#script para analizar datos acerca de la pandemia de COVID-19
#Utilizando los datos
# Estabelcer Folder de trabajo
setwd("~/ONLINEDS")
#Biblioteca de lectura de datos
library(readr)
## Warning: package 'readr' was built under R version 3.6.1
#Declaracion de URL de variables
url_conf <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
url_decesos <- "https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv"
url_recuperados <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
#variables en marcos de datos
datos_conf <- read.csv(url_conf)
datos_decesos <-read.csv(url_decesos)
datos_recuperados <- read.csv(url_recuperados)
#Explorando datos
class(datos_conf)
## [1] "data.frame"
dim(datos_conf)
## [1] 264 95
head(datos_conf)
## Province.State Country.Region Lat Long X1.22.20 X1.23.20
## 1 Afghanistan 33.0000 65.0000 0 0
## 2 Albania 41.1533 20.1683 0 0
## 3 Algeria 28.0339 1.6596 0 0
## 4 Andorra 42.5063 1.5218 0 0
## 5 Angola -11.2027 17.8739 0 0
## 6 Antigua and Barbuda 17.0608 -61.7964 0 0
## X1.24.20 X1.25.20 X1.26.20 X1.27.20 X1.28.20 X1.29.20 X1.30.20 X1.31.20
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X2.1.20 X2.2.20 X2.3.20 X2.4.20 X2.5.20 X2.6.20 X2.7.20 X2.8.20 X2.9.20
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0
## X2.10.20 X2.11.20 X2.12.20 X2.13.20 X2.14.20 X2.15.20 X2.16.20 X2.17.20
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X2.18.20 X2.19.20 X2.20.20 X2.21.20 X2.22.20 X2.23.20 X2.24.20 X2.25.20
## 1 0 0 0 0 0 0 1 1
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 1
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X2.26.20 X2.27.20 X2.28.20 X2.29.20 X3.1.20 X3.2.20 X3.3.20 X3.4.20
## 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 0 0 0
## 3 1 1 1 1 1 3 5 12
## 4 0 0 0 0 0 1 1 1
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X3.5.20 X3.6.20 X3.7.20 X3.8.20 X3.9.20 X3.10.20 X3.11.20 X3.12.20
## 1 1 1 1 4 4 5 7 7
## 2 0 0 0 0 2 10 12 23
## 3 12 17 17 19 20 20 20 24
## 4 1 1 1 1 1 1 1 1
## 5 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## X3.13.20 X3.14.20 X3.15.20 X3.16.20 X3.17.20 X3.18.20 X3.19.20 X3.20.20
## 1 7 11 16 21 22 22 22 24
## 2 33 38 42 51 55 59 64 70
## 3 26 37 48 54 60 74 87 90
## 4 1 1 1 2 39 39 53 75
## 5 0 0 0 0 0 0 0 1
## 6 1 1 1 1 1 1 1 1
## X3.21.20 X3.22.20 X3.23.20 X3.24.20 X3.25.20 X3.26.20 X3.27.20 X3.28.20
## 1 24 40 40 74 84 94 110 110
## 2 76 89 104 123 146 174 186 197
## 3 139 201 230 264 302 367 409 454
## 4 88 113 133 164 188 224 267 308
## 5 2 2 3 3 3 4 4 5
## 6 1 1 3 3 3 7 7 7
## X3.29.20 X3.30.20 X3.31.20 X4.1.20 X4.2.20 X4.3.20 X4.4.20 X4.5.20
## 1 120 170 174 237 273 281 299 349
## 2 212 223 243 259 277 304 333 361
## 3 511 584 716 847 986 1171 1251 1320
## 4 334 370 376 390 428 439 466 501
## 5 7 7 7 8 8 8 10 14
## 6 7 7 7 7 9 15 15 15
## X4.6.20 X4.7.20 X4.8.20 X4.9.20 X4.10.20 X4.11.20 X4.12.20 X4.13.20
## 1 367 423 444 484 521 555 607 665
## 2 377 383 400 409 416 433 446 467
## 3 1423 1468 1572 1666 1761 1825 1914 1983
## 4 525 545 564 583 601 601 638 646
## 5 16 17 19 19 19 19 19 19
## 6 15 19 19 19 19 21 21 23
## X4.14.20 X4.15.20 X4.16.20 X4.17.20 X4.18.20 X4.19.20 X4.20.20 X4.21.20
## 1 714 784 840 906 933 996 1026 1092
## 2 475 494 518 539 548 562 584 609
## 3 2070 2160 2268 2418 2534 2629 2718 2811
## 4 659 673 673 696 704 713 717 717
## 5 19 19 19 19 24 24 24 24
## 6 23 23 23 23 23 23 23 23
tail(datos_conf)
## Province.State Country.Region Lat Long
## 259 Falkland Islands (Malvinas) United Kingdom -51.79630 -59.523600
## 260 Saint Pierre and Miquelon France 46.88520 -56.315900
## 261 South Sudan 6.87700 31.307000
## 262 Western Sahara 24.21550 -12.885800
## 263 Sao Tome and Principe 0.18636 6.613081
## 264 Yemen 15.55273 48.516388
## X1.22.20 X1.23.20 X1.24.20 X1.25.20 X1.26.20 X1.27.20 X1.28.20
## 259 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0
## X1.29.20 X1.30.20 X1.31.20 X2.1.20 X2.2.20 X2.3.20 X2.4.20 X2.5.20
## 259 0 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0 0
## X2.6.20 X2.7.20 X2.8.20 X2.9.20 X2.10.20 X2.11.20 X2.12.20 X2.13.20
## 259 0 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0 0
## X2.14.20 X2.15.20 X2.16.20 X2.17.20 X2.18.20 X2.19.20 X2.20.20
## 259 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0
## X2.21.20 X2.22.20 X2.23.20 X2.24.20 X2.25.20 X2.26.20 X2.27.20
## 259 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0
## X2.28.20 X2.29.20 X3.1.20 X3.2.20 X3.3.20 X3.4.20 X3.5.20 X3.6.20
## 259 0 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0 0
## X3.7.20 X3.8.20 X3.9.20 X3.10.20 X3.11.20 X3.12.20 X3.13.20 X3.14.20
## 259 0 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0 0
## X3.15.20 X3.16.20 X3.17.20 X3.18.20 X3.19.20 X3.20.20 X3.21.20
## 259 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0
## X3.22.20 X3.23.20 X3.24.20 X3.25.20 X3.26.20 X3.27.20 X3.28.20
## 259 0 0 0 0 0 0 0
## 260 0 0 0 0 0 0 0
## 261 0 0 0 0 0 0 0
## 262 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0
## X3.29.20 X3.30.20 X3.31.20 X4.1.20 X4.2.20 X4.3.20 X4.4.20 X4.5.20
## 259 0 0 0 0 0 0 1 2
## 260 0 0 0 0 0 0 0 1
## 261 0 0 0 0 0 0 0 1
## 262 0 0 0 0 0 0 0 4
## 263 0 0 0 0 0 0 0 0
## 264 0 0 0 0 0 0 0 0
## X4.6.20 X4.7.20 X4.8.20 X4.9.20 X4.10.20 X4.11.20 X4.12.20 X4.13.20
## 259 2 2 5 5 5 5 5 5
## 260 1 1 1 1 1 1 1 1
## 261 1 2 2 3 4 4 4 4
## 262 4 4 4 4 4 4 6 6
## 263 4 4 4 4 4 4 4 4
## 264 0 0 0 0 1 1 1 1
## X4.14.20 X4.15.20 X4.16.20 X4.17.20 X4.18.20 X4.19.20 X4.20.20
## 259 11 11 11 11 11 11 11
## 260 1 1 1 1 1 1 1
## 261 4 4 4 4 4 4 4
## 262 6 6 6 6 6 6 6
## 263 4 4 4 4 4 4 4
## 264 1 1 1 1 1 1 1
## X4.21.20
## 259 11
## 260 1
## 261 4
## 262 6
## 263 4
## 264 1
names(datos_conf)
## [1] "Province.State" "Country.Region" "Lat" "Long"
## [5] "X1.22.20" "X1.23.20" "X1.24.20" "X1.25.20"
## [9] "X1.26.20" "X1.27.20" "X1.28.20" "X1.29.20"
## [13] "X1.30.20" "X1.31.20" "X2.1.20" "X2.2.20"
## [17] "X2.3.20" "X2.4.20" "X2.5.20" "X2.6.20"
## [21] "X2.7.20" "X2.8.20" "X2.9.20" "X2.10.20"
## [25] "X2.11.20" "X2.12.20" "X2.13.20" "X2.14.20"
## [29] "X2.15.20" "X2.16.20" "X2.17.20" "X2.18.20"
## [33] "X2.19.20" "X2.20.20" "X2.21.20" "X2.22.20"
## [37] "X2.23.20" "X2.24.20" "X2.25.20" "X2.26.20"
## [41] "X2.27.20" "X2.28.20" "X2.29.20" "X3.1.20"
## [45] "X3.2.20" "X3.3.20" "X3.4.20" "X3.5.20"
## [49] "X3.6.20" "X3.7.20" "X3.8.20" "X3.9.20"
## [53] "X3.10.20" "X3.11.20" "X3.12.20" "X3.13.20"
## [57] "X3.14.20" "X3.15.20" "X3.16.20" "X3.17.20"
## [61] "X3.18.20" "X3.19.20" "X3.20.20" "X3.21.20"
## [65] "X3.22.20" "X3.23.20" "X3.24.20" "X3.25.20"
## [69] "X3.26.20" "X3.27.20" "X3.28.20" "X3.29.20"
## [73] "X3.30.20" "X3.31.20" "X4.1.20" "X4.2.20"
## [77] "X4.3.20" "X4.4.20" "X4.5.20" "X4.6.20"
## [81] "X4.7.20" "X4.8.20" "X4.9.20" "X4.10.20"
## [85] "X4.11.20" "X4.12.20" "X4.13.20" "X4.14.20"
## [89] "X4.15.20" "X4.16.20" "X4.17.20" "X4.18.20"
## [93] "X4.19.20" "X4.20.20" "X4.21.20"
#Hacer lo mismo con otros dos datasets
#datos decesos y datos recuperados
#mostrar el numero de infectados actualmente
conf_china <- t(datos_conf [datos_conf$Country.Region=="China" ,])
conf_mex <- t(datos_conf [datos_conf$Country.Region=="Mexico" ,])
#mostrando resultados
cat("Confirmados Mexico", conf_mex[nrow(conf_mex)], "\n")
## Confirmados Mexico 8772