Informe 1
INTRODUCCION: El propósito de este informe radica en analizar la base de datos de deslizamientos globales con énfasis único en el subcontinente centroamericano, esto con el fin de interpretar de forma más diáfana la información que nos suministra la base de datos mencionada anteriormente. Dicho esto, los mecanismos gráficos que utilizaremos serán: Diagramas de barras en diferentes tipos, tablas de frecuencia, diagramas de Pareto, series temporales, diagramas de caja y bigote, tallo y hojas, estadísticos, entre otros…
#COSTA RICA
library(readr)
library(knitr)
df <- read.csv("https://raw.githubusercontent.com/lihkir/AnalisisEstadisticoUN/main/Data/catalog.csv")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncolnames(df)[5] <- "Country"
colnames(df)[7] <- "State"
colnames(df)[9] <- "City"
colnames(df)[10] <- "Distance"library(readr)
library(knitr)
df_cr <- subset (df, Country == "Costa Rica")
knitr::kable(head(df_cr))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 249 | 9/9/07 | NA | Costa Rica | CR | Heredia | 21947 | Heredia | 0.26208 | 10.0000 | -84.1167 | (10, -84.116699999999994) | Landslide | Landslide | Medium | Rain | NA | NA | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_09/0911072.htm | |||
| 44 | 299 | 10/9/07 | NA | Costa Rica | CR | San José | 3072 | San Ignacio | 4.57763 | 9.7789 | -84.1250 | (9.7789000000000001, -84.125) | Landslide | Complex | Medium | Rain | NA | 4 | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_10/1010071.htm | |||
| 45 | 301 | 10/11/07 | NA | Costa Rica | CR | Alajuela | 7014 | Atenas | 3.08459 | 9.9869 | -84.4070 | (9.9869000000000003, -84.406999999999996) | Landslide | Mudslide | Large | Rain | NA | 14 | Agence France-Presse, afp.google.com | http://afp.google.com/article/ALeqM5hu6a8oyAM1ycq9nU_6Zyj_l7F0AA | |||
| 46 | 302 | 10/11/07 | NA | Costa Rica | CR | San José | 26669 | 9.56251 | 10.0214 | -83.9451 | (10.0214, -83.945099999999996) | Landslide | Landslide | Large | Rain | NA | 10 | International Herald | http://www.iht.com/articles/ap/2007/10/12/america/LA-GEN-Costa-Rica-Mudslide.php | ||||
| 51 | 323 | 10/24/07 | NA | Costa Rica | CR | Puntarenas | 6540 | Miramar | 3.82425 | Mine construction | 10.0715 | -84.7575 | (10.0715, -84.757499999999993) | Landslide | Mudslide | Medium | Downpour | NA | NA | Reuters - AlertNet.org | http://www.reuters.com/article/companyNewsAndPR/idUSN2435152820071025 | ||
| 102 | 556 | 5/29/08 | NA | Costa Rica | CR | Guanacaste | 4108 | Bagaces | 17.65521 | 10.4024 | -85.3555 | (10.4024, -85.355500000000006) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Alma | NA | NA | http://www.reliefweb.int/rw/RWB.NSF/db900SID/ASAZ-7FHCHL?OpenDocument |
df_cr %>%
select(Country, State, City, Distance)## Country State City Distance
## 38 Costa Rica Heredia Heredia 0.26208
## 44 Costa Rica San José San Ignacio 4.57763
## 45 Costa Rica Alajuela Atenas 3.08459
## 46 Costa Rica San José 9.56251
## 51 Costa Rica Puntarenas Miramar 3.82425
## 102 Costa Rica Guanacaste Bagaces 17.65521
## 147 Costa Rica San José Daniel Flores 1.85787
## 153 Costa Rica San José San Isidro 16.24937
## 154 Costa Rica San José Santiago 12.85801
## 156 Costa Rica Puntarenas Golfito 11.74074
## 157 Costa Rica Puntarenas Miramar 8.92048
## 229 Costa Rica Puntarenas San Vito 18.00524
## 302 Costa Rica Alajuela Desamparados 6.88715
## 311 Costa Rica Heredia Ã\201ngeles 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 Ã\201ngeles 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 Orosà 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 Ã\201ngeles 19.54581
## 579 Costa Rica Limón Guápiles 17.23264
## 702 Costa Rica Heredia Ã\201ngeles 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 Ã\201ngeles 9.53611
## 1529 Costa Rica San José Dulce Nombre de Jesus 3.71407
## 1600 Costa Rica San José San Juan 0.72957
## 1642 Costa Rica Alajuela Santo Domingo 3.21979
## 1643 Costa Rica Alajuela Alajuela 3.08916
## 1644 Costa Rica Alajuela Naranjo 2.08469
## 1646 Costa Rica Cartago 5.15142
## 1647 Costa Rica Cartago Cot 9.53493
## 1648 Costa Rica Cartago Cartago 2.94804
## 1649 Costa Rica Puntarenas Buenos Aires 0.35225
## 1650 Costa Rica San José San José 1.16705
## 1651 Costa Rica San José Mercedes 10.01198
## 1652 Costa Rica San José Santiago 8.27042library(ggplot2)
ggplot(data=df_cr, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="yellow")
library(ggplot2)
library(dplyr)
ggplot(data=df_cr, aes(fill=City, x="Costa Rica", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_cr, aes(fill=City, x="Costa Rica", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_cr, aes(x = "Costa Rica", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
library(qcc)## Package 'qcc' version 2.7## Type 'citation("qcc")' for citing this R package in publications.Distance <- df_cr$Distance
names(Distance) <- df_cr$City
pareto.chart(Distance,
ylab="Distance",
col = heat.colors(length(Distance)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "CONCENTRACION DE LAS INUNDACIONES EN LAS CIUDADES")
##
## Pareto chart analysis for Distance
## Frequency Cum.Freq. Percentage Cum.Percent.
## San Isidro 22.32368000 22.32368000 3.45492296 3.45492296
## Santo Domingo 21.95470000 44.27838000 3.39781780 6.85274076
## San Isidro 21.67452000 65.95290000 3.35445576 10.20719651
## Ciudad Cortés 20.06633000 86.01923000 3.10556433 13.31276084
## Ã\201ngeles 19.54581000 105.56504000 3.02500608 16.33776693
## Ã\201ngeles 19.51432000 125.07936000 3.02013254 19.35789946
## Orosà 19.28722000 144.36658000 2.98498542 22.34288488
## San Vito 18.00524000 162.37182000 2.78657986 25.12946474
## Bagaces 17.65521000 180.02703000 2.73240749 27.86187223
## Guápiles 17.23264000 197.25967000 2.66700847 30.52888070
## San Isidro 16.24937000 213.50904000 2.51483275 33.04371345
## San Isidro 15.64997000 229.15901000 2.42206664 35.46578009
## Ã\201ngeles 15.05161000 244.21062000 2.32946150 37.79524159
## Ã\201ngeles 14.81614000 259.02676000 2.29301900 40.08826059
## Parrita 13.48919000 272.51595000 2.08765366 42.17591425
## Santiago 12.85801000 285.37396000 1.98996913 44.16588338
## Tilarán 12.33807000 297.71203000 1.90950065 46.07538402
## Tilarán 12.21952000 309.93155000 1.89115326 47.96653728
## Tilarán 12.18115000 322.11270000 1.88521493 49.85175221
## Rio Segundo 11.96524000 334.07794000 1.85179963 51.70355185
## Golfito 11.74074000 345.81868000 1.81705490 53.52060675
## San Isidro 11.31047000 357.12915000 1.75046419 55.27107094
## San Isidro 10.73752000 367.86667000 1.66179162 56.93286257
## Sabanilla 10.32968000 378.19635000 1.59867229 58.53153486
## Tilarán 10.21631000 388.41266000 1.58112659 60.11266145
## Dulce Nombre de Jesus 10.01310000 398.42576000 1.54967681 61.66233825
## Mercedes 10.01198000 408.43774000 1.54950347 63.21184172
## Santo Domingo 9.85736000 418.29510000 1.52557371 64.73741544
## La Fortuna 9.84213000 428.13723000 1.52321664 66.26063208
## Cot 9.63616000 437.77339000 1.49133971 67.75197179
## San Rafael 9.61692000 447.39031000 1.48836203 69.24033382
## 9.56251000 456.95282000 1.47994127 70.72027510
## Ã\201ngeles 9.53611000 466.48893000 1.47585548 72.19613057
## Cot 9.53493000 476.02386000 1.47567286 73.67180343
## Miramar 8.92048000 484.94434000 1.38057754 75.05238097
## Dulce Nombre de Jesus 8.39161000 493.33595000 1.29872701 76.35110797
## Santiago 8.27042000 501.60637000 1.27997104 77.63107902
## Mercedes 8.21372000 509.82009000 1.27119587 78.90227489
## Golfito 7.87044000 517.69053000 1.21806816 80.12034305
## Desamparados 6.92174000 524.61227000 1.07124267 81.19158572
## Desamparados 6.88715000 531.49942000 1.06588935 82.25747506
## Atenas 6.80061000 538.30003000 1.05249599 83.30997105
## Tejar 6.49523000 544.79526000 1.00523387 84.31520492
## La Fortuna 5.96634000 550.76160000 0.92338024 85.23858516
## Desamparados 5.95519000 556.71679000 0.92165461 86.16023978
## 5.57523000 562.29202000 0.86285013 87.02308991
## Santiago 5.43516000 567.72718000 0.84117220 87.86426211
## Siquirres 5.36500000 573.09218000 0.83031390 88.69457601
## 5.15142000 578.24360000 0.79725920 89.49183521
## Desamparados 5.12667000 583.37027000 0.79342877 90.28526397
## Corredor 4.93053000 588.30080000 0.76307317 91.04833715
## Daniel Flores 4.89954000 593.20034000 0.75827701 91.80661415
## Sabanilla 4.87432000 598.07466000 0.75437384 92.56098799
## San Ignacio 4.57763000 602.65229000 0.70845663 93.26944461
## Desamparados 4.24199000 606.89428000 0.65651132 93.92595593
## Miramar 3.82425000 610.71853000 0.59185982 94.51781575
## Dulce Nombre de Jesus 3.71407000 614.43260000 0.57480782 95.09262357
## Escazú 3.67691000 618.10951000 0.56905675 95.66168032
## Santo Domingo 3.21979000 621.32930000 0.49831060 96.15999092
## Alajuela 3.08916000 624.41846000 0.47809366 96.63808457
## Atenas 3.08459000 627.50305000 0.47738638 97.11547096
## Cartago 3.07297000 630.57602000 0.47558801 97.59105897
## Cartago 2.94804000 633.52406000 0.45625323 98.04731220
## Dulce Nombre de Jesus 2.92605000 636.45011000 0.45284995 98.50016215
## Naranjo 2.08469000 638.53480000 0.32263692 98.82279907
## Daniel Flores 1.85787000 640.39267000 0.28753314 99.11033220
## San Rafael 1.47396000 641.86663000 0.22811733 99.33844953
## San José 1.16705000 643.03368000 0.18061842 99.51906795
## San Juan 0.72957000 643.76325000 0.11291186 99.63197981
## Upala 0.70048000 644.46373000 0.10840974 99.74038955
## San Marcos 0.55804000 645.02177000 0.08636503 99.82675458
## Buenos Aires 0.35225000 645.37402000 0.05451595 99.88127053
## Heredia 0.26208000 645.63610000 0.04056080 99.92183132
## Salitral 0.25254000 645.88864000 0.03908434 99.96091566
## Salitral 0.25254000 646.14118000 0.03908434 100.00000000stem(df_cr$"Distance")##
## The decimal point is at the |
##
## 0 | 3334677259
## 2 | 1991112778
## 4 | 2699912446
## 6 | 0058999
## 8 | 23495566689
## 10 | 0023737
## 12 | 022395
## 14 | 816
## 16 | 227
## 18 | 0355
## 20 | 17
## 22 | 03head(df_cr)## id date time continent_code Country country_code State
## 38 249 9/9/07 <NA> Costa Rica CR Heredia
## 44 299 10/9/07 <NA> Costa Rica CR San José
## 45 301 10/11/07 <NA> Costa Rica CR Alajuela
## 46 302 10/11/07 <NA> Costa Rica CR San José
## 51 323 10/24/07 <NA> Costa Rica CR Puntarenas
## 102 556 5/29/08 <NA> Costa Rica CR Guanacaste
## population City Distance location_description latitude longitude
## 38 21947 Heredia 0.26208 10.0000 -84.1167
## 44 3072 San Ignacio 4.57763 9.7789 -84.1250
## 45 7014 Atenas 3.08459 9.9869 -84.4070
## 46 26669 9.56251 10.0214 -83.9451
## 51 6540 Miramar 3.82425 Mine construction 10.0715 -84.7575
## 102 4108 Bagaces 17.65521 10.4024 -85.3555
## geolocation hazard_type landslide_type
## 38 (10, -84.116699999999994) Landslide Landslide
## 44 (9.7789000000000001, -84.125) Landslide Complex
## 45 (9.9869000000000003, -84.406999999999996) Landslide Mudslide
## 46 (10.0214, -83.945099999999996) Landslide Landslide
## 51 (10.0715, -84.757499999999993) Landslide Mudslide
## 102 (10.4024, -85.355500000000006) Landslide Landslide
## landslide_size trigger storm_name injuries fatalities
## 38 Medium Rain NA NA
## 44 Medium Rain NA 4
## 45 Large Rain NA 14
## 46 Large Rain NA 10
## 51 Medium Downpour NA NA
## 102 Medium Tropical cyclone Tropical Storm Alma NA NA
## source_name
## 38 ticotimes.net
## 44 ticotimes.net
## 45 Agence France-Presse, afp.google.com
## 46 International Herald
## 51 Reuters - AlertNet.org
## 102
## source_link
## 38 http://www.ticotimes.net/dailyarchive/2007_09/0911072.htm
## 44 http://www.ticotimes.net/dailyarchive/2007_10/1010071.htm
## 45 http://afp.google.com/article/ALeqM5hu6a8oyAM1ycq9nU_6Zyj_l7F0AA
## 46 http://www.iht.com/articles/ap/2007/10/12/america/LA-GEN-Costa-Rica-Mudslide.php
## 51 http://www.reuters.com/article/companyNewsAndPR/idUSN2435152820071025
## 102 http://www.reliefweb.int/rw/RWB.NSF/db900SID/ASAZ-7FHCHL?OpenDocumentknitr::kable(head(df_cr))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 249 | 9/9/07 | NA | Costa Rica | CR | Heredia | 21947 | Heredia | 0.26208 | 10.0000 | -84.1167 | (10, -84.116699999999994) | Landslide | Landslide | Medium | Rain | NA | NA | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_09/0911072.htm | |||
| 44 | 299 | 10/9/07 | NA | Costa Rica | CR | San José | 3072 | San Ignacio | 4.57763 | 9.7789 | -84.1250 | (9.7789000000000001, -84.125) | Landslide | Complex | Medium | Rain | NA | 4 | ticotimes.net | http://www.ticotimes.net/dailyarchive/2007_10/1010071.htm | |||
| 45 | 301 | 10/11/07 | NA | Costa Rica | CR | Alajuela | 7014 | Atenas | 3.08459 | 9.9869 | -84.4070 | (9.9869000000000003, -84.406999999999996) | Landslide | Mudslide | Large | Rain | NA | 14 | Agence France-Presse, afp.google.com | http://afp.google.com/article/ALeqM5hu6a8oyAM1ycq9nU_6Zyj_l7F0AA | |||
| 46 | 302 | 10/11/07 | NA | Costa Rica | CR | San José | 26669 | 9.56251 | 10.0214 | -83.9451 | (10.0214, -83.945099999999996) | Landslide | Landslide | Large | Rain | NA | 10 | International Herald | http://www.iht.com/articles/ap/2007/10/12/america/LA-GEN-Costa-Rica-Mudslide.php | ||||
| 51 | 323 | 10/24/07 | NA | Costa Rica | CR | Puntarenas | 6540 | Miramar | 3.82425 | Mine construction | 10.0715 | -84.7575 | (10.0715, -84.757499999999993) | Landslide | Mudslide | Medium | Downpour | NA | NA | Reuters - AlertNet.org | http://www.reuters.com/article/companyNewsAndPR/idUSN2435152820071025 | ||
| 102 | 556 | 5/29/08 | NA | Costa Rica | CR | Guanacaste | 4108 | Bagaces | 17.65521 | 10.4024 | -85.3555 | (10.4024, -85.355500000000006) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Alma | NA | NA | http://www.reliefweb.int/rw/RWB.NSF/db900SID/ASAZ-7FHCHL?OpenDocument |
stem(df_cr$"Distance")##
## The decimal point is at the |
##
## 0 | 3334677259
## 2 | 1991112778
## 4 | 2699912446
## 6 | 0058999
## 8 | 23495566689
## 10 | 0023737
## 12 | 022395
## 14 | 816
## 16 | 227
## 18 | 0355
## 20 | 17
## 22 | 03stem(df_cr$"Distance", scale = 2)##
## The decimal point is at the |
##
## 0 | 3334677259
## 2 | 1991112778
## 4 | 2699912446
## 6 | 0058999
## 8 | 23495566689
## 10 | 0023737
## 12 | 022395
## 14 | 816
## 16 | 227
## 18 | 0355
## 20 | 17
## 22 | 03library(forecast)## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoodata<- ts(df_cr$Distance, frequency=12, start=2008)
knitr::kable(head(data))| x |
|---|
| 0.26208 |
| 4.57763 |
| 3.08459 |
| 9.56251 |
| 3.82425 |
| 17.65521 |
autoplot(data) + labs(title = "Gráfico de series temporales", x="date", y = "distancia", colour = "green") +theme_bw()
library(questionr)
table <- questionr::freq(Distance, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| 0.25254 | 2 | 2.7 | 2.7 | 2.7 | 2.7 |
| 0.26208 | 1 | 1.3 | 1.3 | 4.0 | 4.0 |
| 0.35225 | 1 | 1.3 | 1.3 | 5.3 | 5.3 |
| 0.55804 | 1 | 1.3 | 1.3 | 6.7 | 6.7 |
| 0.70048 | 1 | 1.3 | 1.3 | 8.0 | 8.0 |
| 0.72957 | 1 | 1.3 | 1.3 | 9.3 | 9.3 |
| 1.16705 | 1 | 1.3 | 1.3 | 10.7 | 10.7 |
| 1.47396 | 1 | 1.3 | 1.3 | 12.0 | 12.0 |
| 1.85787 | 1 | 1.3 | 1.3 | 13.3 | 13.3 |
| 2.08469 | 1 | 1.3 | 1.3 | 14.7 | 14.7 |
| 2.92605 | 1 | 1.3 | 1.3 | 16.0 | 16.0 |
| 2.94804 | 1 | 1.3 | 1.3 | 17.3 | 17.3 |
| 3.07297 | 1 | 1.3 | 1.3 | 18.7 | 18.7 |
| 3.08459 | 1 | 1.3 | 1.3 | 20.0 | 20.0 |
| 3.08916 | 1 | 1.3 | 1.3 | 21.3 | 21.3 |
| 3.21979 | 1 | 1.3 | 1.3 | 22.7 | 22.7 |
| 3.67691 | 1 | 1.3 | 1.3 | 24.0 | 24.0 |
| 3.71407 | 1 | 1.3 | 1.3 | 25.3 | 25.3 |
| 3.82425 | 1 | 1.3 | 1.3 | 26.7 | 26.7 |
| 4.24199 | 1 | 1.3 | 1.3 | 28.0 | 28.0 |
| 4.57763 | 1 | 1.3 | 1.3 | 29.3 | 29.3 |
| 4.87432 | 1 | 1.3 | 1.3 | 30.7 | 30.7 |
| 4.89954 | 1 | 1.3 | 1.3 | 32.0 | 32.0 |
| 4.93053 | 1 | 1.3 | 1.3 | 33.3 | 33.3 |
| 5.12667 | 1 | 1.3 | 1.3 | 34.7 | 34.7 |
| 5.15142 | 1 | 1.3 | 1.3 | 36.0 | 36.0 |
| 5.365 | 1 | 1.3 | 1.3 | 37.3 | 37.3 |
| 5.43516 | 1 | 1.3 | 1.3 | 38.7 | 38.7 |
| 5.57523 | 1 | 1.3 | 1.3 | 40.0 | 40.0 |
| 5.95519 | 1 | 1.3 | 1.3 | 41.3 | 41.3 |
| 5.96634 | 1 | 1.3 | 1.3 | 42.7 | 42.7 |
| 6.49523 | 1 | 1.3 | 1.3 | 44.0 | 44.0 |
| 6.80061 | 1 | 1.3 | 1.3 | 45.3 | 45.3 |
| 6.88715 | 1 | 1.3 | 1.3 | 46.7 | 46.7 |
| 6.92174 | 1 | 1.3 | 1.3 | 48.0 | 48.0 |
| 7.87044 | 1 | 1.3 | 1.3 | 49.3 | 49.3 |
| 8.21372 | 1 | 1.3 | 1.3 | 50.7 | 50.7 |
| 8.27042 | 1 | 1.3 | 1.3 | 52.0 | 52.0 |
| 8.39161 | 1 | 1.3 | 1.3 | 53.3 | 53.3 |
| 8.92048 | 1 | 1.3 | 1.3 | 54.7 | 54.7 |
| 9.53493 | 1 | 1.3 | 1.3 | 56.0 | 56.0 |
| 9.53611 | 1 | 1.3 | 1.3 | 57.3 | 57.3 |
| 9.56251 | 1 | 1.3 | 1.3 | 58.7 | 58.7 |
| 9.61692 | 1 | 1.3 | 1.3 | 60.0 | 60.0 |
| 9.63616 | 1 | 1.3 | 1.3 | 61.3 | 61.3 |
| 9.84213 | 1 | 1.3 | 1.3 | 62.7 | 62.7 |
| 9.85736 | 1 | 1.3 | 1.3 | 64.0 | 64.0 |
| 10.01198 | 1 | 1.3 | 1.3 | 65.3 | 65.3 |
| 10.0131 | 1 | 1.3 | 1.3 | 66.7 | 66.7 |
| 10.21631 | 1 | 1.3 | 1.3 | 68.0 | 68.0 |
| 10.32968 | 1 | 1.3 | 1.3 | 69.3 | 69.3 |
| 10.73752 | 1 | 1.3 | 1.3 | 70.7 | 70.7 |
| 11.31047 | 1 | 1.3 | 1.3 | 72.0 | 72.0 |
| 11.74074 | 1 | 1.3 | 1.3 | 73.3 | 73.3 |
| 11.96524 | 1 | 1.3 | 1.3 | 74.7 | 74.7 |
| 12.18115 | 1 | 1.3 | 1.3 | 76.0 | 76.0 |
| 12.21952 | 1 | 1.3 | 1.3 | 77.3 | 77.3 |
| 12.33807 | 1 | 1.3 | 1.3 | 78.7 | 78.7 |
| 12.85801 | 1 | 1.3 | 1.3 | 80.0 | 80.0 |
| 13.48919 | 1 | 1.3 | 1.3 | 81.3 | 81.3 |
| 14.81614 | 1 | 1.3 | 1.3 | 82.7 | 82.7 |
| 15.05161 | 1 | 1.3 | 1.3 | 84.0 | 84.0 |
| 15.64997 | 1 | 1.3 | 1.3 | 85.3 | 85.3 |
| 16.24937 | 1 | 1.3 | 1.3 | 86.7 | 86.7 |
| 17.23264 | 1 | 1.3 | 1.3 | 88.0 | 88.0 |
| 17.65521 | 1 | 1.3 | 1.3 | 89.3 | 89.3 |
| 18.00524 | 1 | 1.3 | 1.3 | 90.7 | 90.7 |
| 19.28722 | 1 | 1.3 | 1.3 | 92.0 | 92.0 |
| 19.51432 | 1 | 1.3 | 1.3 | 93.3 | 93.3 |
| 19.54581 | 1 | 1.3 | 1.3 | 94.7 | 94.7 |
| 20.06633 | 1 | 1.3 | 1.3 | 96.0 | 96.0 |
| 21.67452 | 1 | 1.3 | 1.3 | 97.3 | 97.3 |
| 21.9547 | 1 | 1.3 | 1.3 | 98.7 | 98.7 |
| 22.32368 | 1 | 1.3 | 1.3 | 100.0 | 100.0 |
| Total | 75 | 100.0 | 100.0 | 100.0 | 100.0 |
str(table)## Classes 'freqtab' and 'data.frame': 75 obs. of 5 variables:
## $ n : num 2 1 1 1 1 1 1 1 1 1 ...
## $ % : num 2.7 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 ...
## $ val% : num 2.7 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 ...
## $ %cum : num 2.7 4 5.3 6.7 8 9.3 10.7 12 13.3 14.7 ...
## $ val%cum: num 2.7 4 5.3 6.7 8 9.3 10.7 12 13.3 14.7 ...x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]
df_cr <- data.frame(x = names, y = freqs)
knitr::kable(df_cr)| x | y |
|---|---|
| 0.25254 | 2 |
| 0.26208 | 1 |
| 0.35225 | 1 |
| 0.55804 | 1 |
| 0.70048 | 1 |
| 0.72957 | 1 |
| 1.16705 | 1 |
| 1.47396 | 1 |
| 1.85787 | 1 |
| 2.08469 | 1 |
| 2.92605 | 1 |
| 2.94804 | 1 |
| 3.07297 | 1 |
| 3.08459 | 1 |
| 3.08916 | 1 |
| 3.21979 | 1 |
| 3.67691 | 1 |
| 3.71407 | 1 |
| 3.82425 | 1 |
| 4.24199 | 1 |
| 4.57763 | 1 |
| 4.87432 | 1 |
| 4.89954 | 1 |
| 4.93053 | 1 |
| 5.12667 | 1 |
| 5.15142 | 1 |
| 5.365 | 1 |
| 5.43516 | 1 |
| 5.57523 | 1 |
| 5.95519 | 1 |
| 5.96634 | 1 |
| 6.49523 | 1 |
| 6.80061 | 1 |
| 6.88715 | 1 |
| 6.92174 | 1 |
| 7.87044 | 1 |
| 8.21372 | 1 |
| 8.27042 | 1 |
| 8.39161 | 1 |
| 8.92048 | 1 |
| 9.53493 | 1 |
| 9.53611 | 1 |
| 9.56251 | 1 |
| 9.61692 | 1 |
| 9.63616 | 1 |
| 9.84213 | 1 |
| 9.85736 | 1 |
| 10.01198 | 1 |
| 10.0131 | 1 |
| 10.21631 | 1 |
| 10.32968 | 1 |
| 10.73752 | 1 |
| 11.31047 | 1 |
| 11.74074 | 1 |
| 11.96524 | 1 |
| 12.18115 | 1 |
| 12.21952 | 1 |
| 12.33807 | 1 |
| 12.85801 | 1 |
| 13.48919 | 1 |
| 14.81614 | 1 |
| 15.05161 | 1 |
| 15.64997 | 1 |
| 16.24937 | 1 |
| 17.23264 | 1 |
| 17.65521 | 1 |
| 18.00524 | 1 |
| 19.28722 | 1 |
| 19.51432 | 1 |
| 19.54581 | 1 |
| 20.06633 | 1 |
| 21.67452 | 1 |
| 21.9547 | 1 |
| 22.32368 | 1 |
library(ggplot2)
ggplot(data=df_cr, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de inundaciones") +
ylab("Frecuencia")
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.25254 4.25254 8.25254 12.25254 16.25254 20.25254 24.25254Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.25254 | 2 | 0.0266667 | 2 |
| 0.26208 | 1 | 0.0133333 | 3 |
| 0.35225 | 1 | 0.0133333 | 4 |
| 0.55804 | 1 | 0.0133333 | 5 |
| 0.70048 | 1 | 0.0133333 | 6 |
| 0.72957 | 1 | 0.0133333 | 7 |
| 1.16705 | 1 | 0.0133333 | 8 |
| 1.47396 | 1 | 0.0133333 | 9 |
| 1.85787 | 1 | 0.0133333 | 10 |
| 2.08469 | 1 | 0.0133333 | 11 |
| 2.92605 | 1 | 0.0133333 | 12 |
| 2.94804 | 1 | 0.0133333 | 13 |
| 3.07297 | 1 | 0.0133333 | 14 |
| 3.08459 | 1 | 0.0133333 | 15 |
| 3.08916 | 1 | 0.0133333 | 16 |
| 3.21979 | 1 | 0.0133333 | 17 |
| 3.67691 | 1 | 0.0133333 | 18 |
| 3.71407 | 1 | 0.0133333 | 19 |
| 3.82425 | 1 | 0.0133333 | 20 |
| 4.24199 | 1 | 0.0133333 | 21 |
| 4.57763 | 1 | 0.0133333 | 22 |
| 4.87432 | 1 | 0.0133333 | 23 |
| 4.89954 | 1 | 0.0133333 | 24 |
| 4.93053 | 1 | 0.0133333 | 25 |
| 5.12667 | 1 | 0.0133333 | 26 |
| 5.15142 | 1 | 0.0133333 | 27 |
| 5.365 | 1 | 0.0133333 | 28 |
| 5.43516 | 1 | 0.0133333 | 29 |
| 5.57523 | 1 | 0.0133333 | 30 |
| 5.95519 | 1 | 0.0133333 | 31 |
| 5.96634 | 1 | 0.0133333 | 32 |
| 6.49523 | 1 | 0.0133333 | 33 |
| 6.80061 | 1 | 0.0133333 | 34 |
| 6.88715 | 1 | 0.0133333 | 35 |
| 6.92174 | 1 | 0.0133333 | 36 |
| 7.87044 | 1 | 0.0133333 | 37 |
| 8.21372 | 1 | 0.0133333 | 38 |
| 8.27042 | 1 | 0.0133333 | 39 |
| 8.39161 | 1 | 0.0133333 | 40 |
| 8.92048 | 1 | 0.0133333 | 41 |
| 9.53493 | 1 | 0.0133333 | 42 |
| 9.53611 | 1 | 0.0133333 | 43 |
| 9.56251 | 1 | 0.0133333 | 44 |
| 9.61692 | 1 | 0.0133333 | 45 |
| 9.63616 | 1 | 0.0133333 | 46 |
| 9.84213 | 1 | 0.0133333 | 47 |
| 9.85736 | 1 | 0.0133333 | 48 |
| 10.01198 | 1 | 0.0133333 | 49 |
| 10.0131 | 1 | 0.0133333 | 50 |
| 10.21631 | 1 | 0.0133333 | 51 |
| 10.32968 | 1 | 0.0133333 | 52 |
| 10.73752 | 1 | 0.0133333 | 53 |
| 11.31047 | 1 | 0.0133333 | 54 |
| 11.74074 | 1 | 0.0133333 | 55 |
| 11.96524 | 1 | 0.0133333 | 56 |
| 12.18115 | 1 | 0.0133333 | 57 |
| 12.21952 | 1 | 0.0133333 | 58 |
| 12.33807 | 1 | 0.0133333 | 59 |
| 12.85801 | 1 | 0.0133333 | 60 |
| 13.48919 | 1 | 0.0133333 | 61 |
| 14.81614 | 1 | 0.0133333 | 62 |
| 15.05161 | 1 | 0.0133333 | 63 |
| 15.64997 | 1 | 0.0133333 | 64 |
| 16.24937 | 1 | 0.0133333 | 65 |
| 17.23264 | 1 | 0.0133333 | 66 |
| 17.65521 | 1 | 0.0133333 | 67 |
| 18.00524 | 1 | 0.0133333 | 68 |
| 19.28722 | 1 | 0.0133333 | 69 |
| 19.51432 | 1 | 0.0133333 | 70 |
| 19.54581 | 1 | 0.0133333 | 71 |
| 20.06633 | 1 | 0.0133333 | 72 |
| 21.67452 | 1 | 0.0133333 | 73 |
| 21.9547 | 1 | 0.0133333 | 74 |
| 22.32368 | 1 | 0.0133333 | 75 |
str(Freq_table)## 'data.frame': 74 obs. of 4 variables:
## $ Distance: Factor w/ 74 levels "0.25254","0.26208",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 2 1 1 1 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0267 0.0133 0.0133 0.0133 0.0133 ...
## $ Cum_Freq: int 2 3 4 5 6 7 8 9 10 11 ...df_cr <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_cr)| x | y |
|---|---|
| 0.25254 | 2 |
| 0.26208 | 1 |
| 0.35225 | 1 |
| 0.55804 | 1 |
| 0.70048 | 1 |
| 0.72957 | 1 |
| 1.16705 | 1 |
| 1.47396 | 1 |
| 1.85787 | 1 |
| 2.08469 | 1 |
| 2.92605 | 1 |
| 2.94804 | 1 |
| 3.07297 | 1 |
| 3.08459 | 1 |
| 3.08916 | 1 |
| 3.21979 | 1 |
| 3.67691 | 1 |
| 3.71407 | 1 |
| 3.82425 | 1 |
| 4.24199 | 1 |
| 4.57763 | 1 |
| 4.87432 | 1 |
| 4.89954 | 1 |
| 4.93053 | 1 |
| 5.12667 | 1 |
| 5.15142 | 1 |
| 5.365 | 1 |
| 5.43516 | 1 |
| 5.57523 | 1 |
| 5.95519 | 1 |
| 5.96634 | 1 |
| 6.49523 | 1 |
| 6.80061 | 1 |
| 6.88715 | 1 |
| 6.92174 | 1 |
| 7.87044 | 1 |
| 8.21372 | 1 |
| 8.27042 | 1 |
| 8.39161 | 1 |
| 8.92048 | 1 |
| 9.53493 | 1 |
| 9.53611 | 1 |
| 9.56251 | 1 |
| 9.61692 | 1 |
| 9.63616 | 1 |
| 9.84213 | 1 |
| 9.85736 | 1 |
| 10.01198 | 1 |
| 10.0131 | 1 |
| 10.21631 | 1 |
| 10.32968 | 1 |
| 10.73752 | 1 |
| 11.31047 | 1 |
| 11.74074 | 1 |
| 11.96524 | 1 |
| 12.18115 | 1 |
| 12.21952 | 1 |
| 12.33807 | 1 |
| 12.85801 | 1 |
| 13.48919 | 1 |
| 14.81614 | 1 |
| 15.05161 | 1 |
| 15.64997 | 1 |
| 16.24937 | 1 |
| 17.23264 | 1 |
| 17.65521 | 1 |
| 18.00524 | 1 |
| 19.28722 | 1 |
| 19.51432 | 1 |
| 19.54581 | 1 |
| 20.06633 | 1 |
| 21.67452 | 1 |
| 21.9547 | 1 |
| 22.32368 | 1 |
library(ggplot2)
ggplot(data=df_cr, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_cr$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)##
## Attaching package: 'pastecs'## The following objects are masked from 'package:dplyr':
##
## first, laststat.desc(df_cr)## x y
## nbr.val NA 74.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 75.00000000
## median NA 1.00000000
## mean NA 1.01351351
## SE.mean NA 0.01351351
## CI.mean NA 0.02693239
## var NA 0.01351351
## std.dev NA 0.11624764
## coef.var NA 0.11469767boxplot(Distance, horizontal=TRUE, col='orange')
#COLOMBIA
library(readr)
library(knitr)
df_co <- subset (df, Country == "Colombia")
knitr::kable(head(df_co))| 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 | MedellÃn | 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 | Hacarà | 0.38844 | 8.3200 | -73.1500 | (8.32, -73.150000000000006) | Landslide | Landslide | Medium | Downpour | NA | 10 | http://news.xinhuanet.com/english/2008-06/25/content_8434589.htm | ||||
| 117 | 644 | 7/14/08 | SA | Colombia | CO | Cundinamarca | 1374 | Quetame | 8.58891 | 4.4100 | -73.8600 | (4.41, -73.86) | Landslide | Landslide | Medium | Downpour | NA | 4 | http://news.xinhuanet.com/english/2008-07/15/content_8548107.htm |
df_co %>%
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 MedellÃn 5.12170
## 110 Colombia Norte de Santander Hacarà 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 MedellÃn 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 ApÃa 8.18229
## 455 Colombia Antioquia Giraldo 2.55282
## 479 Colombia Boyacá San Pablo de Borbur 1.53032
## 517 Colombia Risaralda QuinchÃa 0.11421
## 518 Colombia Antioquia MedellÃn 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 VillamarÃa 2.36822
## 621 Colombia Norte de Santander Cúcuta 3.60520
## 623 Colombia Caldas Manizales 1.70850
## 629 Colombia Antioquia MedellÃn 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 VillamarÃa 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 Chucurà 0.80432
## 753 Colombia BolÃvar Santa Rosa del Sur 26.89879
## 821 Colombia Antioquia MedellÃn 1.73101
## 822 Colombia Valle del Cauca Buenaventura 20.25692
## 826 Colombia Antioquia Ciudad BolÃvar 24.48479
## 830 Colombia QuindÃo 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 AgustÃn 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 Ã\201quira 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 Ariguanà 28.50569
## 1305 Colombia Santander San Vicente de Chucurà 1.08964
## 1344 Colombia Cauca Almaguer 17.31514
## 1362 Colombia Antioquia Barbosa 7.78677
## 1456 Colombia Putumayo San Francisco 1.47940
## 1457 Colombia Antioquia Salgar 3.09014
## 1471 Colombia Cauca Jambaló 8.81287
## 1565 Colombia Huila Rivera 20.31227
## 1669 Colombia Antioquia Mutatá 15.04256library(ggplot2)
ggplot(data=df_co, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="yellow")
library(ggplot2)
library(dplyr)
ggplot(data=df_co, aes(fill=City, x="Colombia", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_co, aes(fill=City, x="Colombia", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_co, aes(x = "Colombia", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
library(qcc)Distance <- df_co$Distance
names(Distance) <- df_co$City
pareto.chart(Distance,
ylab="Distance",
col = heat.colors(length(Distance)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "CONCENTRACION DE LAS INUNDACIONES EN LAS CIUDADES")
##
## Pareto chart analysis for Distance
## Frequency Cum.Freq. Percentage
## Ciénaga 5.184125e+01 5.184125e+01 7.082466e+00
## Ariguanà 2.850569e+01 8.034694e+01 3.894400e+00
## Santa Rosa del Sur 2.689879e+01 1.072457e+02 3.674868e+00
## San Marcos 2.551411e+01 1.327598e+02 3.485696e+00
## Ciudad BolÃvar 2.448479e+01 1.572446e+02 3.345072e+00
## Moñitos 2.349217e+01 1.807368e+02 3.209462e+00
## Puerto López 2.253724e+01 2.032740e+02 3.079001e+00
## Rivera 2.031227e+01 2.235863e+02 2.775029e+00
## Buenaventura 2.025692e+01 2.438432e+02 2.767467e+00
## Santa Rosa de Osos 1.891189e+01 2.627551e+02 2.583711e+00
## Marquetalia 1.748659e+01 2.802417e+02 2.388989e+00
## Almaguer 1.731514e+01 2.975568e+02 2.365566e+00
## Cumbitara 1.697776e+01 3.145346e+02 2.319474e+00
## Norcasia 1.694642e+01 3.314810e+02 2.315192e+00
## VillamarÃa 1.582404e+01 3.473051e+02 2.161854e+00
## Florencia 1.542607e+01 3.627311e+02 2.107484e+00
## Florencia 1.516116e+01 3.778923e+02 2.071293e+00
## Mutatá 1.504256e+01 3.929349e+02 2.055090e+00
## Arboledas 1.462503e+01 4.075599e+02 1.998047e+00
## Los Santos 1.261362e+01 4.201735e+02 1.723252e+00
## San Calixto 1.119714e+01 4.313707e+02 1.529735e+00
## Amalfi 1.111685e+01 4.424875e+02 1.518766e+00
## Piedecuesta 1.055986e+01 4.530474e+02 1.442671e+00
## Soacha 1.036239e+01 4.634098e+02 1.415693e+00
## Hobo 1.018040e+01 4.735901e+02 1.390829e+00
## Carmen de Apicalá 9.651570e+00 4.832417e+02 1.318582e+00
## Funza 9.237780e+00 4.924795e+02 1.262050e+00
## Lebrija 9.212170e+00 5.016917e+02 1.258551e+00
## Jambaló 8.812870e+00 5.105045e+02 1.204000e+00
## Quetame 8.588910e+00 5.190934e+02 1.173403e+00
## Cajamarca 8.560860e+00 5.276543e+02 1.169571e+00
## Suárez 8.465790e+00 5.361201e+02 1.156582e+00
## Cali 8.304060e+00 5.444242e+02 1.134487e+00
## ApÃa 8.182290e+00 5.526064e+02 1.117851e+00
## Municipio de Copacabana 7.988380e+00 5.605948e+02 1.091359e+00
## Cajamarca 7.873030e+00 5.684679e+02 1.075600e+00
## San Calixto 7.853690e+00 5.763216e+02 1.072958e+00
## Barbosa 7.786770e+00 5.841083e+02 1.063816e+00
## Palmira 7.702370e+00 5.918107e+02 1.052285e+00
## La Calera 7.289590e+00 5.991003e+02 9.958918e-01
## Ambalema 6.961300e+00 6.060616e+02 9.510413e-01
## San AgustÃn 6.846830e+00 6.129084e+02 9.354026e-01
## Briceño 6.445320e+00 6.193537e+02 8.805490e-01
## Matanza 6.163850e+00 6.255176e+02 8.420950e-01
## San Antonio del Tequendama 6.086280e+00 6.316039e+02 8.314975e-01
## Soacha 5.176500e+00 6.367804e+02 7.072049e-01
## MedellÃn 5.121700e+00 6.419021e+02 6.997182e-01
## Frontino 5.069600e+00 6.469717e+02 6.926004e-01
## San Pablo 4.953530e+00 6.519252e+02 6.767431e-01
## Cáceres 4.291970e+00 6.562172e+02 5.863619e-01
## MedellÃn 4.198670e+00 6.604158e+02 5.736154e-01
## MedellÃn 4.090280e+00 6.645061e+02 5.588073e-01
## El Tambo 3.814450e+00 6.683206e+02 5.211239e-01
## Marquetalia 3.721950e+00 6.720425e+02 5.084867e-01
## Cúcuta 3.605200e+00 6.756477e+02 4.925365e-01
## Ã\201quira 3.142010e+00 6.787897e+02 4.292562e-01
## Salgar 3.090140e+00 6.818799e+02 4.221698e-01
## San Andrés 3.063830e+00 6.849437e+02 4.185754e-01
## Málaga 2.999290e+00 6.879430e+02 4.097581e-01
## Málaga 2.999290e+00 6.909423e+02 4.097581e-01
## Giraldo 2.552820e+00 6.934951e+02 3.487621e-01
## Aranzazu 2.530470e+00 6.960256e+02 3.457086e-01
## VillamarÃa 2.368220e+00 6.983938e+02 3.235423e-01
## Belén 2.284250e+00 7.006780e+02 3.120705e-01
## MedellÃn 2.187760e+00 7.028658e+02 2.988882e-01
## Envigado 2.070810e+00 7.049366e+02 2.829106e-01
## Bello 2.048980e+00 7.069856e+02 2.799283e-01
## Cali 1.849410e+00 7.088350e+02 2.526633e-01
## MedellÃn 1.731010e+00 7.105660e+02 2.364877e-01
## Manizales 1.708500e+00 7.122745e+02 2.334125e-01
## San Pablo de Borbur 1.530320e+00 7.138048e+02 2.090698e-01
## San Francisco 1.479400e+00 7.152842e+02 2.021132e-01
## Calarcá 1.351960e+00 7.166362e+02 1.847025e-01
## Bucaramanga 1.338290e+00 7.179745e+02 1.828350e-01
## Caldas 1.276370e+00 7.192508e+02 1.743756e-01
## San Vicente de Chucurà 1.089640e+00 7.203405e+02 1.488648e-01
## Manizales 1.077650e+00 7.214181e+02 1.472268e-01
## Gigante 9.318400e-01 7.223500e+02 1.273064e-01
## Samacá 8.597600e-01 7.232097e+02 1.174590e-01
## San Vicente de Chucurà 8.043200e-01 7.240140e+02 1.098849e-01
## Santa Marta 7.969400e-01 7.248110e+02 1.088766e-01
## Dos Quebradas 7.420100e-01 7.255530e+02 1.013722e-01
## Marulanda 7.283000e-01 7.262813e+02 9.949914e-02
## Miranda 7.055800e-01 7.269869e+02 9.639518e-02
## Cereté 6.854400e-01 7.276723e+02 9.364368e-02
## La Cruz 6.446900e-01 7.283170e+02 8.807648e-02
## Manizales 6.409400e-01 7.289579e+02 8.756417e-02
## Pereira 6.202200e-01 7.295782e+02 8.473343e-02
## El Tambo 6.059900e-01 7.301842e+02 8.278935e-02
## Florián 4.475300e-01 7.306317e+02 6.114081e-02
## Hacarà 3.884400e-01 7.310201e+02 5.306803e-02
## San Lorenzo 3.780900e-01 7.313982e+02 5.165403e-02
## Girardot 3.058700e-01 7.317041e+02 4.178745e-02
## Sahagún 1.477600e-01 7.318519e+02 2.018673e-02
## QuinchÃa 1.142100e-01 7.319661e+02 1.560318e-02
## Palermo 3.000000e-05 7.319661e+02 4.098551e-06
##
## Pareto chart analysis for Distance
## Cum.Percent.
## Ciénaga 7.082466e+00
## Ariguanà 1.097687e+01
## Santa Rosa del Sur 1.465173e+01
## San Marcos 1.813743e+01
## Ciudad BolÃvar 2.148250e+01
## Moñitos 2.469196e+01
## Puerto López 2.777096e+01
## Rivera 3.054599e+01
## Buenaventura 3.331346e+01
## Santa Rosa de Osos 3.589717e+01
## Marquetalia 3.828616e+01
## Almaguer 4.065173e+01
## Cumbitara 4.297120e+01
## Norcasia 4.528639e+01
## VillamarÃa 4.744825e+01
## Florencia 4.955573e+01
## Florencia 5.162702e+01
## Mutatá 5.368211e+01
## Arboledas 5.568016e+01
## Los Santos 5.740341e+01
## San Calixto 5.893315e+01
## Amalfi 6.045191e+01
## Piedecuesta 6.189458e+01
## Soacha 6.331028e+01
## Hobo 6.470111e+01
## Carmen de Apicalá 6.601969e+01
## Funza 6.728174e+01
## Lebrija 6.854029e+01
## Jambaló 6.974429e+01
## Quetame 7.091769e+01
## Cajamarca 7.208726e+01
## Suárez 7.324384e+01
## Cali 7.437833e+01
## ApÃa 7.549618e+01
## Municipio de Copacabana 7.658754e+01
## Cajamarca 7.766314e+01
## San Calixto 7.873610e+01
## Barbosa 7.979992e+01
## Palmira 8.085220e+01
## La Calera 8.184809e+01
## Ambalema 8.279913e+01
## San AgustÃn 8.373454e+01
## Briceño 8.461509e+01
## Matanza 8.545718e+01
## San Antonio del Tequendama 8.628868e+01
## Soacha 8.699588e+01
## MedellÃn 8.769560e+01
## Frontino 8.838820e+01
## San Pablo 8.906494e+01
## Cáceres 8.965131e+01
## MedellÃn 9.022492e+01
## MedellÃn 9.078373e+01
## El Tambo 9.130485e+01
## Marquetalia 9.181334e+01
## Cúcuta 9.230588e+01
## Ã\201quira 9.273513e+01
## Salgar 9.315730e+01
## San Andrés 9.357588e+01
## Málaga 9.398564e+01
## Málaga 9.439539e+01
## Giraldo 9.474416e+01
## Aranzazu 9.508986e+01
## VillamarÃa 9.541341e+01
## Belén 9.572548e+01
## MedellÃn 9.602437e+01
## Envigado 9.630728e+01
## Bello 9.658720e+01
## Cali 9.683987e+01
## MedellÃn 9.707635e+01
## Manizales 9.730977e+01
## San Pablo de Borbur 9.751884e+01
## San Francisco 9.772095e+01
## Calarcá 9.790565e+01
## Bucaramanga 9.808849e+01
## Caldas 9.826286e+01
## San Vicente de Chucurà 9.841173e+01
## Manizales 9.855896e+01
## Gigante 9.868626e+01
## Samacá 9.880372e+01
## San Vicente de Chucurà 9.891361e+01
## Santa Marta 9.902248e+01
## Dos Quebradas 9.912385e+01
## Marulanda 9.922335e+01
## Miranda 9.931975e+01
## Cereté 9.941339e+01
## La Cruz 9.950147e+01
## Manizales 9.958903e+01
## Pereira 9.967377e+01
## El Tambo 9.975656e+01
## Florián 9.981770e+01
## Hacarà 9.987076e+01
## San Lorenzo 9.992242e+01
## Girardot 9.996421e+01
## Sahagún 9.998439e+01
## QuinchÃa 1.000000e+02
## Palermo 1.000000e+02stem(df_co$"Distance")##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 00000001111111111111111112222222223333333444444555566677788888888999
## 1 | 00011135555677779
## 2 | 00334679
## 3 |
## 4 |
## 5 | 2head(df_co)## id date time continent_code Country country_code State
## 8 77 5/21/07 SA Colombia CO Risaralda
## 49 307 10/13/07 SA Colombia CO Cauca
## 70 397 12/19/07 SA Colombia CO Tolima
## 103 562 5/31/08 SA Colombia CO Antioquia
## 110 605 6/24/08 SA Colombia CO Norte de Santander
## 117 644 7/14/08 SA Colombia CO Cundinamarca
## population City Distance location_description latitude longitude
## 8 440118 Pereira 0.62022 4.8081 -75.6941
## 49 9985 Suárez 8.46579 2.9437 -76.7719
## 70 4892 Ambalema 6.96130 4.8470 -74.7631
## 103 1999979 MedellÃn 5.12170 6.2746 -75.6039
## 110 1502 Hacarà 0.38844 8.3200 -73.1500
## 117 1374 Quetame 8.58891 4.4100 -73.8600
## geolocation hazard_type landslide_type
## 8 (4.8080999999999996, -75.694100000000006) Landslide Mudslide
## 49 (2.9437000000000002, -76.771900000000002) Landslide Mudslide
## 70 (4.8470000000000004, -74.763099999999994) Landslide Landslide
## 103 (6.2746000000000004, -75.603899999999996) Landslide Complex
## 110 (8.32, -73.150000000000006) Landslide Landslide
## 117 (4.41, -73.86) Landslide Landslide
## landslide_size trigger storm_name injuries fatalities
## 8 Large Rain NA 13
## 49 Large Continuous rain NA 24
## 70 Large Rain NA NA
## 103 Large Downpour NA 27
## 110 Medium Downpour NA 10
## 117 Medium Downpour NA 4
## source_name
## 8 Reuters - AlertNet.org
## 49 Reuters - AlertNet.org
## 70 Indiamuslims.info
## 103
## 110
## 117
## source_link
## 8 http://www.reuters.com/news/video/videoStory?videoId=53594&feedType=RSS&rpc=23
## 49 http://www.reuters.com/article/newsOne/idUSN1329387220071013
## 70 http://www.indiamuslims.info/news/2007/dec/20/eight_people_rescued_colombian_landslide.html
## 103 http://english.people.com.cn/90001/90777/90852/6422291.html
## 110 http://news.xinhuanet.com/english/2008-06/25/content_8434589.htm
## 117 http://news.xinhuanet.com/english/2008-07/15/content_8548107.htmknitr::kable(head(df_co))| 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 | MedellÃn | 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 | Hacarà | 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 |
stem(df_co$"Distance")##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 00000001111111111111111112222222223333333444444555566677788888888999
## 1 | 00011135555677779
## 2 | 00334679
## 3 |
## 4 |
## 5 | 2stem(df_co$"Distance", scale = 2)##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 00000001111111111111111112222222223333333444444
## 0 | 55556667778888888899999
## 1 | 0001113
## 1 | 5555677779
## 2 | 00334
## 2 | 679
## 3 |
## 3 |
## 4 |
## 4 |
## 5 | 2library(forecast)data<- ts(df_co$Distance, frequency=12, start=2008)
knitr::kable(head(data))| x |
|---|
| 0.62022 |
| 8.46579 |
| 6.96130 |
| 5.12170 |
| 0.38844 |
| 8.58891 |
autoplot(data) + labs(title = "Gráfico de series temporales", x="date", y = "distancia", colour = "green") +theme_bw()
library(questionr)
table <- questionr::freq(Distance, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| 2.99929 | 2 | 2.1 | 2.1 | 2.1 | 2.1 |
| 3e-05 | 1 | 1.0 | 1.0 | 3.1 | 3.1 |
| 0.11421 | 1 | 1.0 | 1.0 | 4.2 | 4.2 |
| 0.14776 | 1 | 1.0 | 1.0 | 5.2 | 5.2 |
| 0.30587 | 1 | 1.0 | 1.0 | 6.2 | 6.2 |
| 0.37809 | 1 | 1.0 | 1.0 | 7.3 | 7.3 |
| 0.38844 | 1 | 1.0 | 1.0 | 8.3 | 8.3 |
| 0.44753 | 1 | 1.0 | 1.0 | 9.4 | 9.4 |
| 0.60599 | 1 | 1.0 | 1.0 | 10.4 | 10.4 |
| 0.62022 | 1 | 1.0 | 1.0 | 11.5 | 11.5 |
| 0.64094 | 1 | 1.0 | 1.0 | 12.5 | 12.5 |
| 0.64469 | 1 | 1.0 | 1.0 | 13.5 | 13.5 |
| 0.68544 | 1 | 1.0 | 1.0 | 14.6 | 14.6 |
| 0.70558 | 1 | 1.0 | 1.0 | 15.6 | 15.6 |
| 0.7283 | 1 | 1.0 | 1.0 | 16.7 | 16.7 |
| 0.74201 | 1 | 1.0 | 1.0 | 17.7 | 17.7 |
| 0.79694 | 1 | 1.0 | 1.0 | 18.7 | 18.7 |
| 0.80432 | 1 | 1.0 | 1.0 | 19.8 | 19.8 |
| 0.85976 | 1 | 1.0 | 1.0 | 20.8 | 20.8 |
| 0.93184 | 1 | 1.0 | 1.0 | 21.9 | 21.9 |
| 1.07765 | 1 | 1.0 | 1.0 | 22.9 | 22.9 |
| 1.08964 | 1 | 1.0 | 1.0 | 24.0 | 24.0 |
| 1.27637 | 1 | 1.0 | 1.0 | 25.0 | 25.0 |
| 1.33829 | 1 | 1.0 | 1.0 | 26.0 | 26.0 |
| 1.35196 | 1 | 1.0 | 1.0 | 27.1 | 27.1 |
| 1.4794 | 1 | 1.0 | 1.0 | 28.1 | 28.1 |
| 1.53032 | 1 | 1.0 | 1.0 | 29.2 | 29.2 |
| 1.7085 | 1 | 1.0 | 1.0 | 30.2 | 30.2 |
| 1.73101 | 1 | 1.0 | 1.0 | 31.2 | 31.2 |
| 1.84941 | 1 | 1.0 | 1.0 | 32.3 | 32.3 |
| 2.04898 | 1 | 1.0 | 1.0 | 33.3 | 33.3 |
| 2.07081 | 1 | 1.0 | 1.0 | 34.4 | 34.4 |
| 2.18776 | 1 | 1.0 | 1.0 | 35.4 | 35.4 |
| 2.28425 | 1 | 1.0 | 1.0 | 36.5 | 36.5 |
| 2.36822 | 1 | 1.0 | 1.0 | 37.5 | 37.5 |
| 2.53047 | 1 | 1.0 | 1.0 | 38.5 | 38.5 |
| 2.55282 | 1 | 1.0 | 1.0 | 39.6 | 39.6 |
| 3.06383 | 1 | 1.0 | 1.0 | 40.6 | 40.6 |
| 3.09014 | 1 | 1.0 | 1.0 | 41.7 | 41.7 |
| 3.14201 | 1 | 1.0 | 1.0 | 42.7 | 42.7 |
| 3.6052 | 1 | 1.0 | 1.0 | 43.7 | 43.7 |
| 3.72195 | 1 | 1.0 | 1.0 | 44.8 | 44.8 |
| 3.81445 | 1 | 1.0 | 1.0 | 45.8 | 45.8 |
| 4.09028 | 1 | 1.0 | 1.0 | 46.9 | 46.9 |
| 4.19867 | 1 | 1.0 | 1.0 | 47.9 | 47.9 |
| 4.29197 | 1 | 1.0 | 1.0 | 49.0 | 49.0 |
| 4.95353 | 1 | 1.0 | 1.0 | 50.0 | 50.0 |
| 5.0696 | 1 | 1.0 | 1.0 | 51.0 | 51.0 |
| 5.1217 | 1 | 1.0 | 1.0 | 52.1 | 52.1 |
| 5.1765 | 1 | 1.0 | 1.0 | 53.1 | 53.1 |
| 6.08628 | 1 | 1.0 | 1.0 | 54.2 | 54.2 |
| 6.16385 | 1 | 1.0 | 1.0 | 55.2 | 55.2 |
| 6.44532 | 1 | 1.0 | 1.0 | 56.2 | 56.2 |
| 6.84683 | 1 | 1.0 | 1.0 | 57.3 | 57.3 |
| 6.9613 | 1 | 1.0 | 1.0 | 58.3 | 58.3 |
| 7.28959 | 1 | 1.0 | 1.0 | 59.4 | 59.4 |
| 7.70237 | 1 | 1.0 | 1.0 | 60.4 | 60.4 |
| 7.78677 | 1 | 1.0 | 1.0 | 61.5 | 61.5 |
| 7.85369 | 1 | 1.0 | 1.0 | 62.5 | 62.5 |
| 7.87303 | 1 | 1.0 | 1.0 | 63.5 | 63.5 |
| 7.98838 | 1 | 1.0 | 1.0 | 64.6 | 64.6 |
| 8.18229 | 1 | 1.0 | 1.0 | 65.6 | 65.6 |
| 8.30406 | 1 | 1.0 | 1.0 | 66.7 | 66.7 |
| 8.46579 | 1 | 1.0 | 1.0 | 67.7 | 67.7 |
| 8.56086 | 1 | 1.0 | 1.0 | 68.7 | 68.7 |
| 8.58891 | 1 | 1.0 | 1.0 | 69.8 | 69.8 |
| 8.81287 | 1 | 1.0 | 1.0 | 70.8 | 70.8 |
| 9.21217 | 1 | 1.0 | 1.0 | 71.9 | 71.9 |
| 9.23778 | 1 | 1.0 | 1.0 | 72.9 | 72.9 |
| 9.65157 | 1 | 1.0 | 1.0 | 74.0 | 74.0 |
| 10.1804 | 1 | 1.0 | 1.0 | 75.0 | 75.0 |
| 10.36239 | 1 | 1.0 | 1.0 | 76.0 | 76.0 |
| 10.55986 | 1 | 1.0 | 1.0 | 77.1 | 77.1 |
| 11.11685 | 1 | 1.0 | 1.0 | 78.1 | 78.1 |
| 11.19714 | 1 | 1.0 | 1.0 | 79.2 | 79.2 |
| 12.61362 | 1 | 1.0 | 1.0 | 80.2 | 80.2 |
| 14.62503 | 1 | 1.0 | 1.0 | 81.2 | 81.2 |
| 15.04256 | 1 | 1.0 | 1.0 | 82.3 | 82.3 |
| 15.16116 | 1 | 1.0 | 1.0 | 83.3 | 83.3 |
| 15.42607 | 1 | 1.0 | 1.0 | 84.4 | 84.4 |
| 15.82404 | 1 | 1.0 | 1.0 | 85.4 | 85.4 |
| 16.94642 | 1 | 1.0 | 1.0 | 86.5 | 86.5 |
| 16.97776 | 1 | 1.0 | 1.0 | 87.5 | 87.5 |
| 17.31514 | 1 | 1.0 | 1.0 | 88.5 | 88.5 |
| 17.48659 | 1 | 1.0 | 1.0 | 89.6 | 89.6 |
| 18.91189 | 1 | 1.0 | 1.0 | 90.6 | 90.6 |
| 20.25692 | 1 | 1.0 | 1.0 | 91.7 | 91.7 |
| 20.31227 | 1 | 1.0 | 1.0 | 92.7 | 92.7 |
| 22.53724 | 1 | 1.0 | 1.0 | 93.7 | 93.7 |
| 23.49217 | 1 | 1.0 | 1.0 | 94.8 | 94.8 |
| 24.48479 | 1 | 1.0 | 1.0 | 95.8 | 95.8 |
| 25.51411 | 1 | 1.0 | 1.0 | 96.9 | 96.9 |
| 26.89879 | 1 | 1.0 | 1.0 | 97.9 | 97.9 |
| 28.50569 | 1 | 1.0 | 1.0 | 99.0 | 99.0 |
| 51.84125 | 1 | 1.0 | 1.0 | 100.0 | 100.0 |
| Total | 96 | 100.0 | 100.0 | 100.0 | 100.0 |
str(table)## Classes 'freqtab' and 'data.frame': 96 obs. of 5 variables:
## $ n : num 2 1 1 1 1 1 1 1 1 1 ...
## $ % : num 2.1 1 1 1 1 1 1 1 1 1 ...
## $ val% : num 2.1 1 1 1 1 1 1 1 1 1 ...
## $ %cum : num 2.1 3.1 4.2 5.2 6.2 7.3 8.3 9.4 10.4 11.5 ...
## $ val%cum: num 2.1 3.1 4.2 5.2 6.2 7.3 8.3 9.4 10.4 11.5 ...x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]
df_co <- data.frame(x = names, y = freqs)
knitr::kable(df_co)| x | y |
|---|---|
| 2.99929 | 2 |
| 3e-05 | 1 |
| 0.11421 | 1 |
| 0.14776 | 1 |
| 0.30587 | 1 |
| 0.37809 | 1 |
| 0.38844 | 1 |
| 0.44753 | 1 |
| 0.60599 | 1 |
| 0.62022 | 1 |
| 0.64094 | 1 |
| 0.64469 | 1 |
| 0.68544 | 1 |
| 0.70558 | 1 |
| 0.7283 | 1 |
| 0.74201 | 1 |
| 0.79694 | 1 |
| 0.80432 | 1 |
| 0.85976 | 1 |
| 0.93184 | 1 |
| 1.07765 | 1 |
| 1.08964 | 1 |
| 1.27637 | 1 |
| 1.33829 | 1 |
| 1.35196 | 1 |
| 1.4794 | 1 |
| 1.53032 | 1 |
| 1.7085 | 1 |
| 1.73101 | 1 |
| 1.84941 | 1 |
| 2.04898 | 1 |
| 2.07081 | 1 |
| 2.18776 | 1 |
| 2.28425 | 1 |
| 2.36822 | 1 |
| 2.53047 | 1 |
| 2.55282 | 1 |
| 3.06383 | 1 |
| 3.09014 | 1 |
| 3.14201 | 1 |
| 3.6052 | 1 |
| 3.72195 | 1 |
| 3.81445 | 1 |
| 4.09028 | 1 |
| 4.19867 | 1 |
| 4.29197 | 1 |
| 4.95353 | 1 |
| 5.0696 | 1 |
| 5.1217 | 1 |
| 5.1765 | 1 |
| 6.08628 | 1 |
| 6.16385 | 1 |
| 6.44532 | 1 |
| 6.84683 | 1 |
| 6.9613 | 1 |
| 7.28959 | 1 |
| 7.70237 | 1 |
| 7.78677 | 1 |
| 7.85369 | 1 |
| 7.87303 | 1 |
| 7.98838 | 1 |
| 8.18229 | 1 |
| 8.30406 | 1 |
| 8.46579 | 1 |
| 8.56086 | 1 |
| 8.58891 | 1 |
| 8.81287 | 1 |
| 9.21217 | 1 |
| 9.23778 | 1 |
| 9.65157 | 1 |
| 10.1804 | 1 |
| 10.36239 | 1 |
| 10.55986 | 1 |
| 11.11685 | 1 |
| 11.19714 | 1 |
| 12.61362 | 1 |
| 14.62503 | 1 |
| 15.04256 | 1 |
| 15.16116 | 1 |
| 15.42607 | 1 |
| 15.82404 | 1 |
| 16.94642 | 1 |
| 16.97776 | 1 |
| 17.31514 | 1 |
| 17.48659 | 1 |
| 18.91189 | 1 |
| 20.25692 | 1 |
| 20.31227 | 1 |
| 22.53724 | 1 |
| 23.49217 | 1 |
| 24.48479 | 1 |
| 25.51411 | 1 |
| 26.89879 | 1 |
| 28.50569 | 1 |
| 51.84125 | 1 |
library(ggplot2)
ggplot(data=df_co, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de inundaciones") +
ylab("Frecuencia")
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.00003 8.00003 16.00003 24.00003 32.00003 40.00003 48.00003 56.00003Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 3e-05 | 1 | 0.0104167 | 1 |
| 0.11421 | 1 | 0.0104167 | 2 |
| 0.14776 | 1 | 0.0104167 | 3 |
| 0.30587 | 1 | 0.0104167 | 4 |
| 0.37809 | 1 | 0.0104167 | 5 |
| 0.38844 | 1 | 0.0104167 | 6 |
| 0.44753 | 1 | 0.0104167 | 7 |
| 0.60599 | 1 | 0.0104167 | 8 |
| 0.62022 | 1 | 0.0104167 | 9 |
| 0.64094 | 1 | 0.0104167 | 10 |
| 0.64469 | 1 | 0.0104167 | 11 |
| 0.68544 | 1 | 0.0104167 | 12 |
| 0.70558 | 1 | 0.0104167 | 13 |
| 0.7283 | 1 | 0.0104167 | 14 |
| 0.74201 | 1 | 0.0104167 | 15 |
| 0.79694 | 1 | 0.0104167 | 16 |
| 0.80432 | 1 | 0.0104167 | 17 |
| 0.85976 | 1 | 0.0104167 | 18 |
| 0.93184 | 1 | 0.0104167 | 19 |
| 1.07765 | 1 | 0.0104167 | 20 |
| 1.08964 | 1 | 0.0104167 | 21 |
| 1.27637 | 1 | 0.0104167 | 22 |
| 1.33829 | 1 | 0.0104167 | 23 |
| 1.35196 | 1 | 0.0104167 | 24 |
| 1.4794 | 1 | 0.0104167 | 25 |
| 1.53032 | 1 | 0.0104167 | 26 |
| 1.7085 | 1 | 0.0104167 | 27 |
| 1.73101 | 1 | 0.0104167 | 28 |
| 1.84941 | 1 | 0.0104167 | 29 |
| 2.04898 | 1 | 0.0104167 | 30 |
| 2.07081 | 1 | 0.0104167 | 31 |
| 2.18776 | 1 | 0.0104167 | 32 |
| 2.28425 | 1 | 0.0104167 | 33 |
| 2.36822 | 1 | 0.0104167 | 34 |
| 2.53047 | 1 | 0.0104167 | 35 |
| 2.55282 | 1 | 0.0104167 | 36 |
| 2.99929 | 2 | 0.0208333 | 38 |
| 3.06383 | 1 | 0.0104167 | 39 |
| 3.09014 | 1 | 0.0104167 | 40 |
| 3.14201 | 1 | 0.0104167 | 41 |
| 3.6052 | 1 | 0.0104167 | 42 |
| 3.72195 | 1 | 0.0104167 | 43 |
| 3.81445 | 1 | 0.0104167 | 44 |
| 4.09028 | 1 | 0.0104167 | 45 |
| 4.19867 | 1 | 0.0104167 | 46 |
| 4.29197 | 1 | 0.0104167 | 47 |
| 4.95353 | 1 | 0.0104167 | 48 |
| 5.0696 | 1 | 0.0104167 | 49 |
| 5.1217 | 1 | 0.0104167 | 50 |
| 5.1765 | 1 | 0.0104167 | 51 |
| 6.08628 | 1 | 0.0104167 | 52 |
| 6.16385 | 1 | 0.0104167 | 53 |
| 6.44532 | 1 | 0.0104167 | 54 |
| 6.84683 | 1 | 0.0104167 | 55 |
| 6.9613 | 1 | 0.0104167 | 56 |
| 7.28959 | 1 | 0.0104167 | 57 |
| 7.70237 | 1 | 0.0104167 | 58 |
| 7.78677 | 1 | 0.0104167 | 59 |
| 7.85369 | 1 | 0.0104167 | 60 |
| 7.87303 | 1 | 0.0104167 | 61 |
| 7.98838 | 1 | 0.0104167 | 62 |
| 8.18229 | 1 | 0.0104167 | 63 |
| 8.30406 | 1 | 0.0104167 | 64 |
| 8.46579 | 1 | 0.0104167 | 65 |
| 8.56086 | 1 | 0.0104167 | 66 |
| 8.58891 | 1 | 0.0104167 | 67 |
| 8.81287 | 1 | 0.0104167 | 68 |
| 9.21217 | 1 | 0.0104167 | 69 |
| 9.23778 | 1 | 0.0104167 | 70 |
| 9.65157 | 1 | 0.0104167 | 71 |
| 10.1804 | 1 | 0.0104167 | 72 |
| 10.36239 | 1 | 0.0104167 | 73 |
| 10.55986 | 1 | 0.0104167 | 74 |
| 11.11685 | 1 | 0.0104167 | 75 |
| 11.19714 | 1 | 0.0104167 | 76 |
| 12.61362 | 1 | 0.0104167 | 77 |
| 14.62503 | 1 | 0.0104167 | 78 |
| 15.04256 | 1 | 0.0104167 | 79 |
| 15.16116 | 1 | 0.0104167 | 80 |
| 15.42607 | 1 | 0.0104167 | 81 |
| 15.82404 | 1 | 0.0104167 | 82 |
| 16.94642 | 1 | 0.0104167 | 83 |
| 16.97776 | 1 | 0.0104167 | 84 |
| 17.31514 | 1 | 0.0104167 | 85 |
| 17.48659 | 1 | 0.0104167 | 86 |
| 18.91189 | 1 | 0.0104167 | 87 |
| 20.25692 | 1 | 0.0104167 | 88 |
| 20.31227 | 1 | 0.0104167 | 89 |
| 22.53724 | 1 | 0.0104167 | 90 |
| 23.49217 | 1 | 0.0104167 | 91 |
| 24.48479 | 1 | 0.0104167 | 92 |
| 25.51411 | 1 | 0.0104167 | 93 |
| 26.89879 | 1 | 0.0104167 | 94 |
| 28.50569 | 1 | 0.0104167 | 95 |
| 51.84125 | 1 | 0.0104167 | 96 |
str(Freq_table)## 'data.frame': 95 obs. of 4 variables:
## $ Distance: Factor w/ 95 levels "3e-05","0.11421",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0104 0.0104 0.0104 0.0104 0.0104 ...
## $ Cum_Freq: int 1 2 3 4 5 6 7 8 9 10 ...df_co <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_co)| x | y |
|---|---|
| 3e-05 | 1 |
| 0.11421 | 1 |
| 0.14776 | 1 |
| 0.30587 | 1 |
| 0.37809 | 1 |
| 0.38844 | 1 |
| 0.44753 | 1 |
| 0.60599 | 1 |
| 0.62022 | 1 |
| 0.64094 | 1 |
| 0.64469 | 1 |
| 0.68544 | 1 |
| 0.70558 | 1 |
| 0.7283 | 1 |
| 0.74201 | 1 |
| 0.79694 | 1 |
| 0.80432 | 1 |
| 0.85976 | 1 |
| 0.93184 | 1 |
| 1.07765 | 1 |
| 1.08964 | 1 |
| 1.27637 | 1 |
| 1.33829 | 1 |
| 1.35196 | 1 |
| 1.4794 | 1 |
| 1.53032 | 1 |
| 1.7085 | 1 |
| 1.73101 | 1 |
| 1.84941 | 1 |
| 2.04898 | 1 |
| 2.07081 | 1 |
| 2.18776 | 1 |
| 2.28425 | 1 |
| 2.36822 | 1 |
| 2.53047 | 1 |
| 2.55282 | 1 |
| 2.99929 | 2 |
| 3.06383 | 1 |
| 3.09014 | 1 |
| 3.14201 | 1 |
| 3.6052 | 1 |
| 3.72195 | 1 |
| 3.81445 | 1 |
| 4.09028 | 1 |
| 4.19867 | 1 |
| 4.29197 | 1 |
| 4.95353 | 1 |
| 5.0696 | 1 |
| 5.1217 | 1 |
| 5.1765 | 1 |
| 6.08628 | 1 |
| 6.16385 | 1 |
| 6.44532 | 1 |
| 6.84683 | 1 |
| 6.9613 | 1 |
| 7.28959 | 1 |
| 7.70237 | 1 |
| 7.78677 | 1 |
| 7.85369 | 1 |
| 7.87303 | 1 |
| 7.98838 | 1 |
| 8.18229 | 1 |
| 8.30406 | 1 |
| 8.46579 | 1 |
| 8.56086 | 1 |
| 8.58891 | 1 |
| 8.81287 | 1 |
| 9.21217 | 1 |
| 9.23778 | 1 |
| 9.65157 | 1 |
| 10.1804 | 1 |
| 10.36239 | 1 |
| 10.55986 | 1 |
| 11.11685 | 1 |
| 11.19714 | 1 |
| 12.61362 | 1 |
| 14.62503 | 1 |
| 15.04256 | 1 |
| 15.16116 | 1 |
| 15.42607 | 1 |
| 15.82404 | 1 |
| 16.94642 | 1 |
| 16.97776 | 1 |
| 17.31514 | 1 |
| 17.48659 | 1 |
| 18.91189 | 1 |
| 20.25692 | 1 |
| 20.31227 | 1 |
| 22.53724 | 1 |
| 23.49217 | 1 |
| 24.48479 | 1 |
| 25.51411 | 1 |
| 26.89879 | 1 |
| 28.50569 | 1 |
| 51.84125 | 1 |
library(ggplot2)
ggplot(data=df_co, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_co$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_co)## x y
## nbr.val NA 95.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 96.00000000
## median NA 1.00000000
## mean NA 1.01052632
## SE.mean NA 0.01052632
## CI.mean NA 0.02090025
## var NA 0.01052632
## std.dev NA 0.10259784
## coef.var NA 0.10152911boxplot(Distance, horizontal=TRUE, col='orange')
#MEXICO
library(readr)
library(knitr)
df_mx <- subset (df, Country == "Mexico")
knitr::kable(head(df_mx))| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13 | 115 | 7/4/07 | NA | Mexico | MX | Veracruz-Llave | 1947 | Laguna Chica (Pueblo Nuevo) | 9.51003 | 18.5369 | -96.8229 | (18.536899999999999, -96.822900000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | nytimes.com | http://www.nytimes.com/2007/07/04/world/americas/04cnd-mexico.html?_r=1&hp&oref=slogin | |||
| 29 | 201 | 8/23/07 | NA | Mexico | MX | Puebla | 1414 | Xochitlaxco (San Baltazar) | 1.68294 | 19.9333 | -97.8500 | (19.933299999999999, -97.85) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Dean | NA | 6 | Inquirer.com | http://newsinfo.inquirer.net/breakingnews/world/view_article.php?article_id=84452 | ||
| 31 | 225 | 9/2/07 | NA | Mexico | MX | Sinaloa | 3191 | El Limón de los Ramos | 10.88351 | 24.9531 | -107.6220 | (24.953099999999999, -107.622) | Landslide | Complex | Medium | Tropical cyclone | Tropical Storm Henrietta | NA | 3 | ||||
| 42 | 284 | 9/28/07 | NA | Mexico | MX | Puebla | 3761 | Xaltepuxtla | 7.93258 | 20.2000 | -97.9000 | (20.2, -97.9) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Lorenzo | NA | 1 | PressTV.ir | http://www.presstv.ir/detail.aspx?id=25037§ionid=3510207 | ||
| 57 | 342 | 10/31/07 | NA | Mexico | MX | Tabasco | 4468 | Buenavista | 4.19108 | 17.9493 | -92.5534 | (17.949300000000001, -92.553399999999996) | Landslide | Landslide | Medium | Rain | NA | 18 | CapeTimes | http://www.capetimes.co.za/?fArticleId=4109453 | |||
| 59 | 346 | 11/4/07 | NA | Mexico | MX | Chiapas | 3183 | Ostuacán | 3.74149 | 17.3900 | -93.3060 | (17.39, -93.305999999999997) | Landslide | Mudslide | Very_large | Rain | NA | 5 | Reuters - AlertNet.org | http://news.monstersandcritics.com/americas/news/article_1371436.php/Mudslide_is_latest_disaster_for_hard-hit_Mexico__2nd_Roundup_ |
df_mx %>%
select(Country, State, City, Distance)## Country State City Distance
## 13 Mexico Veracruz-Llave Laguna Chica (Pueblo Nuevo) 9.51003
## 29 Mexico Puebla Xochitlaxco (San Baltazar) 1.68294
## 31 Mexico Sinaloa El Limón de los Ramos 10.88351
## 42 Mexico Puebla Xaltepuxtla 7.93258
## 57 Mexico Tabasco Buenavista 4.19108
## 59 Mexico Chiapas Ostuacán 3.74149
## 73 Mexico Baja California Tijuana 12.53758
## 137 Mexico México San Francisco Chimalpa 2.59637
## 177 Mexico The Federal District Mexico City 0.15208
## 207 Mexico Baja California Sur San José del Cabo 0.05648
## 210 Mexico The Federal District Magdalena Contreras 3.49173
## 255 Mexico México Toluca 0.66626
## 256 Mexico Michoacán Heroica Zitácuaro 4.18059
## 257 Mexico Michoacán Jarácuaro 3.42740
## 258 Mexico Michoacán Mineral de Angangueo 1.69508
## 260 Mexico México Zacazonapan 4.40801
## 341 Mexico Baja California Tijuana 2.49770
## 375 Mexico Nuevo León Santa Catarina 3.30074
## 385 Mexico Chiapas Escuintla 1.41805
## 386 Mexico Chiapas San Cristóbal de las Casas 0.94118
## 401 Mexico The Federal District Mexico City 0.15208
## 422 Mexico Oaxaca Santiago Choapam 16.16369
## 423 Mexico Oaxaca Santa MarÃa la Asunción 0.63550
## 445 Mexico Veracruz-Llave El Hatito 8.28739
## 449 Mexico México Villa Guerrero 1.06048
## 457 Mexico Oaxaca Santo Domingo Tepuxtepec 15.74984
## 463 Mexico Chiapas Amatán 0.31118
## 464 Mexico Chiapas Chihuahua 8.46579
## 467 Mexico Oaxaca Tanetze de Zaragoza 24.67589
## 761 Mexico Nayarit La Jarretadera 21.80060
## 764 Mexico Veracruz-Llave Xalapa de EnrÃquez 1.27837
## 787 Mexico Oaxaca Salina Cruz 0.78340
## 788 Mexico Oaxaca San José Chiltepec 0.28905
## 789 Mexico Oaxaca Paso Real de Sarabia 9.56829
## 798 Mexico Chiapas Nueva Libertad 7.93996
## 799 Mexico Colima Colima 0.36051
## 800 Mexico Guerrero Chilpancingo de los Bravos 6.80950
## 801 Mexico Michoacán Jarácuaro 3.36905
## 802 Mexico Nayarit Puga 19.41353
## 825 Mexico Jalisco Cihuatlán 0.08269
## 890 Mexico Oaxaca Oaxaca 3.64682
## 977 Mexico Hidalgo Tepeji de Ocampo 0.64483
## 1095 Mexico Veracruz-Llave Coatzintla 1.52983
## 1096 Mexico Veracruz-Llave Altotonga 2.85382
## 1097 Mexico Veracruz-Llave Yecuatla 3.73160
## 1118 Mexico Veracruz-Llave Heroica Coscomatepec de Bravo 0.09971
## 1147 Mexico Oaxaca San Andrés Huayapam 11.83490
## 1148 Mexico Hidalgo Fontezuelas 9.78251
## 1149 Mexico Puebla Tlatlauquitepec 0.30326
## 1151 Mexico Guerrero Acapulco 0.90692
## 1152 Mexico Veracruz-Llave Altotonga 0.50188
## 1153 Mexico Guerrero Atoyac de Ã\201lvarez 32.12708
## 1154 Mexico Guerrero San Pablo Atzompa 14.04274
## 1155 Mexico Puebla Atzala 5.24855
## 1158 Mexico Veracruz-Llave Tetlaxco 4.51820
## 1159 Mexico Guerrero Chilpancingo de los Bravos 0.88149
## 1185 Mexico Tabasco Olcuatitán 4.32007
## 1196 Mexico Baja California El Sauzal 12.36500
## 1278 Mexico Chihuahua Ciudad Juárez 3.05542
## 1298 Mexico Guerrero Colonia Alborada 4.10830
## 1376 Mexico Veracruz Chocaman 1.93516
## 1493 Mexico Baja California La Esperanza [Granjas Familiares] 6.46156
## 1586 Mexico Tabasco Libertad 4.81680
## 1587 Mexico Tabasco Huapinol 2.15703
## 1601 Mexico Veracruz Cruz Verde 2.47800
## 1621 Mexico Guerrero Acapulco de Juárez 7.07138
## 1625 Mexico Tabasco Olcuatitán 8.93271
## 1635 Mexico Guerrero Pochutla 12.33417
## 1670 Mexico Chiapas Santo Domingo 0.76257
## 1671 Mexico Chiapas Motozintla de Mendoza 2.06743
## 1672 Mexico Chiapas Emiliano Zapata 4.68443
## 1673 Mexico Chiapas Tila 1.73469
## 1674 Mexico Tabasco Tenosique de Pino Suárez 15.22260library(ggplot2)
ggplot(data=df_mx, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="yellow")
library(ggplot2)
library(dplyr)
ggplot(data=df_mx, aes(fill=City, x="Mexico", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_mx, aes(fill=City, x="Mexico", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_mx, aes(x = "Mexico", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
library(qcc)Distance <- df_mx$Distance
names(Distance) <- df_mx$City
pareto.chart(Distance,
ylab="Distance",
col = heat.colors(length(Distance)),
cumperc = seq(0, 100, by = 10),
ylab2 = "Porcentaje acumulado",
main = "CONCENTRACION DE LAS INUNDACIONES EN LAS CIUDADES")
##
## Pareto chart analysis for Distance
## Frequency Cum.Freq. Percentage
## Atoyac de Ã\201lvarez 32.12708000 32.12708000 7.76637116
## Tanetze de Zaragoza 24.67589000 56.80297000 5.96512725
## La Jarretadera 21.80060000 78.60357000 5.27005726
## Puga 19.41353000 98.01710000 4.69300912
## Santiago Choapam 16.16369000 114.18079000 3.90739575
## Santo Domingo Tepuxtepec 15.74984000 129.93063000 3.80735203
## Tenosique de Pino Suárez 15.22260000 145.15323000 3.67989751
## San Pablo Atzompa 14.04274000 159.19597000 3.39467922
## Tijuana 12.53758000 171.73355000 3.03082321
## El Sauzal 12.36500000 184.09855000 2.98910388
## Pochutla 12.33417000 196.43272000 2.98165106
## San Andrés Huayapam 11.83490000 208.26762000 2.86095798
## El Limón de los Ramos 10.88351000 219.15113000 2.63096983
## Fontezuelas 9.78251000 228.93364000 2.36481509
## Paso Real de Sarabia 9.56829000 238.50193000 2.31302974
## Laguna Chica (Pueblo Nuevo) 9.51003000 248.01196000 2.29894602
## Olcuatitán 8.93271000 256.94467000 2.15938521
## Chihuahua 8.46579000 265.41046000 2.04651239
## El Hatito 8.28739000 273.69785000 2.00338614
## Nueva Libertad 7.93996000 281.63781000 1.91939872
## Xaltepuxtla 7.93258000 289.57039000 1.91761469
## Acapulco de Juárez 7.07138000 296.64177000 1.70942898
## Chilpancingo de los Bravos 6.80950000 303.45127000 1.64612235
## La Esperanza [Granjas Familiares] 6.46156000 309.91283000 1.56201165
## Atzala 5.24855000 315.16138000 1.26877971
## Libertad 4.81680000 319.97818000 1.16440886
## Emiliano Zapata 4.68443000 324.66261000 1.13240986
## Tetlaxco 4.51820000 329.18081000 1.09222557
## Zacazonapan 4.40801000 333.58882000 1.06558834
## Olcuatitán 4.32007000 337.90889000 1.04432980
## Buenavista 4.19108000 342.09997000 1.01314787
## Heroica Zitácuaro 4.18059000 346.28056000 1.01061203
## Colonia Alborada 4.10830000 350.38886000 0.99313671
## Ostuacán 3.74149000 354.13035000 0.90446440
## Yecuatla 3.73160000 357.86195000 0.90207360
## Oaxaca 3.64682000 361.50877000 0.88157896
## Magdalena Contreras 3.49173000 365.00050000 0.84408764
## Jarácuaro 3.42740000 368.42790000 0.82853656
## Jarácuaro 3.36905000 371.79695000 0.81443109
## Santa Catarina 3.30074000 375.09769000 0.79791789
## Ciudad Juárez 3.05542000 378.15311000 0.73861446
## Altotonga 2.85382000 381.00693000 0.68987986
## San Francisco Chimalpa 2.59637000 383.60330000 0.62764413
## Tijuana 2.49770000 386.10100000 0.60379173
## Cruz Verde 2.47800000 388.57900000 0.59902947
## Huapinol 2.15703000 390.73603000 0.52143847
## Motozintla de Mendoza 2.06743000 392.80346000 0.49977865
## Chocaman 1.93516000 394.73862000 0.46780382
## Tila 1.73469000 396.47331000 0.41934239
## Mineral de Angangueo 1.69508000 398.16839000 0.40976710
## Xochitlaxco (San Baltazar) 1.68294000 399.85133000 0.40683239
## Coatzintla 1.52983000 401.38116000 0.36981972
## Escuintla 1.41805000 402.79921000 0.34279812
## Xalapa de EnrÃquez 1.27837000 404.07758000 0.30903200
## Villa Guerrero 1.06048000 405.13806000 0.25635947
## San Cristóbal de las Casas 0.94118000 406.07924000 0.22752000
## Acapulco 0.90692000 406.98616000 0.21923802
## Chilpancingo de los Bravos 0.88149000 407.86765000 0.21309059
## Salina Cruz 0.78340000 408.65105000 0.18937840
## Santo Domingo 0.76257000 409.41362000 0.18434298
## Toluca 0.66626000 410.07988000 0.16106109
## Tepeji de Ocampo 0.64483000 410.72471000 0.15588062
## Santa MarÃa la Asunción 0.63550000 411.36021000 0.15362519
## Altotonga 0.50188000 411.86209000 0.12132402
## Colima 0.36051000 412.22260000 0.08714936
## Amatán 0.31118000 412.53378000 0.07522437
## Tlatlauquitepec 0.30326000 412.83704000 0.07330980
## San José Chiltepec 0.28905000 413.12609000 0.06987468
## Mexico City 0.15208000 413.27817000 0.03676368
## Mexico City 0.15208000 413.43025000 0.03676368
## Heroica Coscomatepec de Bravo 0.09971000 413.52996000 0.02410380
## Cihuatlán 0.08269000 413.61265000 0.01998941
## San José del Cabo 0.05648000 413.66913000 0.01365342
##
## Pareto chart analysis for Distance
## Cum.Percent.
## Atoyac de Ã\201lvarez 7.76637116
## Tanetze de Zaragoza 13.73149841
## La Jarretadera 19.00155566
## Puga 23.69456478
## Santiago Choapam 27.60196053
## Santo Domingo Tepuxtepec 31.40931256
## Tenosique de Pino Suárez 35.08921007
## San Pablo Atzompa 38.48388929
## Tijuana 41.51471249
## El Sauzal 44.50381637
## Pochutla 47.48546743
## San Andrés Huayapam 50.34642541
## El Limón de los Ramos 52.97739524
## Fontezuelas 55.34221033
## Paso Real de Sarabia 57.65524007
## Laguna Chica (Pueblo Nuevo) 59.95418609
## Olcuatitán 62.11357130
## Chihuahua 64.16008369
## El Hatito 66.16346982
## Nueva Libertad 68.08286855
## Xaltepuxtla 70.00048324
## Acapulco de Juárez 71.70991222
## Chilpancingo de los Bravos 73.35603457
## La Esperanza [Granjas Familiares] 74.91804622
## Atzala 76.18682593
## Libertad 77.35123479
## Emiliano Zapata 78.48364465
## Tetlaxco 79.57587021
## Zacazonapan 80.64145855
## Olcuatitán 81.68578835
## Buenavista 82.69893622
## Heroica Zitácuaro 83.70954826
## Colonia Alborada 84.70268497
## Ostuacán 85.60714937
## Yecuatla 86.50922296
## Oaxaca 87.39080192
## Magdalena Contreras 88.23488956
## Jarácuaro 89.06342613
## Jarácuaro 89.87785721
## Santa Catarina 90.67577511
## Ciudad Juárez 91.41438956
## Altotonga 92.10426942
## San Francisco Chimalpa 92.73191355
## Tijuana 93.33570528
## Cruz Verde 93.93473475
## Huapinol 94.45617322
## Motozintla de Mendoza 94.95595187
## Chocaman 95.42375570
## Tila 95.84309808
## Mineral de Angangueo 96.25286518
## Xochitlaxco (San Baltazar) 96.65969757
## Coatzintla 97.02951729
## Escuintla 97.37231541
## Xalapa de EnrÃquez 97.68134741
## Villa Guerrero 97.93770688
## San Cristóbal de las Casas 98.16522688
## Acapulco 98.38446490
## Chilpancingo de los Bravos 98.59755549
## Salina Cruz 98.78693390
## Santo Domingo 98.97127688
## Toluca 99.13233796
## Tepeji de Ocampo 99.28821858
## Santa MarÃa la Asunción 99.44184377
## Altotonga 99.56316779
## Colima 99.65031715
## Amatán 99.72554152
## Tlatlauquitepec 99.79885132
## San José Chiltepec 99.86872600
## Mexico City 99.90548968
## Mexico City 99.94225337
## Heroica Coscomatepec de Bravo 99.96635717
## Cihuatlán 99.98634658
## San José del Cabo 100.00000000stem(df_mx$"Distance")##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 000000000111111111111222222222333333344444444
## 0 | 555567788889
## 1 | 000122234
## 1 | 5669
## 2 | 2
## 2 | 5
## 3 | 2head(df_mx)## id date time continent_code Country country_code State
## 13 115 7/4/07 <NA> Mexico MX Veracruz-Llave
## 29 201 8/23/07 <NA> Mexico MX Puebla
## 31 225 9/2/07 <NA> Mexico MX Sinaloa
## 42 284 9/28/07 <NA> Mexico MX Puebla
## 57 342 10/31/07 <NA> Mexico MX Tabasco
## 59 346 11/4/07 <NA> Mexico MX Chiapas
## population City Distance location_description
## 13 1947 Laguna Chica (Pueblo Nuevo) 9.51003
## 29 1414 Xochitlaxco (San Baltazar) 1.68294
## 31 3191 El Limón de los Ramos 10.88351
## 42 3761 Xaltepuxtla 7.93258
## 57 4468 Buenavista 4.19108
## 59 3183 Ostuacán 3.74149
## latitude longitude geolocation hazard_type
## 13 18.5369 -96.8229 (18.536899999999999, -96.822900000000004) Landslide
## 29 19.9333 -97.8500 (19.933299999999999, -97.85) Landslide
## 31 24.9531 -107.6220 (24.953099999999999, -107.622) Landslide
## 42 20.2000 -97.9000 (20.2, -97.9) Landslide
## 57 17.9493 -92.5534 (17.949300000000001, -92.553399999999996) Landslide
## 59 17.3900 -93.3060 (17.39, -93.305999999999997) Landslide
## landslide_type landslide_size trigger storm_name
## 13 Landslide Medium Rain
## 29 Mudslide Medium Tropical cyclone Hurricane Dean
## 31 Complex Medium Tropical cyclone Tropical Storm Henrietta
## 42 Mudslide Medium Tropical cyclone Hurricane Lorenzo
## 57 Landslide Medium Rain
## 59 Mudslide Very_large Rain
## injuries fatalities source_name
## 13 NA 7 nytimes.com
## 29 NA 6 Inquirer.com
## 31 NA 3
## 42 NA 1 PressTV.ir
## 57 NA 18 CapeTimes
## 59 NA 5 Reuters - AlertNet.org
## source_link
## 13 http://www.nytimes.com/2007/07/04/world/americas/04cnd-mexico.html?_r=1&hp&oref=slogin
## 29 http://newsinfo.inquirer.net/breakingnews/world/view_article.php?article_id=84452
## 31
## 42 http://www.presstv.ir/detail.aspx?id=25037§ionid=3510207
## 57 http://www.capetimes.co.za/?fArticleId=4109453
## 59 http://news.monstersandcritics.com/americas/news/article_1371436.php/Mudslide_is_latest_disaster_for_hard-hit_Mexico__2nd_Roundup_knitr::kable(head(df_mx))| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13 | 115 | 7/4/07 | NA | Mexico | MX | Veracruz-Llave | 1947 | Laguna Chica (Pueblo Nuevo) | 9.51003 | 18.5369 | -96.8229 | (18.536899999999999, -96.822900000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | nytimes.com | http://www.nytimes.com/2007/07/04/world/americas/04cnd-mexico.html?_r=1&hp&oref=slogin | |||
| 29 | 201 | 8/23/07 | NA | Mexico | MX | Puebla | 1414 | Xochitlaxco (San Baltazar) | 1.68294 | 19.9333 | -97.8500 | (19.933299999999999, -97.85) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Dean | NA | 6 | Inquirer.com | http://newsinfo.inquirer.net/breakingnews/world/view_article.php?article_id=84452 | ||
| 31 | 225 | 9/2/07 | NA | Mexico | MX | Sinaloa | 3191 | El Limón de los Ramos | 10.88351 | 24.9531 | -107.6220 | (24.953099999999999, -107.622) | Landslide | Complex | Medium | Tropical cyclone | Tropical Storm Henrietta | NA | 3 | ||||
| 42 | 284 | 9/28/07 | NA | Mexico | MX | Puebla | 3761 | Xaltepuxtla | 7.93258 | 20.2000 | -97.9000 | (20.2, -97.9) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Lorenzo | NA | 1 | PressTV.ir | http://www.presstv.ir/detail.aspx?id=25037§ionid=3510207 | ||
| 57 | 342 | 10/31/07 | NA | Mexico | MX | Tabasco | 4468 | Buenavista | 4.19108 | 17.9493 | -92.5534 | (17.949300000000001, -92.553399999999996) | Landslide | Landslide | Medium | Rain | NA | 18 | CapeTimes | http://www.capetimes.co.za/?fArticleId=4109453 | |||
| 59 | 346 | 11/4/07 | NA | Mexico | MX | Chiapas | 3183 | Ostuacán | 3.74149 | 17.3900 | -93.3060 | (17.39, -93.305999999999997) | Landslide | Mudslide | Very_large | Rain | NA | 5 | Reuters - AlertNet.org | http://news.monstersandcritics.com/americas/news/article_1371436.php/Mudslide_is_latest_disaster_for_hard-hit_Mexico__2nd_Roundup_ |
stem(df_mx$"Distance")##
## The decimal point is 1 digit(s) to the right of the |
##
## 0 | 000000000111111111111222222222333333344444444
## 0 | 555567788889
## 1 | 000122234
## 1 | 5669
## 2 | 2
## 2 | 5
## 3 | 2stem(df_mx$"Distance", scale = 2)##
## The decimal point is at the |
##
## 0 | 11122333456678899913457779
## 2 | 12556913445677
## 4 | 122345782
## 6 | 58199
## 8 | 359568
## 10 | 98
## 12 | 345
## 14 | 027
## 16 | 2
## 18 | 4
## 20 | 8
## 22 |
## 24 | 7
## 26 |
## 28 |
## 30 |
## 32 | 1library(forecast)data<- ts(df_mx$Distance, frequency=12, start=2008)
knitr::kable(head(data))| x |
|---|
| 9.51003 |
| 1.68294 |
| 10.88351 |
| 7.93258 |
| 4.19108 |
| 3.74149 |
autoplot(data) + labs(title = "Gráfico de series temporales", x="date", y = "distancia", colour = "green") +theme_bw()
library(questionr)
table <- questionr::freq(Distance, cum = TRUE, sort = "dec", total = TRUE)
knitr::kable(table)| n | % | val% | %cum | val%cum | |
|---|---|---|---|---|---|
| 0.15208 | 2 | 2.7 | 2.7 | 2.7 | 2.7 |
| 0.05648 | 1 | 1.4 | 1.4 | 4.1 | 4.1 |
| 0.08269 | 1 | 1.4 | 1.4 | 5.5 | 5.5 |
| 0.09971 | 1 | 1.4 | 1.4 | 6.8 | 6.8 |
| 0.28905 | 1 | 1.4 | 1.4 | 8.2 | 8.2 |
| 0.30326 | 1 | 1.4 | 1.4 | 9.6 | 9.6 |
| 0.31118 | 1 | 1.4 | 1.4 | 11.0 | 11.0 |
| 0.36051 | 1 | 1.4 | 1.4 | 12.3 | 12.3 |
| 0.50188 | 1 | 1.4 | 1.4 | 13.7 | 13.7 |
| 0.6355 | 1 | 1.4 | 1.4 | 15.1 | 15.1 |
| 0.64483 | 1 | 1.4 | 1.4 | 16.4 | 16.4 |
| 0.66626 | 1 | 1.4 | 1.4 | 17.8 | 17.8 |
| 0.76257 | 1 | 1.4 | 1.4 | 19.2 | 19.2 |
| 0.7834 | 1 | 1.4 | 1.4 | 20.5 | 20.5 |
| 0.88149 | 1 | 1.4 | 1.4 | 21.9 | 21.9 |
| 0.90692 | 1 | 1.4 | 1.4 | 23.3 | 23.3 |
| 0.94118 | 1 | 1.4 | 1.4 | 24.7 | 24.7 |
| 1.06048 | 1 | 1.4 | 1.4 | 26.0 | 26.0 |
| 1.27837 | 1 | 1.4 | 1.4 | 27.4 | 27.4 |
| 1.41805 | 1 | 1.4 | 1.4 | 28.8 | 28.8 |
| 1.52983 | 1 | 1.4 | 1.4 | 30.1 | 30.1 |
| 1.68294 | 1 | 1.4 | 1.4 | 31.5 | 31.5 |
| 1.69508 | 1 | 1.4 | 1.4 | 32.9 | 32.9 |
| 1.73469 | 1 | 1.4 | 1.4 | 34.2 | 34.2 |
| 1.93516 | 1 | 1.4 | 1.4 | 35.6 | 35.6 |
| 2.06743 | 1 | 1.4 | 1.4 | 37.0 | 37.0 |
| 2.15703 | 1 | 1.4 | 1.4 | 38.4 | 38.4 |
| 2.478 | 1 | 1.4 | 1.4 | 39.7 | 39.7 |
| 2.4977 | 1 | 1.4 | 1.4 | 41.1 | 41.1 |
| 2.59637 | 1 | 1.4 | 1.4 | 42.5 | 42.5 |
| 2.85382 | 1 | 1.4 | 1.4 | 43.8 | 43.8 |
| 3.05542 | 1 | 1.4 | 1.4 | 45.2 | 45.2 |
| 3.30074 | 1 | 1.4 | 1.4 | 46.6 | 46.6 |
| 3.36905 | 1 | 1.4 | 1.4 | 47.9 | 47.9 |
| 3.4274 | 1 | 1.4 | 1.4 | 49.3 | 49.3 |
| 3.49173 | 1 | 1.4 | 1.4 | 50.7 | 50.7 |
| 3.64682 | 1 | 1.4 | 1.4 | 52.1 | 52.1 |
| 3.7316 | 1 | 1.4 | 1.4 | 53.4 | 53.4 |
| 3.74149 | 1 | 1.4 | 1.4 | 54.8 | 54.8 |
| 4.1083 | 1 | 1.4 | 1.4 | 56.2 | 56.2 |
| 4.18059 | 1 | 1.4 | 1.4 | 57.5 | 57.5 |
| 4.19108 | 1 | 1.4 | 1.4 | 58.9 | 58.9 |
| 4.32007 | 1 | 1.4 | 1.4 | 60.3 | 60.3 |
| 4.40801 | 1 | 1.4 | 1.4 | 61.6 | 61.6 |
| 4.5182 | 1 | 1.4 | 1.4 | 63.0 | 63.0 |
| 4.68443 | 1 | 1.4 | 1.4 | 64.4 | 64.4 |
| 4.8168 | 1 | 1.4 | 1.4 | 65.8 | 65.8 |
| 5.24855 | 1 | 1.4 | 1.4 | 67.1 | 67.1 |
| 6.46156 | 1 | 1.4 | 1.4 | 68.5 | 68.5 |
| 6.8095 | 1 | 1.4 | 1.4 | 69.9 | 69.9 |
| 7.07138 | 1 | 1.4 | 1.4 | 71.2 | 71.2 |
| 7.93258 | 1 | 1.4 | 1.4 | 72.6 | 72.6 |
| 7.93996 | 1 | 1.4 | 1.4 | 74.0 | 74.0 |
| 8.28739 | 1 | 1.4 | 1.4 | 75.3 | 75.3 |
| 8.46579 | 1 | 1.4 | 1.4 | 76.7 | 76.7 |
| 8.93271 | 1 | 1.4 | 1.4 | 78.1 | 78.1 |
| 9.51003 | 1 | 1.4 | 1.4 | 79.5 | 79.5 |
| 9.56829 | 1 | 1.4 | 1.4 | 80.8 | 80.8 |
| 9.78251 | 1 | 1.4 | 1.4 | 82.2 | 82.2 |
| 10.88351 | 1 | 1.4 | 1.4 | 83.6 | 83.6 |
| 11.8349 | 1 | 1.4 | 1.4 | 84.9 | 84.9 |
| 12.33417 | 1 | 1.4 | 1.4 | 86.3 | 86.3 |
| 12.365 | 1 | 1.4 | 1.4 | 87.7 | 87.7 |
| 12.53758 | 1 | 1.4 | 1.4 | 89.0 | 89.0 |
| 14.04274 | 1 | 1.4 | 1.4 | 90.4 | 90.4 |
| 15.2226 | 1 | 1.4 | 1.4 | 91.8 | 91.8 |
| 15.74984 | 1 | 1.4 | 1.4 | 93.2 | 93.2 |
| 16.16369 | 1 | 1.4 | 1.4 | 94.5 | 94.5 |
| 19.41353 | 1 | 1.4 | 1.4 | 95.9 | 95.9 |
| 21.8006 | 1 | 1.4 | 1.4 | 97.3 | 97.3 |
| 24.67589 | 1 | 1.4 | 1.4 | 98.6 | 98.6 |
| 32.12708 | 1 | 1.4 | 1.4 | 100.0 | 100.0 |
| Total | 73 | 100.0 | 100.0 | 100.0 | 100.0 |
str(table)## Classes 'freqtab' and 'data.frame': 73 obs. of 5 variables:
## $ n : num 2 1 1 1 1 1 1 1 1 1 ...
## $ % : num 2.7 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 ...
## $ val% : num 2.7 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.4 ...
## $ %cum : num 2.7 4.1 5.5 6.8 8.2 9.6 11 12.3 13.7 15.1 ...
## $ val%cum: num 2.7 4.1 5.5 6.8 8.2 9.6 11 12.3 13.7 15.1 ...x <- row.names(table)
y <- table$n
names <- x[1:(length(x)-1)]
freqs <- y[1:(length(y)-1)]
df_co <- data.frame(x = names, y = freqs)
knitr::kable(df_mx)| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13 | 115 | 7/4/07 | NA | Mexico | MX | Veracruz-Llave | 1947 | Laguna Chica (Pueblo Nuevo) | 9.51003 | 18.5369 | -96.8229 | (18.536899999999999, -96.822900000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | nytimes.com | http://www.nytimes.com/2007/07/04/world/americas/04cnd-mexico.html?_r=1&hp&oref=slogin | |||
| 29 | 201 | 8/23/07 | NA | Mexico | MX | Puebla | 1414 | Xochitlaxco (San Baltazar) | 1.68294 | 19.9333 | -97.8500 | (19.933299999999999, -97.85) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Dean | NA | 6 | Inquirer.com | http://newsinfo.inquirer.net/breakingnews/world/view_article.php?article_id=84452 | ||
| 31 | 225 | 9/2/07 | NA | Mexico | MX | Sinaloa | 3191 | El Limón de los Ramos | 10.88351 | 24.9531 | -107.6220 | (24.953099999999999, -107.622) | Landslide | Complex | Medium | Tropical cyclone | Tropical Storm Henrietta | NA | 3 | ||||
| 42 | 284 | 9/28/07 | NA | Mexico | MX | Puebla | 3761 | Xaltepuxtla | 7.93258 | 20.2000 | -97.9000 | (20.2, -97.9) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Lorenzo | NA | 1 | PressTV.ir | http://www.presstv.ir/detail.aspx?id=25037§ionid=3510207 | ||
| 57 | 342 | 10/31/07 | NA | Mexico | MX | Tabasco | 4468 | Buenavista | 4.19108 | 17.9493 | -92.5534 | (17.949300000000001, -92.553399999999996) | Landslide | Landslide | Medium | Rain | NA | 18 | CapeTimes | http://www.capetimes.co.za/?fArticleId=4109453 | |||
| 59 | 346 | 11/4/07 | NA | Mexico | MX | Chiapas | 3183 | Ostuacán | 3.74149 | 17.3900 | -93.3060 | (17.39, -93.305999999999997) | Landslide | Mudslide | Very_large | Rain | NA | 5 | Reuters - AlertNet.org | http://news.monstersandcritics.com/americas/news/article_1371436.php/Mudslide_is_latest_disaster_for_hard-hit_Mexico__2nd_Roundup_ | |||
| 73 | 417 | 1/7/08 | NA | Mexico | MX | Baja California | 1376457 | Tijuana | 12.53758 | 32.5755 | -116.9016 | (32.575499999999998, -116.9016) | Landslide | Landslide | Medium | Rain | NA | 1 | http://www.signonsandiego.com/news/mexico/tijuana/20080108-1858-bn08tj.html | ||||
| 137 | 744 | 8/25/08 | NA | Mexico | MX | México | 7182 | San Francisco Chimalpa | 2.59637 | 19.4360 | -99.3650 | (19.436, -99.364999999999995) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.alertnet.org/thenews/newsdesk/N26335664.htm | ||||
| 177 | 975 | 1/22/09 | NA | Mexico | MX | The Federal District | 12294193 | Mexico City | 0.15208 | 19.4271 | -99.1276 | (19.427099999999999, -99.127600000000001) | Landslide | Landslide | Medium | Downpour | NA | 2 | |||||
| 207 | 1131 | 9/1/09 | NA | Mexico | MX | Baja California Sur | 48518 | San José del Cabo | 0.05648 | 23.0587 | -109.6972 | (23.058700000000002, -109.6972) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Jimena | NA | NA | http://online.wsj.com/article/SB125187243480578733.html | |||
| 210 | 1167 | 9/15/09 | NA | Mexico | MX | The Federal District | 228927 | Magdalena Contreras | 3.49173 | 19.3161 | -99.2398 | (19.316099999999999, -99.239800000000002) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://edition.cnn.com/2009/WORLD/americas/09/16/mexico.mudslide.deaths/ | ||||
| 255 | 1469 | 2/5/10 | NA | Mexico | MX | México | 505881 | Toluca | 0.66626 | 19.2880 | -99.6469 | (19.288, -99.646900000000002) | Landslide | Landslide | Large | Downpour | NA | 11 | http://www.trt.net.tr/trtinternational/en/newsDetail.aspx?HaberKodu=dc2169ea-80de-437d-a57a-eb11725c40e5 | ||||
| 256 | 1470 | 2/5/10 | NA | Mexico | MX | Michoacán | 78950 | Heroica Zitácuaro | 4.18059 | 19.4000 | -100.3667 | (19.399999999999999, -100.36669999999999) | Landslide | Mudslide | Medium | Downpour | NA | 2 | http://www.sott.net/articles/show/202533-Mexico-rainstorms-leave-at-least-15-dead | ||||
| 257 | 1471 | 2/5/10 | NA | Mexico | MX | Michoacán | 2817 | Jarácuaro | 3.42740 | 19.5670 | -101.7098 | (19.567, -101.7098) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.sott.net/articles/show/202533-Mexico-rainstorms-leave-at-least-15-dead | ||||
| 258 | 1472 | 2/5/10 | NA | Mexico | MX | Michoacán | 4943 | Mineral de Angangueo | 1.69508 | 19.6167 | -100.3000 | (19.616700000000002, -100.3) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.aajtv.com/news/World/158635_detail.html | ||||
| 260 | 1477 | 2/4/10 | 22:00 | NA | Mexico | MX | México | 2968 | Zacazonapan | 4.40801 | Above road | 19.1927 | -99.8654 | (19.192699999999999, -99.865399999999994) | Landslide | Mudslide | Large | Downpour | NA | 11 | News Daily | http://www.newsdaily.com/stories/tre6151we-us-mexico-mudslide/ | |
| 341 | 1838 | 5/5/10 | NA | Mexico | MX | Baja California | 1376457 | Tijuana | 2.49770 | 32.5250 | -116.9999 | (32.524999999999999, -116.9999) | Landslide | Mudslide | Medium | Rain | NA | 0 | http://hisz.rsoe.hu/alertmap/woalert_read.php?lang=eng&cid=25962&utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+RsoeEdis-EmergencyAndDisasterInformation+%28RSOE+EDIS+-+Emergency+and+Disaster+Information%29 | ||||
| 375 | 2033 | 7/2/10 | NA | Mexico | MX | Nuevo León | 254472 | Santa Catarina | 3.30074 | Above road | 25.6875 | -100.4870 | (25.6875, -100.48699999999999) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Alex | NA | 1 | Sky News | http://news.sky.com/skynews/Home/World-News/Deadly-Floods-Catch-City-Unprepared-As-Hurricane-Alex-Dumps-A-Years-Worth-of-Rain-In-Mexico/Article/201007115658765?lpos=World_News_Top_Stories_Header_3&lid=ARTICLE_15658765_Deadly_Floods_Catch_City_Unpr | |
| 385 | 2114 | 7/22/10 | Overnight | NA | Mexico | MX | Chiapas | 9570 | Escuintla | 1.41805 | 15.3258 | -92.6698 | (15.325799999999999, -92.669799999999995) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.laht.com/article.asp?ArticleId=361041&CategoryId=14091 | |||
| 386 | 2115 | 7/22/10 | NA | Mexico | MX | Chiapas | 128996 | San Cristóbal de las Casas | 0.94118 | 16.7294 | -92.6389 | (16.729399999999998, -92.638900000000007) | Landslide | Rockfall | Small | Downpour | NA | 0 | http://www.laht.com/article.asp?ArticleId=361041&CategoryId=14091 | ||||
| 401 | 2191 | 8/2/10 | NA | Mexico | MX | The Federal District | 12294193 | Mexico City | 0.15208 | 19.4270 | -99.1276 | (19.427, -99.127600000000001) | Landslide | Mudslide | Medium | Construction | NA | 2 | |||||
| 422 | 2321 | 8/24/10 | Morning | NA | Mexico | MX | Oaxaca | 1099 | Santiago Choapam | 16.16369 | 17.2568 | -96.0289 | (17.256799999999998, -96.028899999999993) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Frank | NA | 2 | |||
| 423 | 2322 | 8/24/10 | Morning | NA | Mexico | MX | Oaxaca | 1701 | Santa MarÃa la Asunción | 0.63550 | 18.1060 | -96.8133 | (18.106000000000002, -96.813299999999998) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Frank | NA | 2 | |||
| 445 | 2438 | 9/17/10 | NA | Mexico | MX | Veracruz-Llave | 1324 | El Hatito | 8.28739 | 19.2818 | -96.3149 | (19.2818, -96.314899999999994) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Karl | NA | 2 | http://www.lfpress.com/news/world/2010/09/17/15382976-reuters.html | |||
| 449 | 2474 | 9/20/10 | Late night | NA | Mexico | MX | México | 9267 | Villa Guerrero | 1.06048 | 18.9642 | -99.6511 | (18.964200000000002, -99.6511) | Landslide | Landslide | Large | Downpour | NA | 8 | http://www.alertnet.org/thenews/newsdesk/N21168920.htm | |||
| 457 | 2514 | 9/28/10 | 4:00:00 | NA | Mexico | MX | Oaxaca | 1813 | Santo Domingo Tepuxtepec | 15.74984 | 17.0977 | -96.0625 | (17.0977, -96.0625) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Karl and Tropical Storm Matthew | NA | 11 | http://technews.tmcnet.com/topics/associated-press/articles/106209-death-toll-southern-mexico-mudslides-up-32.htm | ||
| 463 | 2520 | 9/29/10 | NA | Mexico | MX | Chiapas | 3947 | Amatán | 0.31118 | 17.3738 | -92.8213 | (17.373799999999999, -92.821299999999994) | Landslide | Mudslide | Large | Tropical cyclone | Hurricane Karl and Tropical Storm Matthew | NA | 16 | http://technews.tmcnet.com/topics/associated-press/articles/106209-death-toll-southern-mexico-mudslides-up-32.htm | |||
| 464 | 2521 | 9/29/10 | NA | Mexico | MX | Chiapas | 1088 | Chihuahua | 8.46579 | 16.0203 | -91.8946 | (16.020299999999999, -91.894599999999997) | Landslide | Complex | Medium | Tropical cyclone | Hurricane Karl and Tropical Storm Matthew | NA | 3 | http://technews.tmcnet.com/topics/associated-press/articles/106209-death-toll-southern-mexico-mudslides-up-32.htm | |||
| 467 | 2524 | 9/30/10 | Morning | NA | Mexico | MX | Oaxaca | 1096 | Tanetze de Zaragoza | 24.67589 | 17.1833 | -96.1833 | (17.183299999999999, -96.183300000000003) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Karl and Tropical Storm Matthew | NA | 2 | http://technews.tmcnet.com/topics/associated-press/articles/106209-death-toll-southern-mexico-mudslides-up-32.htm | ||
| 761 | 3639 | 6/22/11 | NA | Mexico | MX | Nayarit | 4463 | La Jarretadera | 21.80060 | 21.9814 | -104.8381 | (21.981400000000001, -104.8381) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Beatriz | NA | 0 | http://channel6newsonline.com/2011/06/missing-teen-found-dead-after-beatriz-slams-mexicos-pacific-coast/ | |||
| 764 | 3684 | 7/1/11 | NA | Mexico | MX | Veracruz-Llave | 425148 | Xalapa de EnrÃquez | 1.27837 | 19.5426 | -96.9137 | (19.5426, -96.913700000000006) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Storm Arlene | NA | 0 | http://edition.cnn.com/2011/WORLD/americas/07/04/mexico.arlene.deaths/ | |||
| 787 | 3795 | 7/17/11 | NA | Mexico | MX | Oaxaca | 73648 | Salina Cruz | 0.78340 | 16.1863 | -95.1924 | (16.186299999999999, -95.192400000000006) | Landslide | Mudslide | Medium | Downpour | NA | 0 | http://latino.foxnews.com/latino/news/2011/07/18/3-die-180000-affected-by-rains-in-southern-mexico/ | ||||
| 788 | 3796 | 7/17/11 | NA | Mexico | MX | Oaxaca | 3294 | San José Chiltepec | 0.28905 | 17.9484 | -96.1674 | (17.948399999999999, -96.167400000000001) | Landslide | Mudslide | Medium | Downpour | NA | 0 | http://www.thenews.com.pk/NewsDetail.aspx?ID=18895&title=Intense-rains-in-Mexico-affect-thousands | ||||
| 789 | 3797 | 7/17/11 | NA | Mexico | MX | Oaxaca | 1019 | Paso Real de Sarabia | 9.56829 | 17.0000 | -95.0000 | (17, -95) | Landslide | Mudslide | Medium | Downpour | NA | 2 | http://www.thenews.com.pk/NewsDetail.aspx?ID=18895&title=Intense-rains-in-Mexico-affect-thousands | ||||
| 798 | 3832 | 7/22/11 | NA | Mexico | MX | Chiapas | 1005 | Nueva Libertad | 7.93996 | 16.1514 | -92.7704 | (16.151399999999999, -92.770399999999995) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dora | NA | 0 | http://www.china.org.cn/environment/2011-07/22/content_23044018.htm | |||
| 799 | 3833 | 7/22/11 | NA | Mexico | MX | Colima | 127235 | Colima | 0.36051 | 19.2444 | -103.7244 | (19.244399999999999, -103.7244) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dora | NA | 0 | ||||
| 800 | 3834 | 7/22/11 | NA | Mexico | MX | Guerrero | 165250 | Chilpancingo de los Bravos | 6.80950 | 17.4974 | -99.5380 | (17.497399999999999, -99.537999999999997) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dora | NA | 0 | ||||
| 801 | 3835 | 7/22/11 | NA | Mexico | MX | Michoacán | 2817 | Jarácuaro | 3.36905 | 19.5805 | -101.7023 | (19.580500000000001, -101.70229999999999) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dora | NA | 0 | ||||
| 802 | 3836 | 7/22/11 | NA | Mexico | MX | Nayarit | 6361 | Puga | 19.41353 | 21.7595 | -104.8334 | (21.759499999999999, -104.8334) | Landslide | Landslide | Medium | Tropical cyclone | Hurricane Dora | NA | 0 | ||||
| 825 | 4009 | 10/12/11 | NA | Mexico | MX | Jalisco | 16520 | Cihuatlán | 0.08269 | 19.2372 | -104.5656 | (19.237200000000001, -104.5656) | Landslide | Landslide | Medium | Downpour | NA | 2 | http://photos.denverpost.com/2011/10/12/photos-hurricane-jova-hits-mexican-coast-kills-2/ | ||||
| 890 | 4412 | 6/15/12 | NA | Mexico | MX | Oaxaca | 262566 | Oaxaca | 3.64682 | 17.0854 | -96.7500 | (17.0854, -96.75) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.earthweek.com/2012/ew120622/ew120622f.html | ||||
| 977 | 4874 | 5/26/13 | NA | Mexico | MX | Hidalgo | 33196 | Tepeji de Ocampo | 0.64483 | 19.9006 | -99.3417 | (19.900600000000001, -99.341700000000003) | Landslide | Landslide | Large | Rain | NA | 7 | en.apa.az | http://en.apa.az/xeber_7_killed_in_landslide_in_central_mexico_193672.html | |||
| 1095 | 5403 | 8/26/13 | 20:20:00 | NA | Mexico | MX | Veracruz-Llave | 30607 | Coatzintla | 1.52983 | 20.5004 | -97.4647 | (20.500399999999999, -97.464699999999993) | Landslide | Landslide | Medium | Tropical cyclone | Fernand | NA | 3 | www.cbc.ca | http://www.cbc.ca/news/world/mexico-storm-related-landslides-kill-13-1.1370491 | |
| 1096 | 5405 | 8/26/13 | NA | Mexico | MX | Veracruz-Llave | 15800 | Altotonga | 2.85382 | 19.7906 | -97.2428 | (19.790600000000001, -97.242800000000003) | Landslide | Landslide | Medium | Tropical cyclone | Fernand | NA | 1 | www.cbc.ca | http://www.cbc.ca/news/world/mexico-storm-related-landslides-kill-13-1.1370491 | ||
| 1097 | 5406 | 8/26/13 | NA | Mexico | MX | Veracruz-Llave | 3198 | Yecuatla | 3.73160 | 19.8413 | -96.8005 | (19.8413, -96.8005) | Landslide | Landslide | Medium | Downpour | NA | 9 | www.cbc.ca | http://www.cbc.ca/news/world/mexico-storm-related-landslides-kill-13-1.1370491 | |||
| 1118 | 5486 | 9/9/13 | Night | NA | Mexico | MX | Veracruz-Llave | 12920 | Heroica Coscomatepec de Bravo | 0.09971 | 19.0719 | -97.0474 | (19.071899999999999, -97.047399999999996) | Landslide | Landslide | Medium | Downpour | 2 | 14 | infosurhoy.com | http://infosurhoy.com/en_GB/articles/saii/newsbriefs/2013/09/12/newsbrief-03 | ||
| 1147 | 5526 | 9/14/13 | NA | Mexico | MX | Oaxaca | 3630 | San Andrés Huayapam | 11.83490 | 17.1933 | -96.6045 | (17.193300000000001, -96.604500000000002) | Landslide | Landslide | Very_large | Tropical cyclone | Ingrid | NA | 1 | www.recordonline.com | http://www.recordonline.com/apps/pbcs.dll/article?AID=/20130915/NEWS/130919814/-1/SITEMAP | ||
| 1148 | 5527 | 9/15/13 | NA | Mexico | MX | Hidalgo | 1236 | Fontezuelas | 9.78251 | 20.5402 | -98.9492 | (20.540199999999999, -98.949200000000005) | Landslide | Landslide | Very_large | Tropical cyclone | Ingrid | NA | 3 | www.recordonline.com | http://www.recordonline.com/apps/pbcs.dll/article?AID=/20130915/NEWS/130919814/-1/SITEMAP | ||
| 1149 | 5528 | 9/15/13 | NA | Mexico | MX | Puebla | 9720 | Tlatlauquitepec | 0.30326 | 19.8490 | -97.4997 | (19.849, -97.499700000000004) | Landslide | Landslide | Very_large | Tropical cyclone | Ingrid | 1 | 3 | www.recordonline.com | http://www.recordonline.com/apps/pbcs.dll/article?AID=/20130915/NEWS/130919814/-1/SITEMAP | ||
| 1151 | 5530 | 9/15/13 | NA | Mexico | MX | Guerrero | 652136 | Acapulco | 0.90692 | 16.8638 | -99.8816 | (16.863800000000001, -99.881600000000006) | Landslide | Mudslide | Very_large | Downpour | NA | NA | www.trust.org | http://www.trust.org/item/20131014162052-6ou24/?source=gep | |||
| 1152 | 5532 | 9/16/13 | 8:00:00 | NA | Mexico | MX | Veracruz-Llave | 15800 | Altotonga | 0.50188 | 19.7615 | -97.2422 | (19.761500000000002, -97.242199999999997) | Landslide | Landslide | Medium | Tropical cyclone | Ingrid | 8 | 12 | www.startribune.com | http://www.startribune.com/223894771.html | |
| 1153 | 5534 | 9/16/13 | 15:30 | NA | Mexico | MX | Guerrero | 21407 | Atoyac de Ãlvarez | 32.12708 | Deforested slope | 17.3461 | -100.1681 | (17.3461, -100.1681) | Landslide | Mudslide | Medium | Tropical cyclone | Manuel | 4 | 71 | Vice News | https://news.vice.com/article/a-mexican-town-mourns-its-missing-one-year-after-being-engulfed-by-mud |
| 1154 | 5538 | 9/16/13 | NA | Mexico | MX | Guerrero | 1252 | San Pablo Atzompa | 14.04274 | 17.2250 | -98.5000 | (17.225000000000001, -98.5) | Landslide | Landslide | Medium | Downpour | NA | 0 | america.aljazeera.com | http://america.aljazeera.com/articles/2013/11/19/after-landslide-poorestofthepoorleftoutinthecoldinmexico.html | |||
| 1155 | 5539 | 9/16/13 | NA | Mexico | MX | Puebla | 1123 | Atzala | 5.24855 | 18.5938 | -98.5539 | (18.593800000000002, -98.553899999999999) | Landslide | Landslide | Medium | Tropical cyclone | Manyi | NA | 0 | america.aljazeera.com | http://america.aljazeera.com/articles/2013/11/19/after-landslide-poorestofthepoorleftoutinthecoldinmexico.html | ||
| 1158 | 5542 | 9/16/13 | NA | Mexico | MX | Veracruz-Llave | 1543 | Tetlaxco | 4.51820 | 19.0603 | -97.1009 | (19.060300000000002, -97.100899999999996) | Landslide | Landslide | Medium | Tropical cyclone | Manuel | NA | 13 | www.raymondvillechroniclenews.com | http://www.raymondvillechroniclenews.com/news/2013-09-18/News/Mexican_village_buries_13_victims_of_mudslide.html | ||
| 1159 | 5543 | 9/16/13 | NA | Mexico | MX | Guerrero | 165250 | Chilpancingo de los Bravos | 0.88149 | 17.5482 | -99.5137 | (17.548200000000001, -99.5137) | Landslide | Mudslide | Medium | Tropical cyclone | Ingrid | NA | 4 | www.washingtonpost.com | http://www.washingtonpost.com/world/two-storms-batter-mexico-killing-dozens/2013/09/17/5cd3d81e-1f8e-11e3-b7d1-7153ad47b549_gallery.html#photo=6 | ||
| 1185 | 5693 | 12/16/13 | NA | Mexico | MX | Tabasco | 1732 | Olcuatitán | 4.32007 | 18.1563 | -92.9790 | (18.156300000000002, -92.978999999999999) | Landslide | Landslide | Medium | Continuous rain | NA | NA | www.aljazeera.com | http://www.aljazeera.com/weather/2013/12/rain-snow-floods-hit-mexico-20131229105245837299.html | |||
| 1196 | 5731 | 12/28/13 | 2:00:00 | NA | Mexico | MX | Baja California | 9085 | El Sauzal | 12.36500 | Below road | 31.9780 | -116.7776 | (31.978000000000002, -116.77760000000001) | Landslide | Mudslide | Medium | Downpour | NA | 0 | www.globalpost.com | http://www.globalpost.com/dispatch/news/agencia-efe/131230/mudslide-closes-major-road-northwestern-mexico | |
| 1278 | 6122 | 7/2/14 | Morning | NA | Mexico | MX | Chihuahua | 1512354 | Ciudad Juárez | 3.05542 | Unknown | 31.7293 | -106.5152 | (31.729299999999999, -106.51519999999999) | Landslide | Landslide | Small | Rain | 0 | 3 | El Paso Times | http://www.elpasotimes.com/news/ci_26072515/mudslide-juarez-kills-mother-two-children | |
| 1298 | 6269 | 10/18/14 | NA | Mexico | MX | Guerrero | 1018 | Colonia Alborada | 4.10830 | Above road | 16.8850 | -99.8562 | (16.885000000000002, -99.856200000000001) | Landslide | Landslide | Small | Tropical cyclone | Trudy | 0 | 0 | Thomson Reuters Foundation | http://www.trust.org/item/20141018173449-r3rca/ | |
| 1376 | 6688 | 1/5/14 | Night | NA | Mexico | MX | Veracruz | 9277 | Chocaman | 1.93516 | Unknown | 19.0294 | -97.0355 | (19.029399999999999, -97.035499999999999) | Landslide | Landslide | Medium | Rain | 0 | 0 | Calor | http://www.alcalorpolitico.com/informacion/saldo-blanco-en-deslave-de-chocaman-familias-salieron-a-tiempo-maza-limon-139202.html#.VLfpVPnF98E | |
| 1493 | 7056 | 5/17/15 | NA | Mexico | MX | Baja California | 1173 | La Esperanza [Granjas Familiares] | 6.46156 | Urban area | 32.5196 | -117.0898 | (32.519599999999997, -117.0898) | Landslide | Landslide | Medium | Rain | 0 | 0 | ||||
| 1586 | 7430 | 10/7/15 | NA | Mexico | MX | Tabasco | 1042 | Libertad | 4.81680 | Unknown | 17.3768 | -92.7439 | (17.376799999999999, -92.743899999999996) | Landslide | Landslide | Medium | Rain | 0 | 0 | Tabasco Hoy | http://www.tabascohoy.com/2/notas/273505/dejan-lluvias-deslizamientos-e-inundaciones-en-la-sierra | ||
| 1587 | 7431 | 10/7/15 | NA | Mexico | MX | Tabasco | 2500 | Huapinol | 2.15703 | Unknown | 17.9100 | -92.8956 | (17.91, -92.895600000000002) | Landslide | Landslide | Medium | Rain | 0 | 0 | Tabasco Hoy | http://www.tabascohoy.com/2/notas/273505/dejan-lluvias-deslizamientos-e-inundaciones-en-la-sierra | ||
| 1601 | 7445 | 9/17/15 | NA | Mexico | MX | Veracruz | 1005 | Cruz Verde | 2.47800 | Deforested slope | 19.1331 | -97.1317 | (19.133099999999999, -97.131699999999995) | Landslide | Landslide | Medium | Rain | 0 | 6 | El Diario | http://www.eldiariony.com/2015/09/17/deslizamiento-de-cerro-en-veracruz-deja-seis-muertos/ | ||
| 1621 | 7465 | 5/13/15 | NA | Mexico | MX | Guerrero | 652136 | Acapulco de Juárez | 7.07138 | Urban area | 16.8989 | -99.9452 | (16.898900000000001, -99.9452) | Landslide | Landslide | Medium | Construction | 0 | 0 | Milenio | http://www.milenio.com/estados/Deslave_de_cerro_en_Acapulco_deja_3_personas_semienterradas_0_517148565.html | ||
| 1625 | 7469 | 12/16/15 | NA | Mexico | MX | Tabasco | 1732 | Olcuatitán | 8.93271 | Below road | 18.2341 | -92.8901 | (18.234100000000002, -92.890100000000004) | Landslide | Riverbank collapse | Medium | Flooding | 0 | 0 | Diario Presente | http://www.diariopresente.com.mx/noticia/tabasco/145872/deslave-carretera-punto-dejar-incomunicados-comunidades-nacajuca/ | ||
| 1635 | 7479 | 10/3/15 | Night | NA | Mexico | MX | Guerrero | 1201 | Pochutla | 12.33417 | Unknown | 17.5586 | -98.9483 | (17.558599999999998, -98.948300000000003) | Landslide | Landslide | Medium | Tropical cyclone | Marty | 0 | 0 | Tiempo | http://www.tiempoenlinea.com.mx/index.php/oaxaca-2/52715-deslave-afecta-62-viviendas-en-la-montana-de-guerrero |
| 1670 | 7514 | 5/23/15 | NA | Mexico | MX | Chiapas | 3796 | Santo Domingo | 0.76257 | Below road | 15.0337 | -92.1124 | (15.0337, -92.112399999999994) | Landslide | Landslide | Medium | Rain | 0 | 0 | Cuarto Poder | http://www.cuartopoder.mx/deslaveafectacarreteradejandouncarril-116564.html | ||
| 1671 | 7515 | 10/21/15 | NA | Mexico | MX | Chiapas | 19092 | Motozintla de Mendoza | 2.06743 | Above road | 15.3482 | -92.2523 | (15.3482, -92.252300000000005) | Landslide | Landslide | Medium | Rain | 0 | 0 | azteca noticias | http://www.aztecanoticias.com.mx/notas/estados/234140/suman-16-deslaves-por-lluvias-en-chiapas | ||
| 1672 | 7516 | 10/21/15 | NA | Mexico | MX | Chiapas | 18608 | Emiliano Zapata | 4.68443 | Below road | 17.7055 | -91.7416 | (17.705500000000001, -91.741600000000005) | Landslide | Landslide | Medium | Rain | cold front No. 7 | 0 | 0 | azteca noticias | http://www.aztecanoticias.com.mx/notas/estados/234140/suman-16-deslaves-por-lluvias-en-chiapas | |
| 1673 | 7517 | 9/12/15 | NA | Mexico | MX | Chiapas | 6089 | Tila | 1.73469 | Below road | 17.3112 | -92.4393 | (17.311199999999999, -92.439300000000003) | Landslide | Landslide | Medium | Rain | 0 | 0 | NVI noticias | http://www.noticiasnet.mx/portal/chiapas/general/agua/317470-lluvias-causan-deslaves-tramos-carreteros | ||
| 1674 | 7518 | 10/15/15 | NA | Mexico | MX | Tabasco | 32415 | Tenosique de Pino Suárez | 15.22260 | Below road | 17.4054 | -91.2988 | (17.4054, -91.2988) | Landslide | Landslide | Medium | Flooding | 0 | 0 | ver tiente global | http://vertienteglobal.com/?p=49401 |
library(ggplot2)
ggplot(data=df_co, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de inundaciones") +
ylab("Frecuencia")
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_mx <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_mx)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_mx, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_mx$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_mx)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#CANADA
library(readr)
library(knitr)
df_ca <- subset (df, Country == "Canada")
knitr::kable(head(df_ca))| 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-/ |
df_ca %>%
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 2.00473library(ggplot2)
ggplot(data=df_ca, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_ca, aes(fill=City, x="Canada", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_ca, aes(fill=City, x="Canada", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_ca, aes(x = "Canada", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_ca <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_ca)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_ca, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_ca$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_ca)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#ESTADOS UNIDOS
library(readr)
library(knitr)
df_us <- subset (df, Country == "United States")
knitr::kable(head(df_us))| 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 |
df_us %>%
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
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## 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
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## 647 United States West Virginia Elizabeth 15.03444
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## 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
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## 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
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## 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
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## 686 United States Ohio Bridgetown 4.26162
## 687 United States Ohio Mack 2.95385
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## 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
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## 710 United States Pennsylvania Masontown 5.82547
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## 712 United States Pennsylvania Maple Glen 1.49457
## 713 United States New York Whitehall 16.90533
## 717 United States Pennsylvania Curtisville 2.01507
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## 721 United States West Virginia Huntington 2.94557
## 722 United States West Virginia Huntington 2.76021
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## 725 United States Pennsylvania Coraopolis 2.45126
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## 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
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## 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
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## 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
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## 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
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## 811 United States Tennessee Columbia 0.72461
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## 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
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## 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
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## 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
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## 933 United States North Carolina Maggie Valley 5.63904
## 934 United States Tennessee Knoxville 5.53703
## 935 United States North Carolina Cherokee 13.86023
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## 938 United States Tennessee Jellico 13.77073
## 939 United States North Carolina Robbinsville 20.45203
## 940 United States Pennsylvania Pittsburgh 2.64611
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## 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
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## 955 United States Illinois East Peoria 2.39991
## 957 United States North Carolina Marshall 6.68251
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## 961 United States Florida Clermont 0.39179
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## 963 United States North Carolina Lenoir 12.88531
## 964 United States North Carolina Canton 4.10329
## 965 United States North Carolina Fruitland 5.76576
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## 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
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## 988 United States Pennsylvania Philadelphia 1.27824
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## 991 United States New York Middleburgh 1.41843
## 992 United States New York Camillus 2.54860
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## 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
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## 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 1.78429library(ggplot2)
ggplot(data=df_us, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")## Warning: Removed 1 rows containing missing values (position_stack).
library(ggplot2)
library(dplyr)
ggplot(data=df_us, aes(fill=City, x="United States", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")## Warning: Removed 1 rows containing missing values (geom_bar).
library(ggplot2)
library(dplyr)
ggplot(data=df_us, aes(fill=City, x="United States", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")## Warning: Removed 1 rows containing missing values (position_stack).
library(ggplot2)
library(dplyr)
ggplot(data=df_us, aes(x = "United States", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)## Warning: Removed 1 rows containing missing values (position_stack).
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_us <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_us)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_us, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_us$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_us)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#ECUADOR
library(readr)
library(knitr)
df_ec <- subset (df, Country == "Ecuador")
knitr::kable(head(df_ec))| 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 | Sangolquà | 33.94603 | -0.3560 | -78.1480 | (-0.35599999999999998, -78.147999999999996) | Landslide | Landslide | Medium | Downpour | NA | NA | Red Cross - Field reports | https://www-secure.ifrc.org/dmis/prepare/view_report.asp?ReportID=2908 | |||
| 78 | 468 | 2/28/08 | SA | Ecuador | EC | Napo | 7309 | Archidona | 50.21741 | -0.4635 | -77.8928 | (-0.46350000000000002, -77.892799999999994) | Landslide | Landslide | Medium | Rain | NA | 7 | http://english.aljazeera.net/NR/exeres/868843D7-B211-4DE5-AFBE-31C29CF79C5A.htm | ||||
| 87 | 489 | 4/1/08 | SA | Ecuador | EC | Pichincha | 1399814 | Quito | 1.56942 | -0.2196 | -78.5347 | (-0.21959999999999999, -78.534700000000001) | Landslide | Landslide | Medium | Rain | NA | NA | http://www.plenglish.com/Article.asp?ID=%7B39BA85A0-5900-4A4A-A329-818B3FC61EA1%7D&language=EN | ||||
| 218 | 1186 | 12/28/09 | Night | SA | Ecuador | EC | Santo Domingo de los Tsáchilas | 200421 | Santo Domingo de los Colorados | 1.16036 | Unknown | -0.3152 | -78.9724 | (-0.31519999999999998, -78.972399999999993) | Landslide | Mudslide | Medium | Downpour | 5 | 3 | Latin American Herald Tribune | http://www.laht.com/article.asp?ArticleId=327938&CategoryId=14089 |
df_ec %>%
select(Country, State, City, Distance)## Country State City
## 9 Ecuador Zamora-Chinchipe Zamora
## 10 Ecuador Loja Loja
## 11 Ecuador Pichincha SangolquÃ
## 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 SaquisilÃ
## 660 Ecuador Morona-Santiago Macas
## 697 Ecuador Pichincha Quito
## 751 Ecuador Carchi El Ã\201ngel
## 868 Ecuador Manabi Chone
## 872 Ecuador Manabi Tosagua
## 942 Ecuador Guayas Balao
## 956 Ecuador Esmeraldas Esmeraldas
## 984 Ecuador Pichincha Cayambe
## 1304 Ecuador Carchi San Gabriel
## 1332 Ecuador Pichincha Quito
## 1333 Ecuador Pichincha Quito
## 1335 Ecuador Sucumbios Gonzalo Pizarro
## 1365 Ecuador Azuay Cuenca
## 1368 Ecuador Zamora-Chinchipe Zamora
## 1451 Ecuador Loja Macará
## 1452 Ecuador Loja Loja
## 1453 Ecuador Loja Catamayo
## 1454 Ecuador Azuay Cuenca
## 1566 Ecuador Pichincha Quito
## Distance
## 9 0.47714
## 10 0.35649
## 11 33.94603
## 78 50.21741
## 87 1.56942
## 218 1.16036
## 238 26.18676
## 253 11.91442
## 339 2.81891
## 586 25.82923
## 587 30.81169
## 660 46.77007
## 697 4.39517
## 751 28.29459
## 868 19.85816
## 872 7.67919
## 942 16.34404
## 956 21.26652
## 984 45.69792
## 1304 10.47204
## 1332 26.72137
## 1333 23.97854
## 1335 11.55916
## 1365 13.21139
## 1368 1.23724
## 1451 18.88784
## 1452 1.82885
## 1453 17.57187
## 1454 10.16196
## 1566 4.25486library(ggplot2)
ggplot(data=df_ec, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_ec, aes(fill=City, x="Ecuador", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_ec, aes(fill=City, x="Ecuador", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_ec, aes(x = "Ecuador", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_ec <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_ec)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_ec, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_ec$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_ec)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#GUATEMALA
library(readr)
library(knitr)
df_gu <- subset (df, Country == "Guatemala")
knitr::kable(head(df_gu))| 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 | LanquÃn | 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 | PalÃn | 3.10150 | 14.4226 | -90.6755 | (14.422599999999999, -90.6755) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Storm Arthur | NA | 1 | http://209.85.215.104/search?q=cache:QU_lPxNfk78J:www.plenglish.com/article.asp?ID=%7B1D4A74F7-CDCA-49D0-ABD4-D2E0FD9D2130%7D&language=EN+Colom+said+the+declaration+came+after+a+death+in+Palin+and+40+houses+partially&hl=en&ct=clnk&cd=1&gl=us&c | |||
| 108 | 591 | 6/18/08 | NA | Guatemala | GT | Guatemala | 994938 | Guatemala City | 3.12614 | 14.6510 | -90.5403 | (14.651, -90.540300000000002) | Landslide | Complex | Medium | Rain | NA | 8 | http://cnnwire.blogs.cnn.com/2008/06/20/8-dead-in-rough-weather-in-guatemala/ |
df_gu %>%
select(Country, State, City, Distance)## Country State City Distance
## 17 Guatemala Guatemala San José Pinula 4.74385
## 27 Guatemala Alta Verapaz LanquÃn 13.39817
## 28 Guatemala Izabal Morales 12.55184
## 41 Guatemala Guatemala Guatemala City 2.79113
## 104 Guatemala Escuintla PalÃn 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 PalÃn 0.18542
## 354 Guatemala Guatemala Amatitlán 2.02891
## 355 Guatemala Guatemala Chinautla 0.44764
## 356 Guatemala Sacatepéquez Santa MarÃa 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 MarÃa Ixhuatán 0.82332
## 1561 Guatemala Suchitepeque San Juan Bautista 3.47803
## 1568 Guatemala Guatemala Fraijanes 6.19218
## 1569 Guatemala Chimaltenango PatzicÃa 5.52205
## 1570 Guatemala Guatemala Mixco 1.87009
## 1571 Guatemala Guatemala Petapa 4.20726
## 1572 Guatemala Sololá San Antonio Palopó 3.18658
## 1573 Guatemala Sololá San Andrés Semetabaj 0.67040
## 1574 Guatemala Guatemala Guatemala City 3.80312
## 1575 Guatemala Guatemala Guatemala City 1.68290
## 1576 Guatemala Guatemala Guatemala City 2.08425
## 1577 Guatemala Guatemala Guatemala City 3.25675
## 1578 Guatemala Guatemala Guatemala City 3.49341
## 1579 Guatemala Guatemala Guatemala City 1.83863
## 1580 Guatemala Guatemala Guatemala City 1.57381
## 1581 Guatemala Guatemala Guatemala City 1.70147
## 1582 Guatemala Guatemala Guatemala City 3.00314
## 1583 Guatemala Quiché Chichicastenango 2.27725
## 1584 Guatemala Guatemala Chinautla 2.36376
## 1585 Guatemala Guatemala Chinautla 2.66358
## 1588 Guatemala Sacatepéquez Santa Catarina Barahona 1.45200
## 1589 Guatemala Alta Verapaz Cahabón 5.14479
## 1590 Guatemala Escuintla Nueva Concepción 8.25465
## 1591 Guatemala Guatemala Villa Canales 0.65744
## 1592 Guatemala Sololá San Andrés Semetabaj 0.75685
## 1595 Guatemala Quetzaltenango Coatepeque 1.81216library(ggplot2)
ggplot(data=df_gu, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_gu, aes(fill=City, x="Guatemala", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_gu, aes(fill=City, x="Guatemala", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_gu, aes(x = "Guatemala", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_gu <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_gu)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_gu, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_gu$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_gu)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#EL SALVADOR
library(readr)
library(knitr)
df_sv <- subset (df, Country == "El Salvador")
knitr::kable(head(df_sv))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | 230 | 9/5/07 | NA | El Salvador | SV | Ahuachapán | 7797 | Concepción de Ataco | 0.00273 | 13.8703 | -89.8486 | (13.8703, -89.848600000000005) | Landslide | Mudslide | Medium | Tropical cyclone | Hurricane Felix | NA | NA | Azcentral.com | http://www.azcentral.com/news/articles/1108sr-fhsistercity1109-ON.html | ||
| 105 | 564 | 6/2/08 | NA | El Salvador | SV | La Libertad | 124694 | Santa Tecla | 4.96416 | 13.7205 | -89.2687 | (13.720499999999999, -89.268699999999995) | Landslide | Landslide | Medium | Tropical cyclone | Tropical Storm Arthur | NA | NA | http://news.xinhuanet.com/english/2008-06/04/content_8310737.htm | |||
| 224 | 1285 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 7.60946 | 13.6409 | -88.8699 | (13.6409, -88.869900000000001) | Landslide | Complex | Very_large | Tropical cyclone | Tropical Cyclone Ida | NA | 23 | http://www.google.com/hostednews/ap/article/ALeqM5j0XCCb1n12DyhoBoDzGj_hTyEtrAD9BRKPRG0 | |||
| 225 | 1286 | 11/8/09 | NA | El Salvador | SV | La Libertad | 33767 | Antiguo Cuscatlán | 4.86219 | 13.7156 | -89.2521 | (13.7156, -89.252099999999999) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | 4 | http://www.google.com/hostednews/ap/article/ALeqM5j0XCCb1n12DyhoBoDzGj_hTyEtrAD9BRKPRG0 | |||
| 226 | 1287 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 5.90726 | 13.6094 | -88.8488 | (13.609400000000001, -88.848799999999997) | Landslide | Rockfall | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | NA | http://news.bbc.co.uk/2/hi/in_depth/8349333.stm | |||
| 227 | 1288 | 11/8/09 | NA | El Salvador | SV | San Vicente | 41504 | San Vicente | 4.03125 | 13.6466 | -88.8347 | (13.646599999999999, -88.834699999999998) | Landslide | Mudslide | Medium | Tropical cyclone | Tropical Cyclone Ida | NA | NA | http://news.yahoo.com/s/afp/20091109/wl_afp/salvadorweatherstorm_20091109100952 |
df_sv %>%
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 MartÃn 8.82525
## 1369 El Salvador Sonsonate Nahuizalco 4.23875
## 1370 El Salvador Sonsonate Sonzacate 3.22235
## 1371 El Salvador La Paz San Pedro Masahuat 0.31933
## 1372 El Salvador San Miguel Chirilagua 9.97227
## 1373 El Salvador Santa Ana Coatepeque 8.83210
## 1374 El Salvador La Libertad Santa Tecla 4.60655
## 1375 El Salvador San Salvador Antiguo Cuscatlán 3.25227
## 1594 El Salvador Santa Ana Ciudad Arce 1.15810
## 1596 El Salvador La Libertad Santa Tecla 4.67722
## 1597 El Salvador La Libertad Santa Tecla 9.87553
## 1598 El Salvador Sonsonate Juayúa 0.49346library(ggplot2)
ggplot(data=df_sv, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_sv, aes(fill=City, x="El Salvador", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_sv, aes(fill=City, x="El Salvador", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_sv, aes(x = "El Salvador", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_sv <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_sv)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_sv, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_sv$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_sv)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#PERU
library(readr)
library(knitr)
df_pe <- subset (df, Country == "Peru")
knitr::kable(head(df_pe))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 80 | 472 | 3/5/08 | SA | Peru | PE | Huanuco | 53177 | Tingo MarÃa | 17.34318 | -9.2114 | -76.1311 | (-9.2113999999999994, -76.131100000000004) | Landslide | Landslide | Medium | Rain | NA | 7 | http://news.xinhuanet.com/english/2008-03/06/content_7727748.htm | ||||
| 90 | 497 | 4/11/08 | SA | Peru | PE | Ancash | 5044 | Carhuaz | 2.43089 | -9.2965 | -77.6613 | (-9.2965, -77.661299999999997) | Landslide | Landslide | Medium | Rain | NA | NA | http://news.xinhuanet.com/english/2008-04/14/content_7972049.htm | ||||
| 184 | 1004 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Parcoy | 9.64894 | -7.9589 | -77.5239 | (-7.9588999999999999, -77.523899999999998) | Landslide | Mudslide | Medium | Downpour | NA | 12 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 185 | 1005 | 4/14/09 | SA | Peru | PE | La Libertad | 0 | Aricapampa | 0.00442 | -7.8058 | -77.7172 | (-7.8057999999999996, -77.717200000000005) | Landslide | Mudslide | Medium | Downpour | NA | 0 | http://rawstory.com/news/afp/Nine_dead_in_Peru_mudslides_04142009.html | ||||
| 252 | 1459 | 1/28/10 | 15:00:00 | SA | Peru | PE | Ancash | 0 | Anta | 0.13147 | -9.3584 | -77.5984 | (-9.3583999999999996, -77.598399999999998) | Landslide | Complex | Medium | Downpour | NA | 0 | http://daveslandslideblog.blogspot.com/2010/01/new-images-of-level-of-destruction-in.html | |||
| 294 | 1612 | 4/1/10 | Early morning | SA | Peru | PE | Huanuco | 0 | Acomayo | 3.49890 | -9.7996 | -76.1038 | (-9.7995999999999999, -76.103800000000007) | Landslide | Lahar | Large | Downpour | NA | 12 | http://www.thejakartapost.com/news/2010/03/18/mudslide-isolates-1500-residents-solok.html |
df_pe %>%
select(Country, State, City, Distance)## Country State City Distance
## 80 Peru Huanuco Tingo MarÃa 17.34318
## 90 Peru Ancash Carhuaz 2.43089
## 184 Peru La Libertad Parcoy 9.64894
## 185 Peru La Libertad Aricapampa 0.00442
## 252 Peru Ancash Anta 0.13147
## 294 Peru Huanuco Acomayo 3.49890
## 295 Peru Huanuco Ambo 0.55865
## 516 Peru Huanuco Huánuco 1.01932
## 578 Peru Ancash Yanac 3.70678
## 864 Peru Ancash Tauca 6.77672
## 912 Peru San MartÃn Yuracyacu 8.89799
## 1378 Peru Ancash Huachis 3.26788
## 1424 Peru San MartÃn Naranjos 12.70296
## 1425 Peru San MartÃn Tarapoto 2.89809library(ggplot2)
ggplot(data=df_pe, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_pe, aes(fill=City, x="Peru", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_pe, aes(fill=City, x="Peru", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_pe, aes(x = "Peru", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_pe <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_pe)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_pe, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_pe$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_pe)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
NICARAGUA
library(readr)
library(knitr)
df_ni <- subset (df, Country == "Nicaragua")
knitr::kable(head(df_ni))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | 229 | 9/4/07 | NA | Nicaragua | NI | Atlántico Norte | 6315 | Bonanza | 54.90196 | 13.6670 | -84.2435 | (13.667, -84.243499999999997) | Landslide | Complex | Medium | Tropical cyclone | Hurricane Felix | NA | NA | United Nations Development Programme - Relief Web | http://www.reliefweb.int/ | ||
| 151 | 826 | 10/3/08 | NA | Nicaragua | NI | Masaya | 5182 | Tisma | 14.49301 | 12.1200 | -85.8900 | (12.12, -85.89) | Landslide | Landslide | Medium | Downpour | NA | 9 | CBC | http://www.cbc.ca/world/story/2008/10/04/nicaragua-flooding.html | |||
| 420 | 2289 | 8/20/10 | NA | Nicaragua | NI | Managua | 16469 | El Crucero | 5.84054 | 12.0420 | -86.2998 | (12.042, -86.299800000000005) | Landslide | Mudslide | Medium | Downpour | NA | 3 | |||||
| 424 | 2330 | 8/25/10 | NA | Nicaragua | NI | Jinotega | 2367 | San José de Bocay | 1.36745 | 13.5317 | -85.5325 | (13.531700000000001, -85.532499999999999) | Landslide | Landslide | Medium | Downpour | NA | NA | |||||
| 1261 | 6089 | 6/23/14 | NA | Nicaragua | NI | Chontales | 5827 | Santo Domingo | 31.14242 | Unknown | 12.3535 | -84.8095 | (12.3535, -84.8095) | Landslide | Landslide | Small | Continuous rain | 0 | 0 | Wilfried Strauch | |||
| 1262 | 6090 | 6/23/14 | NA | Nicaragua | NI | Chontales | 5827 | Santo Domingo | 31.24511 | Unknown | 12.3521 | -84.8080 | (12.3521, -84.808000000000007) | Landslide | Landslide | Medium | Continuous rain | 0 | 0 | Wilfried Strauch |
df_ni %>%
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 RÃo San Juan San Carlos 0.67752
## 1626 Nicaragua Jinotega Wiwilà 25.81514
## 1627 Nicaragua Jinotega Jinotega 2.44880
## 1631 Nicaragua Madriz Las Sabanas 7.21108
## 1632 Nicaragua Madriz Las Sabanas 4.86364
## 1633 Nicaragua Managua Terrabona 18.92056
## 1634 Nicaragua Ogun State Bonanza 10.61568
## 1636 Nicaragua Ogun State Siuna 1.68056
## 1637 Nicaragua Masaya San Juan de Oriente 1.56730library(ggplot2)
ggplot(data=df_ni, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_ni, aes(fill=City, x="Nicaragua", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_ni, aes(fill=City, x="Nicaragua", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_ni, aes(x = "Nicaragua", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_ni <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_ni)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_ni, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_ni$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_ni)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#HONDURAS
library(readr)
library(knitr)
df_hn <- subset (df, Country == "Honduras")
knitr::kable(head(df_hn))| 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 | CorquÃn | 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 LucÃa | 4.75791 | 14.1015 | -87.1607 | (14.1015, -87.160700000000006) | Landslide | Rockfall | Medium | Downpour | NA | 5 |
df_hn %>%
select(Country, State, City, Distance)## Country State City Distance
## 159 Honduras Copán CorquÃn 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 LucÃa 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 ParaÃso 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 RÃo Abajo 3.63962
## 1641 Honduras Francisco Morazán Tegucigalpa 2.91326library(ggplot2)
ggplot(data=df_hn, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_hn, aes(fill=City, x="Honduras", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_hn, aes(fill=City, x="Honduras", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_hn, aes(x = "Honduras", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_hn <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_hn)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_hn, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_hn$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_hn)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#VENEZUELA
library(readr)
library(knitr)
df_vn <- subset (df, Country == "Venezuela")
knitr::kable(head(df_vn))| 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 | MaiquetÃa | 6.65506 | 10.5363 | -66.9492 | (10.536300000000001, -66.949200000000005) | Landslide | Landslide | Medium | Downpour | NA | 7 | http://www.laht.com/article.asp?ArticleId=367610&CategoryId=14091 | ||||
| 530 | 2749 | 11/25/10 | SA | Venezuela | VE | Miranda | 278890 | Santa Teresa | 8.45736 | 10.2452 | -66.5867 | (10.245200000000001, -66.586699999999993) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | ||||
| 531 | 2753 | 11/26/10 | Before dawn | SA | Venezuela | VE | Distrito Federal | 3000000 | Caracas | 2.95706 | 10.4913 | -66.9060 | (10.491300000000001, -66.906000000000006) | Landslide | Mudslide | Medium | Downpour | NA | 3 | http://www.laht.com/article.asp?ArticleId=379809&CategoryId=10717 | |||
| 538 | 2761 | 11/27/10 | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 7.90754 | 10.5030 | -66.9995 | (10.503, -66.999499999999998) | Landslide | Mudslide | Medium | Downpour | NA | 1 | http://www.laht.com/article.asp?ArticleId=380021&CategoryId=10717 | ||||
| 539 | 2762 | 11/27/10 | Night | SA | Venezuela | VE | Distrito Federal | 0 | Caricuao | 5.64050 | 10.4267 | -67.0342 | (10.4267, -67.034199999999998) | Landslide | Landslide | Medium | Downpour | NA | 0 | http://english.eluniversal.com/2010/11/30/en_pol_esp_landslides-hit-sever_30A4792571.shtml |
df_vn %>%
select(Country, State, City, Distance)## Country State City Distance
## 165 Venezuela Distrito Federal Caracas 2.92493
## 448 Venezuela Vargas MaiquetÃa 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 MaiquetÃa 7.89319
## 768 Venezuela Aragua El Consejo 14.28266
## 769 Venezuela Falcón Santo Domingo 19.81345
## 847 Venezuela Distrito Federal Caracas 2.55507library(ggplot2)
ggplot(data=df_vn, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_vn, aes(fill=City, x="Venezuela", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_vn, aes(fill=City, x="Venezuela", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_vn, aes(x = "Venezuela", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_vn <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_vn)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_vn, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_vn$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_vn)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
PANAMA
library(readr)
library(knitr)
df_pa <- subset (df, Country == "Panama")
knitr::kable(head(df_vn))| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
df_pa %>%
select(Country, State, City, Distance)## Country State City Distance
## 141 Panama Panamá Cerro Azul 1.84596
## 167 Panama Chiriquà 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 Chiriquà Volcán 0.35187
## 1388 Panama Chiriquà RÃo Sereno 0.64491
## 1389 Panama Panamá AlcaldedÃaz 1.41526
## 1390 Panama Colón Nuevo San Juan 4.74914
## 1391 Panama Panamá San Miguelito 4.18074
## 1392 Panama Chiriquà Cerro Punta 3.33873
## 1393 Panama Panamá Ancón 2.24069
## 1606 Panama Colón Nueva Providencia 0.18619
## 1607 Panama Colón MarÃa 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 Chiriquà Boca de Balsa 2.44254
## 1659 Panama Chiriquà RÃo Sereno 3.66775
## 1660 Panama Chiriquà Palmira Centro 0.90169
## 1661 Panama Chiriquà Breñón 0.37999
## 1662 Panama Panamá San Miguelito 2.57852
## 1663 Panama Chiriquà Monte Lirio 6.74338
## 1664 Panama Coclé Caballero 0.21605
## 1665 Panama Panamá San Miguelito 2.59449
## 1666 Panama Panamá San Miguelito 3.30848
## 1667 Panama Panamá San Miguelito 3.54386
## 1668 Panama Panamá Arraiján 1.80341library(ggplot2)
ggplot(data=df_pa, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_pa, aes(fill=City, x="Panama", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_pa, aes(fill=City, x="Panama", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_pa, aes(x = "Panama", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_pa <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_pa)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_pa, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_pa$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_pa)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#BRAZIL
library(readr)
library(knitr)
df_br <- subset (df, Country == "Brazil")
knitr::kable(head(df_br))| id | date | time | continent_code | Country | country_code | State | population | City | Distance | location_description | latitude | longitude | geolocation | hazard_type | landslide_type | landslide_size | trigger | storm_name | injuries | fatalities | source_name | source_link | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 193 | 1019 | 5/6/09 | SA | Brazil | BR | Pará | 70888 | Altamira | 1.01848 | -3.1946 | -52.2093 | (-3.1945999999999999, -52.209299999999999) | Landslide | Mudslide | Medium | Rain | NA | 19 | http://en.wikipedia.org/wiki/2009_Brazilian_floods_and_mudslides#cite_note-186k_homeless_from_flood-0 | ||||
| 194 | 1020 | 5/6/09 | SA | Brazil | BR | Maranhão | 48901 | Barra do Corda | 61.75306 | -5.0804 | -45.6010 | (-5.0804, -45.600999999999999) | Landslide | Mudslide | Medium | Rain | NA | 3 | http://www.3news.co.nz/Heavy-rains-leave-186K-homeless-in-northern-Brazil/tabid/417/articleID/102883/cat/782/Default.aspx | ||||
| 947 | 4789 | 3/28/13 | 0:30:00 | SA | Brazil | BR | Amapá | 83927 | Santana | 0.17020 | -0.0568 | -51.1818 | (-0.056800000000000003, -51.181800000000003) | Landslide | Landslide | Medium | Other | NA | 6 | bestonlinebroker.co | http://bestonlinebroker.co/p/3999693490/2013/04/10/beadell-resources-why-the-silence-after-a-major-mining-disaster | ||
| 1348 | 6591 | 1/4/14 | SA | Brazil | BR | Pará | 78050 | Abaetetuba | 1.04062 | Urban area | -1.7225 | -48.8907 | (-1.7224999999999999, -48.890700000000002) | Landslide | Riverbank collapse | Medium | Unknown | 0 | 0 | G1 | http://g1.globo.com/pa/para/noticia/2014/01/area-que-desabou-em-abaetetuba-tinha-aterro-irregular-diz-engenheira.html |
df_br %>%
select(Country, State, City, Distance)## Country State City Distance
## 193 Brazil Pará Altamira 1.01848
## 194 Brazil Maranhão Barra do Corda 61.75306
## 947 Brazil Amapá Santana 0.17020
## 1348 Brazil Pará Abaetetuba 1.04062library(ggplot2)
ggplot(data=df_br, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_br, aes(fill=City, x="Brazil", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_br, aes(fill=City, x="Brazil", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_br, aes(x = "Brazil", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_br <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_br)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_br, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_br$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_br)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
#BELIZE
library(readr)
library(knitr)
df_bz <- subset (df, Country == "Belize")
knitr::kable(head(df_bz))| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1593 | 7437 | 11/24/15 | 10:00 | NA | Belize | BZ | Cayo | 13381 | Belmopan | 9.71758 | Above road | 17.2183 | -88.8519 | (17.218299999999999, -88.851900000000001) | Landslide | Rockfall | Small | Mining digging | 0 | 0 | Plus TV | http://www.plustvbelize.com/landslide-in-arizona-village-blocks-road-for-hours/ |
df_bz %>%
select(Country, State, City, Distance)## Country State City Distance
## 1593 Belize Cayo Belmopan 9.71758library(ggplot2)
ggplot(data=df_bz, aes(x= State , y= Distance)) + geom_bar(stat="identity", color="black", fill="purple")
library(ggplot2)
library(dplyr)
ggplot(data=df_bz, aes(fill=City, x="Belize", y=Distance)) +
geom_bar(position="dodge", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_bz, aes(fill=City, x="Belize", y=Distance)) +
geom_bar(position="stack", stat="identity", color="black")
library(ggplot2)
library(dplyr)
ggplot(data=df_bz, aes(x = "Belize", y = Distance , fill=City)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0)
n_sturges = 1 + log(length(Distance))/log(2)
n_sturgesc = ceiling(n_sturges)
n_sturgesf = floor(n_sturges)
n_clases = 0
if (n_sturgesc%%2 == 0) {
n_clases = n_sturgesf
} else {
n_clases = n_sturgesc
}
R = max(Distance) - min(Distance)
w = ceiling(R/n_clases)bins <- seq(min(Distance), max(Distance) + w, by = w)
bins## [1] 0.05648 5.05648 10.05648 15.05648 20.05648 25.05648 30.05648 35.05648Edades <- cut(Distance, bins)
Freq_table <- transform(table(Distance), Rel_Freq=prop.table(Freq), Cum_Freq=cumsum(Freq))
knitr::kable(Freq_table)| Distance | Freq | Rel_Freq | Cum_Freq |
|---|---|---|---|
| 0.05648 | 1 | 0.0136986 | 1 |
| 0.08269 | 1 | 0.0136986 | 2 |
| 0.09971 | 1 | 0.0136986 | 3 |
| 0.15208 | 2 | 0.0273973 | 5 |
| 0.28905 | 1 | 0.0136986 | 6 |
| 0.30326 | 1 | 0.0136986 | 7 |
| 0.31118 | 1 | 0.0136986 | 8 |
| 0.36051 | 1 | 0.0136986 | 9 |
| 0.50188 | 1 | 0.0136986 | 10 |
| 0.6355 | 1 | 0.0136986 | 11 |
| 0.64483 | 1 | 0.0136986 | 12 |
| 0.66626 | 1 | 0.0136986 | 13 |
| 0.76257 | 1 | 0.0136986 | 14 |
| 0.7834 | 1 | 0.0136986 | 15 |
| 0.88149 | 1 | 0.0136986 | 16 |
| 0.90692 | 1 | 0.0136986 | 17 |
| 0.94118 | 1 | 0.0136986 | 18 |
| 1.06048 | 1 | 0.0136986 | 19 |
| 1.27837 | 1 | 0.0136986 | 20 |
| 1.41805 | 1 | 0.0136986 | 21 |
| 1.52983 | 1 | 0.0136986 | 22 |
| 1.68294 | 1 | 0.0136986 | 23 |
| 1.69508 | 1 | 0.0136986 | 24 |
| 1.73469 | 1 | 0.0136986 | 25 |
| 1.93516 | 1 | 0.0136986 | 26 |
| 2.06743 | 1 | 0.0136986 | 27 |
| 2.15703 | 1 | 0.0136986 | 28 |
| 2.478 | 1 | 0.0136986 | 29 |
| 2.4977 | 1 | 0.0136986 | 30 |
| 2.59637 | 1 | 0.0136986 | 31 |
| 2.85382 | 1 | 0.0136986 | 32 |
| 3.05542 | 1 | 0.0136986 | 33 |
| 3.30074 | 1 | 0.0136986 | 34 |
| 3.36905 | 1 | 0.0136986 | 35 |
| 3.4274 | 1 | 0.0136986 | 36 |
| 3.49173 | 1 | 0.0136986 | 37 |
| 3.64682 | 1 | 0.0136986 | 38 |
| 3.7316 | 1 | 0.0136986 | 39 |
| 3.74149 | 1 | 0.0136986 | 40 |
| 4.1083 | 1 | 0.0136986 | 41 |
| 4.18059 | 1 | 0.0136986 | 42 |
| 4.19108 | 1 | 0.0136986 | 43 |
| 4.32007 | 1 | 0.0136986 | 44 |
| 4.40801 | 1 | 0.0136986 | 45 |
| 4.5182 | 1 | 0.0136986 | 46 |
| 4.68443 | 1 | 0.0136986 | 47 |
| 4.8168 | 1 | 0.0136986 | 48 |
| 5.24855 | 1 | 0.0136986 | 49 |
| 6.46156 | 1 | 0.0136986 | 50 |
| 6.8095 | 1 | 0.0136986 | 51 |
| 7.07138 | 1 | 0.0136986 | 52 |
| 7.93258 | 1 | 0.0136986 | 53 |
| 7.93996 | 1 | 0.0136986 | 54 |
| 8.28739 | 1 | 0.0136986 | 55 |
| 8.46579 | 1 | 0.0136986 | 56 |
| 8.93271 | 1 | 0.0136986 | 57 |
| 9.51003 | 1 | 0.0136986 | 58 |
| 9.56829 | 1 | 0.0136986 | 59 |
| 9.78251 | 1 | 0.0136986 | 60 |
| 10.88351 | 1 | 0.0136986 | 61 |
| 11.8349 | 1 | 0.0136986 | 62 |
| 12.33417 | 1 | 0.0136986 | 63 |
| 12.365 | 1 | 0.0136986 | 64 |
| 12.53758 | 1 | 0.0136986 | 65 |
| 14.04274 | 1 | 0.0136986 | 66 |
| 15.2226 | 1 | 0.0136986 | 67 |
| 15.74984 | 1 | 0.0136986 | 68 |
| 16.16369 | 1 | 0.0136986 | 69 |
| 19.41353 | 1 | 0.0136986 | 70 |
| 21.8006 | 1 | 0.0136986 | 71 |
| 24.67589 | 1 | 0.0136986 | 72 |
| 32.12708 | 1 | 0.0136986 | 73 |
str(Freq_table)## 'data.frame': 72 obs. of 4 variables:
## $ Distance: Factor w/ 72 levels "0.05648","0.08269",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Freq : int 1 1 1 2 1 1 1 1 1 1 ...
## $ Rel_Freq: num 0.0137 0.0137 0.0137 0.0274 0.0137 ...
## $ Cum_Freq: int 1 2 3 5 6 7 8 9 10 11 ...df_bz <- data.frame(x = Freq_table$Distance, y = Freq_table$Freq)
knitr::kable(df_bz)| x | y |
|---|---|
| 0.05648 | 1 |
| 0.08269 | 1 |
| 0.09971 | 1 |
| 0.15208 | 2 |
| 0.28905 | 1 |
| 0.30326 | 1 |
| 0.31118 | 1 |
| 0.36051 | 1 |
| 0.50188 | 1 |
| 0.6355 | 1 |
| 0.64483 | 1 |
| 0.66626 | 1 |
| 0.76257 | 1 |
| 0.7834 | 1 |
| 0.88149 | 1 |
| 0.90692 | 1 |
| 0.94118 | 1 |
| 1.06048 | 1 |
| 1.27837 | 1 |
| 1.41805 | 1 |
| 1.52983 | 1 |
| 1.68294 | 1 |
| 1.69508 | 1 |
| 1.73469 | 1 |
| 1.93516 | 1 |
| 2.06743 | 1 |
| 2.15703 | 1 |
| 2.478 | 1 |
| 2.4977 | 1 |
| 2.59637 | 1 |
| 2.85382 | 1 |
| 3.05542 | 1 |
| 3.30074 | 1 |
| 3.36905 | 1 |
| 3.4274 | 1 |
| 3.49173 | 1 |
| 3.64682 | 1 |
| 3.7316 | 1 |
| 3.74149 | 1 |
| 4.1083 | 1 |
| 4.18059 | 1 |
| 4.19108 | 1 |
| 4.32007 | 1 |
| 4.40801 | 1 |
| 4.5182 | 1 |
| 4.68443 | 1 |
| 4.8168 | 1 |
| 5.24855 | 1 |
| 6.46156 | 1 |
| 6.8095 | 1 |
| 7.07138 | 1 |
| 7.93258 | 1 |
| 7.93996 | 1 |
| 8.28739 | 1 |
| 8.46579 | 1 |
| 8.93271 | 1 |
| 9.51003 | 1 |
| 9.56829 | 1 |
| 9.78251 | 1 |
| 10.88351 | 1 |
| 11.8349 | 1 |
| 12.33417 | 1 |
| 12.365 | 1 |
| 12.53758 | 1 |
| 14.04274 | 1 |
| 15.2226 | 1 |
| 15.74984 | 1 |
| 16.16369 | 1 |
| 19.41353 | 1 |
| 21.8006 | 1 |
| 24.67589 | 1 |
| 32.12708 | 1 |
library(ggplot2)
ggplot(data=df_bz, aes(x=x, y=y)) +
geom_bar(stat="identity", color="black", fill="yellow") +
xlab("Rango de Distance") +
ylab("Frecuencia")
summary(df_bz$Distance)## Length Class Mode
## 0 NULL NULLlibrary(pastecs)stat.desc(df_bz)## x y
## nbr.val NA 72.00000000
## nbr.null NA 0.00000000
## nbr.na NA 0.00000000
## min NA 1.00000000
## max NA 2.00000000
## range NA 1.00000000
## sum NA 73.00000000
## median NA 1.00000000
## mean NA 1.01388889
## SE.mean NA 0.01388889
## CI.mean NA 0.02769366
## var NA 0.01388889
## std.dev NA 0.11785113
## coef.var NA 0.11623673boxplot(Distance, horizontal=TRUE, col='orange')
Conclusión: Después de estudiar y analizar las gráficas derivadas de los datos brindados por la base de datos de deslizamientos globales, podemos concluir que México es el país con mayor número de datos de deslizamientos de Centroamérica, además, el diagrama de Pareto nos arrojó que Guatemala es el país con mayor riesgo ante estos fenómenos, esto debido a la magnitud en la que estos ocurren.