#U1A7
setwd("~/PyE_206145")
#Cargar las bibliotecas con el paquete pacman
library(pacman)
p_load(read,tidyverse,plotly,gganimate,gifsky)
## Installing package into 'C:/Users/Daniela Zazueta D/Documents/R/win-library/3.6'
## (as 'lib' is unspecified)
## Warning: package 'read' is not available (for R version 3.6.3)
## Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/3.6:
## no fue posible abrir la URL 'http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/3.6/PACKAGES'
## Warning: 'BiocManager' not available. Could not check Bioconductor.
##
## Please use `install.packages('BiocManager')` and then retry.
## Warning in p_install(package, character.only = TRUE, ...):
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'read'
## Installing package into 'C:/Users/Daniela Zazueta D/Documents/R/win-library/3.6'
## (as 'lib' is unspecified)
## Warning: package 'gifsky' is not available (for R version 3.6.3)
## Warning: unable to access index for repository http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/3.6:
## no fue posible abrir la URL 'http://www.stats.ox.ac.uk/pub/RWin/bin/windows/contrib/3.6/PACKAGES'
## Warning: 'BiocManager' not available. Could not check Bioconductor.
##
## Please use `install.packages('BiocManager')` and then retry.
## Warning in p_install(package, character.only = TRUE, ...):
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'gifsky'
## Warning in p_load(read, tidyverse, plotly, gganimate, gifsky): Failed to install/load:
## read, gifsky
#Ler datos de la direccion general de epidemiologia (DGE)
datos <- read.csv("Casos_Diarios_Estado_Nacional_Confirmados_20200609.csv")
#dar formato a los datos diarios(absolutos) de Baja California
BC <- t(datos[datos$nombre=="BAJA CALIFORNIA" ,])
BC <- as.vector(BC)
BC <- BC[4:153] #Filtrar datos
BC <- as.numeric(BC) #para convertir en numerico
BC <- as.vector(BC)
BC
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## [37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [55] 0 0 0 0 0 1 0 0 0 0 1 3 4 5 6 3 2 10
## [73] 9 7 11 16 7 21 29 45 65 29 38 24 20 68 40 59 58 48
## [91] 34 44 81 103 102 85 97 46 44 110 104 93 108 86 44 67 109 96
## [109] 108 88 89 77 90 141 138 112 116 140 75 69 130 121 122 107 123 68
## [127] 71 180 147 157 152 183 66 76 132 128 118 106 108 82 58 124 124 98
## [145] 90 66 27 5 1 1
cBC <- cumsum(BC) #datos acumulados diarios
cBC
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [16] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [31] 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [46] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [61] 2 2 2 2 3 6 10 15 21 24 26 36 45 52 63
## [76] 79 86 107 136 181 246 275 313 337 357 425 465 524 582 630
## [91] 664 708 789 892 994 1079 1176 1222 1266 1376 1480 1573 1681 1767 1811
## [106] 1878 1987 2083 2191 2279 2368 2445 2535 2676 2814 2926 3042 3182 3257 3326
## [121] 3456 3577 3699 3806 3929 3997 4068 4248 4395 4552 4704 4887 4953 5029 5161
## [136] 5289 5407 5513 5621 5703 5761 5885 6009 6107 6197 6263 6290 6295 6296 6297
#6297 habitantes confirmados aprox
#Estadistica descriptiva
plot(BC) #Grafico de puntos de datos absolutos
plot(cBC)
#dar formato a los datos diarios (absolutos) de Baja California Sur
BCS <- t(datos[datos$nombre=="BAJA CALIFORNIA SUR" ,])
BCS <- as.vector(BCS)
BCS <- BCS[4:153] #Filtrar datos
BCS <- as.numeric(BCS) #para convertir en numerico
BCS <- as.vector(BCS)
BCS
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 4 2 0 2
## [76] 4 4 1 13 7 5 11 16 6 5 23 24 24 25 9 7 5 14 17 5 9 6 4 8 7
## [101] 12 4 9 13 5 8 10 4 4 14 2 9 7 7 12 10 11 9 6 10 6 16 13 8 5
## [126] 11 3 11 15 17 8 15 7 9 17 13 21 19 21 13 8 29 14 12 18 20 12 15 0 0
cBCS <- cumsum(BCS) #datos acumulados diarios
cBCS
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [19] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [55] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 3 7
## [73] 9 9 11 15 19 20 33 40 45 56 72 78 83 106 130 154 179 188
## [91] 195 200 214 231 236 245 251 255 263 270 282 286 295 308 313 321 331 335
## [109] 339 353 355 364 371 378 390 400 411 420 426 436 442 458 471 479 484 495
## [127] 498 509 524 541 549 564 571 580 597 610 631 650 671 684 692 721 735 747
## [145] 765 785 797 812 812 812
#812 habitantes confirmados aprox
#Estadistica descriptiva
plot(BCS) #Grafico de puntos de datos absolutos
plot(cBCS)
#Construir un vector de fechas
Fecha <- seq(from = as.Date("2020-01-12"), to=as.Date("2020-06-09"), by ="day")
Fecha
## [1] "2020-01-12" "2020-01-13" "2020-01-14" "2020-01-15" "2020-01-16"
## [6] "2020-01-17" "2020-01-18" "2020-01-19" "2020-01-20" "2020-01-21"
## [11] "2020-01-22" "2020-01-23" "2020-01-24" "2020-01-25" "2020-01-26"
## [16] "2020-01-27" "2020-01-28" "2020-01-29" "2020-01-30" "2020-01-31"
## [21] "2020-02-01" "2020-02-02" "2020-02-03" "2020-02-04" "2020-02-05"
## [26] "2020-02-06" "2020-02-07" "2020-02-08" "2020-02-09" "2020-02-10"
## [31] "2020-02-11" "2020-02-12" "2020-02-13" "2020-02-14" "2020-02-15"
## [36] "2020-02-16" "2020-02-17" "2020-02-18" "2020-02-19" "2020-02-20"
## [41] "2020-02-21" "2020-02-22" "2020-02-23" "2020-02-24" "2020-02-25"
## [46] "2020-02-26" "2020-02-27" "2020-02-28" "2020-02-29" "2020-03-01"
## [51] "2020-03-02" "2020-03-03" "2020-03-04" "2020-03-05" "2020-03-06"
## [56] "2020-03-07" "2020-03-08" "2020-03-09" "2020-03-10" "2020-03-11"
## [61] "2020-03-12" "2020-03-13" "2020-03-14" "2020-03-15" "2020-03-16"
## [66] "2020-03-17" "2020-03-18" "2020-03-19" "2020-03-20" "2020-03-21"
## [71] "2020-03-22" "2020-03-23" "2020-03-24" "2020-03-25" "2020-03-26"
## [76] "2020-03-27" "2020-03-28" "2020-03-29" "2020-03-30" "2020-03-31"
## [81] "2020-04-01" "2020-04-02" "2020-04-03" "2020-04-04" "2020-04-05"
## [86] "2020-04-06" "2020-04-07" "2020-04-08" "2020-04-09" "2020-04-10"
## [91] "2020-04-11" "2020-04-12" "2020-04-13" "2020-04-14" "2020-04-15"
## [96] "2020-04-16" "2020-04-17" "2020-04-18" "2020-04-19" "2020-04-20"
## [101] "2020-04-21" "2020-04-22" "2020-04-23" "2020-04-24" "2020-04-25"
## [106] "2020-04-26" "2020-04-27" "2020-04-28" "2020-04-29" "2020-04-30"
## [111] "2020-05-01" "2020-05-02" "2020-05-03" "2020-05-04" "2020-05-05"
## [116] "2020-05-06" "2020-05-07" "2020-05-08" "2020-05-09" "2020-05-10"
## [121] "2020-05-11" "2020-05-12" "2020-05-13" "2020-05-14" "2020-05-15"
## [126] "2020-05-16" "2020-05-17" "2020-05-18" "2020-05-19" "2020-05-20"
## [131] "2020-05-21" "2020-05-22" "2020-05-23" "2020-05-24" "2020-05-25"
## [136] "2020-05-26" "2020-05-27" "2020-05-28" "2020-05-29" "2020-05-30"
## [141] "2020-05-31" "2020-06-01" "2020-06-02" "2020-06-03" "2020-06-04"
## [146] "2020-06-05" "2020-06-06" "2020-06-07" "2020-06-08" "2020-06-09"
#Crear un arreglo matricial (marco de datos "Data frame", "tibble")
BC_BCS <- data.frame(Fecha, BC, BCS) #Datos absolutos
BC_BCS
## Fecha BC BCS
## 1 2020-01-12 0 0
## 2 2020-01-13 0 0
## 3 2020-01-14 0 0
## 4 2020-01-15 0 0
## 5 2020-01-16 0 0
## 6 2020-01-17 0 0
## 7 2020-01-18 0 0
## 8 2020-01-19 0 0
## 9 2020-01-20 0 0
## 10 2020-01-21 0 0
## 11 2020-01-22 0 0
## 12 2020-01-23 0 0
## 13 2020-01-24 0 0
## 14 2020-01-25 0 0
## 15 2020-01-26 0 0
## 16 2020-01-27 0 0
## 17 2020-01-28 0 0
## 18 2020-01-29 0 0
## 19 2020-01-30 0 0
## 20 2020-01-31 0 0
## 21 2020-02-01 0 0
## 22 2020-02-02 0 0
## 23 2020-02-03 0 0
## 24 2020-02-04 0 0
## 25 2020-02-05 0 0
## 26 2020-02-06 0 0
## 27 2020-02-07 0 0
## 28 2020-02-08 0 0
## 29 2020-02-09 0 0
## 30 2020-02-10 0 0
## 31 2020-02-11 0 0
## 32 2020-02-12 1 0
## 33 2020-02-13 0 0
## 34 2020-02-14 0 0
## 35 2020-02-15 0 0
## 36 2020-02-16 0 0
## 37 2020-02-17 0 0
## 38 2020-02-18 0 0
## 39 2020-02-19 0 0
## 40 2020-02-20 0 0
## 41 2020-02-21 0 0
## 42 2020-02-22 0 0
## 43 2020-02-23 0 0
## 44 2020-02-24 0 0
## 45 2020-02-25 0 0
## 46 2020-02-26 0 0
## 47 2020-02-27 0 0
## 48 2020-02-28 0 0
## 49 2020-02-29 0 0
## 50 2020-03-01 0 0
## 51 2020-03-02 0 0
## 52 2020-03-03 0 0
## 53 2020-03-04 0 0
## 54 2020-03-05 0 0
## 55 2020-03-06 0 0
## 56 2020-03-07 0 0
## 57 2020-03-08 0 0
## 58 2020-03-09 0 0
## 59 2020-03-10 0 0
## 60 2020-03-11 1 0
## 61 2020-03-12 0 0
## 62 2020-03-13 0 0
## 63 2020-03-14 0 0
## 64 2020-03-15 0 0
## 65 2020-03-16 1 0
## 66 2020-03-17 3 0
## 67 2020-03-18 4 1
## 68 2020-03-19 5 0
## 69 2020-03-20 6 1
## 70 2020-03-21 3 1
## 71 2020-03-22 2 0
## 72 2020-03-23 10 4
## 73 2020-03-24 9 2
## 74 2020-03-25 7 0
## 75 2020-03-26 11 2
## 76 2020-03-27 16 4
## 77 2020-03-28 7 4
## 78 2020-03-29 21 1
## 79 2020-03-30 29 13
## 80 2020-03-31 45 7
## 81 2020-04-01 65 5
## 82 2020-04-02 29 11
## 83 2020-04-03 38 16
## 84 2020-04-04 24 6
## 85 2020-04-05 20 5
## 86 2020-04-06 68 23
## 87 2020-04-07 40 24
## 88 2020-04-08 59 24
## 89 2020-04-09 58 25
## 90 2020-04-10 48 9
## 91 2020-04-11 34 7
## 92 2020-04-12 44 5
## 93 2020-04-13 81 14
## 94 2020-04-14 103 17
## 95 2020-04-15 102 5
## 96 2020-04-16 85 9
## 97 2020-04-17 97 6
## 98 2020-04-18 46 4
## 99 2020-04-19 44 8
## 100 2020-04-20 110 7
## 101 2020-04-21 104 12
## 102 2020-04-22 93 4
## 103 2020-04-23 108 9
## 104 2020-04-24 86 13
## 105 2020-04-25 44 5
## 106 2020-04-26 67 8
## 107 2020-04-27 109 10
## 108 2020-04-28 96 4
## 109 2020-04-29 108 4
## 110 2020-04-30 88 14
## 111 2020-05-01 89 2
## 112 2020-05-02 77 9
## 113 2020-05-03 90 7
## 114 2020-05-04 141 7
## 115 2020-05-05 138 12
## 116 2020-05-06 112 10
## 117 2020-05-07 116 11
## 118 2020-05-08 140 9
## 119 2020-05-09 75 6
## 120 2020-05-10 69 10
## 121 2020-05-11 130 6
## 122 2020-05-12 121 16
## 123 2020-05-13 122 13
## 124 2020-05-14 107 8
## 125 2020-05-15 123 5
## 126 2020-05-16 68 11
## 127 2020-05-17 71 3
## 128 2020-05-18 180 11
## 129 2020-05-19 147 15
## 130 2020-05-20 157 17
## 131 2020-05-21 152 8
## 132 2020-05-22 183 15
## 133 2020-05-23 66 7
## 134 2020-05-24 76 9
## 135 2020-05-25 132 17
## 136 2020-05-26 128 13
## 137 2020-05-27 118 21
## 138 2020-05-28 106 19
## 139 2020-05-29 108 21
## 140 2020-05-30 82 13
## 141 2020-05-31 58 8
## 142 2020-06-01 124 29
## 143 2020-06-02 124 14
## 144 2020-06-03 98 12
## 145 2020-06-04 90 18
## 146 2020-06-05 66 20
## 147 2020-06-06 27 12
## 148 2020-06-07 5 15
## 149 2020-06-08 1 0
## 150 2020-06-09 1 0
cBC_BCS <- data.frame(Fecha, cBC, cBCS) #Datos acumulados
cBC_BCS
## Fecha cBC cBCS
## 1 2020-01-12 0 0
## 2 2020-01-13 0 0
## 3 2020-01-14 0 0
## 4 2020-01-15 0 0
## 5 2020-01-16 0 0
## 6 2020-01-17 0 0
## 7 2020-01-18 0 0
## 8 2020-01-19 0 0
## 9 2020-01-20 0 0
## 10 2020-01-21 0 0
## 11 2020-01-22 0 0
## 12 2020-01-23 0 0
## 13 2020-01-24 0 0
## 14 2020-01-25 0 0
## 15 2020-01-26 0 0
## 16 2020-01-27 0 0
## 17 2020-01-28 0 0
## 18 2020-01-29 0 0
## 19 2020-01-30 0 0
## 20 2020-01-31 0 0
## 21 2020-02-01 0 0
## 22 2020-02-02 0 0
## 23 2020-02-03 0 0
## 24 2020-02-04 0 0
## 25 2020-02-05 0 0
## 26 2020-02-06 0 0
## 27 2020-02-07 0 0
## 28 2020-02-08 0 0
## 29 2020-02-09 0 0
## 30 2020-02-10 0 0
## 31 2020-02-11 0 0
## 32 2020-02-12 1 0
## 33 2020-02-13 1 0
## 34 2020-02-14 1 0
## 35 2020-02-15 1 0
## 36 2020-02-16 1 0
## 37 2020-02-17 1 0
## 38 2020-02-18 1 0
## 39 2020-02-19 1 0
## 40 2020-02-20 1 0
## 41 2020-02-21 1 0
## 42 2020-02-22 1 0
## 43 2020-02-23 1 0
## 44 2020-02-24 1 0
## 45 2020-02-25 1 0
## 46 2020-02-26 1 0
## 47 2020-02-27 1 0
## 48 2020-02-28 1 0
## 49 2020-02-29 1 0
## 50 2020-03-01 1 0
## 51 2020-03-02 1 0
## 52 2020-03-03 1 0
## 53 2020-03-04 1 0
## 54 2020-03-05 1 0
## 55 2020-03-06 1 0
## 56 2020-03-07 1 0
## 57 2020-03-08 1 0
## 58 2020-03-09 1 0
## 59 2020-03-10 1 0
## 60 2020-03-11 2 0
## 61 2020-03-12 2 0
## 62 2020-03-13 2 0
## 63 2020-03-14 2 0
## 64 2020-03-15 2 0
## 65 2020-03-16 3 0
## 66 2020-03-17 6 0
## 67 2020-03-18 10 1
## 68 2020-03-19 15 1
## 69 2020-03-20 21 2
## 70 2020-03-21 24 3
## 71 2020-03-22 26 3
## 72 2020-03-23 36 7
## 73 2020-03-24 45 9
## 74 2020-03-25 52 9
## 75 2020-03-26 63 11
## 76 2020-03-27 79 15
## 77 2020-03-28 86 19
## 78 2020-03-29 107 20
## 79 2020-03-30 136 33
## 80 2020-03-31 181 40
## 81 2020-04-01 246 45
## 82 2020-04-02 275 56
## 83 2020-04-03 313 72
## 84 2020-04-04 337 78
## 85 2020-04-05 357 83
## 86 2020-04-06 425 106
## 87 2020-04-07 465 130
## 88 2020-04-08 524 154
## 89 2020-04-09 582 179
## 90 2020-04-10 630 188
## 91 2020-04-11 664 195
## 92 2020-04-12 708 200
## 93 2020-04-13 789 214
## 94 2020-04-14 892 231
## 95 2020-04-15 994 236
## 96 2020-04-16 1079 245
## 97 2020-04-17 1176 251
## 98 2020-04-18 1222 255
## 99 2020-04-19 1266 263
## 100 2020-04-20 1376 270
## 101 2020-04-21 1480 282
## 102 2020-04-22 1573 286
## 103 2020-04-23 1681 295
## 104 2020-04-24 1767 308
## 105 2020-04-25 1811 313
## 106 2020-04-26 1878 321
## 107 2020-04-27 1987 331
## 108 2020-04-28 2083 335
## 109 2020-04-29 2191 339
## 110 2020-04-30 2279 353
## 111 2020-05-01 2368 355
## 112 2020-05-02 2445 364
## 113 2020-05-03 2535 371
## 114 2020-05-04 2676 378
## 115 2020-05-05 2814 390
## 116 2020-05-06 2926 400
## 117 2020-05-07 3042 411
## 118 2020-05-08 3182 420
## 119 2020-05-09 3257 426
## 120 2020-05-10 3326 436
## 121 2020-05-11 3456 442
## 122 2020-05-12 3577 458
## 123 2020-05-13 3699 471
## 124 2020-05-14 3806 479
## 125 2020-05-15 3929 484
## 126 2020-05-16 3997 495
## 127 2020-05-17 4068 498
## 128 2020-05-18 4248 509
## 129 2020-05-19 4395 524
## 130 2020-05-20 4552 541
## 131 2020-05-21 4704 549
## 132 2020-05-22 4887 564
## 133 2020-05-23 4953 571
## 134 2020-05-24 5029 580
## 135 2020-05-25 5161 597
## 136 2020-05-26 5289 610
## 137 2020-05-27 5407 631
## 138 2020-05-28 5513 650
## 139 2020-05-29 5621 671
## 140 2020-05-30 5703 684
## 141 2020-05-31 5761 692
## 142 2020-06-01 5885 721
## 143 2020-06-02 6009 735
## 144 2020-06-03 6107 747
## 145 2020-06-04 6197 765
## 146 2020-06-05 6263 785
## 147 2020-06-06 6290 797
## 148 2020-06-07 6295 812
## 149 2020-06-08 6296 812
## 150 2020-06-09 6297 812
#Grafica para Baja California de datos absolutos con ggplot
ggplot(data = BC_BCS) +
ggtitle("Casos diarios COVID-19 en Baja California (DGE)") +
geom_line(mapping = aes(x=Fecha, y=BC))
#Grafica para Baja Californa sur de datos absolutos con ggplot
ggplot(data = BC_BCS) +
ggtitle("Casos diarios COVID-19 en Baja California Sur (DGE)") +
geom_line(mapping = aes(x=Fecha, y=BCS))
#Animacion grafica comparativa de frecuencias acumuladas
#por fechas para los estados de BC y BCS
#Codificados por color
ggplot(data=cBC_BCS) +
geom_line(aes(Fecha, cBC, colour='Baja California')) +
geom_line(aes(Fecha, cBCS, colour='Baja California sur')) +
xlab('Fecha') +
ylab('Casos diarios acumulados') +
labs(colour="Estados") +
transition_reveal(Fecha)
gBC_BCS <- ggplot(data=cBC_BCS)+
geom_line(aes(Fecha, cBC, colour = 'Baja California')) +
geom_line(aes(Fecha, cBCS, colour = 'Baja California Sur')) +
xlab('Fecha') +
ylab('Casos diarios acumulados') +
labs(colour ='Estados')
gBC_BCS
ggplotly(gBC_BCS)
#CONCLUSIONES
#Total 3 315 766 hab. BC aprox
#Densidad 44,1 hab/km²
#0.19% de pobladores infectados aprox
#Total 764 026 hab. BCS aprox
#Densidad 8,4 hab/km²
#0.106% de pobladores infectados
#Aparentemente las graficas parecen mostrar que el estado de BC
#esta mas critico que BCS, pero esto solo demuestra que estos dos
#no pueden ser comparables tomando solo las variables de casos confirmados
#pues, para dar una opinion certera sobre cual estado es mas critico
#se necesita saber los datos mostrados anteriormente (#de habitantes, etc)
#Y con esto podemos concluir que, apesar de que BCS tiene un menor
#numero de confirmados, es el estado que se encuentra mas critico de los dos
#esto debbido a que a pesar de que su poblacion es considerablemente
#mas baja que la de BC, tienen un porcentaje de confirmados bastante parecidos
#lo que lo hace mas critico que BC.
#La dos curvas parecen empezar a actuar simoidalmente
#y los datos de las tablas lo confirman
#haciendo notar que en los dos casos, en los ultimos dias ha
#habido un decremento de contagios diarios,
#lo que demuestra que aparentemente estan en
#la fase de senescencia, despues de haber crecido exponencialemente
#Datos oficiales tomados de: https://coronavirus.gob.mx/datos/#DownZCSV