=df_ES, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_ES,aes(x="Santa Ana",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Santa Ana")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_ESV <- subset (df, Country == "El Salvador")
knitr::kable(head(df_ESV))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | 230 | 9/5/07 | NA | El Salvador | SV | Ahuachapán | 7797 | Concepción de Ataco | 0.00273 | 13.8703 | -89.8486 | (13.8703, -89.848600000000005) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Felix | NA | NA | Azcentral.com | http://www.azcentral.com/news/articles/1108sr-fhsistercity1109-ON.html | ||
| 105 | 564 | 6/2/08 | NA | El Salvador | SV | La Libertad | 124694 | Santa Tecla | 4.96416 | 13.7205 | -89.2687 | (13.720499999999999, -89.268699999999995) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Arthur | NA | NA | http://news.xinhuanet.com/english/2008-06/04/content_8310737.htm | |||
| 224 | 1285 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 7.60946 | 13.6409 | -88.8699 | (13.6409, -88.869900000000001) | Landslide | Complex | Very_large | Tropical cyclone | Tropical Cyclone Ida | NA | 23 | http://www.google.com/hostednews/ap/article/ALeqM5j0XCCb1n12DyhoBoDzGj_hTyEtrAD9BRKPRG0 | |||
| 225 | 1286 | 11/8/09 | NA | El Salvador | SV | La Libertad | 33767 | Antiguo Cuscatlán | 4.86219 | 13.7156 | -89.2521 | (13.7156, -89.252099999999999) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | 4 | http://www.google.com/hostednews/ap/article/ALeqM5j0XCCb1n12DyhoBoDzGj_hTyEtrAD9BRKPRG0 | |||
| 226 | 1287 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 5.90726 | 13.6094 | -88.8488 | (13.609400000000001, -88.848799999999997) | Landslide | Rockfall | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | NA | http://news.bbc.co.uk/2/hi/in_depth/8349333.stm | |||
| 227 | 1288 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 4.03125 | 13.6466 | -88.8347 | (13.646599999999999, -88.834699999999998) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | NA | http://news.yahoo.com/s/afp/20091109/wl_afp/salvadorweatherstorm_20091109100952 |
Distance <- df_ESV$Distance
summary(df_ESV$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00273 3.22983 4.76971 5.09904 7.44344 10.06695library(pastecs)
stat.desc(df_ESV)## id date time continent_code Country country_code State
## nbr.val 2.200000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 2.300000e+02 NA NA NA NA NA NA
## max 7.442000e+03 NA NA NA NA NA NA
## range 7.212000e+03 NA NA NA NA NA NA
## sum 1.086050e+05 NA NA NA NA NA NA
## median 6.680000e+03 NA NA NA NA NA NA
## mean 4.936591e+03 NA NA NA NA NA NA
## SE.mean 5.803080e+02 NA NA NA NA NA NA
## CI.mean.0.95 1.206817e+03 NA NA NA NA NA NA
## var 7.408663e+06 NA NA NA NA NA NA
## std.dev 2.721886e+03 NA NA NA NA NA NA
## coef.var 5.513696e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 2.200000e+01 NA 22.0000000 NA 22.00000000
## nbr.null 0.000000e+00 NA 0.0000000 NA 0.00000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.00000000
## min 2.654000e+03 NA 0.0027300 NA 13.28170000
## max 1.246940e+05 NA 10.0669500 NA 14.00070000
## range 1.220400e+05 NA 10.0642200 NA 0.71900000
## sum 9.383360e+05 NA 112.1789400 NA 301.25530000
## median 2.643100e+04 NA 4.7697050 NA 13.71515000
## mean 4.265164e+04 NA 5.0990427 NA 13.69342273
## SE.mean 9.849090e+03 NA 0.6720603 NA 0.03809807
## CI.mean.0.95 2.048230e+04 NA 1.3976259 NA 0.07922927
## var 2.134100e+09 NA 9.9366315 NA 0.03193218
## std.dev 4.619633e+04 NA 3.1522423 NA 0.17869578
## coef.var 1.083108e+00 NA 0.6182028 NA 0.01304975
## longitude geolocation hazard_type landslide_type
## nbr.val 2.200000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -8.990440e+01 NA NA NA
## max -8.808430e+01 NA NA NA
## range 1.820100e+00 NA NA NA
## sum -1.961688e+03 NA NA NA
## median -8.926030e+01 NA NA NA
## mean -8.916765e+01 NA NA NA
## SE.mean 1.112458e-01 NA NA NA
## CI.mean.0.95 2.313484e-01 NA NA NA
## var 2.722640e-01 NA NA NA
## std.dev 5.217892e-01 NA NA NA
## coef.var -5.851777e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities
## nbr.val NA NA NA 14.0000000 18.000000
## nbr.null NA NA NA 10.0000000 13.000000
## nbr.na NA NA NA 8.0000000 4.000000
## min NA NA NA 0.0000000 0.000000
## max NA NA NA 2.0000000 32.000000
## range NA NA NA 2.0000000 32.000000
## sum NA NA NA 6.0000000 63.000000
## median NA NA NA 0.0000000 0.000000
## mean NA NA NA 0.4285714 3.500000
## SE.mean NA NA NA 0.2020305 2.107022
## CI.mean.0.95 NA NA NA 0.4364604 4.445428
## var NA NA NA 0.5714286 79.911765
## std.dev NA NA NA 0.7559289 8.939338
## coef.var NA NA NA 1.7638342 2.554097
## source_name source_link
## nbr.val NA NA
## nbr.null NA NA
## nbr.na NA NA
## min NA NA
## max NA NA
## range NA NA
## sum NA NA
## median NA NA
## mean NA NA
## SE.mean NA NA
## CI.mean.0.95 NA NA
## var NA NA
## std.dev NA NA
## coef.var NA NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="blue", fill="blue", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para El Salvador están medianamente bien distribuidos con cierto sesgo hacia la derecha.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Guatemala
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_GUA <- subset (df, Country == "Guatemala")
knitr::kable(head(df_GUA))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17 | 165 | 8/9/07 | NA | Guatemala | GT | Guatemala | 47247 | San José Pinula | 4.74385 | 14.5667 | -90.4500 | (14.566700000000001, -90.45) | Landslide | Mudslide | Medium | Rain | NA | 5 | International Herald | http://www.iht.com/articles/ap/2007/08/09/america/LA-GEN-Guatemala-Deadly-Mudslide.php | |||
| 27 | 198 | 8/21/07 | NA | Guatemala | GT | Alta Verapaz | 2006 | Lanquín | 13.39817 | 15.6046 | -90.0853 | (15.6046, -90.085300000000004) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dean | NA | NA | United Nations Development Programme - Relief Web | http://www.reliefweb.int/rw/RWB.NSF/db900SID/EDIS-76BSG6?OpenDocument | ||
| 28 | 199 | 8/21/07 | NA | Guatemala | GT | Izabal | 18994 | Morales | 12.55184 | 15.5163 | -88.9286 | (15.516299999999999, -88.928600000000003) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dean | NA | NA | United Nations Development Programme - Relief Web | http://www.reliefweb.int/rw/RWB.NSF/db900SID/EDIS-76BSG6?OpenDocument | ||
| 41 | 277 | 9/22/07 | NA | Guatemala | GT | Guatemala | 994938 | Guatemala City | 2.79113 | 14.6229 | -90.5316 | (14.6229, -90.531599999999997) | Landslide | Mudslide | Medium | Rain | NA | 3 | Fox News | http://www.foxnews.com/story/0,2933,297714,00.html | |||
| 104 | 563 | 6/1/08 | NA | Guatemala | GT | Escuintla | 31329 | Palín | 3.10150 | 14.4226 | -90.6755 | (14.422599999999999, -90.6755) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Storm Arthur | NA | 1 | http://209.85.215.104/search?q=cache:QU_lPxNfk78J:www.plenglish.com/article.asp?ID=%7B1D4A74F7-CDCA-49D0-ABD4-D2E0FD9D2130%7D&language=EN+Colom+said+the+declaration+came+after+a+death+in+Palin+and+40+houses+partially&hl=en&ct=clnk&cd=1&gl=us&c | |||
| 108 | 591 | 6/18/08 | NA | Guatemala | GT | Guatemala | 994938 | Guatemala City | 3.12614 | 14.6510 | -90.5403 | (14.651, -90.540300000000002) | Landslide | Complex | Medium | Rain | NA | 8 | http://cnnwire.blogs.cnn.com/2008/06/20/8-dead-in-rough-weather-in-guatemala/ |
df_GUA %>%
select(Country, State, City, Distance)## Country State City Distance
## 17 Guatemala Guatemala San José Pinula 4.74385
## 27 Guatemala Alta Verapaz Lanquín 13.39817
## 28 Guatemala Izabal Morales 12.55184
## 41 Guatemala Guatemala Guatemala City 2.79113
## 104 Guatemala Escuintla Palín 3.10150
## 108 Guatemala Guatemala Guatemala City 3.12614
## 120 Guatemala Zacapa La Unión 0.80640
## 158 Guatemala Chimaltenango San José Poaquil 5.31511
## 162 Guatemala Huehuetenango San Sebastián Huehuetenango 1.58358
## 169 Guatemala Izabal Morales 23.92309
## 351 Guatemala Quetzaltenango Almolonga 0.77254
## 353 Guatemala Escuintla Palín 0.18542
## 354 Guatemala Guatemala Amatitlán 2.02891
## 355 Guatemala Guatemala Chinautla 0.44764
## 356 Guatemala Sacatepéquez Santa María De Jesús 6.13527
## 357 Guatemala Guatemala Guatemala City 4.07930
## 358 Guatemala Sololá Santa Cruz La Laguna 6.00513
## 359 Guatemala Chimaltenango Santa Apolonia 0.99952
## 360 Guatemala Sololá San Pedro La Laguna 0.50611
## 361 Guatemala Guatemala San José Pinula 0.89040
## 362 Guatemala Quiché San Juan Cotzal 8.93658
## 363 Guatemala Totonicapán Totonicapán 0.17513
## 372 Guatemala Sololá San Pablo La Laguna 3.85753
## 383 Guatemala Guatemala Chinautla 3.85648
## 427 Guatemala Guatemala Mixco 2.10418
## 428 Guatemala Guatemala Mixco 3.64749
## 429 Guatemala Guatemala Santa Catarina Pinula 2.81128
## 430 Guatemala Chiquimula San Juan Ermita 6.15103
## 431 Guatemala Huehuetenango Malacatancito 0.03280
## 432 Guatemala Sololá San Andrés Semetabaj 0.00359
## 433 Guatemala Alta Verapaz Senahú 2.30104
## 437 Guatemala Sololá Nahualá 3.04642
## 438 Guatemala Quetzaltenango Colomba 0.92729
## 439 Guatemala Quiché Chicamán 21.83272
## 440 Guatemala Sacatepéquez Jocotenango 0.63089
## 441 Guatemala Chimaltenango Chimaltenango 1.36473
## 442 Guatemala Quiché Sacapulas 0.35171
## 818 Guatemala Huehuetenango Barillas 0.45507
## 885 Guatemala Quiché Chajul 7.39906
## 1112 Guatemala San Marcos Pajapita 0.96647
## 1244 Guatemala Huehuetenango San Pedro Necta 0.91108
## 1347 Guatemala Sololá Zunil 7.03115
## 1352 Guatemala Baja Verapaz Purulhá 5.88787
## 1353 Guatemala Guatemala Santa Catarina Pinula 2.70053
## 1354 Guatemala Guatemala Guatemala City 2.59620
## 1356 Guatemala Quiché San Luis Ixcán 22.56101
## 1357 Guatemala Jutiapa Jalpatagua 4.51954
## 1358 Guatemala Huehuetenango Cuilco 3.30989
## 1359 Guatemala Guatemala Chinautla 5.94535
## 1360 Guatemala Quiché Chinique 3.98185
## 1361 Guatemala San Marcos Tacaná 0.75729
## 1557 Guatemala Guatemala Santa Catarina Pinula 0.94245
## 1559 Guatemala Guatemala Santa Catarina Pinula 3.96161
## 1560 Guatemala Santa Rosa Santa María Ixhuatán 0.82332
## 1561 Guatemala Suchitepeque San Juan Bautista 3.47803
## 1568 Guatemala Guatemala Fraijanes 6.19218
## 1569 Guatemala Chimaltenango Patzicía 5.52205
## 1570 Guatemala Guatemala Mixco 1.87009
## 1571 Guatemala Guatemala Petapa 4.20726
## 1572 Guatemala Sololá San Antonio Palopó 3.18658
## 1573 Guatemala Sololá San Andrés Semetabaj 0.67040
## 1574 Guatemala Guatemala Guatemala City 3.80312
## 1575 Guatemala Guatemala Guatemala City 1.68290
## 1576 Guatemala Guatemala Guatemala City 2.08425
## 1577 Guatemala Guatemala Guatemala City 3.25675
## 1578 Guatemala Guatemala Guatemala City 3.49341
## 1579 Guatemala Guatemala Guatemala City 1.83863
## 1580 Guatemala Guatemala Guatemala City 1.57381
## 1581 Guatemala Guatemala Guatemala City 1.70147
## 1582 Guatemala Guatemala Guatemala City 3.00314
## 1583 Guatemala Quiché Chichicastenango 2.27725
## 1584 Guatemala Guatemala Chinautla 2.36376
## 1585 Guatemala Guatemala Chinautla 2.66358
## 1588 Guatemala Sacatepéquez Santa Catarina Barahona 1.45200
## 1589 Guatemala Alta Verapaz Cahabón 5.14479
## 1590 Guatemala Escuintla Nueva Concepción 8.25465
## 1591 Guatemala Guatemala Villa Canales 0.65744
## 1592 Guatemala Sololá San Andrés Semetabaj 0.75685
## 1595 Guatemala Quetzaltenango Coatepeque 1.81216library(ggplot2)
ggplot(data=df_GUA, aes(x = "Guatemala", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_GUA, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Baja Verapaz
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Baja Verapaz")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1352 Guatemala Baja Verapaz Purulhá 5.88787ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Baja Verapaz",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Baja Verapaz")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Chimaltenango
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Chimaltenango")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 158 Guatemala Chimaltenango San José Poaquil 5.31511
## 359 Guatemala Chimaltenango Santa Apolonia 0.99952
## 441 Guatemala Chimaltenango Chimaltenango 1.36473
## 1569 Guatemala Chimaltenango Patzicía 5.52205ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Chimaltenango",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Chimaltenango")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Chiquimula
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Chiquimula")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 430 Guatemala Chiquimula San Juan Ermita 6.15103ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Chiquimula",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Chiquimula")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Escuintla
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Escuintla")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 104 Guatemala Escuintla Palín 3.10150
## 353 Guatemala Escuintla Palín 0.18542
## 1590 Guatemala Escuintla Nueva Concepción 8.25465ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Escuintla",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Escuintla")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Guatemala
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Guatemala")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 17 Guatemala Guatemala San José Pinula 4.74385
## 41 Guatemala Guatemala Guatemala City 2.79113
## 108 Guatemala Guatemala Guatemala City 3.12614
## 354 Guatemala Guatemala Amatitlán 2.02891
## 355 Guatemala Guatemala Chinautla 0.44764
## 357 Guatemala Guatemala Guatemala City 4.07930
## 361 Guatemala Guatemala San José Pinula 0.89040
## 383 Guatemala Guatemala Chinautla 3.85648
## 427 Guatemala Guatemala Mixco 2.10418
## 428 Guatemala Guatemala Mixco 3.64749
## 429 Guatemala Guatemala Santa Catarina Pinula 2.81128
## 1353 Guatemala Guatemala Santa Catarina Pinula 2.70053
## 1354 Guatemala Guatemala Guatemala City 2.59620
## 1359 Guatemala Guatemala Chinautla 5.94535
## 1557 Guatemala Guatemala Santa Catarina Pinula 0.94245
## 1559 Guatemala Guatemala Santa Catarina Pinula 3.96161
## 1568 Guatemala Guatemala Fraijanes 6.19218
## 1570 Guatemala Guatemala Mixco 1.87009
## 1571 Guatemala Guatemala Petapa 4.20726
## 1574 Guatemala Guatemala Guatemala City 3.80312
## 1575 Guatemala Guatemala Guatemala City 1.68290
## 1576 Guatemala Guatemala Guatemala City 2.08425
## 1577 Guatemala Guatemala Guatemala City 3.25675
## 1578 Guatemala Guatemala Guatemala City 3.49341
## 1579 Guatemala Guatemala Guatemala City 1.83863
## 1580 Guatemala Guatemala Guatemala City 1.57381
## 1581 Guatemala Guatemala Guatemala City 1.70147
## 1582 Guatemala Guatemala Guatemala City 3.00314
## 1584 Guatemala Guatemala Chinautla 2.36376
## 1585 Guatemala Guatemala Chinautla 2.66358
## 1591 Guatemala Guatemala Villa Canales 0.65744ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Guatemala",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Guatemala")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Izabal
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Izabal")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 28 Guatemala Izabal Morales 12.55184
## 169 Guatemala Izabal Morales 23.92309ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Izabal",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Izabal")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Jutiapa
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Jutiapa")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1357 Guatemala Jutiapa Jalpatagua 4.51954ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Jutiapa",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Jutiapa")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Quetzaltenango
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Quetzaltenango")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 351 Guatemala Quetzaltenango Almolonga 0.77254
## 438 Guatemala Quetzaltenango Colomba 0.92729
## 1595 Guatemala Quetzaltenango Coatepeque 1.81216ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Quetzaltenango",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Quetzaltenango")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Quiché
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Quiché")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 362 Guatemala Quiché San Juan Cotzal 8.93658
## 439 Guatemala Quiché Chicamán 21.83272
## 442 Guatemala Quiché Sacapulas 0.35171
## 885 Guatemala Quiché Chajul 7.39906
## 1356 Guatemala Quiché San Luis Ixcán 22.56101
## 1360 Guatemala Quiché Chinique 3.98185
## 1583 Guatemala Quiché Chichicastenango 2.27725ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Quiché",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Quiché")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Sacatepéquez
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Sacatepéquez")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 356 Guatemala Sacatepéquez Santa María De Jesús 6.13527
## 440 Guatemala Sacatepéquez Jocotenango 0.63089
## 1588 Guatemala Sacatepéquez Santa Catarina Barahona 1.45200ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Sacatepéquez",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Sacatepéquez")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de San Marcos
library(readr)
library(knitr)
df_GUA <- subset (df, State == "San Marcos")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1112 Guatemala San Marcos Pajapita 0.96647
## 1361 Guatemala San Marcos Tacaná 0.75729ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="San Marcos",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San Marcos")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Santa Rosa
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Santa Rosa")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1560 Guatemala Santa Rosa Santa María Ixhuatán 0.82332ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Santa Rosa",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Santa Rosa")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Sololá
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Sololá")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 358 Guatemala Sololá Santa Cruz La Laguna 6.00513
## 360 Guatemala Sololá San Pedro La Laguna 0.50611
## 372 Guatemala Sololá San Pablo La Laguna 3.85753
## 432 Guatemala Sololá San Andrés Semetabaj 0.00359
## 437 Guatemala Sololá Nahualá 3.04642
## 1347 Guatemala Sololá Zunil 7.03115
## 1572 Guatemala Sololá San Antonio Palopó 3.18658
## 1573 Guatemala Sololá San Andrés Semetabaj 0.67040
## 1592 Guatemala Sololá San Andrés Semetabaj 0.75685ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Sololá",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Sololá")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Suchitepeque
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Suchitepeque")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1561 Guatemala Suchitepeque San Juan Bautista 3.47803ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Suchitepeque",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Suchitepeque")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Totonicapán
library(readr)
library(knitr)
df_GUA <- subset (df, State == "Totonicapán")
df_GUA %>%
select(Country, State, City, Distance) ## Country State City Distance
## 363 Guatemala Totonicapán Totonicapán 0.17513ggplot(data=df_GUA, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_GUA,aes(x="Totonicapán",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Totonicapán")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_GTM <- subset (df, Country == "Guatemala")
knitr::kable(head(df_GTM))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 17 | 165 | 8/9/07 | NA | Guatemala | GT | Guatemala | 47247 | San José Pinula | 4.74385 | 14.5667 | -90.4500 | (14.566700000000001, -90.45) | Landslide | Mudslide | Medium | Rain | NA | 5 | International Herald | http://www.iht.com/articles/ap/2007/08/09/america/LA-GEN-Guatemala-Deadly-Mudslide.php | |||
| 27 | 198 | 8/21/07 | NA | Guatemala | GT | Alta Verapaz | 2006 | Lanquín | 13.39817 | 15.6046 | -90.0853 | (15.6046, -90.085300000000004) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dean | NA | NA | United Nations Development Programme - Relief Web | http://www.reliefweb.int/rw/RWB.NSF/db900SID/EDIS-76BSG6?OpenDocument | ||
| 28 | 199 | 8/21/07 | NA | Guatemala | GT | Izabal | 18994 | Morales | 12.55184 | 15.5163 | -88.9286 | (15.516299999999999, -88.928600000000003) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dean | NA | NA | United Nations Development Programme - Relief Web | http://www.reliefweb.int/rw/RWB.NSF/db900SID/EDIS-76BSG6?OpenDocument | ||
| 41 | 277 | 9/22/07 | NA | Guatemala | GT | Guatemala | 994938 | Guatemala City | 2.79113 | 14.6229 | -90.5316 | (14.6229, -90.531599999999997) | Landslide | Mudslide | Medium | Rain | NA | 3 | Fox News | http://www.foxnews.com/story/0,2933,297714,00.html | |||
| 104 | 563 | 6/1/08 | NA | Guatemala | GT | Escuintla | 31329 | Palín | 3.10150 | 14.4226 | -90.6755 | (14.422599999999999, -90.6755) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Storm Arthur | NA | 1 | http://209.85.215.104/search?q=cache:QU_lPxNfk78J:www.plenglish.com/article.asp?ID=%7B1D4A74F7-CDCA-49D0-ABD4-D2E0FD9D2130%7D&language=EN+Colom+said+the+declaration+came+after+a+death+in+Palin+and+40+houses+partially&hl=en&ct=clnk&cd=1&gl=us&c | |||
| 108 | 591 | 6/18/08 | NA | Guatemala | GT | Guatemala | 994938 | Guatemala City | 3.12614 | 14.6510 | -90.5403 | (14.651, -90.540300000000002) | Landslide | Complex | Medium | Rain | NA | 8 | http://cnnwire.blogs.cnn.com/2008/06/20/8-dead-in-rough-weather-in-guatemala/ |
Distance <- df_GTM$Distance
summary(df_GTM$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00359 0.95446 2.70053 3.83694 4.36340 23.92309library(pastecs)
stat.desc(df_GTM)## id date time continent_code Country country_code State
## nbr.val 7.900000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 1.650000e+02 NA NA NA NA NA NA
## max 7.439000e+03 NA NA NA NA NA NA
## range 7.274000e+03 NA NA NA NA NA NA
## sum 3.572230e+05 NA NA NA NA NA NA
## median 5.475000e+03 NA NA NA NA NA NA
## mean 4.521810e+03 NA NA NA NA NA NA
## SE.mean 3.112730e+02 NA NA NA NA NA NA
## CI.mean.0.95 6.196970e+02 NA NA NA NA NA NA
## var 7.654381e+06 NA NA NA NA NA NA
## std.dev 2.766655e+03 NA NA NA NA NA NA
## coef.var 6.118468e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 7.900000e+01 NA 79.0000000 NA 7.900000e+01
## nbr.null 0.000000e+00 NA 0.0000000 NA 0.000000e+00
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.000000e+00
## min 1.311000e+03 NA 0.0035900 NA 1.419740e+01
## max 9.949380e+05 NA 23.9230900 NA 1.580760e+01
## range 9.936270e+05 NA 23.9195000 NA 1.610200e+00
## sum 1.624255e+07 NA 303.1182300 NA 1.170505e+03
## median 1.899400e+04 NA 2.7005300 NA 1.468080e+01
## mean 2.056019e+05 NA 3.8369396 NA 1.481653e+01
## SE.mean 4.095993e+04 NA 0.5175386 NA 4.216799e-02
## CI.mean.0.95 8.154496e+04 NA 1.0303402 NA 8.395001e-02
## var 1.325396e+11 NA 21.1598488 NA 1.404730e-01
## std.dev 3.640598e+05 NA 4.5999836 NA 3.747973e-01
## coef.var 1.770702e+00 NA 1.1988679 NA 2.529589e-02
## longitude geolocation hazard_type landslide_type
## nbr.val 7.900000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -9.207380e+01 NA NA NA
## max -8.892860e+01 NA NA NA
## range 3.145200e+00 NA NA NA
## sum -7.170565e+03 NA NA NA
## median -9.061580e+01 NA NA NA
## mean -9.076664e+01 NA NA NA
## SE.mean 6.449027e-02 NA NA NA
## CI.mean.0.95 1.283903e-01 NA NA NA
## var 3.285606e-01 NA NA NA
## std.dev 5.732021e-01 NA NA NA
## coef.var -6.315118e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities
## nbr.val NA NA NA 40.0000000 75.000000
## nbr.null NA NA NA 35.0000000 40.000000
## nbr.na NA NA NA 39.0000000 4.000000
## min NA NA NA 0.0000000 0.000000
## max NA NA NA 7.0000000 280.000000
## range NA NA NA 7.0000000 280.000000
## sum NA NA NA 19.0000000 482.000000
## median NA NA NA 0.0000000 0.000000
## mean NA NA NA 0.4750000 6.426667
## SE.mean NA NA NA 0.2401589 3.742773
## CI.mean.0.95 NA NA NA 0.4857672 7.457638
## var NA NA NA 2.3070513 1050.626306
## std.dev NA NA NA 1.5188980 32.413366
## coef.var NA NA NA 3.1976801 5.043574
## source_name source_link
## nbr.val NA NA
## nbr.null NA NA
## nbr.na NA NA
## min NA NA
## max NA NA
## range NA NA
## sum NA NA
## median NA NA
## mean NA NA
## SE.mean NA NA
## CI.mean.0.95 NA NA
## var NA NA
## std.dev NA NA
## coef.var NA NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="blue", fill="light blue", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Guatemala están medianamente bien distribuidos con sesgo hacia la izquierda y con algunos datos aberrantes.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Honduras
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_HON <- subset (df, Country == "Honduras")
knitr::kable(head(df_HON))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | 854 | 10/19/08 | NA | Honduras | HN | Copán | 4752 | Corquín | 0.43391 | 14.5637 | -88.8693 | (14.563700000000001, -88.869299999999996) | Landslide | Landslide | Large | Tropical cyclone | Tropical Depression 16 | NA | 23 | http://www.chron.com/disp/story.mpl/ap/world/6068144.html | |||
| 160 | 855 | 10/20/08 | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 2.99239 | 14.1080 | -87.2137 | (14.108000000000001, -87.213700000000003) | Landslide | Mudslide | Large | Tropical cyclone | Tropical Depression 16 | NA | 29 | http://in.ibtimes.com/articles/20081021/honduras-landslide-tegucigalpa-victim.htm | |||
| 376 | 2062 | 7/12/10 | 5:30:00 | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 0.98377 | 14.0831 | -87.1978 | (14.0831, -87.197800000000001) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://mdn.mainichi.jp/mdnnews/news/20100713p2a00m0na013000c.html | |||
| 381 | 2093 | 7/18/10 | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 1.24404 | 14.0814 | -87.1953 | (14.0814, -87.195300000000003) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.insidecostarica.com/dailynews/2010/july/19/centralamerica10071903.htm | ||||
| 406 | 2217 | 8/7/10 | Overnight | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 2.21442 | 14.0783 | -87.2270 | (14.0783, -87.227000000000004) | Landslide | Mudslide | Medium | Downpour | NA | 3 | ||||
| 435 | 2358 | 8/29/10 | 4:30:00 | NA | Honduras | HN | Francisco Morazán | 2288 | Santa Lucía | 4.75791 | 14.1015 | -87.1607 | (14.1015, -87.160700000000006) | Landslide | Rockfall | Medium | Downpour | NA | 5 |
df_HON %>%
select(Country, State, City, Distance)## Country State City Distance
## 159 Honduras Copán Corquín 0.43391
## 160 Honduras Francisco Morazán Tegucigalpa 2.99239
## 376 Honduras Francisco Morazán Tegucigalpa 0.98377
## 381 Honduras Francisco Morazán Tegucigalpa 1.24404
## 406 Honduras Francisco Morazán Tegucigalpa 2.21442
## 435 Honduras Francisco Morazán Santa Lucía 4.75791
## 474 Honduras Comayagua El Rancho 4.53362
## 485 Honduras Colón Cusuna 36.37629
## 820 Honduras Francisco Morazán Tegucigalpa 1.23639
## 1100 Honduras Cortés Los Caminos 3.53737
## 1279 Honduras Choluteca Ciudad Choluteca 3.69596
## 1288 Honduras Yoro Yoro 0.31238
## 1363 Honduras Ocotepeque Sinuapa 2.00805
## 1377 Honduras Cortés Agua Azul Rancho 0.97057
## 1379 Honduras Santa Bárbara Agualote 2.91594
## 1599 Honduras El Paraíso 1.90052
## 1602 Honduras Francisco Morazán El Lolo 1.85897
## 1603 Honduras Francisco Morazán Tegucigalpa 3.25281
## 1604 Honduras Choluteca Duyure 11.67237
## 1605 Honduras Choluteca Corpus 0.36987
## 1610 Honduras Comayagua El Sauce 7.28575
## 1611 Honduras Comayagua La Libertad 17.28613
## 1612 Honduras Comayagua Concepción de Guasistagua 8.52584
## 1613 Honduras Copán Santa Rosa de Copán 0.74414
## 1614 Honduras Copán Santa Rosa de Copán 0.28887
## 1615 Honduras Copán Ojos de Agua 1.39095
## 1616 Honduras La Paz San José 4.69133
## 1617 Honduras Copán Lucerna 5.89721
## 1618 Honduras Ocotepeque La Labor 5.79867
## 1619 Honduras Francisco Morazán Villa Nueva 2.00830
## 1620 Honduras Santa Bárbara Ilama 2.87349
## 1622 Honduras Francisco Morazán El Guapinol 3.54399
## 1623 Honduras Yoro La Sarrosa 6.66574
## 1624 Honduras Francisco Morazán El Tablón 3.12986
## 1638 Honduras Francisco Morazán Tegucigalpa 0.91552
## 1639 Honduras Francisco Morazán Yaguacire 1.30583
## 1640 Honduras Francisco Morazán Río Abajo 3.63962
## 1641 Honduras Francisco Morazán Tegucigalpa 2.91326library(ggplot2)
ggplot(data=df_HON, aes(x = "Honduras", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_HON, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Choluteca
library(readr)
library(knitr)
df_HON <- subset (df, State == "Choluteca")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1279 Honduras Choluteca Ciudad Choluteca 3.69596
## 1604 Honduras Choluteca Duyure 11.67237
## 1605 Honduras Choluteca Corpus 0.36987ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Choluteca",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Choluteca")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Colón
library(readr)
library(knitr)
df_HON <- subset (df, State == "Colón")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 485 Honduras Colón Cusuna 36.37629
## 557 Panama Colón El Giral 1.80330
## 558 Panama Colón Portobelo 0.09491
## 919 Panama Colón Colón 0.16894
## 979 Panama Colón Portobelo 2.67409
## 980 Panama Colón Margarita 0.74760
## 981 Panama Colón Colón 0.20365
## 1080 Panama Colón Margarita 0.76740
## 1390 Panama Colón Nuevo San Juan 4.74914
## 1606 Panama Colón Nueva Providencia 0.18619
## 1607 Panama Colón María Chiquita 2.28589
## 1608 Panama Colón Cativá 0.63948ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Colón",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Colón")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Comayagua
library(readr)
library(knitr)
df_HON <- subset (df, State == "Comayagua")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 474 Honduras Comayagua El Rancho 4.53362
## 1610 Honduras Comayagua El Sauce 7.28575
## 1611 Honduras Comayagua La Libertad 17.28613
## 1612 Honduras Comayagua Concepción de Guasistagua 8.52584ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Comayagua",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Comayagua")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Copán
library(readr)
library(knitr)
df_HON <- subset (df, State == "Copán")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 159 Honduras Copán Corquín 0.43391
## 1613 Honduras Copán Santa Rosa de Copán 0.74414
## 1614 Honduras Copán Santa Rosa de Copán 0.28887
## 1615 Honduras Copán Ojos de Agua 1.39095
## 1617 Honduras Copán Lucerna 5.89721ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Copán",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Copán")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Cortés
library(readr)
library(knitr)
df_HON <- subset (df, State == "Cortés")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1100 Honduras Cortés Los Caminos 3.53737
## 1377 Honduras Cortés Agua Azul Rancho 0.97057ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Cortés",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Cortés")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de El Paraíso
library(readr)
library(knitr)
df_HON <- subset (df, State == "El Paraíso")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1599 Honduras El Paraíso 1.90052ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="El Paraíso",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - El Paraíso")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de La Paz
library(readr)
library(knitr)
df_HON <- subset (df, State == "La Paz")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1371 El Salvador La Paz San Pedro Masahuat 0.31933
## 1616 Honduras La Paz San José 4.69133ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="La Paz",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - La Paz")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Ocotepeque
library(readr)
library(knitr)
df_HON <- subset (df, State == "Ocotepeque")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1363 Honduras Ocotepeque Sinuapa 2.00805
## 1618 Honduras Ocotepeque La Labor 5.79867ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Ocotepeque",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Ocotepeque")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Santa Bárbara
library(readr)
library(knitr)
df_HON <- subset (df, State == "Santa Bárbara")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1379 Honduras Santa Bárbara Agualote 2.91594
## 1620 Honduras Santa Bárbara Ilama 2.87349ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Santa Bárbara",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Santa Bárbara")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Yoro
library(readr)
library(knitr)
df_HON <- subset (df, State == "Yoro")
df_HON %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1288 Honduras Yoro Yoro 0.31238
## 1623 Honduras Yoro La Sarrosa 6.66574ggplot(data=df_HON, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_HON,aes(x="Yoro",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Yoro")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_HND <- subset (df, Country == "Honduras")
knitr::kable(head(df_HND))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 159 | 854 | 10/19/08 | NA | Honduras | HN | Copán | 4752 | Corquín | 0.43391 | 14.5637 | -88.8693 | (14.563700000000001, -88.869299999999996) | Landslide | Landslide | Large | Tropical cyclone | Tropical Depression 16 | NA | 23 | http://www.chron.com/disp/story.mpl/ap/world/6068144.html | |||
| 160 | 855 | 10/20/08 | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 2.99239 | 14.1080 | -87.2137 | (14.108000000000001, -87.213700000000003) | Landslide | Mudslide | Large | Tropical cyclone | Tropical Depression 16 | NA | 29 | http://in.ibtimes.com/articles/20081021/honduras-landslide-tegucigalpa-victim.htm | |||
| 376 | 2062 | 7/12/10 | 5:30:00 | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 0.98377 | 14.0831 | -87.1978 | (14.0831, -87.197800000000001) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://mdn.mainichi.jp/mdnnews/news/20100713p2a00m0na013000c.html | |||
| 381 | 2093 | 7/18/10 | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 1.24404 | 14.0814 | -87.1953 | (14.0814, -87.195300000000003) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.insidecostarica.com/dailynews/2010/july/19/centralamerica10071903.htm | ||||
| 406 | 2217 | 8/7/10 | Overnight | NA | Honduras | HN | Francisco Morazán | 850848 | Tegucigalpa | 2.21442 | 14.0783 | -87.2270 | (14.0783, -87.227000000000004) | Landslide | Mudslide | Medium | Downpour | NA | 3 | ||||
| 435 | 2358 | 8/29/10 | 4:30:00 | NA | Honduras | HN | Francisco Morazán | 2288 | Santa Lucía | 4.75791 | 14.1015 | -87.1607 | (14.1015, -87.160700000000006) | Landslide | Rockfall | Medium | Downpour | NA | 5 |
Distance <- df_HND$Distance
summary(df_HND$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2889 1.2595 2.9146 4.3730 4.6519 36.3763library(pastecs)
stat.desc(df_HND)## id date time continent_code Country country_code State
## nbr.val 3.800000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 8.540000e+02 NA NA NA NA NA NA
## max 7.485000e+03 NA NA NA NA NA NA
## range 6.631000e+03 NA NA NA NA NA NA
## sum 2.290210e+05 NA NA NA NA NA NA
## median 7.448500e+03 NA NA NA NA NA NA
## mean 6.026868e+03 NA NA NA NA NA NA
## SE.mean 3.671138e+02 NA NA NA NA NA NA
## CI.mean.0.95 7.438432e+02 NA NA NA NA NA NA
## var 5.121356e+06 NA NA NA NA NA NA
## std.dev 2.263041e+03 NA NA NA NA NA NA
## coef.var 3.754921e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 3.800000e+01 NA 38.000000 NA 38.00000000
## nbr.null 0.000000e+00 NA 0.000000 NA 0.00000000
## nbr.na 0.000000e+00 NA 0.000000 NA 0.00000000
## min 1.043000e+03 NA 0.288870 NA 13.28610000
## max 8.508480e+05 NA 36.376290 NA 15.52270000
## range 8.498050e+05 NA 36.087420 NA 2.23660000
## sum 7.001138e+06 NA 166.172050 NA 546.00580000
## median 1.936000e+03 NA 2.914600 NA 14.20695000
## mean 1.842405e+05 NA 4.372949 NA 14.36857368
## SE.mean 5.663200e+04 NA 1.023393 NA 0.08447952
## CI.mean.0.95 1.147473e+05 NA 2.073592 NA 0.17117178
## var 1.218729e+11 NA 39.798695 NA 0.27119802
## std.dev 3.491031e+05 NA 6.308621 NA 0.52076676
## coef.var 1.894823e+00 NA 1.442647 NA 0.03624346
## longitude geolocation hazard_type landslide_type
## nbr.val 3.800000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -8.916660e+01 NA NA NA
## max -8.526500e+01 NA NA NA
## range 3.901600e+00 NA NA NA
## sum -3.329927e+03 NA NA NA
## median -8.728760e+01 NA NA NA
## mean -8.762966e+01 NA NA NA
## SE.mean 1.301577e-01 NA NA NA
## CI.mean.0.95 2.637245e-01 NA NA NA
## var 6.437585e-01 NA NA NA
## std.dev 8.023457e-01 NA NA NA
## coef.var -9.156096e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities
## nbr.val NA NA NA 29.0000000 38.0000000
## nbr.null NA NA NA 26.0000000 25.0000000
## nbr.na NA NA NA 9.0000000 0.0000000
## min NA NA NA 0.0000000 0.0000000
## max NA NA NA 3.0000000 29.0000000
## range NA NA NA 3.0000000 29.0000000
## sum NA NA NA 6.0000000 86.0000000
## median NA NA NA 0.0000000 0.0000000
## mean NA NA NA 0.2068966 2.2631579
## SE.mean NA NA NA 0.1253499 0.9774733
## CI.mean.0.95 NA NA NA 0.2567675 1.9805491
## var NA NA NA 0.4556650 36.3072546
## std.dev NA NA NA 0.6750296 6.0255502
## coef.var NA NA NA 3.2626433 2.6624524
## source_name source_link
## nbr.val NA NA
## nbr.null NA NA
## nbr.na NA NA
## min NA NA
## max NA NA
## range NA NA
## sum NA NA
## median NA NA
## mean NA NA
## SE.mean NA NA
## CI.mean.0.95 NA NA
## var NA NA
## std.dev NA NA
## coef.var NA NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="light blue", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamiento para Honduras están bien distribuidos y se considera la presencia de ciertos datos aberrantes.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Jamaica
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_JAM <- subset (df, Country == "Jamaica")
knitr::kable(head(df_JAM))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | 174 | 8/11/07 | NA | Jamaica | JM | Portland | 14400 | Port Antonio | 7.79027 | 18.1258 | -76.5082 | (18.125800000000002, -76.508200000000002) | Landslide | Landslide | Medium | Rain | NA | NA | Jamaica Gleaner | http://www.jamaica-gleaner.com/gleaner/20070812/lead/lead2.html | |||
| 48 | 304 | 10/12/07 | NA | Jamaica | JM | St. Elizabeth | 1371 | Maggotty | 4.57363 | 18.1257 | -77.7405 | (18.125699999999998, -77.740499999999997) | Landslide | Complex | Medium | Rain | NA | NA | RadioJamaica | http://www.radiojamaica.com/content/view/2039/26/ | |||
| 50 | 314 | 10/17/07 | NA | Jamaica | JM | Saint Thomas | 2382 | Bath | 0.21825 | 17.9500 | -76.3500 | (17.95, -76.349999999999994) | Landslide | Mudslide | Medium | Rain | NA | NA | RadioJamaica | http://www.radiojamaica.com/content/view/2193/26/ | |||
| 54 | 339 | 10/31/07 | NA | Jamaica | JM | Saint Thomas | 2634 | Easington | 6.51940 | 17.9384 | -76.6479 | (17.938400000000001, -76.647900000000007) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Noel | NA | 1 | RadioJamaica | http://www.radiojamaica.com/content/view/2583/26/ | ||
| 55 | 340 | 10/31/07 | NA | Jamaica | JM | Saint Catherine | 4085 | Riversdale | 5.46381 | 18.2152 | -76.9659 | (18.215199999999999, -76.965900000000005) | Landslide | Complex | Medium | Rain | NA | NA | RadioJamaica | http://www.radiojamaica.com/content/view/2933/26/ | |||
| 56 | 341 | 10/31/07 | NA | Jamaica | JM | Saint Andrew | 1088 | Gordon Town | 0.96514 | 18.0370 | -76.7088 | (18.036999999999999, -76.708799999999997) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Noel | NA | NA | Jamaica Observer | http://www.jamaicaobserver.com/news/html/20071101T010000-0500_128916_OBS_DEADLY_RAIN.asp |
df_JAM %>%
select(Country, State, City, Distance)## Country State City Distance
## 18 Jamaica Portland Port Antonio 7.79027
## 48 Jamaica St. Elizabeth Maggotty 4.57363
## 50 Jamaica Saint Thomas Bath 0.21825
## 54 Jamaica Saint Thomas Easington 6.51940
## 55 Jamaica Saint Catherine Riversdale 5.46381
## 56 Jamaica Saint Andrew Gordon Town 0.96514
## 60 Jamaica Saint Ann Saint Annнўд‰ед‹ўs Bay 3.96533
## 93 Jamaica Saint Ann Ocho Rios 0.53886
## 143 Jamaica Saint Andrew Mavis Bank 9.83051
## 146 Jamaica Saint Thomas Bath 1.71217
## 237 Jamaica Portland Moore Town 5.02351
## 305 Jamaica Saint Thomas Easington 4.53632
## 306 Jamaica Clarendon Chapelton 9.53953
## 307 Jamaica Clarendon May Pen 0.56186
## 308 Jamaica Portland Buff Bay 2.78885
## 309 Jamaica Portland Port Antonio 4.46942
## 408 Jamaica Saint Andrew Stony Hill 5.98731
## 444 Jamaica Portland Moore Town 0.59599
## 456 Jamaica Portland Moore Town 7.81269
## 460 Jamaica Saint Mary Richmond 1.24727
## 461 Jamaica Saint Andrew Stony Hill 10.84467
## 462 Jamaica Saint Catherine Bog Walk 5.86530
## 466 Jamaica Saint Andrew Stony Hill 2.13878
## 486 Jamaica Saint Mary Richmond 8.74635
## 522 Jamaica Portland Manchioneal 0.71773
## 588 Jamaica Saint Mary Richmond 3.77580
## 589 Jamaica Portland Buff Bay 7.52317
## 900 Jamaica Portland Manchioneal 4.13454
## 920 Jamaica Portland Moore Town 3.17930
## 1310 Jamaica Portland Port Antonio 3.60799
## 1628 Jamaica Saint Catherine Bog Walk 6.71269
## 1629 Jamaica Saint Ann Saint Annнўд‰ед‹ўs Bay 1.08196
## 1630 Jamaica Portland Port Antonio 4.68732library(ggplot2)
ggplot(data=df_JAM, aes(x = "Jamaica", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_JAM, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Clarendon
library(readr)
library(knitr)
df_JAM <- subset (df, State == "Clarendon")
df_JAM %>%
select(Country, State, City, Distance) ## Country State City Distance
## 306 Jamaica Clarendon Chapelton 9.53953
## 307 Jamaica Clarendon May Pen 0.56186ggplot(data=df_JAM, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_JAM,aes(x="Clarendon",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Clarendon")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Saint Andrew
library(readr)
library(knitr)
df_JAM <- subset (df, State == "Saint Andrew")
df_JAM %>%
select(Country, State, City, Distance) ## Country State City Distance
## 56 Jamaica Saint Andrew Gordon Town 0.96514
## 143 Jamaica Saint Andrew Mavis Bank 9.83051
## 408 Jamaica Saint Andrew Stony Hill 5.98731
## 461 Jamaica Saint Andrew Stony Hill 10.84467
## 466 Jamaica Saint Andrew Stony Hill 2.13878
## 476 Dominica Saint Andrew Calibishie 2.64873ggplot(data=df_JAM, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_JAM,aes(x="Saint Andrew",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Saint Andrew")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Saint Ann
library(readr)
library(knitr)
df_JAM <- subset (df, State == "Saint Ann")
df_JAM %>%
select(Country, State, City, Distance) ## Country State City Distance
## 60 Jamaica Saint Ann Saint Annнўд‰ед‹ўs Bay 3.96533
## 93 Jamaica Saint Ann Ocho Rios 0.53886
## 1629 Jamaica Saint Ann Saint Annнўд‰ед‹ўs Bay 1.08196ggplot(data=df_JAM, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_JAM,aes(x="Saint Ann",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Saint Ann")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Saint Catherine
library(readr)
library(knitr)
df_JAM <- subset (df, State == "Saint Catherine")
df_JAM %>%
select(Country, State, City, Distance) ## Country State City Distance
## 55 Jamaica Saint Catherine Riversdale 5.46381
## 462 Jamaica Saint Catherine Bog Walk 5.86530
## 1628 Jamaica Saint Catherine Bog Walk 6.71269ggplot(data=df_JAM, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_JAM,aes(x="Saint Catherine",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Saint Catherine")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Saint Mary
library(readr)
library(knitr)
df_JAM <- subset (df, State == "Saint Mary")
df_JAM %>%
select(Country, State, City, Distance) ## Country State City Distance
## 460 Jamaica Saint Mary Richmond 1.24727
## 486 Jamaica Saint Mary Richmond 8.74635
## 588 Jamaica Saint Mary Richmond 3.77580ggplot(data=df_JAM, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_JAM,aes(x="Saint Mary",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Saint Mary")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Saint Thomas
library(readr)
library(knitr)
df_JAM <- subset (df, State == "Saint Thomas")
df_JAM %>%
select(Country, State, City, Distance) ## Country State City Distance
## 50 Jamaica Saint Thomas Bath 0.21825
## 54 Jamaica Saint Thomas Easington 6.51940
## 146 Jamaica Saint Thomas Bath 1.71217
## 305 Jamaica Saint Thomas Easington 4.53632ggplot(data=df_JAM, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_JAM,aes(x="Saint Thomas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Saint Thomas")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_JMC <- subset (df, Country == "Jamaica")
knitr::kable(head(df_JMC))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | 174 | 8/11/07 | NA | Jamaica | JM | Portland | 14400 | Port Antonio | 7.79027 | 18.1258 | -76.5082 | (18.125800000000002, -76.508200000000002) | Landslide | Landslide | Medium | Rain | NA | NA | Jamaica Gleaner | http://www.jamaica-gleaner.com/gleaner/20070812/lead/lead2.html | |||
| 48 | 304 | 10/12/07 | NA | Jamaica | JM | St. Elizabeth | 1371 | Maggotty | 4.57363 | 18.1257 | -77.7405 | (18.125699999999998, -77.740499999999997) | Landslide | Complex | Medium | Rain | NA | NA | RadioJamaica | http://www.radiojamaica.com/content/view/2039/26/ | |||
| 50 | 314 | 10/17/07 | NA | Jamaica | JM | Saint Thomas | 2382 | Bath | 0.21825 | 17.9500 | -76.3500 | (17.95, -76.349999999999994) | Landslide | Mudslide | Medium | Rain | NA | NA | RadioJamaica | http://www.radiojamaica.com/content/view/2193/26/ | |||
| 54 | 339 | 10/31/07 | NA | Jamaica | JM | Saint Thomas | 2634 | Easington | 6.51940 | 17.9384 | -76.6479 | (17.938400000000001, -76.647900000000007) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Noel | NA | 1 | RadioJamaica | http://www.radiojamaica.com/content/view/2583/26/ | ||
| 55 | 340 | 10/31/07 | NA | Jamaica | JM | Saint Catherine | 4085 | Riversdale | 5.46381 | 18.2152 | -76.9659 | (18.215199999999999, -76.965900000000005) | Landslide | Complex | Medium | Rain | NA | NA | RadioJamaica | http://www.radiojamaica.com/content/view/2933/26/ | |||
| 56 | 341 | 10/31/07 | NA | Jamaica | JM | Saint Andrew | 1088 | Gordon Town | 0.96514 | 18.0370 | -76.7088 | (18.036999999999999, -76.708799999999997) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Noel | NA | NA | Jamaica Observer | http://www.jamaicaobserver.com/news/html/20071101T010000-0500_128916_OBS_DEADLY_RAIN.asp |
Distance <- df_JMC$Distance
summary(df_JMC$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2182 1.7122 4.4694 4.4593 6.5194 10.8447library(pastecs)
stat.desc(df_JAM)## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced## id date time continent_code Country country_code State
## nbr.val 4.000000e+00 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 3.140000e+02 NA NA NA NA NA NA
## max 1.760000e+03 NA NA NA NA NA NA
## range 1.446000e+03 NA NA NA NA NA NA
## sum 3.187000e+03 NA NA NA NA NA NA
## median 5.565000e+02 NA NA NA NA NA NA
## mean 7.967500e+02 NA NA NA NA NA NA
## SE.mean 3.380028e+02 NA NA NA NA NA NA
## CI.mean.0.95 1.075676e+03 NA NA NA NA NA NA
## var 4.569836e+05 NA NA NA NA NA NA
## std.dev 6.760056e+02 NA NA NA NA NA NA
## coef.var 8.484539e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 4.000000e+00 NA 4.0000000 NA 4.000000e+00
## nbr.null 0.000000e+00 NA 0.0000000 NA 0.000000e+00
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.000000e+00
## min 2.382000e+03 NA 0.2182500 NA 1.793840e+01
## max 2.634000e+03 NA 6.5194000 NA 1.796470e+01
## range 2.520000e+02 NA 6.3011500 NA 2.630000e-02
## sum 1.003200e+04 NA 12.9861400 NA 7.180110e+01
## median 2.508000e+03 NA 3.1242450 NA 1.794900e+01
## mean 2.508000e+03 NA 3.2465350 NA 1.795028e+01
## SE.mean 7.274613e+01 NA 1.4112635 NA 5.434055e-03
## CI.mean.0.95 2.315107e+02 NA 4.4912704 NA 1.729359e-02
## var 2.116800e+04 NA 7.9666589 NA 1.181158e-04
## std.dev 1.454923e+02 NA 2.8225270 NA 1.086811e-02
## coef.var 5.801127e-02 NA 0.8693968 NA 6.054565e-04
## longitude geolocation hazard_type landslide_type
## nbr.val 4.000000e+00 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -7.664790e+01 NA NA NA
## max -7.633300e+01 NA NA NA
## range 3.149000e-01 NA NA NA
## sum -3.059144e+02 NA NA NA
## median -7.646675e+01 NA NA NA
## mean -7.647860e+01 NA NA NA
## SE.mean 8.031387e-02 NA NA NA
## CI.mean.0.95 2.555946e-01 NA NA NA
## var 2.580127e-02 NA NA NA
## std.dev 1.606277e-01 NA NA NA
## coef.var -2.100297e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 0 2.0000000 NA
## nbr.null NA NA NA 0 1.0000000 NA
## nbr.na NA NA NA 4 2.0000000 NA
## min NA NA NA Inf 0.0000000 NA
## max NA NA NA -Inf 1.0000000 NA
## range NA NA NA -Inf 1.0000000 NA
## sum NA NA NA 0 1.0000000 NA
## median NA NA NA NA 0.5000000 NA
## mean NA NA NA NaN 0.5000000 NA
## SE.mean NA NA NA NA 0.5000000 NA
## CI.mean.0.95 NA NA NA NaN 6.3531024 NA
## var NA NA NA NA 0.5000000 NA
## std.dev NA NA NA NA 0.7071068 NA
## coef.var NA NA NA NA 1.4142136 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="light blue", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Jamaica se encuentran medianamente bien distribuidos y presentan un ligero sesgo hacia la izquierda.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Nicaragua
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_NIC <- subset (df, Country == "Nicaragua")
knitr::kable(head(df_NIC))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | 229 | 9/4/07 | NA | Nicaragua | NI | Atlántico Norte | 6315 | Bonanza | 54.90196 | 13.6670 | -84.2435 | (13.667, -84.243499999999997) | Landslide | Complex | Medium | Tropical cyclone | Hurricane Felix | NA | NA | United Nations Development Programme - Relief Web | http://www.reliefweb.int/ | ||
| 151 | 826 | 10/3/08 | NA | Nicaragua | NI | Masaya | 5182 | Tisma | 14.49301 | 12.1200 | -85.8900 | (12.12, -85.89) | Landslide | Landslide | Medium | Downpour | NA | 9 | CBC | http://www.cbc.ca/world/story/2008/10/04/nicaragua-flooding.html | |||
| 420 | 2289 | 8/20/10 | NA | Nicaragua | NI | Managua | 16469 | El Crucero | 5.84054 | 12.0420 | -86.2998 | (12.042, -86.299800000000005) | Landslide | Mudslide | Medium | Downpour | NA | 3 | |||||
| 424 | 2330 | 8/25/10 | NA | Nicaragua | NI | Jinotega | 2367 | San José de Bocay | 1.36745 | 13.5317 | -85.5325 | (13.531700000000001, -85.532499999999999) | Landslide | Landslide | Medium | Downpour | NA | NA | |||||
| 1261 | 6089 | 6/23/14 | NA | Nicaragua | NI | Chontales | 5827 | Santo Domingo | 31.14242 | Unknown | 12.3535 | -84.8095 | (12.3535, -84.8095) | Landslide | Landslide | Small | Continuous rain | 0 | 0 | Wilfried Strauch | |||
| 1262 | 6090 | 6/23/14 | NA | Nicaragua | NI | Chontales | 5827 | Santo Domingo | 31.24511 | Unknown | 12.3521 | -84.8080 | (12.3521, -84.808000000000007) | Landslide | Landslide | Medium | Continuous rain | 0 | 0 | Wilfried Strauch |
df_NIC %>%
select(Country, State, City, Distance)## Country State City Distance
## 33 Nicaragua Atlántico Norte Bonanza 54.90196
## 151 Nicaragua Masaya Tisma 14.49301
## 420 Nicaragua Managua El Crucero 5.84054
## 424 Nicaragua Jinotega San José de Bocay 1.36745
## 1261 Nicaragua Chontales Santo Domingo 31.14242
## 1262 Nicaragua Chontales Santo Domingo 31.24511
## 1263 Nicaragua Chontales Santo Domingo 31.37360
## 1264 Nicaragua Chontales Santo Domingo 31.10125
## 1265 Nicaragua Chontales Santo Domingo 30.99704
## 1266 Nicaragua Chontales Santo Domingo 30.77070
## 1267 Nicaragua Chontales Santo Domingo 30.27546
## 1268 Nicaragua Chontales Santo Domingo 29.95253
## 1269 Nicaragua Chontales Santo Domingo 29.92927
## 1270 Nicaragua Chontales Santo Domingo 28.90294
## 1271 Nicaragua Chontales Santo Domingo 32.69694
## 1272 Nicaragua Chontales Santo Domingo 32.96402
## 1273 Nicaragua Chontales Santo Domingo 32.77401
## 1274 Nicaragua Chontales Santo Domingo 29.94574
## 1299 Nicaragua Managua Ciudad Sandino 5.59574
## 1321 Nicaragua Ogun State Bonanza 0.37593
## 1380 Nicaragua Rivas Altagracia 1.97784
## 1381 Nicaragua Rivas Altagracia 5.77119
## 1382 Nicaragua Río San Juan San Carlos 0.67752
## 1626 Nicaragua Jinotega Wiwilí 25.81514
## 1627 Nicaragua Jinotega Jinotega 2.44880
## 1631 Nicaragua Madriz Las Sabanas 7.21108
## 1632 Nicaragua Madriz Las Sabanas 4.86364
## 1633 Nicaragua Managua Terrabona 18.92056
## 1634 Nicaragua Ogun State Bonanza 10.61568
## 1636 Nicaragua Ogun State Siuna 1.68056
## 1637 Nicaragua Masaya San Juan de Oriente 1.56730library(ggplot2)
ggplot(data=df_NIC, aes(x = "Nicaragua", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_NIC, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Atlántico Norte
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Atlántico Norte")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 33 Nicaragua Atlántico Norte Bonanza 54.90196ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Atlántico Norte",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Atlántico Norte")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Chontales
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Chontales")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1261 Nicaragua Chontales Santo Domingo 31.14242
## 1262 Nicaragua Chontales Santo Domingo 31.24511
## 1263 Nicaragua Chontales Santo Domingo 31.37360
## 1264 Nicaragua Chontales Santo Domingo 31.10125
## 1265 Nicaragua Chontales Santo Domingo 30.99704
## 1266 Nicaragua Chontales Santo Domingo 30.77070
## 1267 Nicaragua Chontales Santo Domingo 30.27546
## 1268 Nicaragua Chontales Santo Domingo 29.95253
## 1269 Nicaragua Chontales Santo Domingo 29.92927
## 1270 Nicaragua Chontales Santo Domingo 28.90294
## 1271 Nicaragua Chontales Santo Domingo 32.69694
## 1272 Nicaragua Chontales Santo Domingo 32.96402
## 1273 Nicaragua Chontales Santo Domingo 32.77401
## 1274 Nicaragua Chontales Santo Domingo 29.94574ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Chontales",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Chontales")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Jinotega
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Jinotega")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 424 Nicaragua Jinotega San José de Bocay 1.36745
## 1626 Nicaragua Jinotega Wiwilí 25.81514
## 1627 Nicaragua Jinotega Jinotega 2.44880ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Jinotega",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Jinotega")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Madriz
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Madriz")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1631 Nicaragua Madriz Las Sabanas 7.21108
## 1632 Nicaragua Madriz Las Sabanas 4.86364ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Madriz",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Madriz")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Managua
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Managua")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 420 Nicaragua Managua El Crucero 5.84054
## 1299 Nicaragua Managua Ciudad Sandino 5.59574
## 1633 Nicaragua Managua Terrabona 18.92056ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Managua",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Managua")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Masaya
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Masaya")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 151 Nicaragua Masaya Tisma 14.49301
## 1637 Nicaragua Masaya San Juan de Oriente 1.56730ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Masaya",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Masaya")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Ogun State
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Ogun State")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1321 Nicaragua Ogun State Bonanza 0.37593
## 1634 Nicaragua Ogun State Bonanza 10.61568
## 1636 Nicaragua Ogun State Siuna 1.68056ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Ogun State",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Ogun State")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Río San Juan
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Río San Juan")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1382 Nicaragua Río San Juan San Carlos 0.67752ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Río San Juan",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Río San Juan")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Rivas
library(readr)
library(knitr)
df_NIC <- subset (df, State == "Rivas")
df_NIC %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1380 Nicaragua Rivas Altagracia 1.97784
## 1381 Nicaragua Rivas Altagracia 5.77119ggplot(data=df_NIC, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_NIC,aes(x="Rivas",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Rivas")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_NCR <- subset (df, Country == "Nicaragua")
knitr::kable(head(df_NCR))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | 229 | 9/4/07 | NA | Nicaragua | NI | Atlántico Norte | 6315 | Bonanza | 54.90196 | 13.6670 | -84.2435 | (13.667, -84.243499999999997) | Landslide | Complex | Medium | Tropical cyclone | Hurricane Felix | NA | NA | United Nations Development Programme - Relief Web | http://www.reliefweb.int/ | ||
| 151 | 826 | 10/3/08 | NA | Nicaragua | NI | Masaya | 5182 | Tisma | 14.49301 | 12.1200 | -85.8900 | (12.12, -85.89) | Landslide | Landslide | Medium | Downpour | NA | 9 | CBC | http://www.cbc.ca/world/story/2008/10/04/nicaragua-flooding.html | |||
| 420 | 2289 | 8/20/10 | NA | Nicaragua | NI | Managua | 16469 | El Crucero | 5.84054 | 12.0420 | -86.2998 | (12.042, -86.299800000000005) | Landslide | Mudslide | Medium | Downpour | NA | 3 | |||||
| 424 | 2330 | 8/25/10 | NA | Nicaragua | NI | Jinotega | 2367 | San José de Bocay | 1.36745 | 13.5317 | -85.5325 | (13.531700000000001, -85.532499999999999) | Landslide | Landslide | Medium | Downpour | NA | NA | |||||
| 1261 | 6089 | 6/23/14 | NA | Nicaragua | NI | Chontales | 5827 | Santo Domingo | 31.14242 | Unknown | 12.3535 | -84.8095 | (12.3535, -84.8095) | Landslide | Landslide | Small | Continuous rain | 0 | 0 | Wilfried Strauch | |||
| 1262 | 6090 | 6/23/14 | NA | Nicaragua | NI | Chontales | 5827 | Santo Domingo | 31.24511 | Unknown | 12.3521 | -84.8080 | (12.3521, -84.808000000000007) | Landslide | Landslide | Medium | Continuous rain | 0 | 0 | Wilfried Strauch |
Distance <- df_NCR$Distance
summary(df_NCR$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3759 5.2297 25.8151 19.2966 31.0491 54.9020library(pastecs)
stat.desc(df_NCR)## id date time continent_code Country country_code State
## nbr.val 3.100000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 2.290000e+02 NA NA NA NA NA NA
## max 7.481000e+03 NA NA NA NA NA NA
## range 7.252000e+03 NA NA NA NA NA NA
## sum 1.835850e+05 NA NA NA NA NA NA
## median 6.100000e+03 NA NA NA NA NA NA
## mean 5.922097e+03 NA NA NA NA NA NA
## SE.mean 3.381976e+02 NA NA NA NA NA NA
## CI.mean.0.95 6.906916e+02 NA NA NA NA NA NA
## var 3.545706e+06 NA NA NA NA NA NA
## std.dev 1.883004e+03 NA NA NA NA NA NA
## coef.var 3.179624e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 3.100000e+01 NA 31.0000000 NA 31.00000000
## nbr.null 0.000000e+00 NA 0.0000000 NA 0.00000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.00000000
## min 1.257000e+03 NA 0.3759300 NA 11.12970000
## max 7.001300e+04 NA 54.9019600 NA 14.02560000
## range 6.875600e+04 NA 54.5260300 NA 2.89590000
## sum 2.941580e+05 NA 598.1949700 NA 390.48210000
## median 5.827000e+03 NA 25.8151400 NA 12.35020000
## mean 9.488968e+03 NA 19.2966119 NA 12.59619677
## SE.mean 2.567661e+03 NA 2.6411002 NA 0.13419490
## CI.mean.0.95 5.243863e+03 NA 5.3938462 NA 0.27406254
## var 2.043793e+08 NA 216.2377168 NA 0.55825639
## std.dev 1.429613e+04 NA 14.7050235 NA 0.74716557
## coef.var 1.506605e+00 NA 0.7620521 NA 0.05931676
## longitude geolocation hazard_type landslide_type
## nbr.val 3.100000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -8.661460e+01 NA NA NA
## max -8.424350e+01 NA NA NA
## range 2.371100e+00 NA NA NA
## sum -2.642577e+03 NA NA NA
## median -8.481950e+01 NA NA NA
## mean -8.524444e+01 NA NA NA
## SE.mean 1.206052e-01 NA NA NA
## CI.mean.0.95 2.463087e-01 NA NA NA
## var 4.509141e-01 NA NA NA
## std.dev 6.715013e-01 NA NA NA
## coef.var -7.877363e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 27.000000 29.0000000 NA
## nbr.null NA NA NA 26.000000 24.0000000 NA
## nbr.na NA NA NA 4.000000 2.0000000 NA
## min NA NA NA 0.000000 0.0000000 NA
## max NA NA NA 38.000000 9.0000000 NA
## range NA NA NA 38.000000 9.0000000 NA
## sum NA NA NA 38.000000 29.0000000 NA
## median NA NA NA 0.000000 0.0000000 NA
## mean NA NA NA 1.407407 1.0000000 NA
## SE.mean NA NA NA 1.407407 0.4862645 NA
## CI.mean.0.95 NA NA NA 2.892967 0.9960678 NA
## var NA NA NA 53.481481 6.8571429 NA
## std.dev NA NA NA 7.313103 2.6186147 NA
## coef.var NA NA NA 5.196152 2.6186147 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="blue", fill="blue", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos para Nicaragua presentan sesgo hacia la izquierda.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Panama
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_PAN <- subset (df, Country == "Panama")
knitr::kable(head(df_PAN))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 141 | 750 | 8/26/08 | NA | Panama | PA | Panamá | 1287 | Cerro Azul | 1.84596 | 9.1559 | -79.4295 | (9.1559000000000008, -79.429500000000004) | Landslide | Landslide | Medium | Downpour | NA | NA | http://news.xinhuanet.com/english/2008-08/27/content_9721298.htm | ||||
| 167 | 905 | 11/25/08 | NA | Panama | PA | Chiriquí | 6123 | Boquete | 9.83700 | 8.7870 | -82.3440 | (8.7870000000000008, -82.343999999999994) | Landslide | Complex | Medium | Downpour | NA | 8 | http://www.iht.com/articles/ap/2008/11/26/news/LT-Panama-Flooding.php | ||||
| 557 | 2794 | 12/8/10 | NA | Panama | PA | Colón | 1310 | El Giral | 1.80330 | 9.2332 | -79.6961 | (9.2332000000000001, -79.696100000000001) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.portworld.com/news/i98731/Panama_Canal_disrupted_by_floods | ||||
| 558 | 2795 | 12/9/10 | Morning | NA | Panama | PA | Colón | 1274 | Portobelo | 0.09491 | Deforested slope | 9.5493 | -79.6505 | (9.5493000000000006, -79.650499999999994) | Landslide | Mudslide | Medium | Downpour | NA | 8 | Fox News Latino | http://latino.foxnews.com/latino/news/2010/12/09/heavy-rains-kill-panama-force-canal-close/ | |
| 591 | 3167 | 3/2/11 | Night | NA | Panama | PA | Los Santos | 1283 | La Palma | 13.40535 | 7.5955 | -80.3800 | (7.5955000000000004, -80.38) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.newsroompanama.com/panama/2434-off-season-rain-creating-crop-worries-and-landslides.html | |||
| 592 | 3168 | 3/2/11 | Night | NA | Panama | PA | Bocas del Toro | 8114 | Almirante | 8.41491 | 9.3224 | -82.4732 | (9.3224, -82.473200000000006) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.newsroompanama.com/panama/2434-off-season-rain-creating-crop-worries-and-landslides.html |
df_PAN %>%
select(Country, State, City, Distance)## Country State City Distance
## 141 Panama Panamá Cerro Azul 1.84596
## 167 Panama Chiriquí Boquete 9.83700
## 557 Panama Colón El Giral 1.80330
## 558 Panama Colón Portobelo 0.09491
## 591 Panama Los Santos La Palma 13.40535
## 592 Panama Bocas del Toro Almirante 8.41491
## 919 Panama Colón Colón 0.16894
## 979 Panama Colón Portobelo 2.67409
## 980 Panama Colón Margarita 0.74760
## 981 Panama Colón Colón 0.20365
## 1080 Panama Colón Margarita 0.76740
## 1166 Panama Panamá Las Cumbres 2.81479
## 1387 Panama Chiriquí Volcán 0.35187
## 1388 Panama Chiriquí Río Sereno 0.64491
## 1389 Panama Panamá Alcaldedíaz 1.41526
## 1390 Panama Colón Nuevo San Juan 4.74914
## 1391 Panama Panamá San Miguelito 4.18074
## 1392 Panama Chiriquí Cerro Punta 3.33873
## 1393 Panama Panamá Ancón 2.24069
## 1606 Panama Colón Nueva Providencia 0.18619
## 1607 Panama Colón María Chiquita 2.28589
## 1608 Panama Colón Cativá 0.63948
## 1609 Panama Panamá Las Margaritas 0.48725
## 1645 Panama Bocas del Toro Punta Peña 0.29869
## 1653 Panama Bocas del Toro Changuinola 5.97977
## 1654 Panama Bocas del Toro Valle del Risco 6.65185
## 1655 Panama Bocas del Toro Pueblo Nuevo 3.92621
## 1656 Panama Bocas del Toro Valle del Risco 5.13913
## 1657 Panama Bocas del Toro Cauchero 4.45630
## 1658 Panama Chiriquí Boca de Balsa 2.44254
## 1659 Panama Chiriquí Río Sereno 3.66775
## 1660 Panama Chiriquí Palmira Centro 0.90169
## 1661 Panama Chiriquí Breñón 0.37999
## 1662 Panama Panamá San Miguelito 2.57852
## 1663 Panama Chiriquí Monte Lirio 6.74338
## 1664 Panama Coclé Caballero 0.21605
## 1665 Panama Panamá San Miguelito 2.59449
## 1666 Panama Panamá San Miguelito 3.30848
## 1667 Panama Panamá San Miguelito 3.54386
## 1668 Panama Panamá Arraiján 1.80341library(ggplot2)
ggplot(data=df_PAN, aes(x = "Panama", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_PAN, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Bocas del Toro
library(readr)
library(knitr)
df_PAN <- subset (df, State == "Bocas del Toro")
df_PAN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 592 Panama Bocas del Toro Almirante 8.41491
## 1645 Panama Bocas del Toro Punta Peña 0.29869
## 1653 Panama Bocas del Toro Changuinola 5.97977
## 1654 Panama Bocas del Toro Valle del Risco 6.65185
## 1655 Panama Bocas del Toro Pueblo Nuevo 3.92621
## 1656 Panama Bocas del Toro Valle del Risco 5.13913
## 1657 Panama Bocas del Toro Cauchero 4.45630ggplot(data=df_PAN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PAN,aes(x="Bocas del Toro",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Bocas del Toro")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Chiriquí
library(readr)
library(knitr)
df_PAN <- subset (df, State == "Chiriquí")
df_PAN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 167 Panama Chiriquí Boquete 9.83700
## 1387 Panama Chiriquí Volcán 0.35187
## 1388 Panama Chiriquí Río Sereno 0.64491
## 1392 Panama Chiriquí Cerro Punta 3.33873
## 1658 Panama Chiriquí Boca de Balsa 2.44254
## 1659 Panama Chiriquí Río Sereno 3.66775
## 1660 Panama Chiriquí Palmira Centro 0.90169
## 1661 Panama Chiriquí Breñón 0.37999
## 1663 Panama Chiriquí Monte Lirio 6.74338ggplot(data=df_PAN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PAN,aes(x="Chiriquí",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Chiriquí")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Coclé
library(readr)
library(knitr)
df_PAN <- subset (df, State == "Coclé")
df_PAN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1664 Panama Coclé Caballero 0.21605ggplot(data=df_PAN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PAN,aes(x="Coclé",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Coclé")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Colón
library(readr)
library(knitr)
df_PAN <- subset (df, State == "Colón")
df_PAN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 485 Honduras Colón Cusuna 36.37629
## 557 Panama Colón El Giral 1.80330
## 558 Panama Colón Portobelo 0.09491
## 919 Panama Colón Colón 0.16894
## 979 Panama Colón Portobelo 2.67409
## 980 Panama Colón Margarita 0.74760
## 981 Panama Colón Colón 0.20365
## 1080 Panama Colón Margarita 0.76740
## 1390 Panama Colón Nuevo San Juan 4.74914
## 1606 Panama Colón Nueva Providencia 0.18619
## 1607 Panama Colón María Chiquita 2.28589
## 1608 Panama Colón Cativá 0.63948ggplot(data=df_PAN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PAN,aes(x="Colón",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Colón")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Los Santos
library(readr)
library(knitr)
df_PAN <- subset (df, State == "Los Santos")
df_PAN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 591 Panama Los Santos La Palma 13.40535ggplot(data=df_PAN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PAN,aes(x="Los Santos",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Los Santos")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Panamá
library(readr)
library(knitr)
df_PAN <- subset (df, State == "Panamá")
df_PAN %>%
select(Country, State, City, Distance) ## Country State City Distance
## 141 Panama Panamá Cerro Azul 1.84596
## 1166 Panama Panamá Las Cumbres 2.81479
## 1389 Panama Panamá Alcaldedíaz 1.41526
## 1391 Panama Panamá San Miguelito 4.18074
## 1393 Panama Panamá Ancón 2.24069
## 1609 Panama Panamá Las Margaritas 0.48725
## 1662 Panama Panamá San Miguelito 2.57852
## 1665 Panama Panamá San Miguelito 2.59449
## 1666 Panama Panamá San Miguelito 3.30848
## 1667 Panama Panamá San Miguelito 3.54386
## 1668 Panama Panamá Arraiján 1.80341ggplot(data=df_PAN, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PAN,aes(x="Panamá",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Panamá")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_PNM <- subset (df, Country == "Panama")
knitr::kable(head(df_PNM))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 141 | 750 | 8/26/08 | NA | Panama | PA | Panamá | 1287 | Cerro Azul | 1.84596 | 9.1559 | -79.4295 | (9.1559000000000008, -79.429500000000004) | Landslide | Landslide | Medium | Downpour | NA | NA | http://news.xinhuanet.com/english/2008-08/27/content_9721298.htm | ||||
| 167 | 905 | 11/25/08 | NA | Panama | PA | Chiriquí | 6123 | Boquete | 9.83700 | 8.7870 | -82.3440 | (8.7870000000000008, -82.343999999999994) | Landslide | Complex | Medium | Downpour | NA | 8 | http://www.iht.com/articles/ap/2008/11/26/news/LT-Panama-Flooding.php | ||||
| 557 | 2794 | 12/8/10 | NA | Panama | PA | Colón | 1310 | El Giral | 1.80330 | 9.2332 | -79.6961 | (9.2332000000000001, -79.696100000000001) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.portworld.com/news/i98731/Panama_Canal_disrupted_by_floods | ||||
| 558 | 2795 | 12/9/10 | Morning | NA | Panama | PA | Colón | 1274 | Portobelo | 0.09491 | Deforested slope | 9.5493 | -79.6505 | (9.5493000000000006, -79.650499999999994) | Landslide | Mudslide | Medium | Downpour | NA | 8 | Fox News Latino | http://latino.foxnews.com/latino/news/2010/12/09/heavy-rains-kill-panama-force-canal-close/ | |
| 591 | 3167 | 3/2/11 | Night | NA | Panama | PA | Los Santos | 1283 | La Palma | 13.40535 | 7.5955 | -80.3800 | (7.5955000000000004, -80.38) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.newsroompanama.com/panama/2434-off-season-rain-creating-crop-worries-and-landslides.html | |||
| 592 | 3168 | 3/2/11 | Night | NA | Panama | PA | Bocas del Toro | 8114 | Almirante | 8.41491 | 9.3224 | -82.4732 | (9.3224, -82.473200000000006) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://www.newsroompanama.com/panama/2434-off-season-rain-creating-crop-worries-and-landslides.html |
Distance <- df_PNM$Distance
summary(df_PNM$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.09491 0.64355 2.36422 2.94825 3.98984 13.40535library(pastecs)
stat.desc(df_PNM)## id date time continent_code Country country_code State
## nbr.val 4.000000e+01 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 7.500000e+02 NA NA NA NA NA NA
## max 7.512000e+03 NA NA NA NA NA NA
## range 6.762000e+03 NA NA NA NA NA NA
## sum 2.479400e+05 NA NA NA NA NA NA
## median 7.450500e+03 NA NA NA NA NA NA
## mean 6.198500e+03 NA NA NA NA NA NA
## SE.mean 3.051354e+02 NA NA NA NA NA NA
## CI.mean.0.95 6.171945e+02 NA NA NA NA NA NA
## var 3.724304e+06 NA NA NA NA NA NA
## std.dev 1.929846e+03 NA NA NA NA NA NA
## coef.var 3.113407e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 4.000000e+01 NA 40.0000000 NA 40.00000000
## nbr.null 8.000000e+00 NA 0.0000000 NA 0.00000000
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.00000000
## min 0.000000e+00 NA 0.0949100 NA 7.59550000
## max 3.215010e+05 NA 13.4053500 NA 9.56760000
## range 3.215010e+05 NA 13.3104400 NA 1.97210000
## sum 2.041483e+06 NA 117.9301600 NA 361.65930000
## median 2.365500e+03 NA 2.3642150 NA 9.07300000
## mean 5.103707e+04 NA 2.9482540 NA 9.04148250
## SE.mean 1.674111e+04 NA 0.4634140 NA 0.05632097
## CI.mean.0.95 3.386208e+04 NA 0.9373432 NA 0.11391991
## var 1.121058e+10 NA 8.5900997 NA 0.12688206
## std.dev 1.058800e+05 NA 2.9308872 NA 0.35620508
## coef.var 2.074571e+00 NA 0.9941095 NA 0.03939676
## longitude geolocation hazard_type landslide_type
## nbr.val 4.000000e+01 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -8.286620e+01 NA NA NA
## max -7.908960e+01 NA NA NA
## range 3.776600e+00 NA NA NA
## sum -3.232397e+03 NA NA NA
## median -7.989490e+01 NA NA NA
## mean -8.080992e+01 NA NA NA
## SE.mean 2.230672e-01 NA NA NA
## CI.mean.0.95 4.511960e-01 NA NA NA
## var 1.990359e+00 NA NA NA
## std.dev 1.410801e+00 NA NA NA
## coef.var -1.745826e-02 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 28.000000 38.0000000 NA
## nbr.null NA NA NA 27.000000 33.0000000 NA
## nbr.na NA NA NA 12.000000 2.0000000 NA
## min NA NA NA 0.000000 0.0000000 NA
## max NA NA NA 45.000000 8.0000000 NA
## range NA NA NA 45.000000 8.0000000 NA
## sum NA NA NA 45.000000 23.0000000 NA
## median NA NA NA 0.000000 0.0000000 NA
## mean NA NA NA 1.607143 0.6052632 NA
## SE.mean NA NA NA 1.607143 0.3099568 NA
## CI.mean.0.95 NA NA NA 3.297585 0.6280321 NA
## var NA NA NA 72.321429 3.6507824 NA
## std.dev NA NA NA 8.504201 1.9107021 NA
## coef.var NA NA NA 5.291503 3.1568121 NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="blue", fill="red", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos para Panamá se encuentran bien distribuidos y se considera la presencial de algunos datos aberrantes.
Gráfico circular y de barras de las distancias de desplazamientos para el país de Puerto Rico
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
library(readr)
library(knitr)
df_PUE <- subset (df, Country == "Puerto Rico")
knitr::kable(head(df_PUE))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 68 | 393 | 12/12/07 | NA | Puerto Rico | PR | San Juan | 418140 | San Juan | 6.91777 | 18.4320 | -66.0510 | (18.431999999999999, -66.051000000000002) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | NA | AP.google.com | http://ap.google.com/article/ALeqM5gVWjsPEiqe1tEu2mhBIRaxxGi8owD8TFVR600 | ||
| 477 | 2550 | 10/6/10 | NA | Puerto Rico | PR | Orocovis | 944 | Orocovis | 6.85760 | 18.1652 | -66.3969 | (18.165199999999999, -66.396900000000002) | Landslide | Complex | Medium | Tropical cyclone | Tropical Storm Otto | NA | 0 | http://www.whitehouse.gov/the-press-office/2010/10/26/president-obama-signs-puerto-rico-disaster-declaration | |||
| 1396 | 6708 | 5/18/14 | 16:30 | NA | Puerto Rico | PR | Vega Alta | 12036 | Vega Alta | 3.49090 | Mine construction | 18.3806 | -66.3319 | (18.380600000000001, -66.331900000000005) | Landslide | Other | Small | Rain | 0 | 0 | Telemundo | http://www.telemundopr.com/telenoticias/puerto-rico/Deslizamiento-deja-a-familias-incomunicadas-en-Vega-Alta-258522361.html | |
| 1397 | 6709 | 9/24/14 | NA | Puerto Rico | PR | Aguada | 4040 | Aguada | 1.40257 | Unknown | 18.3711 | -67.1782 | (18.371099999999998, -67.178200000000004) | Landslide | Landslide | Medium | Downpour | 0 | 0 | Telemundo | http://www.telemundopr.com/telenoticias/puerto-rico/Viviendas-inhabitables-luego-de-deslizamiento-de-tierras-en-Aguada-277123031.html | ||
| 1398 | 6710 | 8/24/14 | 3:00 | NA | Puerto Rico | PR | Ponce | 5080 | Adjuntas | 5.78872 | Unknown | 18.1283 | -66.6810 | (18.128299999999999, -66.680999999999997) | Landslide | Landslide | Small | Downpour | 0 | 0 | Perla del Sur | http://www.periodicolaperla.com/index.php?option=com_content&view=article&id=6371:surgen-nuevos-deslizamientos-en-ponce&catid=135:actualidad-del-sur&Itemid=423 | |
| 1399 | 6711 | 8/24/14 | NA | Puerto Rico | PR | Ponce | 5080 | Adjuntas | 6.89036 | Unknown | 18.1254 | -66.6700 | (18.125399999999999, -66.67) | Landslide | Landslide | Medium | Downpour | 0 | 0 | Perla del Sur | http://www.periodicolaperla.com/index.php?option=com_content&view=article&id=6371:surgen-nuevos-deslizamientos-en-ponce&catid=135:actualidad-del-sur&Itemid=423 |
df_PUE %>%
select(Country, State, City, Distance)## Country State City Distance
## 68 Puerto Rico San Juan San Juan 6.91777
## 477 Puerto Rico Orocovis Orocovis 6.85760
## 1396 Puerto Rico Vega Alta Vega Alta 3.49090
## 1397 Puerto Rico Aguada Aguada 1.40257
## 1398 Puerto Rico Ponce Adjuntas 5.78872
## 1399 Puerto Rico Ponce Adjuntas 6.89036
## 1400 Puerto Rico Villalba Villalba 3.65535library(ggplot2)
ggplot(data=df_PUE, aes(x = "Puerto Rico", y = Distance, fill=State)) +
geom_bar(stat = "identity", width = 1, color = "black") +
coord_polar("y", start = 0)
ggplot(data=df_PUE, aes(fill=State, y=Distance, x="State")) +
geom_bar(position="dodge", stat="identity")
### Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Aguada.
library(readr)
library(knitr)
df_PUE <- subset (df, State == "Aguada")
df_PUE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1397 Puerto Rico Aguada Aguada 1.40257ggplot(data=df_PUE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PUE,aes(x="Aguada",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Aguada")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Orocovis
library(readr)
library(knitr)
df_PUE <- subset (df, State == "Orocovis")
df_PUE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 477 Puerto Rico Orocovis Orocovis 6.8576ggplot(data=df_PUE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PUE,aes(x="Orocovis",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Orocovis")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Ponce
library(readr)
library(knitr)
df_PUE <- subset (df, State == "Ponce")
df_PUE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1398 Puerto Rico Ponce Adjuntas 5.78872
## 1399 Puerto Rico Ponce Adjuntas 6.89036ggplot(data=df_PUE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PUE,aes(x="Ponce",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Ponce")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de San Juan
library(readr)
library(knitr)
df_PUE <- subset (df, State == "San Juan")
df_PUE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 68 Puerto Rico San Juan San Juan 6.91777ggplot(data=df_PUE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PUE,aes(x="San Juan",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - San Juan")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Vega Alta
library(readr)
library(knitr)
df_PUE <- subset (df, State == "Vega Alta")
df_PUE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1396 Puerto Rico Vega Alta Vega Alta 3.4909ggplot(data=df_PUE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PUE,aes(x="Vega Alta",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Vega Alta")
Gráfico circular porcentual y de barras apiladas de las distancias de desplazamientos para el departamento de Villalba
library(readr)
library(knitr)
df_PUE <- subset (df, State == "Villalba")
df_PUE %>%
select(Country, State, City, Distance) ## Country State City Distance
## 1400 Puerto Rico Villalba Villalba 3.65535ggplot(data=df_PUE, aes(x=City, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)
ggplot(df_PUE,aes(x="Villalba",y=Distance, fill=City))+
geom_bar(stat = "identity",
color="black")+
geom_text(aes(label=(Distance*1)),
position=position_stack(vjust=0.5),color="white",size=2)+
coord_polar(theta = "y")+
labs(title="Gráfico de distancia de desplazamiento - Villalba")
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(ggplot2)
colnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"
df_PRC <- subset (df, Country == "Puerto Rico")
knitr::kable(head(df_PRC))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 68 | 393 | 12/12/07 | NA | Puerto Rico | PR | San Juan | 418140 | San Juan | 6.91777 | 18.4320 | -66.0510 | (18.431999999999999, -66.051000000000002) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Olga | NA | NA | AP.google.com | http://ap.google.com/article/ALeqM5gVWjsPEiqe1tEu2mhBIRaxxGi8owD8TFVR600 | ||
| 477 | 2550 | 10/6/10 | NA | Puerto Rico | PR | Orocovis | 944 | Orocovis | 6.85760 | 18.1652 | -66.3969 | (18.165199999999999, -66.396900000000002) | Landslide | Complex | Medium | Tropical cyclone | Tropical Storm Otto | NA | 0 | http://www.whitehouse.gov/the-press-office/2010/10/26/president-obama-signs-puerto-rico-disaster-declaration | |||
| 1396 | 6708 | 5/18/14 | 16:30 | NA | Puerto Rico | PR | Vega Alta | 12036 | Vega Alta | 3.49090 | Mine construction | 18.3806 | -66.3319 | (18.380600000000001, -66.331900000000005) | Landslide | Other | Small | Rain | 0 | 0 | Telemundo | http://www.telemundopr.com/telenoticias/puerto-rico/Deslizamiento-deja-a-familias-incomunicadas-en-Vega-Alta-258522361.html | |
| 1397 | 6709 | 9/24/14 | NA | Puerto Rico | PR | Aguada | 4040 | Aguada | 1.40257 | Unknown | 18.3711 | -67.1782 | (18.371099999999998, -67.178200000000004) | Landslide | Landslide | Medium | Downpour | 0 | 0 | Telemundo | http://www.telemundopr.com/telenoticias/puerto-rico/Viviendas-inhabitables-luego-de-deslizamiento-de-tierras-en-Aguada-277123031.html | ||
| 1398 | 6710 | 8/24/14 | 3:00 | NA | Puerto Rico | PR | Ponce | 5080 | Adjuntas | 5.78872 | Unknown | 18.1283 | -66.6810 | (18.128299999999999, -66.680999999999997) | Landslide | Landslide | Small | Downpour | 0 | 0 | Perla del Sur | http://www.periodicolaperla.com/index.php?option=com_content&view=article&id=6371:surgen-nuevos-deslizamientos-en-ponce&catid=135:actualidad-del-sur&Itemid=423 | |
| 1399 | 6711 | 8/24/14 | NA | Puerto Rico | PR | Ponce | 5080 | Adjuntas | 6.89036 | Unknown | 18.1254 | -66.6700 | (18.125399999999999, -66.67) | Landslide | Landslide | Medium | Downpour | 0 | 0 | Perla del Sur | http://www.periodicolaperla.com/index.php?option=com_content&view=article&id=6371:surgen-nuevos-deslizamientos-en-ponce&catid=135:actualidad-del-sur&Itemid=423 |
Distance <- df_PRC$Distance
summary(df_PRC$Distance)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.403 3.573 5.789 5.000 6.874 6.918library(pastecs)
stat.desc(df_PRC)## id date time continent_code Country country_code State
## nbr.val 7.000000e+00 NA NA NA NA NA NA
## nbr.null 0.000000e+00 NA NA NA NA NA NA
## nbr.na 0.000000e+00 NA NA NA NA NA NA
## min 3.930000e+02 NA NA NA NA NA NA
## max 6.712000e+03 NA NA NA NA NA NA
## range 6.319000e+03 NA NA NA NA NA NA
## sum 3.649300e+04 NA NA NA NA NA NA
## median 6.709000e+03 NA NA NA NA NA NA
## mean 5.213286e+03 NA NA NA NA NA NA
## SE.mean 9.943773e+02 NA NA NA NA NA NA
## CI.mean.0.95 2.433154e+03 NA NA NA NA NA NA
## var 6.921504e+06 NA NA NA NA NA NA
## std.dev 2.630875e+03 NA NA NA NA NA NA
## coef.var 5.046482e-01 NA NA NA NA NA NA
## population City Distance location_description latitude
## nbr.val 7.000000e+00 NA 7.0000000 NA 7.000000e+00
## nbr.null 0.000000e+00 NA 0.0000000 NA 0.000000e+00
## nbr.na 0.000000e+00 NA 0.0000000 NA 0.000000e+00
## min 9.440000e+02 NA 1.4025700 NA 1.812540e+01
## max 4.181400e+05 NA 6.9177700 NA 1.843200e+01
## range 4.171960e+05 NA 5.5152000 NA 3.066000e-01
## sum 4.498750e+05 NA 35.0032700 NA 1.277604e+02
## median 5.080000e+03 NA 5.7887200 NA 1.816520e+01
## mean 6.426786e+04 NA 5.0004671 NA 1.825149e+01
## SE.mean 5.899213e+04 NA 0.8215987 NA 5.137584e-02
## CI.mean.0.95 1.443485e+05 NA 2.0103796 NA 1.257122e-01
## var 2.436050e+10 NA 4.7251712 NA 1.847634e-02
## std.dev 1.560785e+05 NA 2.1737459 NA 1.359277e-01
## coef.var 2.428562e+00 NA 0.4347086 NA 7.447487e-03
## longitude geolocation hazard_type landslide_type
## nbr.val 7.000000e+00 NA NA NA
## nbr.null 0.000000e+00 NA NA NA
## nbr.na 0.000000e+00 NA NA NA
## min -6.717820e+01 NA NA NA
## max -6.605100e+01 NA NA NA
## range 1.127200e+00 NA NA NA
## sum -4.657880e+02 NA NA NA
## median -6.647900e+01 NA NA NA
## mean -6.654114e+01 NA NA NA
## SE.mean 1.336926e-01 NA NA NA
## CI.mean.0.95 3.271341e-01 NA NA NA
## var 1.251161e-01 NA NA NA
## std.dev 3.537175e-01 NA NA NA
## coef.var -5.315771e-03 NA NA NA
## landslide_size trigger storm_name injuries fatalities source_name
## nbr.val NA NA NA 5 6 NA
## nbr.null NA NA NA 5 6 NA
## nbr.na NA NA NA 2 1 NA
## min NA NA NA 0 0 NA
## max NA NA NA 0 0 NA
## range NA NA NA 0 0 NA
## sum NA NA NA 0 0 NA
## median NA NA NA 0 0 NA
## mean NA NA NA 0 0 NA
## SE.mean NA NA NA 0 0 NA
## CI.mean.0.95 NA NA NA 0 0 NA
## var NA NA NA 0 0 NA
## std.dev NA NA NA 0 0 NA
## coef.var NA NA NA NaN NaN NA
## source_link
## nbr.val NA
## nbr.null NA
## nbr.na NA
## min NA
## max NA
## range NA
## sum NA
## median NA
## mean NA
## SE.mean NA
## CI.mean.0.95 NA
## var NA
## std.dev NA
## coef.var NA#### Caja y extensión
library(tidyverse)
library(hrbrthemes)
library(viridis)
df <- data.frame(Distance)
df %>% ggplot(aes(x = "", y = Distance)) +
geom_boxplot(color="black", fill="yellow", alpha=0.5) +
theme_ipsum() +
theme(legend.position="none", plot.title = element_text(size=20)) +
ggtitle("Longitd de deslizamientos") +
coord_flip() +
xlab("") +
ylab("")
Como puede observarse en el diagrama de caja y extensión, los datos de longitud de deslizamientos para Puerto Rico presentan un claro sesgo hacia la izquierda.
CONCLUSIÓN
Por medio de este análisis, interpretación y representación de la información brindada por el Catalogo Global de Deslizamientos de Tierra (GLC), fue posible establecer relaciones entre diferentes variables presentes en los datos disponibles para los países de Colombia, Ecuador, Perú, Trinidad y Tobago, y Venezuela, pertenecientes a Sudamérica. Entre estas se destacan el tipo de deslizamientos asociados a las regiones y el porcentaje de eventos que desatan deslizamientos en los países estudiados. Además, fue posible organizar en un diagrama de caja y extensión los datos asociados a la distancia de deslizamiento en Colombia, en el cual se afirma que la media de los desplazamientos en el país es de al rededor 5 km (Q2) y en donde se permiten valores para el estudio de estos que van aproximadamente de 0 km (Q1) a 25 km (Q3), y en el cual existen datos con magnitud superior a estos límites o bigotes denominados datos aberrantes, los cuales no deberían ser tomados en cuenta al momento de realizar un estudio sobre distancia recorrida en un deslizamiento en el país.
REFERENCIAS
Patel K. Help NASA Create the Largest Landslide Database. (2018). Nasa.gov; NASA Earth Observatory. https://earthobservatory.nasa.gov/blogs/earthmatters/2018/04/18/landslide-citizen-science/
Mergili, M., Marchant Santiago, C. I., & Moreiras, S. M. (2015). Causas, características e impacto de los procesos de remoción en masa, en áreas contrastantes de la región Andina. Cuadernos de Geografía: Revista Colombiana de Geografía, 24(2), 113–131. https://doi.org/10.15446/rcdg.v24n2.50211
https://github.com/lihkir/AnalisisEstadisticoUN/blob/main/Data/catalog.csv#1