EJERCICIO: Utilizando la información brindada por la Secretaria de Salud Federal sobre los casos de COVID-19 detectados en la República Mexicana, y considerando solamente los casos positivos (verificar que la información que se obtenga de la base de datos sea igual a la reportada diariamente por dicha secretaría), obtener lo siguiente.

Covid <- read.csv("C:\\Users\\permi\\Desktop\\CIDE\\Primer semestre\\R\\COVID\\211213COVID19MEXICO.csv")

1. Eligiendo un estado de la república de residencia del paciente que tenga más de 20 municipios, obtener (tablas):

#Filtramos la base de datos por entidad Guerrero = 12 
covid_g<-which(Covid$ENTIDAD_RES == "12")
Covid_Guerrero<-Covid[covid_g,]
#Filtramos la base de datos con  solo datos de Guerrero, ahora por solo datos positivos == 3
casos_positivos<-which(Covid_Guerrero$CLASIFICACION_FINAL == 3)
Covid_G_P<-Covid_Guerrero[casos_positivos,]
#Procedemos a ponerle su respectivo nombre a cada municipio del estado seleccionado
nombres_mun<-read_xlsx("C:\\Users\\permi\\Desktop\\CIDE\\Primer semestre\\R\\COVID\\201128 Catalogos.xlsx", sheet = "Catálogo MUNICIPIOS")
G_mun<-which(nombres_mun$CLAVE_ENTIDAD =="12")
municipios_G<-nombres_mun[G_mun,-3]
Covid_Guerrero_Positivos<-merge(Covid_G_P, municipios_G, by.x ="MUNICIPIO_RES", by.y ="CLAVE_MUNICIPIO")
#Agregamos una columna con unos que nos servirá para conteos futuros
Covid_Guerrero_Positivos$unos<-1

- 1. (5%) % de defunciones por municipio (total de defunciones / total de casos positivos).

#Vemos en qué posición se encuentran las fechas "9999-99-99"
Defunciones<-which(Covid_Guerrero_Positivos$FECHA_DEF=="9999-99-99")
#Filtramos la base de datos en función de Fechas de Defunción que SI tienen fecha
Def_X_Mun<-aggregate(unos~MUNICIPIO,data=Covid_Guerrero_Positivos[-Defunciones,],FUN=sum)
#Sumamos el total de casos positivos por municipio
P_x_Mun<-aggregate(unos~MUNICIPIO, data=Covid_Guerrero_Positivos,FUN=sum)
#Unimos las dos tablas anteriores
Def_Pos_Mun<-merge(Def_X_Mun ,P_x_Mun, by="MUNICIPIO")
#Creamos un data frame con cada municipio y su respectivo porcentahe de defunciones por municipio
Porcentaje<-data.frame(Municipio = Def_Pos_Mun$MUNICIPIO, porcentaje =round(Def_Pos_Mun$unos.x/Def_Pos_Mun$unos.y*100,2))

#Creamos la tabla
Porcentaje %>% kbl(caption= "Porcentaje de defunciones por municipio")%>% kable_classic(full_width = F, html_font = "Cambria") %>% kable_paper(full_width = F) %>% column_spec(column = 2 , color = "White" ,bold = TRUE ,background = spec_color(Porcentaje$porcentaje, end = 1, option = "C", direction = -1 )) %>% scroll_box(width = "350px", height = "250px")
Porcentaje de defunciones por municipio
Municipio porcentaje
ACAPULCO DE JUÁREZ 9.06
ACATEPEC 7.14
AHUACUOTZINGO 5.14
AJUCHITLÁN DEL PROGRESO 17.98
ALCOZAUCA DE GUERRERO 11.11
ALPOYECA 16.83
APAXTLA 21.88
ARCELIA 10.99
ATENANGO DEL RÍO 19.35
ATLAMAJALCINGO DEL MONTE 38.46
ATLIXTAC 7.84
ATOYAC DE ÁLVAREZ 7.27
AYUTLA DE LOS LIBRES 6.43
AZOYÚ 7.14
BENITO JUÁREZ 3.88
BUENAVISTA DE CUÉLLAR 10.42
CHILAPA DE ÁLVAREZ 3.93
CHILPANCINGO DE LOS BRAVO 4.50
COAHUAYUTLA DE JOSÉ MARÍA IZAZAGA 30.77
COCULA 13.11
COPALA 4.39
COPALILLO 12.82
COPANATOYAC 22.86
COYUCA DE BENÍTEZ 13.94
COYUCA DE CATALÁN 13.77
CUAJINICUILAPA 6.72
CUALÁC 10.81
CUAUTEPEC 12.22
CUETZALA DEL PROGRESO 10.34
CUTZAMALA DE PINZÓN 24.31
EDUARDO NERI 11.00
FLORENCIO VILLARREAL 5.56
GENERAL CANUTO A. NERI 25.00
GENERAL HELIODORO CASTILLO 3.53
HUAMUXTITLÁN 13.33
HUITZUCO DE LOS FIGUEROA 13.39
IGUALA DE LA INDEPENDENCIA 9.99
IGUALAPA 4.32
ILIATENCO 66.67
IXCATEOPAN DE CUAUHTÉMOC 5.13
JUAN R. ESCUDERO 8.05
JUCHITÁN 11.01
LA UNIÓN DE ISIDORO MONTES DE OCA 17.77
LEONARDO BRAVO 12.03
MALINALTEPEC 11.43
MARQUELIA 6.13
MÁRTIR DE CUILAPAN 14.81
METLATÓNOC 19.23
MOCHITLÁN 4.08
OLINALÁ 18.31
OMETEPEC 5.84
PEDRO ASCENCIO ALQUISIRAS 50.00
PETATLÁN 10.98
PILCAYA 11.59
PUNGARABATO 8.85
QUECHULTENANGO 6.04
SAN LUIS ACATLÁN 6.95
SAN MARCOS 13.90
SAN MIGUEL TOTOLAPAN 11.24
TAXCO DE ALARCÓN 9.73
TECOANAPA 4.15
TÉCPAN DE GALEANA 10.99
TELOLOAPAN 15.96
TEPECOACUILCO DE TRUJANO 17.22
TETIPAC 18.75
TIXTLA DE GUERRERO 3.48
TLACOACHISTLAHUACA 9.57
TLACOAPA 11.76
TLALCHAPA 8.45
TLALIXTAQUILLA DE MALDONADO 7.81
TLAPA DE COMONFORT 8.67
TLAPEHUALA 9.03
XALPATLÁHUAC 10.00
XOCHIHUEHUETLÁN 14.04
XOCHISTLAHUACA 3.62
ZAPOTITLÁN TABLAS 18.75
ZIHUATANEJO DE AZUETA 5.71
ZIRÁNDARO 5.65
ZITLALA 4.59

- 2. (5%) % de casos positivos con al menos una comorbilidad a nivel municipal.

Covid_Guerrero_Positivos<- Covid_Guerrero_Positivos %>% mutate(comorbilidad = case_when(TABAQUISMO==1|HIPERTENSION==1|DIABETES==1|CARDIOVASCULAR==1|ASMA==1|OTRA_COM==1|EPOC==1|RENAL_CRONICA==1|OBESIDAD==1|INMUSUPR==1 ~ 1, TRUE ~ 0))

Comorbo_Posi <- which(Covid_Guerrero_Positivos$comorbilidad == 1)

Comorbilidades <- aggregate(unos~MUNICIPIO, data = Covid_Guerrero_Positivos[Comorbo_Posi,], FUN = sum)

Comorbilidades1<- merge(x=Comorbilidades, y=P_x_Mun, by="MUNICIPIO")

Porcentaje_Comorbo_Posi<-data.frame(Municipio= Comorbilidades$MUNICIPIO, Porcentaje = round(Comorbilidades1$unos.x/Comorbilidades1$unos.y*100,2))

Porcentaje_Comorbo_Posi %>% kbl(caption= "Porcentaje de casos positivos con al menos una como")%>% kable_classic(full_width = F, html_font = "Cambria") %>% kable_paper(full_width = F) %>% column_spec(column = 2 , color = "White",bold = TRUE ,background = spec_color(Porcentaje_Comorbo_Posi$Porcentaje, option = "C", direction = -1 )) %>% scroll_box(width = "350px", height = "250px")
Porcentaje de casos positivos con al menos una como
Municipio Porcentaje
ACAPULCO DE JUÁREZ 33.97
ACATEPEC 28.57
AHUACUOTZINGO 47.04
AJUCHITLÁN DEL PROGRESO 41.01
ALCOZAUCA DE GUERRERO 36.11
ALPOYECA 44.55
APAXTLA 62.50
ARCELIA 39.27
ATENANGO DEL RÍO 54.84
ATLAMAJALCINGO DEL MONTE 46.15
ATLIXTAC 39.22
ATOYAC DE ÁLVAREZ 32.30
AYUTLA DE LOS LIBRES 37.20
AZOYÚ 41.18
BENITO JUÁREZ 22.01
BUENAVISTA DE CUÉLLAR 45.83
CHILAPA DE ÁLVAREZ 34.03
CHILPANCINGO DE LOS BRAVO 31.28
COAHUAYUTLA DE JOSÉ MARÍA IZAZAGA 46.15
COCHOAPA EL GRANDE 39.13
COCULA 45.63
COPALA 21.46
COPALILLO 51.28
COPANATOYAC 42.86
COYUCA DE BENÍTEZ 30.50
COYUCA DE CATALÁN 33.93
CUAJINICUILAPA 35.85
CUALÁC 29.73
CUAUTEPEC 46.67
CUETZALA DEL PROGRESO 51.72
CUTZAMALA DE PINZÓN 52.78
EDUARDO NERI 36.11
FLORENCIO VILLARREAL 36.27
GENERAL CANUTO A. NERI 33.33
GENERAL HELIODORO CASTILLO 28.84
HUAMUXTITLÁN 53.89
HUITZUCO DE LOS FIGUEROA 48.50
IGUALA DE LA INDEPENDENCIA 46.01
IGUALAPA 32.73
ILIATENCO 50.00
IXCATEOPAN DE CUAUHTÉMOC 29.91
JOSÉ JOAQUÍN DE HERRERA 47.37
JUAN R. ESCUDERO 43.91
JUCHITÁN 36.70
LA UNIÓN DE ISIDORO MONTES DE OCA 45.87
LEONARDO BRAVO 32.28
MALINALTEPEC 51.43
MARQUELIA 40.23
MÁRTIR DE CUILAPAN 27.78
METLATÓNOC 7.69
MOCHITLÁN 31.97
NO ESPECIFICADO 100.00
OLINALÁ 47.89
OMETEPEC 30.63
PEDRO ASCENCIO ALQUISIRAS 66.67
PETATLÁN 34.28
PILCAYA 47.83
PUNGARABATO 37.34
QUECHULTENANGO 30.59
SAN LUIS ACATLÁN 40.73
SAN MARCOS 43.05
SAN MIGUEL TOTOLAPAN 39.33
TAXCO DE ALARCÓN 35.21
TECOANAPA 46.61
TÉCPAN DE GALEANA 25.65
TELOLOAPAN 41.04
TEPECOACUILCO DE TRUJANO 51.20
TETIPAC 37.50
TIXTLA DE GUERRERO 30.05
TLACOACHISTLAHUACA 32.98
TLACOAPA 35.29
TLALCHAPA 26.69
TLALIXTAQUILLA DE MALDONADO 32.03
TLAPA DE COMONFORT 42.25
TLAPEHUALA 33.63
XALPATLÁHUAC 53.33
XOCHIHUEHUETLÁN 54.39
XOCHISTLAHUACA 39.37
ZAPOTITLÁN TABLAS 37.50
ZIHUATANEJO DE AZUETA 26.06
ZIRÁNDARO 45.16
ZITLALA 40.37

- 3. (10%) Total de casos positivos acumulados por día a nivel estatal.

Acumulado_estatal_P<- aggregate(unos~FECHA_SINTOMAS, data =  Covid_Guerrero_Positivos, FUN = sum)
#Acumulado_estatal_P<- as.data.frame(Acumulado_estatal_P)


Acumulado_estatal_P %>% kbl(caption= "Total de casos positivos acumulados por día a nivel estatal")%>% kable_classic(full_width = F, html_font = "Cambria") %>% kable_paper(full_width = F) %>% column_spec(column = 2 , color = "White",bold = TRUE ,background = spec_color(Acumulado_estatal_P$unos , option = "C", direction = -1 )) %>% scroll_box(width = "350px", height = "250px")
Total de casos positivos acumulados por día a nivel estatal
FECHA_SINTOMAS unos
2020-03-08 1
2020-03-09 1
2020-03-11 1
2020-03-12 1
2020-03-13 1
2020-03-15 3
2020-03-16 1
2020-03-17 3
2020-03-18 2
2020-03-19 2
2020-03-20 3
2020-03-21 3
2020-03-22 1
2020-03-23 3
2020-03-24 1
2020-03-25 1
2020-03-26 3
2020-03-28 1
2020-03-29 2
2020-03-30 4
2020-03-31 5
2020-04-01 4
2020-04-02 5
2020-04-03 8
2020-04-04 10
2020-04-05 6
2020-04-06 8
2020-04-07 5
2020-04-08 13
2020-04-09 6
2020-04-10 12
2020-04-11 9
2020-04-12 8
2020-04-13 15
2020-04-14 3
2020-04-15 14
2020-04-16 7
2020-04-17 9
2020-04-18 13
2020-04-19 17
2020-04-20 25
2020-04-21 17
2020-04-22 18
2020-04-23 25
2020-04-24 37
2020-04-25 35
2020-04-26 21
2020-04-27 24
2020-04-28 29
2020-04-29 36
2020-04-30 39
2020-05-01 59
2020-05-02 35
2020-05-03 47
2020-05-04 48
2020-05-05 39
2020-05-06 36
2020-05-07 40
2020-05-08 55
2020-05-09 60
2020-05-10 68
2020-05-11 67
2020-05-12 72
2020-05-13 74
2020-05-14 90
2020-05-15 146
2020-05-16 114
2020-05-17 101
2020-05-18 157
2020-05-19 110
2020-05-20 120
2020-05-21 100
2020-05-22 98
2020-05-23 121
2020-05-24 95
2020-05-25 108
2020-05-26 127
2020-05-27 112
2020-05-28 114
2020-05-29 114
2020-05-30 112
2020-05-31 97
2020-06-01 156
2020-06-02 118
2020-06-03 111
2020-06-04 87
2020-06-05 114
2020-06-06 128
2020-06-07 105
2020-06-08 133
2020-06-09 136
2020-06-10 167
2020-06-11 110
2020-06-12 128
2020-06-13 112
2020-06-14 93
2020-06-15 138
2020-06-16 101
2020-06-17 107
2020-06-18 135
2020-06-19 141
2020-06-20 158
2020-06-21 104
2020-06-22 150
2020-06-23 124
2020-06-24 141
2020-06-25 236
2020-06-26 240
2020-06-27 239
2020-06-28 263
2020-06-29 218
2020-06-30 170
2020-07-01 249
2020-07-02 136
2020-07-03 161
2020-07-04 157
2020-07-05 115
2020-07-06 104
2020-07-07 86
2020-07-08 114
2020-07-09 148
2020-07-10 185
2020-07-11 131
2020-07-12 133
2020-07-13 125
2020-07-14 99
2020-07-15 171
2020-07-16 164
2020-07-17 143
2020-07-18 167
2020-07-19 181
2020-07-20 259
2020-07-21 136
2020-07-22 127
2020-07-23 127
2020-07-24 143
2020-07-25 184
2020-07-26 135
2020-07-27 186
2020-07-28 155
2020-07-29 135
2020-07-30 99
2020-07-31 129
2020-08-01 226
2020-08-02 182
2020-08-03 155
2020-08-04 108
2020-08-05 137
2020-08-06 100
2020-08-07 114
2020-08-08 163
2020-08-09 83
2020-08-10 119
2020-08-11 87
2020-08-12 91
2020-08-13 102
2020-08-14 94
2020-08-15 150
2020-08-16 82
2020-08-17 90
2020-08-18 124
2020-08-19 96
2020-08-20 157
2020-08-21 108
2020-08-22 86
2020-08-23 65
2020-08-24 109
2020-08-25 130
2020-08-26 118
2020-08-27 95
2020-08-28 156
2020-08-29 87
2020-08-30 118
2020-08-31 126
2020-09-01 189
2020-09-02 207
2020-09-03 161
2020-09-04 141
2020-09-05 163
2020-09-06 91
2020-09-07 152
2020-09-08 151
2020-09-09 163
2020-09-10 205
2020-09-11 115
2020-09-12 115
2020-09-13 105
2020-09-14 108
2020-09-15 171
2020-09-16 138
2020-09-17 158
2020-09-18 164
2020-09-19 156
2020-09-20 152
2020-09-21 144
2020-09-22 132
2020-09-23 101
2020-09-24 119
2020-09-25 161
2020-09-26 92
2020-09-27 91
2020-09-28 120
2020-09-29 146
2020-09-30 111
2020-10-01 186
2020-10-02 113
2020-10-03 96
2020-10-04 82
2020-10-05 107
2020-10-06 90
2020-10-07 82
2020-10-08 88
2020-10-09 107
2020-10-10 103
2020-10-11 70
2020-10-12 87
2020-10-13 79
2020-10-14 65
2020-10-15 80
2020-10-16 76
2020-10-17 68
2020-10-18 71
2020-10-19 66
2020-10-20 89
2020-10-21 44
2020-10-22 54
2020-10-23 59
2020-10-24 37
2020-10-25 51
2020-10-26 52
2020-10-27 37
2020-10-28 54
2020-10-29 57
2020-10-30 44
2020-10-31 50
2020-11-01 51
2020-11-02 51
2020-11-03 58
2020-11-04 47
2020-11-05 41
2020-11-06 56
2020-11-07 56
2020-11-08 44
2020-11-09 51
2020-11-10 57
2020-11-11 40
2020-11-12 46
2020-11-13 47
2020-11-14 53
2020-11-15 50
2020-11-16 43
2020-11-17 61
2020-11-18 66
2020-11-19 51
2020-11-20 85
2020-11-21 84
2020-11-22 55
2020-11-23 42
2020-11-24 58
2020-11-25 79
2020-11-26 47
2020-11-27 70
2020-11-28 63
2020-11-29 41
2020-11-30 77
2020-12-01 79
2020-12-02 71
2020-12-03 76
2020-12-04 80
2020-12-05 70
2020-12-06 54
2020-12-07 87
2020-12-08 76
2020-12-09 81
2020-12-10 122
2020-12-11 83
2020-12-12 87
2020-12-13 103
2020-12-14 116
2020-12-15 80
2020-12-16 77
2020-12-17 55
2020-12-18 80
2020-12-19 67
2020-12-20 86
2020-12-21 69
2020-12-22 88
2020-12-23 86
2020-12-24 92
2020-12-25 108
2020-12-26 91
2020-12-27 97
2020-12-28 126
2020-12-29 94
2020-12-30 107
2020-12-31 174
2021-01-01 186
2021-01-02 158
2021-01-03 137
2021-01-04 197
2021-01-05 207
2021-01-06 240
2021-01-07 217
2021-01-08 248
2021-01-09 207
2021-01-10 255
2021-01-11 227
2021-01-12 193
2021-01-13 212
2021-01-14 224
2021-01-15 203
2021-01-16 214
2021-01-17 247
2021-01-18 257
2021-01-19 210
2021-01-20 238
2021-01-21 123
2021-01-22 194
2021-01-23 174
2021-01-24 206
2021-01-25 226
2021-01-26 193
2021-01-27 147
2021-01-28 159
2021-01-29 129
2021-01-30 139
2021-01-31 139
2021-02-01 198
2021-02-02 149
2021-02-03 131
2021-02-04 116
2021-02-05 111
2021-02-06 87
2021-02-07 76
2021-02-08 101
2021-02-09 99
2021-02-10 110
2021-02-11 69
2021-02-12 91
2021-02-13 80
2021-02-14 99
2021-02-15 90
2021-02-16 78
2021-02-17 63
2021-02-18 86
2021-02-19 91
2021-02-20 96
2021-02-21 76
2021-02-22 61
2021-02-23 77
2021-02-24 96
2021-02-25 76
2021-02-26 71
2021-02-27 76
2021-02-28 109
2021-03-01 136
2021-03-02 62
2021-03-03 80
2021-03-04 74
2021-03-05 76
2021-03-06 69
2021-03-07 61
2021-03-08 101
2021-03-09 56
2021-03-10 57
2021-03-11 60
2021-03-12 73
2021-03-13 58
2021-03-14 65
2021-03-15 70
2021-03-16 85
2021-03-17 53
2021-03-18 92
2021-03-19 63
2021-03-20 78
2021-03-21 65
2021-03-22 62
2021-03-23 42
2021-03-24 57
2021-03-25 49
2021-03-26 61
2021-03-27 53
2021-03-28 66
2021-03-29 76
2021-03-30 49
2021-03-31 66
2021-04-01 64
2021-04-02 69
2021-04-03 81
2021-04-04 73
2021-04-05 73
2021-04-06 66
2021-04-07 85
2021-04-08 78
2021-04-09 82
2021-04-10 83
2021-04-11 70
2021-04-12 58
2021-04-13 53
2021-04-14 59
2021-04-15 56
2021-04-16 56
2021-04-17 62
2021-04-18 50
2021-04-19 64
2021-04-20 62
2021-04-21 51
2021-04-22 45
2021-04-23 48
2021-04-24 63
2021-04-25 56
2021-04-26 63
2021-04-27 56
2021-04-28 58
2021-04-29 50
2021-04-30 73
2021-05-01 89
2021-05-02 57
2021-05-03 64
2021-05-04 59
2021-05-05 71
2021-05-06 41
2021-05-07 61
2021-05-08 41
2021-05-09 42
2021-05-10 63
2021-05-11 35
2021-05-12 31
2021-05-13 32
2021-05-14 34
2021-05-15 42
2021-05-16 35
2021-05-17 38
2021-05-18 41
2021-05-19 28
2021-05-20 37
2021-05-21 51
2021-05-22 33
2021-05-23 19
2021-05-24 32
2021-05-25 25
2021-05-26 21
2021-05-27 21
2021-05-28 31
2021-05-29 22
2021-05-30 17
2021-05-31 19
2021-06-01 25
2021-06-02 27
2021-06-03 21
2021-06-04 25
2021-06-05 20
2021-06-06 22
2021-06-07 19
2021-06-08 17
2021-06-09 15
2021-06-10 20
2021-06-11 17
2021-06-12 29
2021-06-13 19
2021-06-14 8
2021-06-15 18
2021-06-16 30
2021-06-17 28
2021-06-18 27
2021-06-19 26
2021-06-20 34
2021-06-21 33
2021-06-22 39
2021-06-23 40
2021-06-24 39
2021-06-25 61
2021-06-26 57
2021-06-27 69
2021-06-28 89
2021-06-29 97
2021-06-30 96
2021-07-01 186
2021-07-02 184
2021-07-03 227
2021-07-04 275
2021-07-05 309
2021-07-06 297
2021-07-07 267
2021-07-08 338
2021-07-09 401
2021-07-10 570
2021-07-11 441
2021-07-12 582
2021-07-13 438
2021-07-14 411
2021-07-15 600
2021-07-16 690
2021-07-17 754
2021-07-18 803
2021-07-19 862
2021-07-20 844
2021-07-21 569
2021-07-22 588
2021-07-23 748
2021-07-24 825
2021-07-25 868
2021-07-26 813
2021-07-27 732
2021-07-28 646
2021-07-29 612
2021-07-30 731
2021-07-31 751
2021-08-01 1026
2021-08-02 743
2021-08-03 582
2021-08-04 537
2021-08-05 619
2021-08-06 668
2021-08-07 591
2021-08-08 626
2021-08-09 594
2021-08-10 553
2021-08-11 414
2021-08-12 436
2021-08-13 489
2021-08-14 438
2021-08-15 463
2021-08-16 428
2021-08-17 309
2021-08-18 316
2021-08-19 296
2021-08-20 418
2021-08-21 298
2021-08-22 249
2021-08-23 245
2021-08-24 208
2021-08-25 192
2021-08-26 182
2021-08-27 204
2021-08-28 219
2021-08-29 184
2021-08-30 229
2021-08-31 182
2021-09-01 203
2021-09-02 196
2021-09-03 179
2021-09-04 169
2021-09-05 160
2021-09-06 168
2021-09-07 103
2021-09-08 96
2021-09-09 117
2021-09-10 122
2021-09-11 93
2021-09-12 83
2021-09-13 59
2021-09-14 64
2021-09-15 84
2021-09-16 65
2021-09-17 72
2021-09-18 70
2021-09-19 57
2021-09-20 80
2021-09-21 43
2021-09-22 38
2021-09-23 51
2021-09-24 58
2021-09-25 48
2021-09-26 56
2021-09-27 33
2021-09-28 46
2021-09-29 31
2021-09-30 34
2021-10-01 62
2021-10-02 49
2021-10-03 21
2021-10-04 58
2021-10-05 27
2021-10-06 23
2021-10-07 25
2021-10-08 41
2021-10-09 23
2021-10-10 38
2021-10-11 19
2021-10-12 32
2021-10-13 26
2021-10-14 33
2021-10-15 35
2021-10-16 29
2021-10-17 47
2021-10-18 29
2021-10-19 51
2021-10-20 36
2021-10-21 25
2021-10-22 23
2021-10-23 29
2021-10-24 39
2021-10-25 47
2021-10-26 31
2021-10-27 30
2021-10-28 18
2021-10-29 28
2021-10-30 20
2021-10-31 29
2021-11-01 29
2021-11-02 29
2021-11-03 32
2021-11-04 28
2021-11-05 36
2021-11-06 26
2021-11-07 20
2021-11-08 17
2021-11-09 20
2021-11-10 15
2021-11-11 17
2021-11-12 17
2021-11-13 14
2021-11-14 8
2021-11-15 14
2021-11-16 7
2021-11-17 9
2021-11-18 11
2021-11-19 15
2021-11-20 36
2021-11-21 15
2021-11-22 18
2021-11-23 10
2021-11-24 4
2021-11-25 18
2021-11-26 9
2021-11-27 13
2021-11-28 14
2021-11-29 11
2021-11-30 11
2021-12-01 14
2021-12-02 2
2021-12-03 8
2021-12-04 9
2021-12-05 11
2021-12-06 8
2021-12-07 8
2021-12-08 5
2021-12-09 3
2021-12-11 1

- 4. (10%) Total acumulado de defunciones por día a nivel estatal.

Acumulado_estatal_D<- aggregate(unos~FECHA_DEF, data = Covid_Guerrero_Positivos[-Defunciones,], FUN = sum)


#Acumulado_estatal_D<- as.data.frame(Acumulado_estatal_D)


Acumulado_estatal_D %>% kbl(caption= "Total acumulado de defunciones por día a nivel estatal")%>% kable_classic(full_width = F, html_font = "Cambria") %>% kable_paper(full_width = F) %>% column_spec(column = 2 , color = "White",bold = TRUE ,background = spec_color(Acumulado_estatal_D$unos , option = "C", direction = -1 )) %>% scroll_box(width = "350px", height = "250px")
Total acumulado de defunciones por día a nivel estatal
FECHA_DEF unos
2020-03-28 1
2020-04-04 2
2020-04-06 2
2020-04-09 1
2020-04-12 1
2020-04-13 1
2020-04-14 1
2020-04-15 3
2020-04-17 1
2020-04-18 3
2020-04-19 4
2020-04-20 3
2020-04-21 7
2020-04-22 3
2020-04-23 3
2020-04-24 1
2020-04-25 4
2020-04-26 1
2020-04-27 8
2020-04-28 5
2020-04-29 4
2020-04-30 2
2020-05-01 2
2020-05-02 8
2020-05-03 4
2020-05-04 5
2020-05-05 6
2020-05-06 10
2020-05-07 6
2020-05-08 4
2020-05-09 11
2020-05-10 6
2020-05-11 9
2020-05-12 7
2020-05-13 9
2020-05-14 9
2020-05-15 14
2020-05-16 13
2020-05-17 16
2020-05-18 18
2020-05-19 13
2020-05-20 23
2020-05-21 17
2020-05-22 19
2020-05-23 19
2020-05-24 21
2020-05-25 28
2020-05-26 23
2020-05-27 26
2020-05-28 17
2020-05-29 22
2020-05-30 17
2020-05-31 24
2020-06-01 22
2020-06-02 28
2020-06-03 18
2020-06-04 18
2020-06-05 23
2020-06-06 19
2020-06-07 24
2020-06-08 24
2020-06-09 21
2020-06-10 24
2020-06-11 18
2020-06-12 18
2020-06-13 27
2020-06-14 23
2020-06-15 17
2020-06-16 20
2020-06-17 27
2020-06-18 21
2020-06-19 30
2020-06-20 14
2020-06-21 16
2020-06-22 29
2020-06-23 16
2020-06-24 16
2020-06-25 16
2020-06-26 24
2020-06-27 26
2020-06-28 21
2020-06-29 20
2020-06-30 22
2020-07-01 13
2020-07-02 11
2020-07-03 16
2020-07-04 21
2020-07-05 14
2020-07-06 15
2020-07-07 23
2020-07-08 9
2020-07-09 12
2020-07-10 17
2020-07-11 15
2020-07-12 10
2020-07-13 17
2020-07-14 24
2020-07-15 14
2020-07-16 9
2020-07-17 14
2020-07-18 16
2020-07-19 16
2020-07-20 14
2020-07-21 16
2020-07-22 12
2020-07-23 15
2020-07-24 12
2020-07-25 16
2020-07-26 17
2020-07-27 9
2020-07-28 7
2020-07-29 7
2020-07-30 13
2020-07-31 11
2020-08-01 11
2020-08-02 12
2020-08-03 12
2020-08-04 14
2020-08-05 9
2020-08-06 7
2020-08-07 15
2020-08-08 7
2020-08-09 10
2020-08-10 7
2020-08-11 12
2020-08-12 4
2020-08-13 14
2020-08-14 4
2020-08-15 13
2020-08-16 16
2020-08-17 8
2020-08-18 12
2020-08-19 19
2020-08-20 10
2020-08-21 6
2020-08-22 9
2020-08-23 6
2020-08-24 9
2020-08-25 11
2020-08-26 8
2020-08-27 7
2020-08-28 6
2020-08-29 14
2020-08-30 5
2020-08-31 4
2020-09-01 4
2020-09-02 5
2020-09-03 8
2020-09-04 4
2020-09-05 8
2020-09-06 2
2020-09-07 13
2020-09-08 2
2020-09-09 11
2020-09-10 6
2020-09-11 3
2020-09-12 15
2020-09-13 14
2020-09-14 5
2020-09-15 12
2020-09-16 7
2020-09-17 4
2020-09-18 5
2020-09-19 2
2020-09-20 7
2020-09-21 13
2020-09-22 7
2020-09-23 15
2020-09-24 7
2020-09-25 5
2020-09-26 14
2020-09-27 11
2020-09-28 8
2020-09-29 5
2020-09-30 9
2020-10-01 13
2020-10-02 10
2020-10-03 13
2020-10-04 4
2020-10-05 10
2020-10-06 7
2020-10-07 9
2020-10-08 14
2020-10-09 11
2020-10-10 9
2020-10-11 9
2020-10-12 12
2020-10-13 11
2020-10-14 5
2020-10-15 12
2020-10-16 7
2020-10-17 6
2020-10-18 9
2020-10-19 8
2020-10-20 2
2020-10-21 4
2020-10-22 6
2020-10-23 6
2020-10-24 5
2020-10-25 7
2020-10-26 5
2020-10-27 8
2020-10-28 5
2020-10-29 8
2020-10-30 5
2020-10-31 7
2020-11-01 11
2020-11-02 2
2020-11-03 8
2020-11-04 7
2020-11-05 5
2020-11-06 6
2020-11-07 8
2020-11-08 5
2020-11-09 7
2020-11-10 4
2020-11-11 6
2020-11-12 4
2020-11-13 3
2020-11-14 10
2020-11-15 4
2020-11-16 4
2020-11-17 6
2020-11-18 3
2020-11-19 8
2020-11-20 4
2020-11-21 3
2020-11-22 3
2020-11-24 4
2020-11-25 4
2020-11-26 9
2020-11-27 2
2020-11-28 2
2020-11-29 7
2020-11-30 2
2020-12-01 5
2020-12-02 7
2020-12-03 6
2020-12-04 6
2020-12-05 8
2020-12-06 8
2020-12-07 9
2020-12-08 2
2020-12-09 11
2020-12-10 5
2020-12-11 6
2020-12-12 6
2020-12-13 11
2020-12-14 3
2020-12-15 18
2020-12-16 5
2020-12-17 5
2020-12-18 6
2020-12-19 5
2020-12-20 10
2020-12-21 9
2020-12-22 8
2020-12-23 6
2020-12-24 4
2020-12-25 8
2020-12-26 6
2020-12-27 8
2020-12-28 8
2020-12-29 8
2020-12-30 8
2020-12-31 5
2021-01-01 4
2021-01-02 12
2021-01-03 10
2021-01-04 10
2021-01-05 17
2021-01-06 13
2021-01-07 11
2021-01-08 6
2021-01-09 8
2021-01-10 18
2021-01-11 13
2021-01-12 10
2021-01-13 19
2021-01-14 16
2021-01-15 18
2021-01-16 24
2021-01-17 12
2021-01-18 18
2021-01-19 24
2021-01-20 22
2021-01-21 17
2021-01-22 28
2021-01-23 17
2021-01-24 31
2021-01-25 20
2021-01-26 30
2021-01-27 28
2021-01-28 24
2021-01-29 21
2021-01-30 21
2021-01-31 27
2021-02-01 19
2021-02-02 20
2021-02-03 28
2021-02-04 19
2021-02-05 17
2021-02-06 14
2021-02-07 23
2021-02-08 23
2021-02-09 15
2021-02-10 10
2021-02-11 24
2021-02-12 23
2021-02-13 15
2021-02-14 19
2021-02-15 22
2021-02-16 22
2021-02-17 12
2021-02-18 14
2021-02-19 22
2021-02-20 12
2021-02-21 16
2021-02-22 19
2021-02-23 16
2021-02-24 18
2021-02-25 8
2021-02-26 12
2021-02-27 9
2021-02-28 17
2021-03-01 8
2021-03-02 8
2021-03-03 14
2021-03-04 14
2021-03-05 6
2021-03-06 10
2021-03-07 12
2021-03-08 15
2021-03-09 9
2021-03-10 9
2021-03-11 11
2021-03-12 15
2021-03-13 5
2021-03-14 9
2021-03-15 14
2021-03-16 11
2021-03-17 11
2021-03-18 12
2021-03-19 8
2021-03-20 8
2021-03-21 9
2021-03-22 2
2021-03-23 10
2021-03-24 7
2021-03-25 7
2021-03-26 5
2021-03-27 3
2021-03-28 8
2021-03-29 11
2021-03-30 9
2021-03-31 10
2021-04-01 3
2021-04-02 6
2021-04-03 2
2021-04-04 9
2021-04-05 9
2021-04-06 8
2021-04-07 3
2021-04-08 5
2021-04-09 3
2021-04-10 7
2021-04-11 9
2021-04-12 7
2021-04-13 12
2021-04-14 10
2021-04-15 6
2021-04-16 6
2021-04-17 6
2021-04-18 5
2021-04-19 8
2021-04-20 9
2021-04-21 7
2021-04-22 3
2021-04-23 4
2021-04-24 2
2021-04-25 7
2021-04-26 8
2021-04-27 2
2021-04-28 2
2021-04-29 4
2021-04-30 4
2021-05-01 9
2021-05-02 5
2021-05-03 6
2021-05-04 2
2021-05-05 3
2021-05-06 5
2021-05-07 2
2021-05-08 2
2021-05-09 5
2021-05-10 3
2021-05-11 4
2021-05-12 3
2021-05-13 3
2021-05-14 2
2021-05-15 1
2021-05-16 3
2021-05-17 5
2021-05-18 2
2021-05-19 3
2021-05-20 4
2021-05-21 4
2021-05-23 1
2021-05-24 4
2021-05-25 3
2021-05-26 3
2021-05-27 1
2021-05-28 3
2021-05-29 5
2021-05-30 5
2021-05-31 6
2021-06-02 2
2021-06-03 1
2021-06-04 2
2021-06-05 2
2021-06-06 1
2021-06-07 4
2021-06-08 1
2021-06-09 3
2021-06-10 1
2021-06-11 2
2021-06-12 3
2021-06-13 2
2021-06-14 6
2021-06-15 3
2021-06-16 3
2021-06-20 3
2021-06-22 1
2021-06-23 1
2021-06-24 2
2021-06-26 5
2021-06-27 2
2021-06-28 1
2021-06-30 1
2021-07-02 2
2021-07-03 1
2021-07-04 3
2021-07-06 2
2021-07-07 3
2021-07-08 3
2021-07-09 2
2021-07-10 3
2021-07-11 6
2021-07-12 7
2021-07-13 4
2021-07-14 6
2021-07-15 7
2021-07-16 7
2021-07-17 11
2021-07-18 8
2021-07-19 13
2021-07-20 18
2021-07-21 15
2021-07-22 7
2021-07-23 27
2021-07-24 26
2021-07-25 17
2021-07-26 16
2021-07-27 27
2021-07-28 27
2021-07-29 22
2021-07-30 23
2021-07-31 35
2021-08-01 22
2021-08-02 22
2021-08-03 31
2021-08-04 44
2021-08-05 33
2021-08-06 34
2021-08-07 32
2021-08-08 26
2021-08-09 37
2021-08-10 22
2021-08-11 29
2021-08-12 24
2021-08-13 27
2021-08-14 22
2021-08-15 30
2021-08-16 26
2021-08-17 21
2021-08-18 30
2021-08-19 25
2021-08-20 30
2021-08-21 23
2021-08-22 27
2021-08-23 30
2021-08-24 24
2021-08-25 23
2021-08-26 29
2021-08-27 21
2021-08-28 19
2021-08-29 27
2021-08-30 22
2021-08-31 19
2021-09-01 15
2021-09-02 16
2021-09-03 20
2021-09-04 13
2021-09-05 12
2021-09-06 13
2021-09-07 11
2021-09-08 14
2021-09-09 15
2021-09-10 13
2021-09-11 10
2021-09-12 12
2021-09-13 13
2021-09-14 5
2021-09-15 13
2021-09-16 8
2021-09-17 3
2021-09-18 5
2021-09-19 13
2021-09-20 9
2021-09-21 5
2021-09-22 6
2021-09-23 5
2021-09-24 7
2021-09-25 9
2021-09-26 7
2021-09-27 13
2021-09-28 2
2021-09-29 3
2021-09-30 1
2021-10-01 5
2021-10-02 3
2021-10-03 5
2021-10-04 7
2021-10-05 6
2021-10-06 2
2021-10-07 5
2021-10-08 7
2021-10-09 6
2021-10-10 2
2021-10-11 4
2021-10-12 6
2021-10-13 9
2021-10-14 4
2021-10-15 6
2021-10-16 4
2021-10-17 6
2021-10-18 2
2021-10-19 6
2021-10-20 2
2021-10-21 2
2021-10-22 2
2021-10-23 7
2021-10-24 4
2021-10-25 2
2021-10-26 3
2021-10-27 2
2021-10-28 1
2021-10-29 3
2021-10-30 2
2021-10-31 4
2021-11-01 3
2021-11-02 3
2021-11-03 6
2021-11-04 4
2021-11-05 4
2021-11-06 2
2021-11-07 2
2021-11-08 2
2021-11-09 1
2021-11-10 1
2021-11-11 1
2021-11-12 1
2021-11-13 1
2021-11-14 4
2021-11-15 5
2021-11-17 3
2021-11-18 3
2021-11-19 1
2021-11-20 3
2021-11-21 2
2021-11-23 1
2021-11-25 1
2021-11-26 2
2021-11-27 6
2021-11-29 1
2021-11-30 1
2021-12-01 3
2021-12-02 1
2021-12-03 4
2021-12-05 1
2021-12-06 1
2021-12-10 1

2. (10%) Gráfico de barras de los 10 municipios con mayor % de defunciones.

Top_10_Mun_P<-Porcentaje %>% arrange(desc(porcentaje)) %>% top_n(10)
## Selecting by porcentaje
textoss2<-paste("Municipio:",Top_10_Mun_P$Municipio,"\n", "Porcentaje defunciones por COVID:", Top_10_Mun_P$porcentaje)

bar_graf2<- Top_10_Mun_P %>% ggplot(aes(x=porcentaje, y=Municipio, text=textoss2)) + theme_dark() + theme(text = element_text(size = 9), plot.title = element_text(hjust = 0.5),)+ geom_bar(stat = "identity",color = "white",fill = "darkcyan",width = .7,lwd=0.1) +labs(title = "Top 10 - Porcentaje de defunciones por Covid en Guerrero", x = "Porcentaje", y = "Municipio")

ggplotly(bar_graf2,tooltip = c("text"))

3. (15%) Gráfico de barras de los 10 municipios con mayor número de casos positivos detectados, divido por hombres y por mujeres.

Casos_Sex_Mun<-aggregate(unos~MUNICIPIO + SEXO, Covid_Guerrero_Positivos,sum)
Casos_Sex_Mun<- Casos_Sex_Mun %>% mutate(SEXO= case_when(SEXO == 1 ~ "Mujeres", SEXO == 2 ~ "Hombres", SEXO == 99 ~ "no especificado")) %>% arrange(desc(unos)) %>% group_by(SEXO) %>% top_n(10)
## Selecting by unos
textoss3<-paste("Municipio:",Casos_Sex_Mun$MUNICIPIO,"\n", "Número de defunciones de", Casos_Sex_Mun$SEXO, "por municipio:", Casos_Sex_Mun$unos)

bar_graf3<- Casos_Sex_Mun %>% ggplot(aes(x=Casos_Sex_Mun$unos, y=Casos_Sex_Mun$MUNICIPIO ,text=textoss3)) + theme_dark() + theme(text = element_text(size = 9), plot.title = element_text(hjust = 0.5),)+ geom_bar(stat = "identity", aes(fill=SEXO)) + labs(title = "Top 10 - Número de casos Covid por municipio y sexo", x = "Porcentaje", y = "Municipio") 

ggplotly(bar_graf3,tooltip = c("text"))

4. (15%) Gráfico de líneas que representa el número acumulado de defunciones a nivel estatal por día. Al desplazar el cursor sobre la línea, se debe de mostrar la cantidad representada en la línea.

textoss<-paste("Fecha:",Acumulado_estatal_D$FECHA_DEF, "\n", "Número de defunciones acumuladas:", Acumulado_estatal_D$unos )

grafica_def_est_acu<- Acumulado_estatal_D %>% ggplot(aes(x=FECHA_DEF, y=unos , text=textoss)) + theme_dark()+ theme(text = element_text(size=12), plot.title = element_text(hjust = 1),) + geom_line(group = 1) + geom_point() + labs(title = "Acumulado de defunciones por Covid a nivel estatal por día", x = "Fecha", y = "Defunciones a nivel estatal por día")

ggplotly(grafica_def_est_acu, tooltip = c("text"))

5. (20%) Mapa que muestre la distribución espacial, por municipio, del % de defunciones. Utilizar el corte por cuantiles. Al situar el cursor sobre el municipio, se debe de desplegar el % de defunciones y el total de casos de defunciones del correspondiente.

mapa_guerrero<-readOGR("C:\\Users\\permi\\Desktop\\CIDE\\Primer semestre\\R\\Guerrero\\conjunto_de_datos",layer = "guerrero_mun")
## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\permi\Desktop\CIDE\Primer semestre\R\Guerrero\conjunto_de_datos", layer: "guerrero_mun"
## with 81 features
## It has 4 fields
mapa_guerrero$CVE_MUN<-as.numeric(mapa_guerrero$CVE_MUN)

Porcentaje_D<-merge(Porcentaje,municipios_G, by.x="Municipio", by.y="MUNICIPIO")

Num_D<-merge(x=Def_X_Mun,y=municipios_G, by.x="MUNICIPIO", by.y="MUNICIPIO")

mapa_G3<-merge(x=Porcentaje_D , y=Num_D, by.x="Municipio", by.y="MUNICIPIO")

mapa_G<-merge(x=mapa_guerrero@data, y=mapa_G3 , by.x= "CVE_MUN", by.y="CLAVE_MUNICIPIO.x", sort = FALSE)

mapa_guerrero@data<-mapa_G

#levels(cut(as.numeric(mapa_guerrero@data$porcentaje.x),4))
cortess<- c(0,19.3,35.1,50.9,Inf)

colores <- colorBin( palette="Reds", domain=mapa_guerrero$porcentaje.x ,na.color="transparent", bins=cortess)

textoss <- paste(
      "Municipio : ",mapa_guerrero$Municipio.y ,"<br/>",
      "Porcentaje de defunciones por Covid: ", round(mapa_guerrero$porcentaje ,2),"<br/>","Total de casos de defunciones", mapa_guerrero$unos ,"<br/>" )  %>% lapply(htmltools::HTML)

leaflet(data=mapa_guerrero ) %>% 
 addTiles() %>% 
 addPolygons(label = textoss,fillColor = colores(mapa_guerrero$porcentaje), weight = 2, opacity = 1, color = "white", dashArray = "3",
                  fillOpacity = 0.9) %>% 
   addLegend("topleft", colors = c("#FEE5D9", "#FB9779", "#E74132", "#A50F15"),
             labels= c("(3.42%-19.3%)","(19.3%-35.1%)","(35.1%-50.9%)","(50.9%-66.7%)"),
             title= "Porcentaje de defunciones por municipio. ", opacity = 0.9)