Informe de deslizamientos en países de América

Introducción

En este informe se presenta el desarrollo de tablas de frecuencia simple y agrupada, medidas de tendencia central, medidas de variabilidad, medidas de posición y diagramas de caja y bigotes con el fin organizar de una mejor manera la información y así tener una buena comprensión de los de diferentes datos en los países de América como lo son Barbados, Belice, Bermuda, Brasil, Canadá, Colombia, Costa Rica, Cuba, Dominicana, Ecuador, El Salvador, Guatemala, Haití, Honduras, Jamaica, México, Puerto Rico, Perú, Panamá, República Dominicana, Saint lucia, Saint vincent and the Grenadine, Trinidad y Tobago, Estados unidos, Islas vírgenes y Venezuela. Para realizar el siguiente informe usamos R estudio el cual es un software creado para el análisis estadístico, además de facilitar la creación de gráficos como lo son el circular, el de barras, etc. Haciendo este trabajo ponemos en práctica los conocimientos previamente adquiridos poniéndolos una situación, en este caso el deslizamiento de los suelos a causa de la lluvia en todo el mundo la cual es un evento al que nos podemos enfrentar diariamente como geólogos. Además, tenemos como objetivo principal el poder desarrollar las habilidades mediante el uso de la informática y el análisis estadístico.

Barbados

Para Barbados, solo hubo un deslizamiento en el estado de Saint Joseph, siendo esta la única ciudad donde aparecen datos.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Barbados")

table(pais$state.province)

## 
## Saint Joseph 
##            1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Saint Joseph	1	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="yellow", fill="darkblue", width=0.5) +
  xlab("Barbados") +
  ylab("Frecuencia") +
  
theme(axis.text.x = element_text(angle = 90))

### Diagrama de caja

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, union

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Barbados")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

## 
## Attaching package: 'pastecs'

## The following objects are masked from 'package:dplyr':
## 
##     first, last

stat.desc(pais$population)

## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced

##      nbr.val     nbr.null       nbr.na          min          max        range 
##            1            0            0         1765         1765            0 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##         1765         1765         1765           NA          NaN           NA 
##      std.dev     coef.var 
##           NA           NA

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced

##      nbr.val     nbr.null       nbr.na          min          max        range 
##      1.00000      0.00000      0.00000      2.87363      2.87363      0.00000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##      2.87363      2.87363      2.87363           NA          NaN           NA 
##      std.dev     coef.var 
##           NA           NA

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

Belize

Para Belize, solo hay un estado para la cual se tomaron datos y esta fue Cayo, donde se reportó un deslizamiento.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Belize")

table(pais$state.province)

## 
## Cayo 
##    1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Cayo	1	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue4", fill="red", width=0.5) +
  xlab("Belize") +
  ylab("Frecuencia") +
  
theme(axis.text.x = element_text(angle = 90))

### Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Belize")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced

##      nbr.val     nbr.null       nbr.na          min          max        range 
##            1            0            0        13381        13381            0 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##        13381        13381        13381           NA          NaN           NA 
##      std.dev     coef.var 
##           NA           NA

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

## Warning in qt((0.5 + p/2), (Nbrval - 1)): NaNs produced

##      nbr.val     nbr.null       nbr.na          min          max        range 
##      1.00000      0.00000      0.00000      9.71758      9.71758      0.00000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##      9.71758      9.71758      9.71758           NA          NaN           NA 
##      std.dev     coef.var 
##           NA           NA

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

Bermuda

En Bermuda, se reportaron dos deslizamientos en Hamilton city.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Bermuda")

table(pais$state.province)

## 
## Hamilton city 
##             2

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Hamilton city	2	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow", width=0.5) +
  xlab("Bermuda") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

Brazil

En el caso de Brazil, se reportaron deslizamiento en 3 estados: Pará, Amapá y Maranhão, siendo el primero donde más deslizamientos hubo (2) y en los otros solo 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Brazil")

table(pais$state.province)

## 
##    Amapá Maranhão     Pará 
##        1        1        2

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Pará	2	50	50	50	50
Amapá	1	25	25	75	75
Maranhão	1	25	25	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Brazil") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Pará","Amapá","Maranhão"), 
                   value = c(50, 25, 25))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)

## Loading required package: scales

ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

library(readr)

## 
## Attaching package: 'readr'

## The following object is masked from 'package:scales':
## 
##     col_factor

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)
BRA <- subset (df, Country == "Brazil")
knitr::kable(head(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

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  1.04062

ggplot(data= BRA, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Diagrama de Caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Brazil")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 4.000000e+00 0.000000e+00 0.000000e+00 4.890100e+04 8.392700e+04 3.502600e+04 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 2.817660e+05 7.446900e+04 7.044150e+04 7.659093e+03 2.437465e+04 2.346468e+08 
##      std.dev     coef.var 
## 1.531819e+04 2.174597e-01

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##     4.000000     0.000000     0.000000     0.170200    61.753060    61.582860 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##    63.982360     1.029550    15.995590    15.253836    48.544513   930.717994 
##      std.dev     coef.var 
##    30.507671     1.907255

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Pará","Amapá","Maranhão") , 
                 Frecuencia = c(1,1,2))
knitr::kable(df)

Error	Frecuencia
Pará	1
Amapá	1
Maranhão	2

library(qcc)

## Package 'qcc' version 2.7

## Type 'citation("qcc")' for citing this R package in publications.

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores"
)

##            
## Pareto chart analysis for Frecuencia
##             Frequency Cum.Freq. Percentage Cum.Percent.
##   Maranhão         2         2         50           50
##   Pará             1         3         25           75
##   Amapá            1         4         25          100

Canada

Para Canada se presentaron deslizamientos en 3 estados: Ontario, Quebec y Nova Scotia, para las cuales el número de deslizamientos fue de 6, 3 y 1 respectivamente.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Canada")

table(pais$state.province)

## 
## Nova Scotia     Ontario      Quebec 
##           1           6           3

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Ontario	6	60	60	60	60
Quebec	3	30	30	90	90
Nova Scotia	1	10	10	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Canada") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Ontario","Quebec","Nova Scotia"), 
                   value = c(60, 30, 10))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
CAN <- subset (df, Country == "Canada")
knitr::kable(head(CAN))

	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
4	59	4/14/07		NA	Canada	CA	Quebec	42786	Châteauguay	2.98682	Above river	45.3226	-73.7771	(45.322600000000001, -73.777100000000004)	Landslide	Riverbank collapse	Small	Rain		NA	NA	Le Soleil	http://www.hebdos.net/lsc/edition162007/articles.asp?article_id=166976
14	119	7/8/07		NA	Canada	CA	Ontario	812129	Ottawa	1.74759		45.4257	-75.6896	(45.425699999999999, -75.689599999999999)	Landslide	Landslide	Small	Unknown		NA	NA		http://www.cfra.com/headlines/index.asp?cat=1&nid=50672
379	2085	7/16/10	6:00:00	NA	Canada	CA	Ontario	33232	Ancaster	5.11510	Above road	43.2453	-79.9367	(43.2453, -79.936700000000002)	Landslide	Mudslide	Medium	Downpour		NA	0	Hamilton Spectator	http://www.thespec.com/news-story/2163626-mudslide-causes-four-vehicle-crash-on-highway-403/
451	2490	9/23/10	23:00:00	NA	Canada	CA	Quebec	17016	Saint-Lazare	3.16544		45.3717	-74.1359	(45.371699999999997, -74.135900000000007)	Landslide	Landslide	Medium	Downpour		NA	0		http://www.torontosun.com/news/canada/2010/09/24/15463551.html
929	4710	1/13/13		NA	Canada	CA	Ontario	115760	Guelph	12.17933		43.6187	-80.1432	(43.618699999999997, -80.143199999999993)	Landslide	Mudslide	Medium	Rain		NA	NA	www.lfpress.com	http://www.lfpress.com/2013/01/14/ministry-of-the-environment-investigating-mudslide-in-rockwood-near-guelph
1320	6404	11/28/14	8:30	NA	Canada	CA	Ontario	519949	Hamilton	4.79449	Above road	43.2804	-79.8919	(43.2804, -79.891900000000007)	Landslide	Landslide	Small	Unknown		0	0	Hamilton Spectator	http://www.thespec.com/news-story/5161560--landslide-closes-lanes-of-toronto-bound-hwy-403-at-york-blvd-/

CAN %>% 
  select(Country, State, City, Distance)

##      Country       State         City Distance
## 4     Canada      Quebec  Châteauguay  2.98682
## 14    Canada     Ontario       Ottawa  1.74759
## 379   Canada     Ontario     Ancaster  5.11510
## 451   Canada      Quebec Saint-Lazare  3.16544
## 929   Canada     Ontario       Guelph 12.17933
## 1320  Canada     Ontario     Hamilton  4.79449
## 1349  Canada      Quebec       Sutton  4.33884
## 1492  Canada     Ontario   Owen Sound  1.84109
## 1512  Canada Nova Scotia        Digby 11.62624
## 1680  Canada     Ontario     Hamilton  2.00473

ggplot(data= CAN, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Driagrama de caja En Canada, el tamaño máximo de la población donde ha ocurrido un deslizamiento resgistrado en esta base de datos es 812129. Por otro lado, el mayor deslizamiento tuvo una longitud de 2.74 kilometros.

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


pais <- subset(df, country_name == "Canada")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 1.000000e+01 0.000000e+00 0.000000e+00 2.052000e+03 8.121290e+05 8.100770e+05 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 2.089303e+06 3.800900e+04 2.089303e+05 9.314769e+04 2.107147e+05 8.676492e+10 
##      std.dev     coef.var 
## 2.945588e+05 1.409843e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   10.0000000    0.0000000    0.0000000    1.7475900   12.1793300   10.4317400 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##   49.7996700    3.7521400    4.9799670    1.2151153    2.7487818   14.7650519 
##      std.dev     coef.var 
##    3.8425320    0.7715979

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

En general, los valores de distancia de los desplazamientos en Canada son uniformes, con solo dos eventos atípicos.

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Ontario","Quebec","Nova Scotia") , 
                 Frecuencia = c(6,3,1))
knitr::kable(df)

Error	Frecuencia
Ontario	6
Quebec	3
Nova Scotia	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores"
)

##              
## Pareto chart analysis for Frecuencia
##               Frequency Cum.Freq. Percentage Cum.Percent.
##   Ontario             6         6         60           60
##   Quebec              3         9         30           90
##   Nova Scotia         1        10         10          100

Colombia

En Colombia, el número de deslizamientos se rpeortó de acuerdo al departamento donde ocurrió, siendo el de mayor frecuencia Antioquia con 19 deslizamientos, y los de menor frecuencia fueron: Bolívar, Meta, Putumayo, Quindío y Sucre, que solo tuvieron 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Colombia")

table(pais$state.province)

## 
##          Antioquia            Bolívar             Boyacá             Caldas 
##                 19                  1                  3                 10 
##            Caquetá              Cauca            Córdoba       Cundinamarca 
##                  2                  5                  3                  7 
##              Huila          Magdalena               Meta             Nariño 
##                  6                  3                  1                  5 
## Norte de Santander           Putumayo            Quindío          Risaralda 
##                  5                  1                  1                  4 
##          Santander              Sucre             Tolima    Valle del Cauca 
##                 11                  1                  4                  4

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Antioquia	19	19.8	19.8	19.8	19.8
Santander	11	11.5	11.5	31.2	31.2
Caldas	10	10.4	10.4	41.7	41.7
Cundinamarca	7	7.3	7.3	49.0	49.0
Huila	6	6.2	6.2	55.2	55.2
Cauca	5	5.2	5.2	60.4	60.4
Nariño	5	5.2	5.2	65.6	65.6
Norte de Santander	5	5.2	5.2	70.8	70.8
Risaralda	4	4.2	4.2	75.0	75.0
Tolima	4	4.2	4.2	79.2	79.2
Valle del Cauca	4	4.2	4.2	83.3	83.3
Boyacá	3	3.1	3.1	86.5	86.5
Córdoba	3	3.1	3.1	89.6	89.6
Magdalena	3	3.1	3.1	92.7	92.7
Caquetá	2	2.1	2.1	94.8	94.8
Bolívar	1	1.0	1.0	95.8	95.8
Meta	1	1.0	1.0	96.9	96.9
Putumayo	1	1.0	1.0	97.9	97.9
Quindío	1	1.0	1.0	99.0	99.0
Sucre	1	1.0	1.0	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Colombia") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)


library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Antioquia","Santander","Caldas","Cundinamarca","Huila","Cauca","Nariño","Norte de Santander","Risaralda","Tolima","Valle del Cauca","Boyacá","Córdoba","Magdalena","Caquetá","Bolìvar","Meta","Putumayo","Quindìo","Sucre"), 
                   value = c(19.8, 11.5, 10.4, 7.3, 6.2, 5.2, 5.2, 5.2, 4.2, 4.2, 4.2, 3.1, 3.1, 3.1, 2.1,1,1,1,1,1))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=4) +
  scale_fill_manual(values = c("darkseagreen2","darkred","darkorchid1","gainsboro","gray2","cyan","cyan4","darkgoldenrod1","darkgoldenrod4","brown4","burlywood1","burlywood3","blue1","blue4","blueviolet","chartreuse","cornflowerblue","aliceblue","springgreen","yellow"))

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
COL <- subset (df, Country == "Colombia")
knitr::kable(head(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

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 15.04256

ggplot(data= COL, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Diagrama de caja

En promedio poblacional en zonas con deslizamientos en Colombia es 222180 personas; mientras que el promedio de la distancia de dichos deslizamientos es 7.62 kilometros.

library(dplyr)


df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Colombia")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 9.600000e+01 0.000000e+00 0.000000e+00 1.091000e+03 2.392877e+06 2.391786e+06 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 2.132931e+07 9.940500e+03 2.221804e+05 5.633984e+04 1.118487e+05 3.047210e+11 
##      std.dev     coef.var 
## 5.520154e+05 2.484538e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   96.0000000    0.0000000    0.0000000    0.0000300   51.8412500   51.8412200 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  731.9660900    5.0115650    7.6246468    0.8623174    1.7119165   71.3847624 
##      std.dev     coef.var 
##    8.4489504    1.1081104

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

Para Colombia, hay seis datos atípicos sobre la magnitud de los deslizamientos, todos hacia valores mayores al último cuartil (hacia la derecha), y de distancias no tan uniformes.

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Antioquia","Santander","Caldas","Cundinamarca","Huila","Cauca","Nariño","Norte de Santander","Risaralda","Tolima","Valle del Cauca","Boyacá","Córdoba","Magdalena","Caquetá","Bolìvar","Meta","Putumayo","Quindìo","Sucre") , 
                 Frecuencia = c(19,11,10,7,6,5,5,5,5,4,4,4,3,3,2,1,1,1,1,1))
knitr::kable(df)

Error	Frecuencia
Antioquia	19
Santander	11
Caldas	10
Cundinamarca	7
Huila	6
Cauca	5
Nariño	5
Norte de Santander	5
Risaralda	5
Tolima	4
Valle del Cauca	4
Boyacá	4
Córdoba	3
Magdalena	3
Caquetá	2
Bolìvar	1
Meta	1
Putumayo	1
Quindìo	1
Sucre	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores"
)

##                     
## Pareto chart analysis for Frecuencia
##                       Frequency  Cum.Freq. Percentage Cum.Percent.
##   Antioquia           19.000000  19.000000  19.387755    19.387755
##   Santander           11.000000  30.000000  11.224490    30.612245
##   Caldas              10.000000  40.000000  10.204082    40.816327
##   Cundinamarca         7.000000  47.000000   7.142857    47.959184
##   Huila                6.000000  53.000000   6.122449    54.081633
##   Cauca                5.000000  58.000000   5.102041    59.183673
##   Nariño              5.000000  63.000000   5.102041    64.285714
##   Norte de Santander   5.000000  68.000000   5.102041    69.387755
##   Risaralda            5.000000  73.000000   5.102041    74.489796
##   Tolima               4.000000  77.000000   4.081633    78.571429
##   Valle del Cauca      4.000000  81.000000   4.081633    82.653061
##   Boyacá              4.000000  85.000000   4.081633    86.734694
##   Córdoba             3.000000  88.000000   3.061224    89.795918
##   Magdalena            3.000000  91.000000   3.061224    92.857143
##   Caquetá             2.000000  93.000000   2.040816    94.897959
##   Bolìvar             1.000000  94.000000   1.020408    95.918367
##   Meta                 1.000000  95.000000   1.020408    96.938776
##   Putumayo             1.000000  96.000000   1.020408    97.959184
##   Quindìo             1.000000  97.000000   1.020408    98.979592
##   Sucre                1.000000  98.000000   1.020408   100.000000

Costa Rica

Para el país de Costa Rica se presentaron datos en 7 estados donde el que tuvo mayor número de deslizamientos fue San José con 25 y el de menor fue Limón con solo 2.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Costa Rica")

table(pais$state.province)

## 
##   Alajuela    Cartago Guanacaste    Heredia      Limón Puntarenas   San José 
##         20          6          5          8          2          9         25

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
San José	25	33.3	33.3	33.3	33.3
Alajuela	20	26.7	26.7	60.0	60.0
Puntarenas	9	12.0	12.0	72.0	72.0
Heredia	8	10.7	10.7	82.7	82.7
Cartago	6	8.0	8.0	90.7	90.7
Guanacaste	5	6.7	6.7	97.3	97.3
Limón	2	2.7	2.7	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Costa Rica") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("San José", "Alajuela","Puntarenas","Heredia","Cartago","Guanacaste","Limón"), 
                   value = c(33.3, 26.7, 12, 10.7, 8, 6.7, 2.7))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
COS <- subset (df, Country == "Costa Rica")
knitr::kable(head(COS))

	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

COS %>% 
  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  8.27042

ggplot(data= COS, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Diagrama de Caja

La diferencia entre las mayores poblaciones y menores poblaciones donde ha ocurrido algún deslizamiento en Costa Rica es 335007 habitantes. Mientras que el rango para la distancia de los delizamientos es 22.07 kilometros.

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Costa Rica")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 7.500000e+01 4.000000e+00 0.000000e+00 0.000000e+00 3.350070e+05 3.350070e+05 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 1.127522e+06 6.784000e+03 1.503363e+04 4.498709e+03 8.963873e+03 1.517878e+09 
##      std.dev     coef.var 
## 3.895996e+04 2.591521e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   75.0000000    0.0000000    0.0000000    0.2525400   22.3236800   22.0711400 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  646.1411800    8.2137200    8.6152157    0.6924979    1.3798322   35.9665004 
##      std.dev     coef.var 
##    5.9972077    0.6961181

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

En el gráfico de caja para Costa Rica, es posible distinguir fácilmente que el valor de la mediana es 8,2 aproximandamente, y que los valores a la izquierda de esta medida, son más dispersos a comparación de los valores hacia la derecha.

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("San José", "Alajuela","Puntarenas","Heredia","Cartago","Guanacaste","Limón") , 
                 Frecuencia = c(25,20,9,8,6,5,2))
knitr::kable(df)

Error	Frecuencia
San José	25
Alajuela	20
Puntarenas	9
Heredia	8
Cartago	6
Guanacaste	5
Limón	2

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##             
## Pareto chart analysis for Frecuencia
##               Frequency  Cum.Freq. Percentage Cum.Percent.
##   San José   25.000000  25.000000  33.333333    33.333333
##   Alajuela    20.000000  45.000000  26.666667    60.000000
##   Puntarenas   9.000000  54.000000  12.000000    72.000000
##   Heredia      8.000000  62.000000  10.666667    82.666667
##   Cartago      6.000000  68.000000   8.000000    90.666667
##   Guanacaste   5.000000  73.000000   6.666667    97.333333
##   Limón       2.000000  75.000000   2.666667   100.000000

Cuba

En CUba solo se reportaron 3 estados con deslizamientos, 1 para cada uno de ellos.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Cuba")

table(pais$state.province)

## 
##      Artemisa Province             Guantanamo Provincia de La Habana 
##                      1                      1                      1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Artemisa Province	1	33.3	33.3	33.3	33.3
Guantanamo	1	33.3	33.3	66.7	66.7
Provincia de La Habana	1	33.3	33.3	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Cuba") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Artemisa Province","Guantanamo","Provincia de La Habana"), 
                   value = c(33.3, 33.3, 33.3))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
CUB <- subset (df, Country == "Cuba")
knitr::kable(head(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

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 11.87914

ggplot(data= CUB, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Cuba")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 3.000000e+00 0.000000e+00 0.000000e+00 7.205000e+03 1.323510e+05 1.251460e+05 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 1.879180e+05 4.836200e+04 6.263933e+04 3.682509e+04 1.584456e+05 4.068262e+09 
##      std.dev     coef.var 
## 6.378293e+04 1.018257e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##    3.0000000    0.0000000    0.0000000    0.8986500   11.8791400   10.9804900 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##   23.2357400   10.4579500    7.7452467    3.4477944   14.8346622   35.6618597 
##      std.dev     coef.var 
##    5.9717552    0.7710219

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Artemisa Province","Guantanamo","Provincia de La Habana") , 
                 Frecuencia = c(1,1,1))
knitr::kable(df)

Error	Frecuencia
Artemisa Province	1
Guantanamo	1
Provincia de La Habana	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##                         
## Pareto chart analysis for Frecuencia
##                          Frequency Cum.Freq. Percentage Cum.Percent.
##   Artemisa Province        1.00000   1.00000   33.33333     33.33333
##   Guantanamo               1.00000   2.00000   33.33333     66.66667
##   Provincia de La Habana   1.00000   3.00000   33.33333    100.00000

Dominican Republic

En República Dominicanahubo 7 estados son deslizamientos, siendo Santiago el que más tuvo (4), y Hato Mayor, San Cristobal y santo Domingo con solo 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Dominican Republic")

table(pais$state.province)

## 
## Distrito Nacional        Hato Mayor           La Vega      Puerto Plata 
##                 3                 1                 2                 3 
##     San Cristóbal          Santiago     Santo Domingo 
##                 1                 4                 1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Santiago	4	26.7	26.7	26.7	26.7
Distrito Nacional	3	20.0	20.0	46.7	46.7
Puerto Plata	3	20.0	20.0	66.7	66.7
La Vega	2	13.3	13.3	80.0	80.0
Hato Mayor	1	6.7	6.7	86.7	86.7
San Cristóbal	1	6.7	6.7	93.3	93.3
Santo Domingo	1	6.7	6.7	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Domician Republic") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Santiago","Distrito Nacional","Puerto Plata","La Vega","Hato Mayor","San Cristóbal","Santo Domingo"), 
                   value = c(26.7, 20, 20, 13.3, 6.7, 6.7, 6.7))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
DOM_RE <- subset (df, Country == "Dominican Republic")
knitr::kable(head(DOM_RE))

	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
15	124	7/13/07	Night	NA	Dominican Republic	DO	Distrito Nacional	13456	San Carlos	1.70298		18.4757	-69.9140	(18.4757, -69.914000000000001)	Landslide	Landslide	Small	Unknown		NA	NA	Dominican Today	http://www.dominicantoday.com/app/article.aspx?id=24682
52	333	10/29/07		NA	Dominican Republic	DO	San Cristóbal	66784	Bajos de Haina	1.72138		18.4270	-70.0440	(18.427, -70.043999999999997)	Landslide	Mudslide	Medium	Tropical cyclone	Tropical Storm Noel	NA	3	United Nations Development Programme - Relief Web	http://news.scotsman.com/international.cfm?id=1730152007
58	343	11/1/07		NA	Dominican Republic	DO	La Vega	3613	Río Verde Abajo	3.72637		19.3050	-70.6000	(19.305, -70.599999999999994)	Landslide	Complex	Large	Tropical cyclone	Tropical Storm Noel	NA	68	United Nations Development Programme - Relief Web	http://www.reliefweb.int/rw/fullMaps_Am.nsf/luFullMap/CEB72F0756431A7CC125738D003E2EF4/$File/ifrc_TC_carib071108.pdf?OpenElement
64	388	12/11/07		NA	Dominican Republic	DO	Santiago	1200000	Santiago de los Caballeros	1.10868		19.4550	-70.7070	(19.454999999999998, -70.706999999999994)	Landslide	Landslide	Medium	Tropical cyclone	Tropical Storm Olga	NA	17	news.gossip.info	http://clutchmagonline.com/newsgossipinfo/caribbean-storm-death-toll-rises/
132	724	8/17/08		NA	Dominican Republic	DO	Hato Mayor	13977	Sabana de La Mar	0.75284		19.0560	-69.3822	(19.056000000000001, -69.382199999999997)	Landslide	Complex	Medium	Tropical cyclone	Tropical Storm Fay	NA	NA		http://www.dominicantoday.com/dr/economy/2008/8/18/29085/Storms-downpours-block-transit-on-newest-Dominican-highway
138	746	8/26/08		NA	Dominican Republic	DO	Distrito Nacional	10457	La Agustina	5.71058		18.5500	-69.9200	(18.55, -69.92)	Landslide	Mudslide	Medium	Tropical cyclone	Hurricane Gustav	NA	8		http://www.reuters.com/article/worldNews/idUSN2541891320080827?pageNumber=1&virtualBrandChannel=0

DOM_RE %>% 
  select(Country, State, City, Distance)

##                 Country             State                       City Distance
## 15   Dominican Republic Distrito Nacional                 San Carlos  1.70298
## 52   Dominican Republic     San Cristóbal             Bajos de Haina  1.72138
## 58   Dominican Republic           La Vega            Río Verde Abajo  3.72637
## 64   Dominican Republic          Santiago Santiago de los Caballeros  1.10868
## 132  Dominican Republic        Hato Mayor           Sabana de La Mar  0.75284
## 138  Dominican Republic Distrito Nacional                La Agustina  5.71058
## 178  Dominican Republic          Santiago               Pedro García  4.86398
## 211  Dominican Republic      Puerto Plata                   Altamira  0.88500
## 212  Dominican Republic          Santiago                   Tamboril  4.31327
## 750  Dominican Republic          Santiago      San José de Las Matas  2.72462
## 774  Dominican Republic Distrito Nacional              Santo Domingo  0.55721
## 833  Dominican Republic           La Vega                  Constanza  0.52969
## 923  Dominican Republic      Puerto Plata               Puerto Plata  1.19636
## 1394 Dominican Republic     Santo Domingo         Santo Domingo Este  3.98059
## 1395 Dominican Republic      Puerto Plata                    Luperón  1.54885

ggplot(data= DOM_RE, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Dominican Republic")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 1.500000e+01 1.000000e+00 0.000000e+00 0.000000e+00 2.201941e+06 2.201941e+06 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 3.729279e+06 1.345600e+04 2.486186e+05 1.601963e+05 3.435870e+05 3.849430e+11 
##      std.dev     coef.var 
## 6.204378e+05 2.495540e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   15.0000000    0.0000000    0.0000000    0.5296900    5.7105800    5.1808900 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##   35.3224000    1.7029800    2.3548267    0.4456851    0.9558995    2.9795286 
##      std.dev     coef.var 
##    1.7261311    0.7330183

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Santiago","Distrito Nacional","Puerto Plata","La Vega","Hato Mayor","San Cristóbal","Santo Domingo") , 
                 Frecuencia = c(4,3,3,2,1,1,1))
knitr::kable(df)

Error	Frecuencia
Santiago	4
Distrito Nacional	3
Puerto Plata	3
La Vega	2
Hato Mayor	1
San Cristóbal	1
Santo Domingo	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##                    
## Pareto chart analysis for Frecuencia
##                      Frequency  Cum.Freq. Percentage Cum.Percent.
##   Santiago            4.000000   4.000000  26.666667    26.666667
##   Distrito Nacional   3.000000   7.000000  20.000000    46.666667
##   Puerto Plata        3.000000  10.000000  20.000000    66.666667
##   La Vega             2.000000  12.000000  13.333333    80.000000
##   Hato Mayor          1.000000  13.000000   6.666667    86.666667
##   San Cristóbal      1.000000  14.000000   6.666667    93.333333
##   Santo Domingo       1.000000  15.000000   6.666667   100.000000

Dominica

Para el país de Dominica se presentaron 7 estados con deslizamientos donde el de mayor número de deslizamientos reportados fue Saint Paul con 4 y Saint Patrick con 4, el resto tuvo solamente 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Dominica")

table(pais$state.province)

## 
##  Saint Andrew  Saint George    Saint John  Saint Joseph    Saint Mark 
##             1             1             1             1             1 
## Saint Patrick    Saint Paul 
##             4             5

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Saint Paul	5	35.7	35.7	35.7	35.7
Saint Patrick	4	28.6	28.6	64.3	64.3
Saint Andrew	1	7.1	7.1	71.4	71.4
Saint George	1	7.1	7.1	78.6	78.6
Saint John	1	7.1	7.1	85.7	85.7
Saint Joseph	1	7.1	7.1	92.9	92.9
Saint Mark	1	7.1	7.1	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Dominica") +
  ylab("Frecuencia") +
  
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Saint Paul","Saint Patrick","Saint Andrew","Saint George","Saint John","Saint Joseph","Saint Mark"), 
                   value = c(35.7, 28.6, 7.1, 7.1, 7.1, 7.1, 7.1))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
DOM <- subset (df, Country == "Dominica")
knitr::kable(head(DOM))

	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
20	186	8/17/07		NA	Dominica	DM	Saint Paul	702	Pont Cassé	3.39516		15.3379	-61.3610	(15.337899999999999, -61.360999999999997)	Landslide	Mudslide	Small	Tropical cyclone	Hurricane Dean	NA	2	Tribune India	http://www.tribuneindia.com/2007/20070817/himachal.htm
39	250	9/9/07		NA	Dominica	DM	Saint George	16571	Roseau	2.59849		15.3055	-61.3642	(15.3055, -61.364199999999997)	Landslide	Landslide	Medium	Rain	Tropical Wave	NA	NA	RadioJamaica	http://www.radiojamaica.com/content/view/1156/88/
267	1552	3/11/10		NA	Dominica	DM	Saint Paul	702	Pont Cassé	3.98646		15.3356	-61.3312	(15.335599999999999, -61.331200000000003)	Landslide	Landslide	Medium	Rain		NA	0		http://stormcarib.com/reports/current/report.php?id=1268397271_8827
297	1743	4/12/10		NA	Dominica	DM	Saint Patrick	2608	Berekua	2.08997		15.2454	-61.3017	(15.2454, -61.301699999999997)	Landslide	Landslide	Medium	Downpour		NA	0		http://www.dominicacentral.com/general/community/heavy-overnight-rains-cause-landslides-across-island.html
298	1744	4/12/10		NA	Dominica	DM	Saint Paul	702	Pont Cassé	3.78784		15.4004	-61.3440	(15.400399999999999, -61.344000000000001)	Landslide	Landslide	Medium	Downpour		NA	0		http://www.dominicacentral.com/general/community/heavy-overnight-rains-cause-landslides-across-island.html
299	1745	4/12/10		NA	Dominica	DM	Saint Patrick	2608	Berekua	4.08252		15.2458	-61.2809	(15.245799999999999, -61.280900000000003)	Landslide	Landslide	Small	Downpour		NA	0		http://www.dominicacentral.com/general/community/heavy-overnight-rains-cause-landslides-across-island.html

DOM %>% 
  select(Country, State, City, Distance)

##       Country         State         City Distance
## 20   Dominica    Saint Paul   Pont Cassé  3.39516
## 39   Dominica  Saint George       Roseau  2.59849
## 267  Dominica    Saint Paul   Pont Cassé  3.98646
## 297  Dominica Saint Patrick      Berekua  2.08997
## 298  Dominica    Saint Paul   Pont Cassé  3.78784
## 299  Dominica Saint Patrick      Berekua  4.08252
## 300  Dominica Saint Patrick      Berekua  5.61495
## 301  Dominica Saint Patrick    La Plaine  5.11600
## 304  Dominica    Saint Paul   Pont Cassé  6.45930
## 476  Dominica  Saint Andrew   Calibishie  2.64873
## 1190 Dominica    Saint Paul   Pont Cassé  4.20239
## 1193 Dominica    Saint John   Portsmouth  5.92994
## 1194 Dominica    Saint Mark    Soufrière  1.80847
## 1201 Dominica  Saint Joseph Saint Joseph  2.38605

ggplot(data= DOM, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Dominica")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 1.400000e+01 0.000000e+00 0.000000e+00 7.020000e+02 1.657100e+04 1.586900e+04 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 3.749000e+04 1.374000e+03 2.677857e+03 1.099107e+03 2.374475e+03 1.691249e+07 
##      std.dev     coef.var 
## 4.112480e+03 1.535735e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   14.0000000    0.0000000    0.0000000    1.8084700    6.4593000    4.6508300 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##   54.1062700    3.8871500    3.8647336    0.3969319    0.8575192    2.2057691 
##      std.dev     coef.var 
##    1.4851832    0.3842912

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Saint Paul","Saint Patrick","Saint Andrew","Saint George","Saint John","Saint Joseph","Saint Mark") , 
                 Frecuencia = c(5,4,1,1,1,1,1))
knitr::kable(df)

Error	Frecuencia
Saint Paul	5
Saint Patrick	4
Saint Andrew	1
Saint George	1
Saint John	1
Saint Joseph	1
Saint Mark	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##                
## Pareto chart analysis for Frecuencia
##                  Frequency  Cum.Freq. Percentage Cum.Percent.
##   Saint Paul      5.000000   5.000000  35.714286    35.714286
##   Saint Patrick   4.000000   9.000000  28.571429    64.285714
##   Saint Andrew    1.000000  10.000000   7.142857    71.428571
##   Saint George    1.000000  11.000000   7.142857    78.571429
##   Saint John      1.000000  12.000000   7.142857    85.714286
##   Saint Joseph    1.000000  13.000000   7.142857    92.857143
##   Saint Mark      1.000000  14.000000   7.142857   100.000000

Ecuador

Para el caso de Ecuador se muestran datos en 14 estados, el doble de los anteriores, donde el de mayor frecuencia de deslizamientos fue Pichincha con 9.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Ecuador")

table(pais$state.province)

## 
##                          Azuay                         Carchi 
##                              2                              2 
##                       Cotopaxi                     Esmeraldas 
##                              1                              2 
##                         Guayas                           Loja 
##                              1                              4 
##                        Manabi                 Morona-Santiago 
##                              2                              1 
##                           Napo                      Pichincha 
##                              1                              9 
## Santo Domingo de los Tsáchilas                      Sucumbios 
##                              1                              1 
##                     Tungurahua               Zamora-Chinchipe 
##                              1                              2

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Pichincha	9	30.0	30.0	30.0	30.0
Loja	4	13.3	13.3	43.3	43.3
Azuay	2	6.7	6.7	50.0	50.0
Carchi	2	6.7	6.7	56.7	56.7
Esmeraldas	2	6.7	6.7	63.3	63.3
Manabi	2	6.7	6.7	70.0	70.0
Zamora-Chinchipe	2	6.7	6.7	76.7	76.7
Cotopaxi	1	3.3	3.3	80.0	80.0
Guayas	1	3.3	3.3	83.3	83.3
Morona-Santiago	1	3.3	3.3	86.7	86.7
Napo	1	3.3	3.3	90.0	90.0
Santo Domingo de los Tsáchilas	1	3.3	3.3	93.3	93.3
Sucumbios	1	3.3	3.3	96.7	96.7
Tungurahua	1	3.3	3.3	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Ecuador") +
  ylab("Frecuencia") +
  
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Pichincha","Loja","Azuay","Carchi","Esmeraldas","Manabi","Zamora-Chinchipe","Cotopaxi","Guayas","Morona-Santiago","Napo","Santo Domingo de los Tsáchilas13","Sucumbios","Tungurahua"), 
                   value = c(30, 13.3, 6.7, 6.7, 6.7, 6.7, 6.7, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3, 3.3))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=4) +
  scale_fill_manual(values = c("darkseagreen2","darkred","darkorchid1","gainsboro","gray2","cyan","cyan4","darkgoldenrod1","brown4","burlywood1","blue1","blueviolet","chartreuse","aliceblue"))

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
ECU <- subset (df, Country == "Ecuador")
knitr::kable(head(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

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  4.25486

ggplot(data= ECU, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Diagrama de caja

En Ecuador, el tamaño máximo de la población donde ha ocurrido un deslizamiento resgistrado en esta base de datos es 1399814 personas. Por otro lado, el mayor deslizamiento tuvo una longitud de 50.21 kilometros.

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Ecuador")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 3.000000e+01 1.000000e+00 0.000000e+00 0.000000e+00 1.399814e+06 1.399814e+06 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 8.599781e+06 2.574200e+04 2.866594e+05 9.355366e+04 1.913387e+05 2.625686e+11 
##      std.dev     coef.var 
## 5.124145e+05 1.787538e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   30.0000000    0.0000000    0.0000000    0.3564900   50.2174100   49.8609200 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  515.4786400   14.7777150   17.1826213    2.6348903    5.3889557  208.2794055 
##      std.dev     coef.var 
##   14.4318885    0.8399119

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

Para las longitudes de los deslizamientos en Ecuador no son tan dispersos, con una mediana de 14.77 kilometros; y cuartiles no tan dispersos, a excepción del último.

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Pichincha","Loja","Azuay","Carchi","Esmeraldas","Manabi","Zamora-Chinchipe","Cotopaxi","Guayas","Morona-Santiago","Napo","Santo Domingo de los Tsáchilas13","Sucumbios","Tungurahua") , 
                 Frecuencia = c(9,4,2,2,2,2,2,1,1,1,1,1,1,1))
knitr::kable(df)

Error	Frecuencia
Pichincha	9
Loja	4
Azuay	2
Carchi	2
Esmeraldas	2
Manabi	2
Zamora-Chinchipe	2
Cotopaxi	1
Guayas	1
Morona-Santiago	1
Napo	1
Santo Domingo de los Tsáchilas13	1
Sucumbios	1
Tungurahua	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##                                    
## Pareto chart analysis for Frecuencia
##                                      Frequency  Cum.Freq. Percentage
##   Pichincha                           9.000000   9.000000  30.000000
##   Loja                                4.000000  13.000000  13.333333
##   Azuay                               2.000000  15.000000   6.666667
##   Carchi                              2.000000  17.000000   6.666667
##   Esmeraldas                          2.000000  19.000000   6.666667
##   Manabi                              2.000000  21.000000   6.666667
##   Zamora-Chinchipe                    2.000000  23.000000   6.666667
##   Cotopaxi                            1.000000  24.000000   3.333333
##   Guayas                              1.000000  25.000000   3.333333
##   Morona-Santiago                     1.000000  26.000000   3.333333
##   Napo                                1.000000  27.000000   3.333333
##   Santo Domingo de los Tsáchilas13   1.000000  28.000000   3.333333
##   Sucumbios                           1.000000  29.000000   3.333333
##   Tungurahua                          1.000000  30.000000   3.333333
##                                    
## Pareto chart analysis for Frecuencia
##                                     Cum.Percent.
##   Pichincha                            30.000000
##   Loja                                 43.333333
##   Azuay                                50.000000
##   Carchi                               56.666667
##   Esmeraldas                           63.333333
##   Manabi                               70.000000
##   Zamora-Chinchipe                     76.666667
##   Cotopaxi                             80.000000
##   Guayas                               83.333333
##   Morona-Santiago                      86.666667
##   Napo                                 90.000000
##   Santo Domingo de los Tsáchilas13    93.333333
##   Sucumbios                            96.666667
##   Tungurahua                          100.000000

El Salvador

En El Salvador se presentaron datos en 9 estados donde el de mayor número de deslizamientos fue La Libertad con 9, y los de menor número fueron Cabañas y La Paz con 1 deslizamiento reportado.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "El Salvador")

table(pais$state.province)

## 
##   Ahuachapán      Cabañas  La Libertad       La Paz   San Miguel San Salvador 
##            2            1            5            1            3            2 
##  San Vicente    Santa Ana    Sonsonate 
##            3            2            3

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
La Libertad	5	22.7	22.7	22.7	22.7
San Miguel	3	13.6	13.6	36.4	36.4
San Vicente	3	13.6	13.6	50.0	50.0
Sonsonate	3	13.6	13.6	63.6	63.6
Ahuachapán	2	9.1	9.1	72.7	72.7
San Salvador	2	9.1	9.1	81.8	81.8
Santa Ana	2	9.1	9.1	90.9	90.9
Cabañas	1	4.5	4.5	95.5	95.5
La Paz	1	4.5	4.5	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("El Salvador") +
  ylab("Frecuencia") +
  
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("La Libertad","San Miguel","San Vicente","Sonsonate","Ahuachapán","San Salvador","Santa Ana","Cabañas","La Paz"), 
                   value = c(22.7, 13.6, 13.6, 13.6, 9.1, 9.1, 9.1, 4.5, 4.5))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
EL_SAL <- subset (df, Country == "El Salvador")
knitr::kable(head(EL_SAL))

	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

EL_SAL %>% 
  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  0.49346

ggplot(data= EL_SAL, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "El Salvador")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 2.200000e+01 0.000000e+00 0.000000e+00 2.654000e+03 1.246940e+05 1.220400e+05 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 9.383360e+05 2.643100e+04 4.265164e+04 9.849090e+03 2.048230e+04 2.134100e+09 
##      std.dev     coef.var 
## 4.619633e+04 1.083108e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   22.0000000    0.0000000    0.0000000    0.0027300   10.0669500   10.0642200 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  112.1789400    4.7697050    5.0990427    0.6720603    1.3976259    9.9366315 
##      std.dev     coef.var 
##    3.1522423    0.6182028

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

### Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("La Libertad","San Miguel","San Vicente","Sonsonate","Ahuachapán","San Salvador","Santa Ana","Cabañas","La Paz") , 
                 Frecuencia = c(5,3,3,3,2,2,2,1,1))
knitr::kable(df)

Error	Frecuencia
La Libertad	5
San Miguel	3
San Vicente	3
Sonsonate	3
Ahuachapán	2
San Salvador	2
Santa Ana	2
Cabañas	1
La Paz	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##               
## Pareto chart analysis for Frecuencia
##                 Frequency  Cum.Freq. Percentage Cum.Percent.
##   La Libertad    5.000000   5.000000  22.727273    22.727273
##   San Miguel     3.000000   8.000000  13.636364    36.363636
##   San Vicente    3.000000  11.000000  13.636364    50.000000
##   Sonsonate      3.000000  14.000000  13.636364    63.636364
##   Ahuachapán    2.000000  16.000000   9.090909    72.727273
##   San Salvador   2.000000  18.000000   9.090909    81.818182
##   Santa Ana      2.000000  20.000000   9.090909    90.909091
##   Cabañas       1.000000  21.000000   4.545455    95.454545
##   La Paz         1.000000  22.000000   4.545455   100.000000

Guatemala

Para Guatmela, el número de estados con deslizamientos aumentó, siendo 18, y teniendo un número máximo de eventos en el estado de Guatemala con 31 deslizamientos registrados.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Guatemala")

table(pais$state.province)

## 
##   Alta Verapaz   Baja Verapaz  Chimaltenango     Chiquimula      Escuintla 
##              3              1              4              1              3 
##      Guatemala  Huehuetenango         Izabal        Jutiapa Quetzaltenango 
##             31              5              2              1              3 
##         Quiché   Sacatepéquez     San Marcos     Santa Rosa         Sololá 
##              7              3              2              1              9 
##   Suchitepeque    Totonicapán         Zacapa 
##              1              1              1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Guatemala	31	39.2	39.2	39.2	39.2
Sololá	9	11.4	11.4	50.6	50.6
Quiché	7	8.9	8.9	59.5	59.5
Huehuetenango	5	6.3	6.3	65.8	65.8
Chimaltenango	4	5.1	5.1	70.9	70.9
Alta Verapaz	3	3.8	3.8	74.7	74.7
Escuintla	3	3.8	3.8	78.5	78.5
Quetzaltenango	3	3.8	3.8	82.3	82.3
Sacatepéquez	3	3.8	3.8	86.1	86.1
Izabal	2	2.5	2.5	88.6	88.6
San Marcos	2	2.5	2.5	91.1	91.1
Baja Verapaz	1	1.3	1.3	92.4	92.4
Chiquimula	1	1.3	1.3	93.7	93.7
Jutiapa	1	1.3	1.3	94.9	94.9
Santa Rosa	1	1.3	1.3	96.2	96.2
Suchitepeque	1	1.3	1.3	97.5	97.5
Totonicapán	1	1.3	1.3	98.7	98.7
Zacapa	1	1.3	1.3	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Guatemala") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Guatemala","Sololá","Quiché","Huehuetenango","Chimaltenango","Alta Verapaz33","Escuintla","Quetzaltenango","Sacatepéquez33","Izabal","San Marcos","Baja Verapaz","Chiquimula","Jutiapa","Santa Rosa","Suchitepeque","Totonicapán","Zacapa"), 
                   value = c(39.2, 11.4, 8.9, 6.3, 5.1, 3.8, 3.8, 3.8, 3.8, 2.5, 2.5, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=4) +
  scale_fill_manual(values = c("darkseagreen2","darkred","darkorchid1","gainsboro","gray2","cyan","cyan4","darkgoldenrod1","darkgoldenrod4","brown4","burlywood1","burlywood3","blue1","blue4","blueviolet","chartreuse","springgreen","yellow"))

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
GUA <- subset (df, Country == "Guatemala")
knitr::kable(head(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/

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  1.81216

ggplot(data= GUA, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Guatemala")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 7.900000e+01 0.000000e+00 0.000000e+00 1.311000e+03 9.949380e+05 9.936270e+05 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 1.624255e+07 1.899400e+04 2.056019e+05 4.095993e+04 8.154496e+04 1.325396e+11 
##      std.dev     coef.var 
## 3.640598e+05 1.770702e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   79.0000000    0.0000000    0.0000000    0.0035900   23.9230900   23.9195000 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  303.1182300    2.7005300    3.8369396    0.5175386    1.0303402   21.1598488 
##      std.dev     coef.var 
##    4.5999836    1.1988679

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Guatemala","Sololá","Quiché","Huehuetenango","Chimaltenango","Alta Verapaz33","Escuintla","Quetzaltenango","Sacatepéquez33","Izabal","San Marcos","Baja Verapaz","Chiquimula","Jutiapa","Santa Rosa","Suchitepeque","Totonicapán","Zacapa") , 
                 Frecuencia = c(31,9,7,5,4,3,3,3,3,2,2,1,1,1,1,1,1,1))
knitr::kable(df)

Error	Frecuencia
Guatemala	31
Sololá	9
Quiché	7
Huehuetenango	5
Chimaltenango	4
Alta Verapaz33	3
Escuintla	3
Quetzaltenango	3
Sacatepéquez33	3
Izabal	2
San Marcos	2
Baja Verapaz	1
Chiquimula	1
Jutiapa	1
Santa Rosa	1
Suchitepeque	1
Totonicapán	1
Zacapa	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##                  
## Pareto chart analysis for Frecuencia
##                    Frequency  Cum.Freq. Percentage Cum.Percent.
##   Guatemala        31.000000  31.000000  39.240506    39.240506
##   Sololá           9.000000  40.000000  11.392405    50.632911
##   Quiché           7.000000  47.000000   8.860759    59.493671
##   Huehuetenango     5.000000  52.000000   6.329114    65.822785
##   Chimaltenango     4.000000  56.000000   5.063291    70.886076
##   Alta Verapaz33    3.000000  59.000000   3.797468    74.683544
##   Escuintla         3.000000  62.000000   3.797468    78.481013
##   Quetzaltenango    3.000000  65.000000   3.797468    82.278481
##   Sacatepéquez33   3.000000  68.000000   3.797468    86.075949
##   Izabal            2.000000  70.000000   2.531646    88.607595
##   San Marcos        2.000000  72.000000   2.531646    91.139241
##   Baja Verapaz      1.000000  73.000000   1.265823    92.405063
##   Chiquimula        1.000000  74.000000   1.265823    93.670886
##   Jutiapa           1.000000  75.000000   1.265823    94.936709
##   Santa Rosa        1.000000  76.000000   1.265823    96.202532
##   Suchitepeque      1.000000  77.000000   1.265823    97.468354
##   Totonicapán      1.000000  78.000000   1.265823    98.734177
##   Zacapa            1.000000  79.000000   1.265823   100.000000

Haití

Para Haití solo se ven 5 estados en los que se presentaron 10 deslizamientos para Quest, 4 en Nord, 3 en Artibonite, 2 en Sud-Est y solo 1 en Centre.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Haiti")

table(pais$state.province)

## 
## Artibonite     Centre       Nord      Ouest    Sud-Est 
##          3          1          4         10          2

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Ouest	10	50	50	50	50
Nord	4	20	20	70	70
Artibonite	3	15	15	85	85
Sud-Est	2	10	10	95	95
Centre	1	5	5	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Haiti") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Ouest","Nord","Artibonite","Sud-Est","Centre"), 
                   value = c(50, 20, 15, 10, 5))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
HAI <- subset (df, Country == "Haití")
knitr::kable(head(HAI))

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

HAI %>% 
  select(Country, State, City, Distance)

## [1] Country  State    City     Distance
## <0 rows> (or 0-length row.names)

ggplot(data= HAI, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

### Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
pais <- subset(df, country_name == "Haiti")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 2.000000e+01 0.000000e+00 0.000000e+00 3.951000e+03 1.234742e+06 1.230791e+06 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 6.625032e+06 1.363905e+05 3.312516e+05 9.247531e+04 1.935530e+05 1.710337e+11 
##      std.dev     coef.var 
## 4.135622e+05 1.248483e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##    20.000000     0.000000     0.000000     0.034710    17.298360    17.263650 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##    84.071730     2.678665     4.203587     1.019568     2.133979    20.790360 
##      std.dev     coef.var 
##     4.559645     1.084703

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Ouest","Nord","Artibonite","Sud-Est","Centre") , 
                 Frecuencia = c(10,4,3,2,1))
knitr::kable(df)

Error	Frecuencia
Ouest	10
Nord	4
Artibonite	3
Sud-Est	2
Centre	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##             
## Pareto chart analysis for Frecuencia
##              Frequency Cum.Freq. Percentage Cum.Percent.
##   Ouest             10        10         50           50
##   Nord               4        14         20           70
##   Artibonite         3        17         15           85
##   Sud-Est            2        19         10           95
##   Centre             1        20          5          100

Honduras

En Honduras se reportaron 11 estados con deslizamientos, siendo el de mayor frecuencia de estos sucesos, el estado de Francisco Morazán, y los de meor frecuencia: Colón, El Paraíso y La Paz.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Honduras")

table(pais$state.province)

## 
##          Choluteca              Colón          Comayagua              Copán 
##                  3                  1                  4                  5 
##             Cortés         El Paraíso Francisco Morazán              La Paz 
##                  2                  1                 15                  1 
##         Ocotepeque      Santa Bárbara               Yoro 
##                  2                  2                  2

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Francisco Morazán	15	39.5	39.5	39.5	39.5
Copán	5	13.2	13.2	52.6	52.6
Comayagua	4	10.5	10.5	63.2	63.2
Choluteca	3	7.9	7.9	71.1	71.1
Cortés	2	5.3	5.3	76.3	76.3
Ocotepeque	2	5.3	5.3	81.6	81.6
Santa Bárbara	2	5.3	5.3	86.8	86.8
Yoro	2	5.3	5.3	92.1	92.1
Colón	1	2.6	2.6	94.7	94.7
El Paraíso	1	2.6	2.6	97.4	97.4
La Paz	1	2.6	2.6	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Honduras") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Francisco Morazán","Copán","Comayagua","Choluteca","Cortés","Ocotepeque","Santa Bárbara","Yoro","Colón","El Paraíso","La Paz"), 
                   value = c(39.5, 13.2, 10.5, 7.9, 5.3, 5.3, 5.3, 5.3, 2.6, 2.6, 2.6))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=5) +
  scale_fill_manual(values = c("darkseagreen2","darkred","darkorchid1","gainsboro","gray2","cyan","cyan4","darkgoldenrod1","brown4","burlywood1","burlywood3"))

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
HON <- subset (df, Country == "Honduras")
knitr::kable(head(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

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  2.91326

ggplot(data= HON, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Diagrama de caja

library(dplyr)

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Honduras")

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 3.800000e+01 0.000000e+00 0.000000e+00 1.043000e+03 8.508480e+05 8.498050e+05 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 7.001138e+06 1.936000e+03 1.842405e+05 5.663200e+04 1.147473e+05 1.218729e+11 
##      std.dev     coef.var 
## 3.491031e+05 1.894823e+00

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##    38.000000     0.000000     0.000000     0.288870    36.376290    36.087420 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##   166.172050     2.914600     4.372949     1.023393     2.073592    39.798695 
##      std.dev     coef.var 
##     6.308621     1.442647

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

data <- c(pais$distance)
boxplot(data, horizontal=TRUE, col='steelblue')

Diagrama de Pareto

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")


df <- data.frame(Error = c("Francisco Morazán","Copán","Comayagua","Choluteca","Cortés","Ocotepeque","Santa Bárbara","Yoro","Colón","El ParaÃso","La Paz") , 
                 Frecuencia = c(15,5,4,3,2,2,2,2,1,1,1))
knitr::kable(df)

Error	Frecuencia
Francisco Morazán	15
Copán	5
Comayagua	4
Choluteca	3
Cortés	2
Ocotepeque	2
Santa Bárbara	2
Yoro	2
Colón	1
El ParaÃso	1
La Paz	1

library(qcc)

Frecuencia <- df$Frecuencia
names(Frecuencia) <- df$Error 

pareto.chart(Frecuencia, 
             ylab="Frecuencia",
             col = heat.colors(length(Frecuencia)),
             cumperc = seq(0, 100, by = 10),
             ylab2 = "Porcentaje acumulado",
             main = "Grafico de Pareto para Errores")

##                     
## Pareto chart analysis for Frecuencia
##                       Frequency  Cum.Freq. Percentage Cum.Percent.
##   Francisco Morazán  15.000000  15.000000  39.473684    39.473684
##   Copán               5.000000  20.000000  13.157895    52.631579
##   Comayagua            4.000000  24.000000  10.526316    63.157895
##   Choluteca            3.000000  27.000000   7.894737    71.052632
##   Cortés              2.000000  29.000000   5.263158    76.315789
##   Ocotepeque           2.000000  31.000000   5.263158    81.578947
##   Santa Bárbara       2.000000  33.000000   5.263158    86.842105
##   Yoro                 2.000000  35.000000   5.263158    92.105263
##   Colón               1.000000  36.000000   2.631579    94.736842
##   El ParaÃso           1.000000  37.000000   2.631579    97.368421
##   La Paz               1.000000  38.000000   2.631579   100.000000

Jamaica

Para el caso de jamaica se reportaron 8 estados con deslizamientos, siendo Portland donde más se presentaron (8), y St. Elizabeth donde menos se presentaron (1).

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Jamaica")

table(pais$state.province)

## 
##       Clarendon        Portland    Saint Andrew       Saint Ann Saint Catherine 
##               2              12               5               3               3 
##      Saint Mary    Saint Thomas   St. Elizabeth 
##               3               4               1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Portland	12	36.4	36.4	36.4	36.4
Saint Andrew	5	15.2	15.2	51.5	51.5
Saint Thomas	4	12.1	12.1	63.6	63.6
Saint Ann	3	9.1	9.1	72.7	72.7
Saint Catherine	3	9.1	9.1	81.8	81.8
Saint Mary	3	9.1	9.1	90.9	90.9
Clarendon	2	6.1	6.1	97.0	97.0
St. Elizabeth	1	3.0	3.0	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Jamaica") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Portland","Saint Andrew","Saint Thomas","Saint Ann","Saint Catherine","Saint Mary","Clarendon","St. Elizabeth"), 
                   value = c(36.4, 15.2, 12.1, 9.1, 9.1, 9.1, 6.1, 3))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
JAM <- subset (df, Country == "Jamaica")
knitr::kable(head(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

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  4.68732

ggplot(data= JAM, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

La longitud mínina de un deslizamiento de tierra en Jamaica ha sido 0.21 kilometros; y la menor cantidad de personas afectadas por un deslizamiento es 1088 personas.

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)

pais <- subset(df, country_name == "Jamaica")

knitr::kable(head(pais))

	id	date	time	continent_code	country_name	country_code	state.province	population	city.town	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

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(dplyr)
library(pastecs)


stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 3.300000e+01 0.000000e+00 0.000000e+00 1.088000e+03 4.475500e+04 4.366700e+04 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 2.342270e+05 2.757000e+03 7.097788e+03 1.474547e+03 3.003553e+03 7.175151e+07 
##      std.dev     coef.var 
## 8.470626e+03 1.193418e+00

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##   33.0000000    0.0000000    0.0000000    0.2182500   10.8446700   10.6264200 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
##  147.1557200    4.4694200    4.4592642    0.5248336    1.0690510    9.0898595 
##      std.dev     coef.var 
##    3.0149394    0.6761069

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

El cuarto percentil con base en la distancia de los deslizamientos, tiene valores dispersos acorde a los demás percentiles; además, no han habído valores atípicos de los deslizamientos en Jamaica.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ tibble  3.1.3     ✓ stringr 1.4.0
## ✓ tidyr   1.1.3     ✓ forcats 0.5.1
## ✓ purrr   0.3.4

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x readr::col_factor() masks scales::col_factor()
## x purrr::discard()    masks scales::discard()
## x tidyr::extract()    masks pastecs::extract()
## x dplyr::filter()     masks stats::filter()
## x pastecs::first()    masks dplyr::first()
## x dplyr::lag()        masks stats::lag()
## x pastecs::last()     masks dplyr::last()

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/arialnarrow

library(viridis)

## Loading required package: viridisLite

## 
## Attaching package: 'viridis'

## The following object is masked from 'package:scales':
## 
##     viridis_pal

data<-pais$distance

caja <- data.frame(data)
caja %>% ggplot(aes(x = "", y = data)) +
  geom_boxplot(color="black", fill="blue", alpha=0.5) +
  theme_ipsum() +
  theme(legend.position="none", plot.title = element_text(size=11)) +
  ggtitle("Basic boxplot") +
  coord_flip() +
  xlab("") +
  ylab("")

Mexico

En México, 19 estados tuvieron deslizamientos según los datos reportados, siendo Chiapas el de mayor número con 10, y Colima el de menor con solo 1 al igual que otros como Sinaloa o Nuevo León.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Mexico")

table(pais$state.province)

## 
##      Baja California  Baja California Sur              Chiapas 
##                    4                    1                   10 
##            Chihuahua               Colima             Guerrero 
##                    1                    1                    8 
##              Hidalgo              Jalisco               México 
##                    2                    1                    4 
##            Michoacán              Nayarit           Nuevo León 
##                    4                    2                    1 
##               Oaxaca               Puebla              Sinaloa 
##                    9                    4                    1 
##              Tabasco The Federal District             Veracruz 
##                    6                    3                    2 
##       Veracruz-Llave 
##                    9

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Chiapas	10	13.7	13.7	13.7	13.7
Oaxaca	9	12.3	12.3	26.0	26.0
Veracruz-Llave	9	12.3	12.3	38.4	38.4
Guerrero	8	11.0	11.0	49.3	49.3
Tabasco	6	8.2	8.2	57.5	57.5
Baja California	4	5.5	5.5	63.0	63.0
México	4	5.5	5.5	68.5	68.5
Michoacán	4	5.5	5.5	74.0	74.0
Puebla	4	5.5	5.5	79.5	79.5
The Federal District	3	4.1	4.1	83.6	83.6
Hidalgo	2	2.7	2.7	86.3	86.3
Nayarit	2	2.7	2.7	89.0	89.0
Veracruz	2	2.7	2.7	91.8	91.8
Baja California Sur	1	1.4	1.4	93.2	93.2
Chihuahua	1	1.4	1.4	94.5	94.5
Colima	1	1.4	1.4	95.9	95.9
Jalisco	1	1.4	1.4	97.3	97.3
Nuevo León	1	1.4	1.4	98.6	98.6
Sinaloa	1	1.4	1.4	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Mexico") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Chiapas","Oaxaca","Veracruz-Llave","Guerrero","Tabasco","Baja California","México","Michoacán","Puebla","The Federal District","Hidalgo","Hidalgo","Veracruz","Baja California Sur","Chihuahua","Chihuahua","Jalisco","Nuevo León","Sinaloa"), 
                   value = c(13.7, 12.3, 12.3, 11, 8.2, 5.5, 5.5, 5.5, 5.5, 4.1, 2.7, 2.7, 2.7, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=4) +
  scale_fill_manual(values = c("darkseagreen2","darkred","darkorchid1","gainsboro","gray2","cyan","cyan4","darkgoldenrod1","brown4","burlywood1","burlywood3","blue1","blue4","blueviolet","chartreuse","springgreen","yellow"))

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
MEX <- subset (df, Country == "México")
knitr::kable(head(MEX))

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

MEX %>% 
  select(Country, State, City, Distance)

## [1] Country  State    City     Distance
## <0 rows> (or 0-length row.names)

ggplot(data= MEX, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Nicaragua

En Nicaragua hubo 9 estados donde se obtuvieron datos de deslizamientos, de los cuales el que mayor tuvo fue Chontales con 14 y los que menos tuvieron fueron Atlántico Norte y Río San Juan con 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Nicaragua")

table(pais$state.province)

## 
## Atlántico Norte       Chontales        Jinotega          Madriz         Managua 
##               1              14               3               2               3 
##          Masaya      Ogun State    Río San Juan           Rivas 
##               2               3               1               2

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Chontales	14	45.2	45.2	45.2	45.2
Jinotega	3	9.7	9.7	54.8	54.8
Managua	3	9.7	9.7	64.5	64.5
Ogun State	3	9.7	9.7	74.2	74.2
Madriz	2	6.5	6.5	80.6	80.6
Masaya	2	6.5	6.5	87.1	87.1
Rivas	2	6.5	6.5	93.5	93.5
Atlántico Norte	1	3.2	3.2	96.8	96.8
Río San Juan	1	3.2	3.2	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Nicaragua") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Chontales","Jinotega","Managua","Ogun State","Madriz","Masaya","Rivas","Atlántico Norte","Río San Juan"), 
                   value = c(45.2, 9.7, 9.7, 9.7, 6.5, 6.5, 6.5, 3.2, 3.2))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=5) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
NIC <- subset (df, Country == "Nicaragua")
knitr::kable(head(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

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  1.56730

ggplot(data= NIC, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Puerto Rico

Para Puerto rico hubo 6 estados con deslizamientos, estando Ponce con 2 y el resto como Agua, Orocovis, etc. con solamente 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Puerto Rico")

table(pais$state.province)

## 
##    Aguada  Orocovis     Ponce  San Juan Vega Alta  Villalba 
##         1         1         2         1         1         1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Ponce	2	28.6	28.6	28.6	28.6
Aguada	1	14.3	14.3	42.9	42.9
Orocovis	1	14.3	14.3	57.1	57.1
San Juan	1	14.3	14.3	71.4	71.4
Vega Alta	1	14.3	14.3	85.7	85.7
Villalba	1	14.3	14.3	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Puerto Rico") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Ponce","Aguada","Orocovis","San Juan","Vega Alta","Villalba"), 
                   value = c(28.6, 14.3, 14.3, 14.3, 14.3, 14.3))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
PUE <- subset (df, Country == "Puerto Rico")
knitr::kable(head(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

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  3.65535

ggplot(data= PUE, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Peru

Para Perú hubo 4 estados con deslizamientos reportados, Ancash con 5, Huanuco con 4, San Martín con 3 y La Libertad con 2.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Peru")

table(pais$state.province)

## 
##      Ancash     Huanuco La Libertad  San Martín 
##           5           4           2           3

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Ancash	5	35.7	35.7	35.7	35.7
Huanuco	4	28.6	28.6	64.3	64.3
San Martín	3	21.4	21.4	85.7	85.7
La Libertad	2	14.3	14.3	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Peru") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Ancash","Huanuco","San Martín","La Libertad"), 
                   value = c(35.7, 28.6, 21.4, 14.3))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
PER <- subset (df, Country == "Perú")
knitr::kable(head(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

PER %>% 
  select(Country, State, City, Distance)

## [1] Country  State    City     Distance
## <0 rows> (or 0-length row.names)

ggplot(data= PER, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Panama

En panamá donde se presentaron más deslizamientos fue en el estado de Colón (11) y donde menos hubo fue en Los Santos (1).

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Panama")

table(pais$state.province)

## 
## Bocas del Toro       Chiriquí          Coclé          Colón     Los Santos 
##              7              9              1             11              1 
##         Panamá 
##             11

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Colón	11	27.5	27.5	27.5	27.5
Panamá	11	27.5	27.5	55.0	55.0
Chiriquí	9	22.5	22.5	77.5	77.5
Bocas del Toro	7	17.5	17.5	95.0	95.0
Coclé	1	2.5	2.5	97.5	97.5
Los Santos	1	2.5	2.5	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Panama") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Colón","Panamá","Chiriquí","Bocas del Toro","Coclé","Los Santos"), 
                   value = c(27.5, 27.5, 22.5, 17.5, 2.5, 2.5))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
PAN <- subset (df, Country == "Panama")
knitr::kable(head(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

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  1.80341

ggplot(data= PAN, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Saint Vincent and the Grenadines

En Saint Vincent and the Grenadines también se reportó un deslizamiento solo en un estado: Charlotte.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Saint Vincent and the Grenadines")

table(pais$state.province)

## 
## Charlotte 
##         4

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Charlotte	4	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow", width=0.5) +
  xlab("Saint Vincent and the Grenadines") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

Saint Lucia

En Santa Lucía se portaron 5 estados con 8 deslizamientos en total: Soufriére (4), Dennery (1), Laborie (1), Praslin (1) y Vieux-Fort (1).

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Saint Lucia")

table(pais$state.province)

## 
##    Dennery    Laborie    Praslin  Soufrière Vieux-Fort 
##          1          1          1          4          1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Soufrière	4	50.0	50.0	50.0	50.0
Dennery	1	12.5	12.5	62.5	62.5
Laborie	1	12.5	12.5	75.0	75.0
Praslin	1	12.5	12.5	87.5	87.5
Vieux-Fort	1	12.5	12.5	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Saint Lucia") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Soufrière","Dennery","Laborie","Praslin","Vieux-Fort"), 
                   value = c(50, 12.5, 12.5, 12.5, 12.5))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
SAIN <- subset (df, Country == "Saint Lucia")
knitr::kable(head(SAIN))

	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
155	841	10/12/08		NA	Saint Lucia	LC	Vieux-Fort	4574	Vieux Fort	2.61752		13.7400	-60.9460	(13.74, -60.945999999999998)	Landslide	Landslide	Medium	Downpour		NA	NA		http://www.htsstlucia.com/2008_News/October/HTS_News_October_13th_2008.html
488	2658	10/30/10		NA	Saint Lucia	LC	Laborie	1131	Laborie	2.74715		13.7705	-60.9690	(13.7705, -60.969000000000001)	Landslide	Landslide	Medium	Tropical cyclone	Hurricane Tomas	NA	0
489	2659	10/31/10		NA	Saint Lucia	LC	Soufrière	2918	Soufrière	1.56516		13.8516	-61.0429	(13.851599999999999, -61.042900000000003)	Landslide	Landslide	Medium	Tropical cyclone	Hurricane Tomas	NA	2		http://gocaribbean.about.com/b/2010/11/02/hurricane-triggers-deadly-landslides-on-st-lucia.htm
490	2660	10/31/10		NA	Saint Lucia	LC	Soufrière	2918	Soufrière	1.24758		13.8491	-61.0656	(13.8491, -61.065600000000003)	Landslide	Landslide	Medium	Tropical cyclone	Hurricane Tomas	NA	2		http://gocaribbean.about.com/b/2010/11/02/hurricane-triggers-deadly-landslides-on-st-lucia.htm
491	2661	10/31/10		NA	Saint Lucia	LC	Dennery	2870	Dennery	6.13309		13.9210	-60.9402	(13.920999999999999, -60.940199999999997)	Landslide	Landslide	Medium	Tropical cyclone	Hurricane Tomas	NA	0		http://www.youtube.com/watch?v=TbPh_Rh7hvE&feature=player_embedded
492	2662	10/31/10		NA	Saint Lucia	LC	Soufrière	2918	Soufrière	2.60663		13.8328	-61.0535	(13.832800000000001, -61.0535)	Landslide	Landslide	Medium	Tropical cyclone	Hurricane Tomas	NA	0		http://one-little-house.blogspot.com/2010/11/tomas-update.html

SAIN %>% 
  select(Country, State, City, Distance)

##          Country      State       City Distance
## 155  Saint Lucia Vieux-Fort Vieux Fort  2.61752
## 488  Saint Lucia    Laborie    Laborie  2.74715
## 489  Saint Lucia  Soufrière  Soufrière  1.56516
## 490  Saint Lucia  Soufrière  Soufrière  1.24758
## 491  Saint Lucia    Dennery    Dennery  6.13309
## 492  Saint Lucia  Soufrière  Soufrière  2.60663
## 493  Saint Lucia  Soufrière  Soufrière  0.96933
## 1191 Saint Lucia    Praslin    Praslin  3.81469

ggplot(data= SAIN, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Trinidad and Tobago

Para Trinidad y Tobago hubo 15 estados donde se reportaron deslizamientos, siendo Diego Martín donde más se presentaron (16).

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Trinidad and Tobago")

table(pais$state.province)

## 
##                                Borough of Arima   City of Port of Spain 
##                       1                       1                       5 
##    City of San Fernando Couva-Tabaquite-Talparo            Diego Martin 
##                       2                       3                      16 
##          Eastern Tobago              Penal/Debe            Point Fortin 
##                      10                       2                       1 
##            Princes Town     San Juan/Laventille           Sangre Grande 
##                       1                       2                       7 
##                 Siparia                  Tobago         Tunapuna/Piarco 
##                       1                       7                       4

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Diego Martin	16	25.4	25.4	25.4	25.4
Eastern Tobago	10	15.9	15.9	41.3	41.3
Sangre Grande	7	11.1	11.1	52.4	52.4
Tobago	7	11.1	11.1	63.5	63.5
City of Port of Spain	5	7.9	7.9	71.4	71.4
Tunapuna/Piarco	4	6.3	6.3	77.8	77.8
Couva-Tabaquite-Talparo	3	4.8	4.8	82.5	82.5
City of San Fernando	2	3.2	3.2	85.7	85.7
Penal/Debe	2	3.2	3.2	88.9	88.9
San Juan/Laventille	2	3.2	3.2	92.1	92.1
	1	1.6	1.6	93.7	93.7
Borough of Arima	1	1.6	1.6	95.2	95.2
Point Fortin	1	1.6	1.6	96.8	96.8
Princes Town	1	1.6	1.6	98.4	98.4
Siparia	1	1.6	1.6	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Trinidad and Tobago") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Diego Martin","Eastern Tobago","Sangre Grande","Tobago","City of Port of Spain","Tunapuna/Piarco","Couva-Tabaquite-Talparo","City of San Fernando","Penal/Debe","San Juan/Laventille","Borough of Arima","Point Fortin","Princes Town","Siparia"), 
                   value = c(25.4, 15.9, 11.1, 11.1, 7.9, 6.3, 4.8, 3.2, 3.2, 3.2, 1.6, 1.6, 1.6, 1.6))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=4) +
  scale_fill_manual(values = c("darkseagreen2","darkred","darkorchid1","gainsboro","gray2","cyan","cyan4","darkgoldenrod1","darkgoldenrod4","brown4","burlywood1","burlywood3","blue1","blue4","blueviolet"))

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
TRI <- subset (df, Country == "Trinidad and Tobago")
knitr::kable(head(TRI))

	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

TRI %>% 
  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  3.75200

ggplot(data= TRI, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

U.S. Virgin Islands

En U.S Virgin Islands solo se reportaron dos estados con deslizamientos: Saint Croix Island y Saint John Island ambos con 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "U.S. Virgin Islands")

table(pais$state.province)

## 
## Saint Croix Island  Saint John Island 
##                  1                  1

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Saint Croix Island	1	50	50	50	50
Saint John Island	1	50	50	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("U.S. Virgin Islands") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Saint Croix Island","Saint John Island"), 
                   value = c(50, 50))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
VIRG <- subset (df, Country == "U.S Virgin Islands")
knitr::kable(head(VIRG))

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

VIRG %>% 
  select(Country, State, City, Distance)

## [1] Country  State    City     Distance
## <0 rows> (or 0-length row.names)

ggplot(data= VIRG, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

United States

Para el caso de Estados Unidos, que fue el país con más estados (42) con deslizamientos, hubo 124 de ellos en kentucky y 108 en colorado siendo los de mayor número, y Michigan, Montana y Oregon con solo 1, los de menor número.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "United States")

table(pais$state.province)

## 
##        Alabama        Arizona       Arkansas     California       Colorado 
##              9             16              6             57            108 
##    Connecticut        Florida        Georgia          Idaho       Illinois 
##              6              2             11             37              9 
##        Indiana           Iowa         Kansas       Kentucky          Maine 
##              5             11              2            124              2 
##       Maryland  Massachusetts       Michigan      Minnesota    Mississippi 
##              8             10              1             23              2 
##       Missouri        Montana         Nevada  New Hampshire     New Jersey 
##              9              1              7              7             11 
##     New Mexico       New York North Carolina           Ohio       Oklahoma 
##             11             31             52             61              4 
##         Oregon   Pennsylvania South Carolina   South Dakota      Tennessee 
##              1             97              2              2             39 
##          Texas           Utah        Vermont       Virginia  West Virginia 
##              4             65              7             16             80 
##      Wisconsin        Wyoming 
##             13             17

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Kentucky	124	12.6	12.6	12.6	12.6
Colorado	108	11.0	11.0	23.5	23.5
Pennsylvania	97	9.8	9.8	33.4	33.4
West Virginia	80	8.1	8.1	41.5	41.5
Utah	65	6.6	6.6	48.1	48.1
Ohio	61	6.2	6.2	54.3	54.3
California	57	5.8	5.8	60.0	60.0
North Carolina	52	5.3	5.3	65.3	65.3
Tennessee	39	4.0	4.0	69.3	69.3
Idaho	37	3.8	3.8	73.0	73.0
New York	31	3.1	3.1	76.2	76.2
Minnesota	23	2.3	2.3	78.5	78.5
Wyoming	17	1.7	1.7	80.2	80.2
Arizona	16	1.6	1.6	81.8	81.8
Virginia	16	1.6	1.6	83.5	83.5
Wisconsin	13	1.3	1.3	84.8	84.8
Georgia	11	1.1	1.1	85.9	85.9
Iowa	11	1.1	1.1	87.0	87.0
New Jersey	11	1.1	1.1	88.1	88.1
New Mexico	11	1.1	1.1	89.2	89.2
Massachusetts	10	1.0	1.0	90.3	90.3
Alabama	9	0.9	0.9	91.2	91.2
Illinois	9	0.9	0.9	92.1	92.1
Missouri	9	0.9	0.9	93.0	93.0
Maryland	8	0.8	0.8	93.8	93.8
Nevada	7	0.7	0.7	94.5	94.5
New Hampshire	7	0.7	0.7	95.2	95.2
Vermont	7	0.7	0.7	95.9	95.9
Arkansas	6	0.6	0.6	96.6	96.6
Connecticut	6	0.6	0.6	97.2	97.2
Indiana	5	0.5	0.5	97.7	97.7
Oklahoma	4	0.4	0.4	98.1	98.1
Texas	4	0.4	0.4	98.5	98.5
Florida	2	0.2	0.2	98.7	98.7
Kansas	2	0.2	0.2	98.9	98.9
Maine	2	0.2	0.2	99.1	99.1
Mississippi	2	0.2	0.2	99.3	99.3
South Carolina	2	0.2	0.2	99.5	99.5
South Dakota	2	0.2	0.2	99.7	99.7
Michigan	1	0.1	0.1	99.8	99.8
Montana	1	0.1	0.1	99.9	99.9
Oregon	1	0.1	0.1	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("United States") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Kentucky","Colorado","Pennsylvania","West Virginia","Utah","Ohio","California","North Carolina","Tennessee","Idaho","New York","Minnesota","Wyoming","Arizona","Virginia","Wisconsin","Georgia","Iowa","New Jersey","New Mexico","Massachusetts","Alabama","Illinois","Missouri","Maryland","Nevada","New Hampshire","Vermont","Arkansas","Connecticut","Indiana","Oklahoma","Texas","Florida","Kansas","Maine","Mississippi","South Carolina","South Dakota","Michigan","Montana","Oregon"), 
                   value = c(12.6, 11, 9.8, 8.1, 6.6, 6.2, 5.8, 5.3, 4, 3.8, 3.1, 2.3, 1.7, 1.6, 1.6, 1.3, 1.1, 1.1, 1.1, 1.1, 1, 0.9, 0.9, 0.9, 0.8, 0.7, 0.7, 0.7, 0.6, 0.6, 0.5, 0.4, 0.4, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.1, 0.1, 0.1))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=3,) +
  scale_fill_manual(values = c("darkseagreen2","darkred","darkorchid1","gainsboro","gray2","cyan","cyan4","darkgoldenrod1","darkgoldenrod4","brown4","burlywood1","burlywood3","blue1","blue4","blueviolet","chartreuse","cornflowerblue","aliceblue","springgreen","yellow","red","rosybrown1","rosybrown2","rosybrown4","salmon","sandybrown","seagreen1","plum","pink","plum1","slateblue4","turquoise","turquoise4","violet","violetred2","violetred4","yellowgreen","steelblue1","snow2","skyblue3","palegoldenrod","mintcream"))

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
USA <- subset (df, Country == "United States")
knitr::kable(head(USA))

	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
1	34	3/2/07	Night	NA	United States	US	Virginia	16000	Cherry Hill	3.40765	Unknown	38.6009	-77.2682	(38.600900000000003, -77.268199999999993)	Landslide	Landslide	Small	Rain		NA	NA	NBC 4 news	http://www.nbc4.com/news/11186871/detail.html
2	42	3/22/07		NA	United States	US	Ohio	17288	New Philadelphia	3.33522		40.5175	-81.4305	(40.517499999999998, -81.430499999999995)	Landslide	Landslide	Small	Rain		NA	NA	Canton Rep.com	http://www.cantonrep.com/index.php?ID=345054&Category=9&subCategoryID=0
3	56	4/6/07		NA	United States	US	Pennsylvania	15930	Wilkinsburg	2.91977	Urban area	40.4377	-79.9160	(40.4377, -79.915999999999997)	Landslide	Landslide	Small	Rain		NA	NA	The Pittsburgh Channel.com	https://web.archive.org/web/20080423132842/http://www.thepittsburghchannel.com/news/11846833/detail.html
5	61	4/15/07		NA	United States	US	Kentucky	6903	Pikeville	5.66542	Below road	37.4325	-82.4931	(37.432499999999997, -82.493099999999998)	Landslide	Landslide	Small	Downpour		NA	0	Matthew Crawford (KGS)
6	64	4/20/07		NA	United States	US	Kentucky	6903	Pikeville	0.23715		37.4814	-82.5186	(37.481400000000001, -82.518600000000006)	Landslide	Landslide	Small	Rain		NA	NA	Applalachain news-express	http://www.news-expressky.com/articles/2007/04/19/top_story/01mudslide.txt
7	67	4/24/07		NA	United States	US	South Dakota	2540	Dakota Dunes	2.48033		42.4941	-96.4576	(42.494100000000003, -96.457599999999999)	Landslide	Landslide	Small	Rain		NA	NA	Sioux City Journnal	http://www.siouxcityjournal.com/articles/2007/04/25/news/top/02a92f1defdd220f862572c800126dfc.txt

USA %>% 
  select(Country, State, City, Distance)

##            Country          State                       City Distance
## 1    United States       Virginia                Cherry Hill  3.40765
## 2    United States           Ohio           New Philadelphia  3.33522
## 3    United States   Pennsylvania                Wilkinsburg  2.91977
## 5    United States       Kentucky                  Pikeville  5.66542
## 6    United States       Kentucky                  Pikeville  0.23715
## 7    United States   South Dakota               Dakota Dunes  2.48033
## 12   United States          Texas                Haltom City  0.03668
## 16   United States          Texas              Grand Prairie  5.66936
## 19   United States       Colorado                     Meeker 10.87949
## 21   United States      Minnesota                  Caledonia 22.31062
## 22   United States        Wyoming                       Cody 64.74594
## 23   United States      Minnesota                  Rochester  0.99990
## 24   United States      Wisconsin                  La Crosse 13.62525
## 25   United States      Wisconsin                  La Crosse 12.91337
## 26   United States  West Virginia                   Glendale  3.49200
## 32   United States     New Mexico               Jemez Pueblo 12.89915
## 35   United States        Wyoming                     Hoback  9.42463
## 36   United States          Idaho                    Ketchum  4.51471
## 37   United States       Colorado          Steamboat Springs  2.63588
## 40   United States       Colorado                 New Castle  4.25758
## 62   United States     California            Rancho Santa Fe  6.48824
## 63   United States        Indiana               Lawrenceburg  2.66572
## 69   United States   Pennsylvania                   Trafford  0.27058
## 71   United States   Pennsylvania                Level Green  0.03251
## 72   United States     California                   La Jolla  8.13382
## 74   United States     California                  Encinitas  3.23702
## 75   United States     California                   La Jolla  1.15586
## 76   United States   Pennsylvania     Wickerham Manor-Fisher  1.09139
## 77   United States   Pennsylvania                       Etna  1.23232
## 79   United States   Pennsylvania                  Homestead  2.97055
## 81   United States       New York               Green Island  4.36125
## 82   United States   Pennsylvania                   Pitcairn  0.36435
## 83   United States           Ohio                    Toronto  1.02992
## 84   United States           Ohio               Mount Carmel  1.17753
## 85   United States           Ohio                 Wellsville  7.05134
## 86   United States       Kentucky                     Ludlow  2.26622
## 88   United States       Illinois                Jerseyville 18.72748
## 89   United States   Pennsylvania              Franklin Park  3.60364
## 91   United States      Wisconsin                     Westby  0.62955
## 92   United States       Colorado                       Avon  3.03321
## 95   United States      Minnesota                     Winona  1.72940
## 96   United States  New Hampshire                    Newbury  0.08548
## 97   United States  New Hampshire                 Whitefield  8.19938
## 98   United States       Virginia                   Chatmoss  8.27691
## 99   United States  West Virginia                 Montgomery  7.85746
## 100  United States       Kentucky                Fort Wright  0.90111
## 101  United States       Colorado                      Aspen 20.14967
## 106  United States        Indiana               Martinsville  0.63962
## 107  United States       New York                    Hancock  4.46079
## 109  United States       Arkansas                     Jasper 30.98796
## 111  United States     New Jersey                     Orange  3.31974
## 112  United States     New Mexico                  Las Vegas 15.17455
## 113  United States  West Virginia                 Williamson  2.20331
## 114  United States  West Virginia                 Charleston  3.05313
## 115  United States           Iowa                 Guttenberg  2.88694
## 116  United States       Colorado                    Durango  2.94432
## 118  United States      Wisconsin              Brice Prairie  6.28666
## 121  United States       Colorado           Snowmass Village 10.78549
## 122  United States          Idaho                     McCall 29.97781
## 123  United States          Idaho                     Salmon 25.30277
## 124  United States       Colorado                 Walsenburg 32.15868
## 125  United States     New Mexico               Jemez Pueblo 12.68493
## 126  United States       Colorado                 Walsenburg 41.47439
## 127  United States  New Hampshire                 Woodsville  5.61303
## 128  United States     California                     Ramona  3.32897
## 129  United States       Colorado                 Carbondale 22.50782
## 130  United States           Utah                   Mapleton  5.66291
## 131  United States           Utah           East Carbon City 67.58127
## 135  United States       Colorado                     Salida  8.16325
## 136  United States       Colorado                 Perry Park 17.16659
## 142  United States North Carolina                  Lake Lure  0.85349
## 144  United States North Carolina                  Lake Lure  5.17876
## 148  United States  Massachusetts                    Holyoke  2.84100
## 150  United States          Idaho                 Sun Valley  1.58679
## 168  United States     California              Valley Center 14.43770
## 170  United States     California            Fairbanks Ranch  8.33509
## 174  United States     California            Harbison Canyon  1.91206
## 175  United States      Tennessee                   Kingston  2.61361
## 176  United States North Carolina              Maggie Valley  2.71542
## 179  United States   Pennsylvania                    Crafton  2.84144
## 181  United States      Tennessee                 Sale Creek 11.51020
## 182  United States           Utah                      Provo  3.35726
## 183  United States       Virginia             East Lexington 10.60458
## 186  United States           Utah         Cottonwood Heights  1.90486
## 187  United States           Utah             East Millcreek  1.68712
## 188  United States           Utah                 Canyon Rim  8.13917
## 189  United States       Virginia             East Lexington 13.41471
## 190  United States       Colorado                    Boulder 15.14802
## 192  United States       Missouri                   Hannibal  1.93676
## 195  United States       Kentucky                    Hindman 15.41975
## 196  United States       Kentucky                    Jackson  9.34774
## 197  United States       Kentucky                  Pikeville 11.07489
## 198  United States North Carolina                     Lenoir  9.72793
## 199  United States  West Virginia                     Hamlin 11.50494
## 200  United States           Utah                     Draper  0.79003
## 201  United States North Carolina                     Hudson 10.33560
## 202  United States          Idaho                    Ketchum  1.27144
## 203  United States       Kentucky                     Dayton  2.30977
## 204  United States       Kentucky               Hopkinsville 15.49105
## 205  United States      Wisconsin           Prairie du Chien  3.41664
## 209  United States          Texas                     Sparks  1.34835
## 213  United States        Georgia                  Cleveland 15.92157
## 214  United States        Alabama                 Livingston 26.97874
## 215  United States        Georgia             Stone Mountain  2.07317
## 216  United States        Georgia           Lookout Mountain  0.80279
## 217  United States North Carolina                    Brevard  3.61918
## 219  United States        Georgia                    Trenton  1.12077
## 220  United States      Tennessee                 Gatlinburg  4.11813
## 221  United States          Texas                 Cedar Hill  6.20618
## 222  United States       Kentucky                      Hyden 10.07650
## 228  United States      Tennessee                 East Ridge  2.60684
## 230  United States       Illinois                      Alton  1.54044
## 231  United States      Tennessee                Chattanooga  5.97545
## 232  United States      Tennessee            Signal Mountain  2.05274
## 233  United States      Tennessee            Signal Mountain  2.47169
## 234  United States       Missouri                  Louisiana  7.60154
## 235  United States     California            Running Springs  8.91845
## 236  United States    Mississippi                     Purvis 17.40130
## 239  United States     California              Valley Center  5.71250
## 240  United States        Arizona                Tonto Basin 16.48124
## 241  United States     California             Morongo Valley  1.35292
## 242  United States     California                    Aguanga 12.95887
## 243  United States        Arizona               Cordes Lakes 12.98377
## 244  United States North Carolina                 Rural Hall  7.54867
## 245  United States      Tennessee               Madisonville  8.55137
## 246  United States      Tennessee            Signal Mountain  2.56584
## 247  United States      Tennessee                Soddy-Daisy  3.20504
## 248  United States   Pennsylvania                 Pittsburgh  1.49656
## 249  United States    Connecticut                     Canaan 10.75115
## 250  United States North Carolina               Tobaccoville  2.39331
## 251  United States       Kentucky                 Manchester  5.79962
## 254  United States South Carolina                    Clemson  4.34093
## 259  United States North Carolina              Maggie Valley  0.91282
## 261  United States       Kentucky               Williamsburg  5.29680
## 262  United States       Kentucky                    Jenkins 19.17000
## 263  United States      Tennessee                 Livingston 14.90347
## 265  United States     California                    Banning  2.18534
## 266  United States     California             San Bernardino  1.18379
## 268  United States   Pennsylvania                 Pittsburgh  1.29986
## 269  United States   Pennsylvania                    Crafton  2.37904
## 270  United States      Wisconsin          Saint Croix Falls  3.90084
## 271  United States  West Virginia                      Belle  0.02907
## 272  United States  West Virginia                  Princeton  7.04356
## 273  United States     New Jersey                Lake Mohawk  4.41013
## 274  United States  West Virginia               Summersville  6.25122
## 275  United States           Ohio                    Jackson 13.05284
## 276  United States  Massachusetts                  Topsfield  1.03712
## 277  United States     New Jersey                 Ogdensburg  0.77081
## 278  United States North Carolina               Robbinsville 23.26062
## 279  United States  Massachusetts                    Clinton  1.25138
## 280  United States  Massachusetts                    Walpole  2.73316
## 281  United States   Pennsylvania                   New Hope  6.71244
## 282  United States      Tennessee                 Gatlinburg 11.83863
## 283  United States           Ohio                Salineville 10.58008
## 284  United States        Alabama                  Good Hope 22.45613
## 285  United States       New York                  Worcester  7.59235
## 286  United States      Tennessee                 Gatlinburg 14.56419
## 287  United States    Connecticut                 Middletown  1.40994
## 288  United States       New York                Sound Beach  0.94386
## 289  United States       New York                    Yonkers  3.28687
## 290  United States           Iowa               Fort Madison  2.69877
## 291  United States           Iowa               Fort Madison  2.69877
## 292  United States          Maine                    Winslow  0.76489
## 293  United States  New Hampshire                 Greenville  0.29408
## 296  United States           Iowa                 Burlington  3.37828
## 310  United States      Tennessee                    Clifton 10.07129
## 312  United States       Kentucky                   Morehead 10.11538
## 313  United States       Kentucky               Salyersville  9.51647
## 314  United States      Tennessee                Belle Meade  4.06928
## 315  United States      Tennessee               Forest Hills  5.92638
## 316  United States      Tennessee                   Fairview 11.67837
## 317  United States      Tennessee          Brentwood Estates  6.61569
## 318  United States      Tennessee                   Columbia 15.51031
## 319  United States       Kentucky                    Hindman  7.91631
## 320  United States       Kentucky                      Hyden  0.00435
## 321  United States       Kentucky                 Cumberland 18.27072
## 322  United States       Kentucky                  Pineville  5.84944
## 323  United States       Kentucky                Paintsville 13.47073
## 324  United States       Kentucky                       Inez  7.32052
## 325  United States  West Virginia                 Williamson 15.29574
## 326  United States       Kentucky                       Inez  9.90252
## 327  United States       Kentucky                     Hazard 12.34170
## 328  United States       Kentucky                    Hindman 10.50499
## 329  United States       Kentucky                    Hindman 21.31051
## 330  United States       Kentucky                 Cumberland  1.09107
## 331  United States       Kentucky                 Cumberland 16.31604
## 332  United States  West Virginia              Gilbert Creek 18.71035
## 333  United States       Virginia                  Clintwood 21.45604
## 334  United States       Kentucky                    Jenkins 10.20803
## 335  United States       Kentucky                  Pikeville  8.90602
## 336  United States           Ohio                    Ironton 17.02627
## 337  United States           Ohio                    Ironton  6.42411
## 338  United States           Ohio                 Coal Grove 18.88870
## 342  United States      Tennessee                Belle Meade  0.01307
## 343  United States           Ohio                    Ironton 10.40449
## 344  United States       Missouri          North Kansas City  5.24463
## 345  United States       Kentucky               Salyersville  7.52656
## 346  United States  West Virginia                 Charleston  1.68022
## 348  United States North Carolina                   Marshall 16.47911
## 350  United States           Utah                 Canyon Rim  0.75491
## 364  United States   Pennsylvania                 Pittsburgh  1.93338
## 365  United States   Pennsylvania                 Green Tree  1.46465
## 366  United States North Carolina                    Brevard  5.80525
## 367  United States         Nevada               Spring Creek 16.13996
## 368  United States  West Virginia                    Mallory  5.45758
## 369  United States        Indiana                  Lafayette  6.08613
## 370  United States      Minnesota                  Caledonia 18.20633
## 371  United States      Wisconsin            Richland Center 16.88781
## 373  United States       Colorado                Buena Vista 13.11084
## 374  United States       New York                Canandaigua  8.27871
## 377  United States       New York                     Naples 14.77042
## 378  United States  West Virginia                Saint Marys  0.76491
## 380  United States        Georgia                  Jefferson  7.58207
## 382  United States        Arizona                  Flagstaff 11.50738
## 384  United States  West Virginia                 Huntington  2.97869
## 387  United States       Colorado                    Durango  4.89100
## 388  United States     New Mexico                    Socorro 25.68212
## 389  United States       Colorado           Mountain Village 17.96218
## 390  United States       Colorado           Mountain Village 27.47506
## 391  United States       Kentucky                   Bellevue  0.99029
## 397  United States       Colorado                 Carbondale 23.83031
## 398  United States       Colorado                  Leadville 20.97634
## 399  United States           Utah             Woodland Hills 13.47828
## 407  United States           Iowa                    Ottumwa  0.92861
## 409  United States      Tennessee                Banner Hill  1.77572
## 410  United States           Utah                     Draper  3.22031
## 411  United States       Colorado                 Carbondale 24.13440
## 412  United States       Colorado                 Carbondale 22.55763
## 413  United States       Colorado                 Carbondale 23.56048
## 414  United States       Colorado                 Carbondale 21.37674
## 415  United States       Colorado           Snowmass Village  4.60739
## 416  United States       Colorado           Snowmass Village  3.84400
## 417  United States       Colorado                 Carbondale  4.51715
## 418  United States       Colorado           Glenwood Springs  3.60875
## 419  United States           Utah                       Moab 38.50248
## 426  United States     California                    Yucaipa 11.06641
## 436  United States   Pennsylvania                  Smethport  6.19784
## 443  United States      Tennessee                  Knoxville  5.00430
## 447  United States       Virginia            Madison Heights  1.20498
## 450  United States        Arizona                Tonto Basin  1.86032
## 452  United States      Wisconsin                Lake Hallie  2.43595
## 458  United States   Pennsylvania               Mount Oliver  2.03608
## 465  United States   Pennsylvania                 Heidelberg  1.22035
## 468  United States       New York                     McGraw  6.65468
## 472  United States  New Hampshire                 Woodsville  5.48538
## 473  United States       Maryland   Chesapeake Ranch Estates  2.52659
## 484  United States     California             Thousand Palms  7.04978
## 521  United States   Pennsylvania                 Manchester  5.03527
## 550  United States       New York               Little Falls  2.73513
## 551  United States      Tennessee                  Knoxville  4.92279
## 552  United States North Carolina                  Flat Rock  5.16572
## 559  United States     California            Running Springs  7.63522
## 560  United States         Nevada            Summerlin South 19.35795
## 561  United States     California                  Encinitas  4.45005
## 562  United States     California                     Muscoy  4.51520
## 563  United States     California                  Encinitas  3.31222
## 564  United States     California                   Highland  1.85916
## 565  United States     California                San Jacinto  2.92944
## 566  United States     California            Running Springs  6.40077
## 567  United States     California                   Highland  1.95744
## 568  United States     California            Running Springs  3.35384
## 569  United States     California                   Carlsbad  5.86771
## 570  United States     California                 Loma Linda  3.23578
## 571  United States     California                   La Jolla  2.01316
## 572  United States     California                   La Jolla  2.24058
## 573  United States     California                 Loma Linda  0.87634
## 575  United States     California                    La Mesa  6.02579
## 576  United States     California              Moreno Valley  9.07891
## 577  United States           Ohio             Mingo Junction  2.81727
## 580  United States          Idaho                 Idaho City 34.46887
## 581  United States          Idaho                      Boise 14.32586
## 582  United States          Idaho                      Boise 14.99911
## 583  United States          Idaho                 Idaho City 38.27504
## 584  United States  West Virginia                 Chesapeake  6.15192
## 585  United States North Carolina              Maggie Valley  1.20337
## 590  United States     California                   Descanso  9.82301
## 593  United States   Pennsylvania                  Glen Lyon  8.31157
## 595  United States North Carolina               Blowing Rock 13.11943
## 596  United States    Connecticut                     Bethel  2.63291
## 597  United States    Connecticut                New Milford       NA
## 598  United States  Massachusetts                 Greenfield  0.81961
## 600  United States   Pennsylvania                 Heidelberg  1.54687
## 601  United States       Missouri                   Wildwood  2.41198
## 602  United States       Kentucky                     Hazard  0.70654
## 603  United States       New York                   Lewiston  3.95434
## 604  United States  West Virginia                    Bethany  2.52454
## 605  United States  West Virginia                    Bethany  1.92763
## 606  United States   Pennsylvania                Tunkhannock 13.66120
## 607  United States       Virginia                  Arlington  3.23558
## 608  United States       Virginia                 Hillsville 13.60254
## 609  United States           Ohio                   Bellaire  3.66747
## 610  United States           Ohio                  Brilliant  4.85999
## 611  United States   Pennsylvania                Tunkhannock  4.84701
## 612  United States           Ohio                 Wellsville  0.79841
## 613  United States           Ohio                  Brilliant  4.85999
## 614  United States        Alabama               Jacksonville  1.79927
## 615  United States   Pennsylvania             Bradford Woods  4.62567
## 616  United States           Ohio             Powhatan Point  1.01208
## 617  United States           Ohio                 Wellsville  3.09494
## 618  United States       New York                     Hadley  6.60211
## 620  United States           Utah                Summit Park  5.09388
## 622  United States     California              Valley Center  5.13929
## 624  United States      Tennessee                Sevierville  2.43605
## 625  United States  West Virginia                  Bethlehem  1.94038
## 626  United States   Pennsylvania                 Coraopolis  2.47173
## 627  United States           Ohio                Rocky River  1.57593
## 628  United States  West Virginia                 Chesapeake 25.09618
## 630  United States   Pennsylvania                   Millvale  2.94131
## 636  United States       Kentucky                   Bellevue  1.26817
## 639  United States   Pennsylvania                 Pittsburgh  1.29731
## 641  United States   Pennsylvania             West Homestead  1.91993
## 642  United States   Pennsylvania             Bradford Woods  2.06885
## 645  United States     New Jersey                    Hoboken  0.84845
## 646  United States           Ohio               Tiltonsville  3.77028
## 647  United States  West Virginia                  Elizabeth 15.03444
## 648  United States           Ohio                  Brilliant  1.76001
## 649  United States   Pennsylvania                Bala Cynwyd  3.74706
## 650  United States       Maryland                   Thurmont  3.50924
## 651  United States       Kentucky               Independence  2.19418
## 652  United States       Kentucky                Cold Spring  1.22389
## 653  United States       Kentucky               Williamstown 12.53751
## 654  United States       Kentucky                Villa Hills  2.03469
## 655  United States           Ohio                Delhi Hills  3.45556
## 656  United States       Kentucky                     Wilder  1.24385
## 657  United States       Kentucky                 Alexandria  4.61879
## 661  United States  West Virginia                Sissonville  3.90856
## 662  United States           Ohio            West Portsmouth  6.43139
## 663  United States           Ohio                    Waverly 12.99718
## 664  United States       Missouri                  Hollister  8.78605
## 665  United States           Ohio                     Cleves  4.50908
## 666  United States       Kentucky                     Ludlow  1.26285
## 667  United States       Kentucky                   Bellevue  1.42927
## 668  United States       Kentucky                  Eddyville  7.88518
## 669  United States           Ohio                 Cincinnati  2.59168
## 670  United States       Kentucky                    Newport  1.73096
## 671  United States           Ohio                 Cincinnati  2.68376
## 672  United States           Ohio                  Wolfhurst  7.12905
## 673  United States       Arkansas                     Midway  6.74198
## 674  United States           Ohio                  Dry Ridge  2.92400
## 675  United States  West Virginia               Barrackville  1.60330
## 676  United States           Ohio                     Amelia  4.45788
## 677  United States      Tennessee                    Bolivar  9.34376
## 678  United States      Tennessee                    Dickson  8.14549
## 679  United States       Kentucky                     Ludlow  2.08830
## 680  United States      Tennessee                     McEwen 12.81745
## 681  United States       Kentucky             Dawson Springs  8.74503
## 682  United States       Kentucky                     Harlan 15.76570
## 683  United States       Missouri             Willow Springs 13.48426
## 684  United States       Kentucky                 Cumberland  8.68702
## 685  United States           Ohio               Turpin Hills  2.88328
## 686  United States           Ohio                 Bridgetown  4.26162
## 687  United States           Ohio                       Mack  2.95385
## 688  United States           Ohio             New Burlington  1.81969
## 689  United States       Kentucky                     Ludlow  1.27755
## 691  United States       Kentucky                    Augusta  8.35471
## 692  United States       Kentucky                  Frankfort  2.05395
## 693  United States       Kentucky                  Frankfort  3.61757
## 694  United States       Kentucky                  Frankfort  3.19663
## 695  United States       Kentucky                  Maysville  2.33611
## 696  United States           Ohio                   Aberdeen  5.88319
## 698  United States        Vermont                    Swanton  5.80477
## 699  United States        Vermont                    Swanton  5.93705
## 700  United States        Vermont                      Barre  6.58098
## 703  United States          Idaho                     Victor 14.40898
## 704  United States  West Virginia                 Charleston  6.02220
## 705  United States           Ohio                 Burlington  4.35442
## 706  United States           Ohio                    Ironton 19.76853
## 707  United States           Ohio                 Coal Grove  9.74624
## 708  United States           Ohio                    Ironton 20.40835
## 709  United States           Ohio           Brooklyn Heights  0.81717
## 710  United States   Pennsylvania                  Masontown  5.82547
## 711  United States   Pennsylvania                 Washington 18.14779
## 712  United States   Pennsylvania                 Maple Glen  1.49457
## 713  United States       New York                  Whitehall 16.90533
## 717  United States   Pennsylvania                Curtisville  2.01507
## 719  United States  West Virginia                 Huntington  1.71788
## 720  United States  West Virginia                 Huntington  2.13503
## 721  United States  West Virginia                 Huntington  2.94557
## 722  United States  West Virginia                 Huntington  2.76021
## 723  United States  West Virginia                 Huntington  0.01392
## 724  United States  West Virginia               West Liberty 11.01130
## 725  United States   Pennsylvania                 Coraopolis  2.45126
## 726  United States   Pennsylvania                 Russellton  4.06157
## 727  United States   Pennsylvania                       Plum  1.09370
## 728  United States   Pennsylvania                  Youngwood  3.51907
## 729  United States           Utah                 Wolf Creek  8.65312
## 730  United States   Pennsylvania                     Ingram  0.71260
## 731  United States   Pennsylvania                 Pittsburgh  0.26199
## 732  United States           Utah                  Santaquin  7.91495
## 733  United States   Pennsylvania                  Leetsdale  1.11499
## 734  United States   Pennsylvania                  Leetsdale  1.11499
## 735  United States           Utah                 Farmington  3.12368
## 736  United States           Utah                     Beaver 14.71664
## 737  United States           Utah                Grantsville 30.28399
## 738  United States           Utah                Centerville  4.54095
## 739  United States   Pennsylvania                     Arnold  3.02440
## 740  United States           Iowa                 Sioux City  4.38210
## 741  United States       Michigan                  Ann Arbor  1.89658
## 742  United States       New York                Lake Placid 11.70793
## 743  United States       Illinois                      Alton  1.71596
## 744  United States        Vermont                      Barre  0.65528
## 745  United States           Utah                 Wolf Creek  8.65312
## 746  United States        Wyoming                   Sundance  7.07507
## 747  United States        Vermont                      Barre  1.15359
## 752  United States       Colorado                   Keystone  5.82093
## 755  United States       Kentucky                     Dayton  1.06742
## 756  United States           Iowa                     Keokuk  2.92601
## 757  United States   Pennsylvania                    Windsor  7.93844
## 758  United States      Wisconsin                 Eau Claire  4.98787
## 759  United States        Indiana                  Southport  2.61312
## 760  United States      Tennessee                  Harrogate  3.07236
## 762  United States       Kentucky                    Stanton  5.95618
## 763  United States       Colorado                 Georgetown 11.46832
## 765  United States       Kentucky                 Burlington  3.77076
## 766  United States           Ohio                    Batavia  1.91217
## 767  United States       Kentucky                    Newport  1.90865
## 770  United States   Pennsylvania                  Homestead  2.97852
## 771  United States       Colorado                      Aspen  0.21641
## 772  United States   Pennsylvania                       Plum  7.45648
## 773  United States           Utah                   Highland  9.18061
## 775  United States       Colorado               Silverthorne  2.66276
## 776  United States        Wyoming                   Sheridan  6.32702
## 777  United States        Wyoming                   Sheridan  0.00178
## 778  United States        Arizona               Sierra Vista 12.28777
## 779  United States        Arizona               Sierra Vista  9.92245
## 781  United States       Colorado                     Paonia  7.50275
## 782  United States       Colorado                     Paonia  0.01994
## 783  United States        Arizona                      Eagar 52.11489
## 784  United States       Colorado                    Boulder  9.47622
## 785  United States       Colorado                    Boulder 10.42230
## 786  United States        Arizona     Sierra Vista Southeast 12.52763
## 790  United States       Colorado           Mountain Village  1.82803
## 791  United States       Colorado           Glenwood Springs  5.78301
## 792  United States   Pennsylvania                  Homestead  2.96418
## 793  United States           Ohio             New California  5.63505
## 794  United States       Kentucky                    Campton 12.29943
## 795  United States       Colorado                       Avon  2.35855
## 796  United States        Florida                  Williston  3.75018
## 797  United States       Kentucky                     Hazard  8.30475
## 803  United States       Colorado                   San Luis 25.38161
## 804  United States     New Mexico                     Hurley 17.95414
## 805  United States       Colorado                   El Jebel  4.90954
## 806  United States       Colorado           Glenwood Springs 10.57117
## 807  United States       Colorado                      Rifle  1.11647
## 808  United States   Pennsylvania                  Wellsboro  0.17013
## 809  United States       Colorado                  Silverton  0.01766
## 810  United States      Tennessee                 Gatlinburg  1.01331
## 811  United States      Tennessee                   Columbia  0.72461
## 812  United States   Pennsylvania                   Imperial  7.26376
## 813  United States     New Jersey              Highland Lake  5.27337
## 815  United States       New York                     Inwood  1.91324
## 816  United States     New Jersey                  Belvidere  0.07578
## 817  United States     New Jersey            Warren Township  3.64818
## 823  United States           Utah                 Cedar City  7.52240
## 834  United States   Pennsylvania                   Kutztown  0.74333
## 837  United States        Alabama                 Scottsboro  0.64281
## 856  United States   Pennsylvania                   Emsworth  0.79265
## 857  United States       Kentucky                     Dayton  1.43697
## 860  United States       Kentucky                     Dayton  1.52592
## 861  United States   Pennsylvania                 Pittsburgh  1.87265
## 862  United States       Kentucky                     Ludlow  1.31411
## 863  United States   Pennsylvania                  Homestead  2.19916
## 865  United States  West Virginia               Chapmanville 13.52866
## 866  United States       Kentucky                   Richmond  9.87331
## 867  United States      Tennessee                  Jacksboro 13.15700
## 869  United States           Ohio                    Cheviot  4.21790
## 870  United States       Kentucky                     Loyall 10.22349
## 871  United States         Kansas               Roeland Park  2.32126
## 874  United States       New York                  Boonville  9.95180
## 880  United States       Kentucky                     Dayton  2.40444
## 881  United States  West Virginia               Fayetteville  8.06254
## 882  United States       Kentucky                   Bellevue  1.30932
## 883  United States           Ohio                 Youngstown  2.51579
## 886  United States      Minnesota                Arden Hills  0.13052
## 891  United States       Colorado                    Laporte 15.65712
## 892  United States       Colorado            Leadville North 13.02782
## 893  United States       Colorado       Cascade-Chipita Park  5.78163
## 894  United States        Wyoming                       Cody 72.64680
## 895  United States       Arkansas                     Jasper  5.06464
## 896  United States     California                    Yucaipa 12.93443
## 899  United States        Arizona                    Phoenix  3.36522
## 901  United States           Utah           Saratoga Springs  2.02291
## 902  United States           Utah                   Fairview 21.80213
## 903  United States           Utah                   Fairview 22.92807
## 904  United States           Utah                   Fairview 23.82853
## 905  United States           Utah             Mount Pleasant 24.64059
## 908  United States      Tennessee                 East Ridge  1.24635
## 909  United States       Colorado                    Laporte 18.34452
## 910  United States       Colorado                 Estes Park 37.11379
## 914  United States  New Hampshire                  Woodstock 14.34429
## 915  United States   Pennsylvania               West Mifflin  0.14473
## 916  United States   Pennsylvania               Brackenridge  2.08818
## 917  United States   Pennsylvania                 Pittsburgh  3.07837
## 918  United States North Carolina                  Lake Lure  4.23278
## 922  United States          Idaho                     McCall 41.54001
## 924  United States   Pennsylvania                 Pittsburgh  1.33115
## 930  United States       Kentucky               Mount Vernon  1.23577
## 931  United States       Kentucky                  Lexington  2.41140
## 932  United States North Carolina                  Mars Hill  0.04550
## 933  United States North Carolina              Maggie Valley  5.63904
## 934  United States      Tennessee                  Knoxville  5.53703
## 935  United States North Carolina                   Cherokee 13.86023
## 936  United States     New Jersey             Monmouth Beach  0.09035
## 937  United States North Carolina                    Woodfin  4.29814
## 938  United States      Tennessee                    Jellico 13.77073
## 939  United States North Carolina               Robbinsville 20.45203
## 940  United States   Pennsylvania                 Pittsburgh  2.64611
## 941  United States North Carolina              Maggie Valley  1.07913
## 943  United States       Kentucky                    Hindman  1.63625
## 944  United States        Arizona                     LeChee 31.61210
## 945  United States   Pennsylvania                West Easton  1.78114
## 946  United States       Kentucky              Nicholasville 11.48269
## 948  United States           Utah                   Herriman  9.55190
## 949  United States   Pennsylvania                Murrysville  4.82123
## 950  United States       Kentucky                  Pikeville  3.38725
## 951  United States           Iowa                  Iowa City  1.40658
## 952  United States       Kentucky                     Loyall  1.40599
## 953  United States       Kentucky                     Harlan  0.94084
## 954  United States       Illinois                  Granville  2.12940
## 955  United States       Illinois                East Peoria  2.39991
## 957  United States North Carolina                   Marshall  6.68251
## 959  United States           Ohio                   Glouster  2.06907
## 960  United States North Carolina                 Cove Creek  2.06558
## 961  United States        Florida                   Clermont  0.39179
## 962  United States North Carolina             Black Mountain  0.17766
## 963  United States North Carolina                     Lenoir 12.88531
## 964  United States North Carolina                     Canton  4.10329
## 965  United States North Carolina                  Fruitland  5.76576
## 966  United States       Kentucky                     Ludlow  4.02153
## 967  United States       Kentucky                  Pineville 12.36833
## 968  United States North Carolina                     Canton  4.10329
## 969  United States North Carolina                  Lake Lure  0.23032
## 970  United States North Carolina                Bryson City  6.61994
## 972  United States      Tennessee                    Jellico 15.98301
## 973  United States North Carolina             Black Mountain  7.32191
## 974  United States        Georgia                Dawsonville 12.44051
## 975  United States South Carolina                 Tigerville  7.92482
## 976  United States      Minnesota            West Saint Paul  1.89742
## 978  United States       Missouri                   Hannibal  1.97078
## 982  United States       Illinois                     Quincy  2.38934
## 983  United States       New York                   Camillus  2.54860
## 985  United States   South Dakota                       Lead  1.39751
## 986  United States       New York                   Camillus  2.54860
## 987  United States    Connecticut                 Old Mystic  0.78631
## 988  United States   Pennsylvania               Philadelphia  1.27824
## 989  United States       Kentucky                     Loyall  2.36880
## 991  United States       New York                Middleburgh  1.41843
## 992  United States       New York                   Camillus  2.54860
## 993  United States        Vermont               Williamstown  8.90467
## 994  United States           Iowa                     Waukon 23.47684
## 995  United States       New York                  Frankfort 17.52573
## 996  United States      Wisconsin                   Boscobel  2.89624
## 997  United States      Wisconsin           Prairie du Chien 12.86889
## 998  United States      Minnesota                  Caledonia 24.86266
## 999  United States       New York                   Camillus  2.54860
## 1000 United States      Wisconsin                    Seymour  2.68314
## 1001 United States   Pennsylvania                 Pottsville  0.10261
## 1002 United States       New York                 Binghamton  2.11425
## 1003 United States       Colorado                    Laporte 25.57232
## 1004 United States   Pennsylvania                   Duquesne  0.89493
## 1005 United States       Colorado            Manitou Springs  0.40970
## 1006 United States       Kentucky                     Dayton  1.11204
## 1007 United States North Carolina                  Hazelwood 11.16865
## 1008 United States North Carolina                      Sylva  4.59472
## 1009 United States   Pennsylvania                Carnot-Moon  1.51482
## 1010 United States       Virginia                Cave Spring  5.42545
## 1011 United States North Carolina                  Asheville  2.03173
## 1012 United States        Wyoming                       Cody  1.13439
## 1013 United States North Carolina                    Woodfin  5.02716
## 1014 United States North Carolina                  Asheville  4.34897
## 1015 United States North Carolina                    Woodfin  5.14161
## 1016 United States North Carolina                  Asheville  1.27543
## 1017 United States North Carolina                    Woodfin  4.03738
## 1018 United States North Carolina               Blowing Rock 11.48809
## 1019 United States North Carolina                    Brevard 18.50736
## 1020 United States           Utah                    Granite  9.12556
## 1021 United States           Utah                    Granite 10.78115
## 1022 United States           Utah                Cedar Hills  2.64441
## 1023 United States           Utah                   Highland  5.32884
## 1024 United States           Utah                Cedar Hills  7.08894
## 1025 United States       New York                      Ilion  0.85072
## 1026 United States       Kentucky                    Jackson  7.39282
## 1027 United States North Carolina               Blowing Rock  1.05589
## 1028 United States     New Mexico                     Questa 21.76837
## 1029 United States       New York                Morrisville  5.55677
## 1030 United States       Kentucky                    Jackson  7.44703
## 1032 United States       Colorado              Colorado City 13.17259
## 1033 United States       Colorado            Manitou Springs  2.43897
## 1034 United States       Colorado            Manitou Springs  0.40970
## 1035 United States  West Virginia                      Logan  7.51462
## 1036 United States       Colorado                    Laporte 14.72352
## 1037 United States       Colorado                    Laporte 20.26585
## 1038 United States         Nevada            Summerlin South 30.37462
## 1039 United States       Colorado                    Laporte  4.00974
## 1040 United States  West Virginia                 Charleston  5.79774
## 1041 United States North Carolina                   Gamewell  9.34488
## 1042 United States North Carolina                    Newland  1.41898
## 1043 United States       Colorado           Mountain Village 10.80667
## 1044 United States       Colorado           Mountain Village 15.04331
## 1045 United States           Utah                     Alpine  1.82493
## 1046 United States           Utah                       Lehi  1.75260
## 1047 United States           Utah                   Highland  5.27973
## 1048 United States           Utah                     Ferron 21.43097
## 1049 United States           Utah                   Highland  3.86093
## 1050 United States       Colorado           Snowmass Village 27.54476
## 1051 United States        Montana           West Yellowstone 51.55941
## 1052 United States        Arizona                       Page 28.51122
## 1053 United States       Colorado                 Estes Park 13.74294
## 1055 United States       Colorado                   Palisade  0.73594
## 1056 United States       Colorado                    Minturn  3.19723
## 1057 United States       Colorado           Snowmass Village 27.05169
## 1058 United States       Colorado                 Carbondale 24.70278
## 1059 United States        Arizona                      Eagar 39.22402
## 1060 United States     New Mexico                     Questa  4.75219
## 1061 United States     California                  Idyllwild  7.79933
## 1062 United States   Pennsylvania                Monroeville  1.19691
## 1063 United States   Pennsylvania                Monroeville  1.67287
## 1064 United States   Pennsylvania                  White Oak  1.50672
## 1065 United States   Pennsylvania                  White Oak  1.80010
## 1066 United States           Ohio             Powhatan Point 21.79988
## 1067 United States  West Virginia                      Logan  5.91065
## 1068 United States           Ohio             Powhatan Point  7.63247
## 1069 United States  West Virginia                 Chesapeake  7.84336
## 1070 United States       Colorado                    Laporte 13.16549
## 1072 United States North Carolina                    Hickory  0.97570
## 1073 United States North Carolina              Millers Creek 17.74135
## 1074 United States       Colorado                    Laporte 19.55437
## 1075 United States       Colorado                    Laporte 12.14412
## 1076 United States       Colorado                     Paonia  2.33014
## 1077 United States        Georgia                     Jasper  5.05140
## 1078 United States        Georgia                     Jasper  4.33442
## 1079 United States        Alabama                   Fairhope  0.73828
## 1081 United States           Utah             Mount Pleasant 26.06760
## 1082 United States           Utah             Mount Pleasant 25.75090
## 1083 United States       Colorado            Manitou Springs  1.60621
## 1084 United States       Kentucky           Coal Run Village 10.79203
## 1085 United States  West Virginia           New Martinsville 24.21776
## 1086 United States        Alabama                  Andalusia  1.69766
## 1087 United States         Nevada            Summerlin South 23.60286
## 1088 United States         Nevada                    Pahrump 31.00660
## 1089 United States           Utah                     Alpine  1.96154
## 1090 United States   Pennsylvania                 Bear Rocks  3.06650
## 1091 United States          Idaho                  Pocatello  0.57038
## 1092 United States           Utah                 Huntington 22.93764
## 1093 United States      Tennessee                Soddy-Daisy  5.71688
## 1094 United States     California             Morongo Valley  1.82682
## 1099 United States       Colorado                  Telluride  9.89859
## 1101 United States          Idaho                     Salmon 26.56626
## 1102 United States     California                    Banning  9.26566
## 1103 United States         Nevada                    Pahrump 18.54588
## 1104 United States           Utah                   Mapleton 33.45684
## 1105 United States           Utah                   Mapleton 34.35735
## 1106 United States           Utah                   Mapleton 34.82356
## 1107 United States           Utah                 Huntington 13.94274
## 1108 United States           Utah                      Manti  1.65946
## 1109 United States          Idaho                     Hailey  2.19542
## 1110 United States          Idaho                 Idaho City 38.48521
## 1111 United States     California                     Julian 10.67044
## 1113 United States           Utah                Cedar Hills  2.37130
## 1114 United States           Utah                   Highland  5.49158
## 1115 United States           Utah                   Highland  3.06220
## 1116 United States           Utah                      Provo  2.97871
## 1117 United States          Idaho                    Preston 19.50083
## 1119 United States        Arizona                  Flagstaff  7.39122
## 1120 United States           Utah             Mount Pleasant 24.76989
## 1121 United States           Utah         Cottonwood Heights  5.23498
## 1122 United States           Utah                Orangeville 12.95090
## 1123 United States       Colorado                    Boulder 15.39663
## 1124 United States           Utah                   Duchesne  7.44914
## 1125 United States       Colorado                Fort Carson  5.13887
## 1126 United States          Idaho              Mountain Home 22.84271
## 1127 United States        Wyoming                  Marbleton 30.39279
## 1128 United States        Wyoming                  Marbleton 41.69882
## 1129 United States       Colorado                    Boulder  2.37394
## 1130 United States       Colorado                 Coal Creek  3.76200
## 1131 United States       Colorado                 Coal Creek  6.81843
## 1132 United States          Idaho              Mountain Home 28.84649
## 1133 United States          Idaho              Mountain Home 27.81611
## 1134 United States          Idaho              Mountain Home 30.71907
## 1135 United States          Idaho              Mountain Home 31.78115
## 1136 United States          Idaho              Mountain Home 27.87603
## 1137 United States          Idaho              Mountain Home 32.71570
## 1138 United States       Colorado                    Boulder  9.54612
## 1139 United States          Idaho                 Idaho City 13.92607
## 1140 United States          Idaho                 Idaho City 13.92607
## 1141 United States          Idaho              Mountain Home 39.14705
## 1142 United States          Idaho              Mountain Home 40.70609
## 1143 United States          Idaho              Mountain Home 32.66456
## 1144 United States           Utah                   Herriman 10.80925
## 1145 United States     New Mexico                Albuquerque  2.54139
## 1146 United States          Idaho                      Eagle  8.21552
## 1150 United States       Colorado                 Estes Park  9.31567
## 1160 United States           Utah                   Herriman  9.55190
## 1161 United States       Colorado                    Durango  7.53236
## 1162 United States       Colorado                    Durango  5.78799
## 1163 United States       Colorado                    Durango  8.65408
## 1164 United States          Idaho                 Idaho City 39.35619
## 1165 United States          Idaho                 Idaho City 44.26251
## 1167 United States       Colorado                Buena Vista 16.21071
## 1168 United States  Massachusetts                Southbridge  2.82925
## 1170 United States      Minnesota                     Winona  4.83306
## 1171 United States      Wisconsin                Trempealeau  5.10967
## 1172 United States        Wyoming                    Jackson 70.82902
## 1176 United States  West Virginia                 Montgomery 13.31637
## 1177 United States     New Jersey                    Hoboken  0.90146
## 1178 United States  West Virginia                      Logan  8.76650
## 1179 United States  West Virginia                 Alum Creek  2.09924
## 1180 United States  West Virginia                      Logan  8.76650
## 1181 United States  West Virginia              Gilbert Creek 14.11991
## 1182 United States  West Virginia                      Logan  9.11798
## 1184 United States           Utah                    Hildale 19.18959
## 1186 United States     California                     Ramona  3.30093
## 1187 United States   Pennsylvania                   Cheswick  1.60422
## 1188 United States   Pennsylvania              Lower Burrell  3.06925
## 1189 United States       Kentucky                    Hindman  9.94567
## 1195 United States  West Virginia                     Oceana 22.89616
## 1197 United States  New Hampshire                     Gorham 17.23167
## 1198 United States  West Virginia                      Welch 12.79020
## 1199 United States           Ohio              Newcomerstown  7.31418
## 1200 United States North Carolina                Bryson City 13.70206
## 1202 United States   Pennsylvania                 Pittsburgh  1.31322
## 1203 United States   Pennsylvania                  Brentwood  1.87113
## 1204 United States       Kentucky                   Morehead 10.34977
## 1205 United States       Kentucky               Prestonsburg 13.34216
## 1206 United States         Oregon                     Joseph 22.16606
## 1209 United States       Kentucky                     Ludlow  1.22118
## 1210 United States       Kentucky               Prestonsburg 11.74439
## 1211 United States       Kentucky                    Newport  1.39322
## 1212 United States           Utah                South Weber  0.87758
## 1213 United States       Kentucky                  Pikeville  7.48881
## 1214 United States       Kentucky                 Booneville 22.52336
## 1215 United States   Pennsylvania                  Aspinwall  1.01041
## 1216 United States       Kentucky                 Park Hills  1.35697
## 1217 United States           Ohio              Saint Bernard  3.98882
## 1218 United States   Pennsylvania                      Manor  1.72576
## 1219 United States   Pennsylvania               Mount Oliver  3.52327
## 1223 United States   Pennsylvania                 Pittsburgh  1.91563
## 1224 United States    Connecticut                  Stratford  0.76857
## 1225 United States       Kentucky                Fort Thomas  0.84033
## 1226 United States       New York                     Nedrow  7.01558
## 1227 United States       Kentucky                 Carrollton  5.24704
## 1228 United States       Kentucky                 Carrollton  5.11169
## 1229 United States        Alabama             Vestavia Hills  0.90573
## 1230 United States   Pennsylvania                 Pittsburgh  1.55600
## 1231 United States           Ohio The Village of Indian Hill  2.22788
## 1232 United States       New York                Warrensburg  2.17558
## 1233 United States  West Virginia                      Wayne  8.50992
## 1234 United States       New York                  Sea Cliff  0.69230
## 1235 United States       Maryland                  Baltimore  5.94285
## 1236 United States       Kentucky               Prestonsburg 13.32465
## 1237 United States       Maryland                Charlestown  4.24402
## 1238 United States     New Jersey                     Alpine  1.62138
## 1239 United States   Pennsylvania                 Pittsburgh  1.27111
## 1240 United States       New York                     Shokan 26.62969
## 1241 United States       Virginia                  Lexington 20.62473
## 1242 United States       Colorado                  Cedaredge 16.89741
## 1243 United States       Colorado                   Keystone  8.62122
## 1245 United States      Minnesota            Mendota Heights  1.73479
## 1246 United States      Minnesota               Eden Prairie  0.15271
## 1247 United States        Georgia                     Canton  9.42335
## 1248 United States      Minnesota            Mendota Heights  1.93378
## 1249 United States          Maine                  Wiscasset  9.00019
## 1250 United States      Minnesota              North Mankato  5.90746
## 1251 United States      Minnesota                    Mankato  1.73178
## 1252 United States      Minnesota                   Le Sueur  6.99107
## 1253 United States      Minnesota               Belle Plaine  3.37657
## 1254 United States      Minnesota                Minneapolis  0.25471
## 1255 United States      Minnesota                 Saint Paul  2.31638
## 1256 United States      Minnesota                Minneapolis  2.50336
## 1257 United States      Minnesota                   Le Sueur  7.07406
## 1258 United States      Minnesota                 Saint Paul  2.99793
## 1259 United States      Minnesota               Belle Plaine  7.97257
## 1260 United States      Minnesota               Belle Plaine  7.02197
## 1275 United States   Pennsylvania                  Boalsburg  2.14720
## 1276 United States      Minnesota                    Preston 14.33305
## 1277 United States           Iowa               Cedar Rapids  4.98458
## 1280 United States        Arizona                     Sedona  5.43356
## 1281 United States       Colorado           Colorado Springs  3.60998
## 1282 United States        Wyoming                       Cody 66.07162
## 1283 United States       Colorado                     Salida  7.69870
## 1284 United States       Colorado       Cascade-Chipita Park  1.37598
## 1285 United States     California            Borrego Springs 20.08692
## 1286 United States           Utah                      Nephi  5.80331
## 1287 United States     California              Cherry Valley  8.96214
## 1291 United States           Iowa                   Woodbine  5.71009
## 1292 United States        Wyoming                    Jackson  1.70313
## 1293 United States      Tennessee           Lookout Mountain  0.58260
## 1295 United States       Colorado                     Granby  5.53226
## 1296 United States   Pennsylvania                 Versailles  1.57546
## 1297 United States           Utah            North Salt Lake  2.55023
## 1300 United States       Maryland            Fort Washington  2.15609
## 1303 United States           Utah                      Logan  1.79637
## 1306 United States           Utah                 Washington  1.34549
## 1307 United States     California                Valle Vista  7.42146
## 1309 United States   Pennsylvania                 Wilmerding  0.35963
## 1311 United States  Massachusetts               Valley Falls  3.45417
## 1312 United States        Georgia                   Columbus  6.88634
## 1313 United States          Idaho                 Idaho City 26.67215
## 1314 United States        Wyoming                       Cody 59.36537
## 1315 United States           Ohio                 Huntington  3.37218
## 1316 United States        Alabama                     Hoover  3.51822
## 1317 United States     California                San Jacinto  4.90583
## 1318 United States       Illinois                East Peoria  2.36449
## 1319 United States       Illinois                East Peoria  2.33139
## 1322 United States     California                  Escondido  5.53682
## 1323 United States     California                San Jacinto  5.62939
## 1324 United States     California                      Crest  2.75389
## 1325 United States     California                    Yucaipa 13.02571
## 1326 United States       Colorado                     Salida 14.50415
## 1327 United States  Massachusetts                  Topsfield  1.82352
## 1328 United States     New Mexico                   Corrales  0.35759
## 1329 United States       Colorado                     Salida  3.89595
## 1330 United States       Colorado                     Salida  7.04118
## 1331 United States       Colorado                   San Luis 25.29753
## 1334 United States           Ohio                 Huntington  7.32387
## 1336 United States          Idaho                 Idaho City  9.77449
## 1337 United States         Nevada               Boulder City  2.66000
## 1338 United States        Wyoming                     Hoback 17.26708
## 1339 United States       Colorado                  Telluride  0.63448
## 1340 United States       Kentucky                    Hindman  1.90244
## 1341 United States       New York                Canajoharie 10.30458
## 1343 United States       Kentucky           Coal Run Village 10.17779
## 1345 United States   Pennsylvania                 Waynesburg  2.22576
## 1346 United States       Maryland               Camp Springs  1.87540
## 1350 United States       Colorado                    Minturn  4.47152
## 1351 United States          Idaho                    Challis  0.36084
## 1355 United States       Illinois                   Highwood  2.92584
## 1403 United States          Idaho                     Joseph 44.08725
## 1405 United States     California                     Alpine  1.38372
## 1407 United States   Pennsylvania               Beaver Falls  7.52989
## 1408 United States       Virginia              Clifton Forge  2.30910
## 1409 United States       Kentucky               Prestonsburg  5.08152
## 1410 United States       Kentucky                    Stearns  6.35327
## 1411 United States       Kentucky           Coal Run Village 16.40897
## 1412 United States       Kentucky                   Morehead  1.53883
## 1413 United States  West Virginia              Gilbert Creek  5.06344
## 1414 United States  West Virginia              Gilbert Creek  9.35294
## 1415 United States  West Virginia                Sissonville  8.93549
## 1416 United States  West Virginia                       Inez 12.32235
## 1417 United States  West Virginia                 Williamson 16.94705
## 1418 United States       Kentucky                     Harlan 12.77232
## 1419 United States  West Virginia                       Rand  5.77112
## 1420 United States  West Virginia                 Charleston  6.40459
## 1421 United States  West Virginia                 Montgomery  3.66745
## 1422 United States  West Virginia                      Logan  2.59792
## 1423 United States  West Virginia                      Welch 18.30387
## 1426 United States       Kentucky                 Cumberland  7.52192
## 1427 United States  West Virginia                 Williamson  9.26151
## 1428 United States  West Virginia              Gilbert Creek  7.28713
## 1429 United States       Kentucky                 Whitesburg  1.49011
## 1430 United States       Kentucky                     Hazard 11.13295
## 1431 United States  West Virginia                     Sophia  2.71773
## 1432 United States  West Virginia                Moundsville 16.23811
## 1433 United States       Virginia                       Wise  2.94501
## 1434 United States       Kentucky               West Liberty  5.64478
## 1435 United States       Kentucky                    Hindman  9.63545
## 1436 United States       Virginia              Big Stone Gap  4.51597
## 1437 United States       Virginia                     Grundy 17.52647
## 1438 United States  West Virginia                    Fairlea  2.82396
## 1439 United States       Kentucky                   Hartford  3.14055
## 1440 United States       New York                    Yonkers  1.05895
## 1441 United States           Ohio                   Bellevue  1.19499
## 1442 United States        Georgia         Chattanooga Valley  3.71673
## 1443 United States  West Virginia                 Charleston  3.35071
## 1444 United States  West Virginia                 Williamson  0.84799
## 1445 United States  West Virginia                 Williamson  0.51702
## 1446 United States           Ohio                 Youngstown  1.20691
## 1447 United States   Pennsylvania                  Aspinwall  0.87791
## 1448 United States       Kentucky                  Pikeville  0.29989
## 1449 United States       Kentucky                Fort Thomas  0.78550
## 1450 United States           Ohio               Wintersville  7.93943
## 1455 United States       Kentucky                       Inez  7.14177
## 1458 United States       Arkansas                   Harrison 10.15474
## 1459 United States           Ohio                Day Heights  2.40349
## 1460 United States       Colorado                  Stratmoor  6.75748
## 1462 United States       Maryland                Saint James  4.05258
## 1463 United States       Oklahoma                   Longtown 19.40495
## 1464 United States   Pennsylvania                    Blawnox  2.15077
## 1465 United States       Oklahoma                      Davis  7.00621
## 1466 United States        Wyoming                       Cody 74.46097
## 1467 United States       Arkansas                      Ozark 25.20737
## 1468 United States   Pennsylvania                  Johnstown  0.59899
## 1469 United States   Pennsylvania                    Milford  6.74021
## 1470 United States        Alabama                  Northport  7.27361
## 1472 United States   Pennsylvania                    Oakland  2.35897
## 1473 United States        Arizona                       Page  3.58700
## 1474 United States       Colorado          Steamboat Springs  0.83297
## 1476 United States     New Mexico                   Cimarron 10.23698
## 1477 United States   Pennsylvania          West Conshohocken  0.98504
## 1478 United States     New Jersey                   Paterson  0.67691
## 1479 United States           Utah                      Provo  2.90388
## 1480 United States   Pennsylvania               New Brighton  1.00035
## 1481 United States       Oklahoma                   Muskogee 12.21464
## 1482 United States        Wyoming                Thermopolis 16.58878
## 1483 United States         Kansas                     Oregon 14.54524
## 1484 United States       Oklahoma                   Talihina 33.62663
## 1485 United States       Colorado                     Golden  2.66154
## 1486 United States       Colorado           Colorado Springs  2.19300
## 1487 United States        Wyoming           West Yellowstone 48.56269
## 1488 United States       Colorado               Lincoln Park 15.35824
## 1489 United States       Colorado                    Penrose 15.46134
## 1490 United States       Colorado                    Penrose 15.49683
## 1491 United States       Kentucky                 Whitesburg  2.84945
## 1494 United States       Colorado          Air Force Academy  8.38116
## 1495 United States       Arkansas                  Coal Hill  7.09417
## 1496 United States   Pennsylvania                   Millvale  2.96684
## 1497 United States       Colorado                Buena Vista 15.55972
## 1498 United States           Ohio              Chagrin Falls  0.71363
## 1499 United States       Colorado                  Silverton  3.25081
## 1500 United States       Kentucky                 Rising Sun  1.04697
## 1501 United States       Colorado                       Loma  7.43165
## 1502 United States  West Virginia                 Burlington  2.92655
## 1503 United States       Kentucky                     Hazard  2.49307
## 1504 United States       Kentucky                 Whitesburg  0.05520
## 1505 United States       New York                     Delmar  2.22929
## 1506 United States       Kentucky               Prestonsburg 11.00729
## 1507 United States    Mississippi                D'Iberville  0.98037
## 1508 United States   Pennsylvania                West Newton  6.55886
## 1509 United States       Maryland                     Keyser  3.89837
## 1510 United States  West Virginia           South Charleston  3.94457
## 1511 United States   Pennsylvania                   Cheswick  0.91573
## 1513 United States       Colorado                  Telluride  9.99621
## 1514 United States       Kentucky                  Pikeville  8.67856
## 1515 United States       Kentucky               Prestonsburg  9.99553
## 1516 United States       Kentucky                   Richmond 11.13705
## 1517 United States       Kentucky               Williamsburg  9.77108
## 1518 United States       Kentucky                 Frenchburg 14.80972
## 1519 United States       Kentucky               Prestonsburg 12.65382
## 1520 United States       Kentucky                     Hazard  4.39348
## 1521 United States  West Virginia                Saint Marys  7.15698
## 1522 United States           Ohio                   Glendale  3.55282
## 1523 United States  West Virginia                  Brilliant  4.71099
## 1524 United States  West Virginia               Teays Valley  3.37673
## 1525 United States   Pennsylvania                    Oakmont  1.28701
## 1526 United States     New Mexico                     Questa 14.70736
## 1527 United States        Indiana                   Rockport  8.43158
## 1530 United States       Kentucky                  Pikeville  6.27087
## 1531 United States  West Virginia                     Lesage  0.02568
## 1532 United States       Kentucky               Prestonsburg  3.12273
## 1533 United States           Ohio                  Peninsula  4.27554
## 1534 United States  West Virginia                     Sutton 18.39748
## 1535 United States  West Virginia                    Bethany  1.55911
## 1536 United States           Ohio                   Oak Hill 10.37446
## 1537 United States      Tennessee                      Erwin  7.88881
## 1538 United States  West Virginia                  Clendenin  0.17162
## 1539 United States  West Virginia            Webster Springs  9.96486
## 1540 United States  West Virginia            Webster Springs  0.16161
## 1541 United States       Missouri                   Hannibal  2.42844
## 1542 United States       Kentucky                  Pikeville 10.52676
## 1543 United States     California                    La Mesa  4.97056
## 1544 United States     California                     Ramona  4.85697
## 1545 United States     California                     Julian  4.69959
## 1546 United States     California  San Diego Country Estates  7.62002
## 1547 United States     California                     Ramona  3.13754
## 1548 United States     California                     Niland 53.76298
## 1549 United States           Ohio                     Dayton  1.56996
## 1550 United States       Missouri                     Fulton 16.39489
## 1551 United States     California                    Yucaipa 10.37615
## 1552 United States  West Virginia                 Montgomery  1.33142
## 1553 United States  Massachusetts                   Hatfield  3.49144
## 1554 United States  Massachusetts               Chesterfield  6.53153
## 1555 United States           Utah             Woodland Hills  0.75074
## 1556 United States       Colorado           Colorado Springs  3.81487
## 1558 United States North Carolina             Black Mountain  7.96582
## 1562 United States     California              Moreno Valley  5.33395
## 1563 United States        Arizona                     LeChee 43.57781
## 1564 United States   Pennsylvania                  Homestead  3.74490
## 1567 United States           Utah                   Mapleton 21.52246
## 1675 United States       Virginia                Springville  8.58443
## 1676 United States   Pennsylvania                    Crafton  1.45894
## 1677 United States       Colorado           Glenwood Springs 13.85194
## 1678 United States       Virginia                   Pembroke  3.21144
## 1679 United States  West Virginia                 Charleston  6.43349
## 1681 United States North Carolina               Blowing Rock  4.20387
## 1682 United States           Ohio                  New Haven  6.83465
## 1683 United States          Idaho                    Cascade 22.78728
## 1684 United States          Idaho                 Idaho City 24.16064
## 1685 United States       Colorado                    Durango 22.57467
## 1686 United States      Tennessee                  Jacksboro  8.43321
## 1687 United States  West Virginia                   Bluewell  5.07093
## 1688 United States North Carolina                      Tryon  7.69666
## 1689 United States North Carolina                      Tryon  7.80866
## 1690 United States  West Virginia                 Charleston  6.84721
## 1691 United States  West Virginia                      Welch 14.19735
## 1692 United States  West Virginia                     Athens 12.00678
## 1693 United States        Vermont                    Windsor  1.78429

ggplot(data= USA, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

## Warning: Removed 1 rows containing missing values (position_stack).

En las zonas donde se han registrado los deslizamientos, el valor promedio de la población es 2509 habitantes. Mientras que los deslizamientos de tierra tienen una longitud de 8,46 kilometros promedio.

Medidas de tendencia y variabilidad de la población en zonas de deslizamientos

library(knitr)
library(readr)


df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

knitr::kable(head(df))

id	date	time	continent_code	country_name	country_code	state.province	population	city.town	distance	location_description	latitude	longitude	geolocation	hazard_type	landslide_type	landslide_size	trigger	storm_name	injuries	fatalities	source_name	source_link
34	3/2/07	Night	NA	United States	US	Virginia	16000	Cherry Hill	3.40765	Unknown	38.6009	-77.2682	(38.600900000000003, -77.268199999999993)	Landslide	Landslide	Small	Rain		NA	NA	NBC 4 news	http://www.nbc4.com/news/11186871/detail.html
42	3/22/07		NA	United States	US	Ohio	17288	New Philadelphia	3.33522		40.5175	-81.4305	(40.517499999999998, -81.430499999999995)	Landslide	Landslide	Small	Rain		NA	NA	Canton Rep.com	http://www.cantonrep.com/index.php?ID=345054&Category=9&subCategoryID=0
56	4/6/07		NA	United States	US	Pennsylvania	15930	Wilkinsburg	2.91977	Urban area	40.4377	-79.9160	(40.4377, -79.915999999999997)	Landslide	Landslide	Small	Rain		NA	NA	The Pittsburgh Channel.com	https://web.archive.org/web/20080423132842/http://www.thepittsburghchannel.com/news/11846833/detail.html
59	4/14/07		NA	Canada	CA	Quebec	42786	Châteauguay	2.98682	Above river	45.3226	-73.7771	(45.322600000000001, -73.777100000000004)	Landslide	Riverbank collapse	Small	Rain		NA	NA	Le Soleil	http://www.hebdos.net/lsc/edition162007/articles.asp?article_id=166976
61	4/15/07		NA	United States	US	Kentucky	6903	Pikeville	5.66542	Below road	37.4325	-82.4931	(37.432499999999997, -82.493099999999998)	Landslide	Landslide	Small	Downpour		NA	0	Matthew Crawford (KGS)
64	4/20/07		NA	United States	US	Kentucky	6903	Pikeville	0.23715		37.4814	-82.5186	(37.481400000000001, -82.518600000000006)	Landslide	Landslide	Small	Rain		NA	NA	Applalachain news-express	http://www.news-expressky.com/articles/2007/04/19/top_story/01mudslide.txt

library(dplyr)

colnames(df)[7] <- "state"
pais <- subset(df, country_name == "United States")

knitr::kable(head(pais))

	id	date	time	continent_code	country_name	country_code	state	population	city.town	distance	location_description	latitude	longitude	geolocation	hazard_type	landslide_type	landslide_size	trigger	storm_name	injuries	fatalities	source_name	source_link
1	34	3/2/07	Night	NA	United States	US	Virginia	16000	Cherry Hill	3.40765	Unknown	38.6009	-77.2682	(38.600900000000003, -77.268199999999993)	Landslide	Landslide	Small	Rain		NA	NA	NBC 4 news	http://www.nbc4.com/news/11186871/detail.html
2	42	3/22/07		NA	United States	US	Ohio	17288	New Philadelphia	3.33522		40.5175	-81.4305	(40.517499999999998, -81.430499999999995)	Landslide	Landslide	Small	Rain		NA	NA	Canton Rep.com	http://www.cantonrep.com/index.php?ID=345054&Category=9&subCategoryID=0
3	56	4/6/07		NA	United States	US	Pennsylvania	15930	Wilkinsburg	2.91977	Urban area	40.4377	-79.9160	(40.4377, -79.915999999999997)	Landslide	Landslide	Small	Rain		NA	NA	The Pittsburgh Channel.com	https://web.archive.org/web/20080423132842/http://www.thepittsburghchannel.com/news/11846833/detail.html
5	61	4/15/07		NA	United States	US	Kentucky	6903	Pikeville	5.66542	Below road	37.4325	-82.4931	(37.432499999999997, -82.493099999999998)	Landslide	Landslide	Small	Downpour		NA	0	Matthew Crawford (KGS)
6	64	4/20/07		NA	United States	US	Kentucky	6903	Pikeville	0.23715		37.4814	-82.5186	(37.481400000000001, -82.518600000000006)	Landslide	Landslide	Small	Rain		NA	NA	Applalachain news-express	http://www.news-expressky.com/articles/2007/04/19/top_story/01mudslide.txt
7	67	4/24/07		NA	United States	US	South Dakota	2540	Dakota Dunes	2.48033		42.4941	-96.4576	(42.494100000000003, -96.457599999999999)	Landslide	Landslide	Small	Rain		NA	NA	Sioux City Journnal	http://www.siouxcityjournal.com/articles/2007/04/25/news/top/02a92f1defdd220f862572c800126dfc.txt

Medidas de tendencia y variabilidad de la longitud de los deslizamientos

library(knitr)
library(readr)


df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

knitr::kable(head(df))

id	date	time	continent_code	country_name	country_code	state.province	population	city.town	distance	location_description	latitude	longitude	geolocation	hazard_type	landslide_type	landslide_size	trigger	storm_name	injuries	fatalities	source_name	source_link
34	3/2/07	Night	NA	United States	US	Virginia	16000	Cherry Hill	3.40765	Unknown	38.6009	-77.2682	(38.600900000000003, -77.268199999999993)	Landslide	Landslide	Small	Rain		NA	NA	NBC 4 news	http://www.nbc4.com/news/11186871/detail.html
42	3/22/07		NA	United States	US	Ohio	17288	New Philadelphia	3.33522		40.5175	-81.4305	(40.517499999999998, -81.430499999999995)	Landslide	Landslide	Small	Rain		NA	NA	Canton Rep.com	http://www.cantonrep.com/index.php?ID=345054&Category=9&subCategoryID=0
56	4/6/07		NA	United States	US	Pennsylvania	15930	Wilkinsburg	2.91977	Urban area	40.4377	-79.9160	(40.4377, -79.915999999999997)	Landslide	Landslide	Small	Rain		NA	NA	The Pittsburgh Channel.com	https://web.archive.org/web/20080423132842/http://www.thepittsburghchannel.com/news/11846833/detail.html
59	4/14/07		NA	Canada	CA	Quebec	42786	Châteauguay	2.98682	Above river	45.3226	-73.7771	(45.322600000000001, -73.777100000000004)	Landslide	Riverbank collapse	Small	Rain		NA	NA	Le Soleil	http://www.hebdos.net/lsc/edition162007/articles.asp?article_id=166976
61	4/15/07		NA	United States	US	Kentucky	6903	Pikeville	5.66542	Below road	37.4325	-82.4931	(37.432499999999997, -82.493099999999998)	Landslide	Landslide	Small	Downpour		NA	0	Matthew Crawford (KGS)
64	4/20/07		NA	United States	US	Kentucky	6903	Pikeville	0.23715		37.4814	-82.5186	(37.481400000000001, -82.518600000000006)	Landslide	Landslide	Small	Rain		NA	NA	Applalachain news-express	http://www.news-expressky.com/articles/2007/04/19/top_story/01mudslide.txt

library(dplyr)
library(pastecs)


stat.desc(pais$population)

##      nbr.val     nbr.null       nbr.na          min          max        range 
## 9.860000e+02 0.000000e+00 0.000000e+00 8.100000e+01 1.526006e+06 1.525925e+06 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 2.474413e+07 4.144500e+03 2.509547e+04 2.840471e+03 5.574069e+03 7.955317e+09 
##      std.dev     coef.var 
## 8.919258e+04 3.554131e+00

stat.desc(pais$distance)

##      nbr.val     nbr.null       nbr.na          min          max        range 
##  985.0000000    0.0000000    1.0000000    0.0017800   74.4609700   74.4591900 
##          sum       median         mean      SE.mean CI.mean.0.95          var 
## 8334.9682600    5.0815200    8.4618967    0.3178238    0.6236904   99.4968078 
##      std.dev     coef.var 
##    9.9748087    1.1787911

Diagrama de caja y bigotes respecto a la distancia de los deslizamientos

En este gráfico, resalta la canitidad de datos atípicos respecto a la distancia que pueden alcanzar los deslizamientos de tierra, llegando incluso a distancias mayores a los 70 kilometros.

library(tidyverse)
library(hrbrthemes)
library(viridis)

data<-pais$distance

caja <- data.frame(data)
caja %>% ggplot(aes(x = "", y = data)) +
  geom_boxplot(color="black", fill="blue", alpha=0.5) +
  theme_ipsum() +
  theme(legend.position="none", plot.title = element_text(size=11)) +
  ggtitle("Basic boxplot") +
  coord_flip() +
  xlab("") +
  ylab("")

## Warning: Removed 1 rows containing non-finite values (stat_boxplot).

Venezuela

Para Venezuela solo se reportaron 5 estados, Distrito Federal con 12 deslizamientos, Miranda con 4, Vargas con 2, y Aragua y Falcón con 1.

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, country_name == "Venezuela")

table(pais$state.province)

## 
##           Aragua Distrito Federal           Falcón          Miranda 
##                1               12                1                4 
##           Vargas 
##                2

Capital <- c(pais$state.province)

library(questionr)

table <- questionr::freq(Capital, cum = TRUE, sort = "dec", total = FALSE)
knitr::kable(table)

	n	%	val%	%cum	val%cum
Distrito Federal	12	60	60	60	60
Miranda	4	20	20	80	80
Vargas	2	10	10	90	90
Aragua	1	5	5	95	95
Falcón	1	5	5	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 = table, y = table$n)

library(ggplot2)

ggplot(data=df, aes(x=x, y=y)) + 
  geom_bar(stat="identity", color="blue", fill="yellow") +
  xlab("Venezuela") +
  ylab("Frecuencia") +
theme(axis.text.x = element_text(angle = 90))

x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]

df <- data.frame(x = table, y = table$n)

library(ggplot2)
library(dplyr)

data <- data.frame(group = 
                     c("Distrito Federal","Miranda","Vargas","Aragua","Falcón"), 
                   value = c(60, 20, 10, 5, 5))

library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )
require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6) +
  scale_fill_brewer(palette="Set1")

ggplot(data, aes(x = "", y = value, fill=group)) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0)

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)
VEN <- subset (df, Country == "Venezuela")
knitr::kable(head(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

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  2.55507

ggplot(data= VEN, aes(x=State, y=Distance)) + geom_bar(stat="identity", color="black", fill="blue", size=0.5)

Discusión

Estas fueron las 5 ciudades (con su respectivo país) donde ocurrieron la mayor parte de eventos geológicos asociados con los deslizamientos. Siendo el primero Estados Unidos con 124 deslizamientos en el estado de Kentucky y el quinto Guatemala con 20 deslizamientos en la ciudad de Alajuela. En el caso de Colombia, este posee una cifra cercana 19 eventos geológicos en el departamento de Antioquia.

df <- data.frame(Ciudad_pais = 
                   c("Kentucky/US","Colorado/US","City of Guatemala/GT","San josé/CR","Alajuela/CR"), 
                 Numero_de_eventos = 
                   c(124,108,31,25,20))
knitr::kable(df)

Ciudad_pais	Numero_de_eventos
Kentucky/US	124
Colorado/US	108
City of Guatemala/GT	31
San josé/CR	25
Alajuela/CR	20

df$Ciudad_pais = factor(df$Ciudad_pais, levels=c("Kentucky/US","Colorado/US","City of Guatemala/GT","San josé/CR","Alajuela/CR"))
levels(df$Ciudad_pais)

## [1] "Kentucky/US"          "Colorado/US"          "City of Guatemala/GT"
## [4] "San josé/CR"          "Alajuela/CR"

library(ggplot2)
ggplot(data=df, aes(x=Ciudad_pais, y=Numero_de_eventos)) + geom_bar(stat="identity") +
  theme(axis.text.x = element_text(angle = 90))

Gráfico Circular

library(ggplot2)
library(dplyr)

data <- data.frame(group = c("Kentucky/US","Colorado/US","City of Guatemala/GT","San josé/CR","Alajuela/CR"), 
                   value = c(40,35,10,8,7))
library(ggplot2)
library(dplyr)

data <- data %>% 
  arrange(desc(group)) %>%
  mutate(prop = value / sum(data$value) *100) %>%
  mutate(ypos = cumsum(prop)- 0.5*prop )

require(scales)
ggplot(data, aes(x="", y = prop, fill=group)) +
  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(value/100)), color = "white", size=6, angle = 90) +
  scale_fill_brewer(palette="Set1")

Series de tiempo

A continuación mostraremos una serie de tiempo para estos 5 estados con su respectivo país donde hubieron la mayor cantidad de eventos, y al mismo tiempo se mostrará que en algunos días se presentaron varios eventos simultaneos.

library(readr)
library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, state.province == "Kentucky")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90))

library(readr)
library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, state.province == "Colorado")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90))

library(readr)
library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, state.province == "Guatemala")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90))

library(readr)
library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, state.province == "San José")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90))

library(readr)
library(knitr)  

df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")

pais <- subset(df, state.province == "Alajuela")

write.csv(x = pais, file = "pais.csv") 

data2 <- read.csv("pais.csv")

library(ggplot2)
library(dplyr)

ggplot(data2, aes(x = date, y = distance )) +
  geom_line(color="#69b3a2", size = 1)+
  geom_point()+
  theme(axis.text.x = element_text(angle = 90))

## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?

Conclusión

En conclusión, se pudo analizar el deslizamiento de suelos en cada uno de estos países de América y se crearon gráficas a partir del software R estudio en el que se logró hacer el análisis por medio de las mismas y diversas tablas con el fin de poder organizar de una mejor manera la información y fuera más clara. Se logró el objetivo del informe el cual pone en práctica los conocimientos como lo son el diagrama de caja y bigotes, tablas de frecuencia simples y agrupadas, conceptos como media, mediana, moda y rango. Además, del desarrollo de habilidades fundamentales para nuestra vida profesional.