Actividad 4
Richard Mondragon Aguilar
1/12/2021
Ejercicio 1.
Utilizando el reporte “ANEXO ESTAD´ISTICO DE POBREZA A NIVEL MUNICIPIO 2010 Y 2015”, elegir un estado (y sus municipios), y representar el nivel de pobreza y pobreza extrema para 2010 y 2015. Esto es:
Inciso 1)
Para 2010, considerando la division (cut) por cuantiles, representar en un mapa la distribucion de la pobreza de los municipios.
Paqueterias:
library(rgdal)## Warning: package 'rgdal' was built under R version 4.1.2## Loading required package: sp## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-27, (SVN revision 1148)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/richa/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/richa/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-6
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/richa/Documents/R/win-library/4.1/rgdal/projlibrary(readxl)
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, unionlibrary(RColorBrewer)
library(htmltools)
library(leaflet)## Warning: package 'leaflet' was built under R version 4.1.2library(dplyr)
library(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowsSe cargan los datos del mapa y los indices:
mapa_mex <- readOGR("C:\\Users\\richa\\OneDrive - CIDE\\1er Semestre-DESKTOP-RPQUJJA\\Manejo de bases de datos\\Actividad 4", layer="mex_mun")## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\richa\OneDrive - CIDE\1er Semestre-DESKTOP-RPQUJJA\Manejo de bases de datos\Actividad 4", layer: "mex_mun"
## with 125 features
## It has 4 fieldsdatos_coneval <- read_xlsx("C:\\Users\\richa\\OneDrive - CIDE\\1er Semestre-DESKTOP-RPQUJJA\\Manejo de bases de datos\\Actividad 4\\Pobreza.xlsx")posis_mun <- which(datos_coneval$Clave_entidad =="15")
datos_coneval_mex <- datos_coneval[posis_mun,]datos_coneval_mex$Prop_pob <- as.numeric(datos_coneval_mex$Pob_2010)/as.numeric(datos_coneval_mex$Población_2010)
datos_coneval_mex$Clave_municipio <- substr(datos_coneval_mex$Clave_municipio,3,5)
red_mex <- datos_coneval_mex[,c(3,15)]
kable(red_mex)| Clave_municipio | Prop_pob |
|---|---|
| 001 | 0.6973908 |
| 002 | 0.4160594 |
| 003 | 0.5460865 |
| 004 | 0.6600423 |
| 005 | 0.5281915 |
| 006 | 0.5060775 |
| 007 | 0.6411333 |
| 008 | 0.5898869 |
| 009 | 0.4374819 |
| 010 | 0.4717216 |
| 011 | 0.5162915 |
| 012 | 0.6099731 |
| 013 | 0.3141752 |
| 014 | 0.5128362 |
| 015 | 0.6969271 |
| 016 | 0.5576678 |
| 017 | 0.4427251 |
| 018 | 0.4344639 |
| 019 | 0.5107966 |
| 020 | 0.2050285 |
| 021 | 0.5421804 |
| 022 | 0.3591644 |
| 023 | 0.5044717 |
| 024 | 0.2795572 |
| 025 | 0.5053304 |
| 026 | 0.6171212 |
| 027 | 0.4014772 |
| 028 | 0.3662017 |
| 029 | 0.3062649 |
| 030 | 0.5245327 |
| 031 | 0.6186522 |
| 032 | 0.8000255 |
| 033 | 0.3755179 |
| 034 | 0.7473672 |
| 035 | 0.4036163 |
| 036 | 0.6133961 |
| 037 | 0.3179125 |
| 038 | 0.3919406 |
| 039 | 0.3070163 |
| 040 | 0.6187926 |
| 041 | 0.7757134 |
| 042 | 0.6775822 |
| 043 | 0.5411716 |
| 044 | 0.3736064 |
| 045 | 0.5540888 |
| 046 | 0.4120228 |
| 047 | 0.6486362 |
| 048 | 0.4631634 |
| 049 | 0.6293442 |
| 050 | 0.6266945 |
| 051 | 0.5299533 |
| 052 | 0.6626470 |
| 053 | 0.3912000 |
| 054 | 0.2500101 |
| 055 | 0.5171226 |
| 056 | 0.6458190 |
| 057 | 0.3291800 |
| 058 | 0.3737508 |
| 059 | 0.4635496 |
| 060 | 0.3928913 |
| 061 | 0.5288315 |
| 062 | 0.4355227 |
| 063 | 0.5703499 |
| 064 | 0.7282525 |
| 065 | 0.5076307 |
| 066 | 0.7581443 |
| 067 | 0.5999070 |
| 068 | 0.6218738 |
| 069 | 0.3907011 |
| 070 | 0.5129107 |
| 071 | 0.3883099 |
| 072 | 0.3952575 |
| 073 | 0.4570170 |
| 074 | 0.7838393 |
| 075 | 0.3893154 |
| 076 | 0.5501592 |
| 077 | 0.5916297 |
| 078 | 0.5445445 |
| 079 | 0.4637025 |
| 080 | 0.8182945 |
| 081 | 0.3277667 |
| 082 | 0.7040352 |
| 083 | 0.3301847 |
| 084 | 0.5266356 |
| 085 | 0.7234226 |
| 086 | 0.6564871 |
| 087 | 0.6452986 |
| 088 | 0.5385605 |
| 089 | 0.4187052 |
| 090 | 0.5476466 |
| 091 | 0.4433839 |
| 092 | 0.4556087 |
| 093 | 0.4654052 |
| 094 | 0.5297637 |
| 095 | 0.3865480 |
| 096 | 0.6074039 |
| 097 | 0.6214125 |
| 098 | 0.5241650 |
| 099 | 0.3912568 |
| 100 | 0.4735739 |
| 101 | 0.5123501 |
| 102 | 0.5440263 |
| 103 | 0.3990771 |
| 104 | 0.2868656 |
| 105 | 0.7217673 |
| 106 | 0.3804678 |
| 107 | 0.4981595 |
| 108 | 0.4326863 |
| 109 | 0.3207873 |
| 110 | 0.5943383 |
| 111 | 0.8751880 |
| 112 | 0.6841926 |
| 113 | 0.5759798 |
| 114 | 0.6268925 |
| 115 | 0.5737822 |
| 116 | 0.6892026 |
| 117 | 0.7774533 |
| 118 | 0.5732682 |
| 119 | 0.7427755 |
| 120 | 0.4218790 |
| 121 | 0.2184254 |
| 122 | 0.5697665 |
| 123 | 0.8200384 |
| 124 | 0.8136758 |
| 125 | 0.4271288 |
merge(x=mapa_mex@data, y=red_mex, by.x = "CVE_MUN", by.y = "Clave_municipio", sort = FALSE)## CVE_MUN CVEGEO CVE_ENT NOMGEO Prop_pob
## 1 003 15003 15 Aculco 0.5460865
## 2 004 15004 15 Almoloya de Alquisiras 0.6600423
## 3 005 15005 15 Almoloya de Juárez 0.5281915
## 4 010 15010 15 Apaxco 0.4717216
## 5 019 15019 15 Capulhuac 0.5107966
## 6 024 15024 15 Cuautitlán 0.2795572
## 7 034 15034 15 Ecatzingo 0.7473672
## 8 038 15038 15 Isidro Fabela 0.3919406
## 9 041 15041 15 Ixtapan del Oro 0.7757134
## 10 044 15044 15 Jaltenco 0.3736064
## 11 046 15046 15 Jilotzingo 0.4120228
## 12 049 15049 15 Joquicingo 0.6293442
## 13 052 15052 15 Malinalco 0.6626470
## 14 062 15062 15 Ocoyoacac 0.4355227
## 15 063 15063 15 Ocuilan 0.5703499
## 16 068 15068 15 Ozumba 0.6218738
## 17 073 15073 15 San Antonio la Isla 0.4570170
## 18 077 15077 15 San Simón de Guerrero 0.5916297
## 19 081 15081 15 Tecámac 0.3277667
## 20 109 15109 15 Tultitlán 0.3207873
## 21 118 15118 15 Zinacantepec 0.5732682
## 22 119 15119 15 Zumpahuacán 0.7427755
## 23 120 15120 15 Zumpango 0.4218790
## 24 125 15125 15 Tonanitla 0.4271288
## 25 001 15001 15 Acambay de RuÃz Castañeda 0.6973908
## 26 014 15014 15 Atlacomulco 0.5128362
## 27 069 15069 15 Papalotla 0.3907011
## 28 070 15070 15 La Paz 0.5129107
## 29 071 15071 15 Polotitlán 0.3883099
## 30 072 15072 15 Rayón 0.3952575
## 31 075 15075 15 San MartÃn de las Pirámides 0.3893154
## 32 078 15078 15 Santo Tomás 0.5445445
## 33 079 15079 15 Soyaniquilpan de Juárez 0.4637025
## 34 080 15080 15 Sultepec 0.8182945
## 35 099 15099 15 Texcoco 0.3912568
## 36 085 15085 15 Temascalcingo 0.7234226
## 37 088 15088 15 Tenancingo 0.5385605
## 38 123 15123 15 Luvianos 0.8200384
## 39 057 15057 15 Naucalpan de Juárez 0.3291800
## 40 037 15037 15 Huixquilucan 0.3179125
## 41 107 15107 15 Tonatico 0.4981595
## 42 008 15008 15 Amatepec 0.5898869
## 43 101 15101 15 Tianguistenco 0.5123501
## 44 103 15103 15 Tlalmanalco 0.3990771
## 45 106 15106 15 Toluca 0.3804678
## 46 108 15108 15 Tultepec 0.4326863
## 47 110 15110 15 Valle de Bravo 0.5943383
## 48 111 15111 15 Villa de Allende 0.8751880
## 49 112 15112 15 Villa del Carbón 0.6841926
## 50 113 15113 15 Villa Guerrero 0.5759798
## 51 114 15114 15 Villa Victoria 0.6268925
## 52 115 15115 15 Xonacatlán 0.5737822
## 53 116 15116 15 Zacazonapan 0.6892026
## 54 117 15117 15 Zacualpan 0.7774533
## 55 121 15121 15 Cuautitlán Izcalli 0.2184254
## 56 124 15124 15 San José del Rincón 0.8136758
## 57 002 15002 15 Acolman 0.4160594
## 58 007 15007 15 Amanalco 0.6411333
## 59 011 15011 15 Atenco 0.5162915
## 60 013 15013 15 Atizapán de Zaragoza 0.3141752
## 61 045 15045 15 Jilotepec 0.5540888
## 62 059 15059 15 Nextlalpan 0.4635496
## 63 016 15016 15 Axapusco 0.5576678
## 64 104 15104 15 Tlalnepantla de Baz 0.2868656
## 65 020 15020 15 Coacalco de Berriozábal 0.2050285
## 66 021 15021 15 Coatepec Harinas 0.5421804
## 67 022 15022 15 Cocotitlán 0.3591644
## 68 027 15027 15 Chapultepec 0.4014772
## 69 030 15030 15 Chiconcuac 0.5245327
## 70 053 15053 15 Melchor Ocampo 0.3912000
## 71 055 15055 15 Mexicaltzingo 0.5171226
## 72 083 15083 15 Temamatla 0.3301847
## 73 087 15087 15 Temoaya 0.6452986
## 74 094 15094 15 Tepetlixpa 0.5297637
## 75 096 15096 15 Tequixquiac 0.6074039
## 76 102 15102 15 Timilpan 0.5440263
## 77 105 15105 15 Tlatlaya 0.7217673
## 78 012 15012 15 Atizapán 0.6099731
## 79 015 15015 15 Atlautla 0.6969271
## 80 017 15017 15 Ayapango 0.4427251
## 81 018 15018 15 Calimaya 0.4344639
## 82 023 15023 15 Coyotepec 0.5044717
## 83 025 15025 15 Chalco 0.5053304
## 84 026 15026 15 Chapa de Mota 0.6171212
## 85 028 15028 15 Chiautla 0.3662017
## 86 029 15029 15 Chicoloapan 0.3062649
## 87 031 15031 15 Chimalhuacán 0.6186522
## 88 032 15032 15 Donato Guerra 0.8000255
## 89 033 15033 15 Ecatepec de Morelos 0.3755179
## 90 035 15035 15 Huehuetoca 0.4036163
## 91 036 15036 15 Hueypoxtla 0.6133961
## 92 039 15039 15 Ixtapaluca 0.3070163
## 93 040 15040 15 Ixtapan de la Sal 0.6187926
## 94 042 15042 15 Ixtlahuaca 0.6775822
## 95 043 15043 15 Xalatlaco 0.5411716
## 96 047 15047 15 Jiquipilco 0.6486362
## 97 048 15048 15 Jocotitlán 0.4631634
## 98 051 15051 15 Lerma 0.5299533
## 99 054 15054 15 Metepec 0.2500101
## 100 056 15056 15 Morelos 0.6458190
## 101 058 15058 15 Nezahualcóyotl 0.3737508
## 102 060 15060 15 Nicolás Romero 0.3928913
## 103 061 15061 15 Nopaltepec 0.5288315
## 104 064 15064 15 El Oro 0.7282525
## 105 065 15065 15 Otumba 0.5076307
## 106 066 15066 15 Otzoloapan 0.7581443
## 107 067 15067 15 Otzolotepec 0.5999070
## 108 076 15076 15 San Mateo Atenco 0.5501592
## 109 082 15082 15 Tejupilco 0.7040352
## 110 084 15084 15 Temascalapa 0.5266356
## 111 086 15086 15 Temascaltepec 0.6564871
## 112 089 15089 15 Tenango del Aire 0.4187052
## 113 090 15090 15 Tenango del Valle 0.5476466
## 114 091 15091 15 Teoloyucan 0.4433839
## 115 092 15092 15 Teotihuacán 0.4556087
## 116 093 15093 15 Tepetlaoxtoc 0.4654052
## 117 095 15095 15 Tepotzotlán 0.3865480
## 118 097 15097 15 Texcaltitlán 0.6214125
## 119 098 15098 15 Texcalyacac 0.5241650
## 120 100 15100 15 Tezoyuca 0.4735739
## 121 006 15006 15 Almoloya del RÃo 0.5060775
## 122 050 15050 15 Juchitepec 0.6266945
## 123 074 15074 15 San Felipe del Progreso 0.7838393
## 124 009 15009 15 Amecameca 0.4374819
## 125 122 15122 15 Valle de Chalco Solidaridad 0.5697665mapa_mex@data$Pobreza <- datos_coneval_mex$Prop_pobSe generan los cuantiles:
cut(mapa_mex@data$Pobreza,4)## [1] (0.54,0.708] (0.373,0.54] (0.54,0.708] (0.54,0.708] (0.373,0.54]
## [6] (0.373,0.54] (0.54,0.708] (0.54,0.708] (0.373,0.54] (0.373,0.54]
## [11] (0.373,0.54] (0.54,0.708] (0.204,0.373] (0.373,0.54] (0.54,0.708]
## [16] (0.54,0.708] (0.373,0.54] (0.373,0.54] (0.373,0.54] (0.204,0.373]
## [21] (0.54,0.708] (0.204,0.373] (0.373,0.54] (0.204,0.373] (0.373,0.54]
## [26] (0.54,0.708] (0.373,0.54] (0.204,0.373] (0.204,0.373] (0.373,0.54]
## [31] (0.54,0.708] (0.708,0.876] (0.373,0.54] (0.708,0.876] (0.373,0.54]
## [36] (0.54,0.708] (0.204,0.373] (0.373,0.54] (0.204,0.373] (0.54,0.708]
## [41] (0.708,0.876] (0.54,0.708] (0.54,0.708] (0.373,0.54] (0.54,0.708]
## [46] (0.373,0.54] (0.54,0.708] (0.373,0.54] (0.54,0.708] (0.54,0.708]
## [51] (0.373,0.54] (0.54,0.708] (0.373,0.54] (0.204,0.373] (0.373,0.54]
## [56] (0.54,0.708] (0.204,0.373] (0.373,0.54] (0.373,0.54] (0.373,0.54]
## [61] (0.373,0.54] (0.373,0.54] (0.54,0.708] (0.708,0.876] (0.373,0.54]
## [66] (0.708,0.876] (0.54,0.708] (0.54,0.708] (0.373,0.54] (0.373,0.54]
## [71] (0.373,0.54] (0.373,0.54] (0.373,0.54] (0.708,0.876] (0.373,0.54]
## [76] (0.54,0.708] (0.54,0.708] (0.54,0.708] (0.373,0.54] (0.708,0.876]
## [81] (0.204,0.373] (0.54,0.708] (0.204,0.373] (0.373,0.54] (0.708,0.876]
## [86] (0.54,0.708] (0.54,0.708] (0.373,0.54] (0.373,0.54] (0.54,0.708]
## [91] (0.373,0.54] (0.373,0.54] (0.373,0.54] (0.373,0.54] (0.373,0.54]
## [96] (0.54,0.708] (0.54,0.708] (0.373,0.54] (0.373,0.54] (0.373,0.54]
## [101] (0.373,0.54] (0.54,0.708] (0.373,0.54] (0.204,0.373] (0.708,0.876]
## [106] (0.373,0.54] (0.373,0.54] (0.373,0.54] (0.204,0.373] (0.54,0.708]
## [111] (0.708,0.876] (0.54,0.708] (0.54,0.708] (0.54,0.708] (0.54,0.708]
## [116] (0.54,0.708] (0.708,0.876] (0.54,0.708] (0.708,0.876] (0.373,0.54]
## [121] (0.204,0.373] (0.54,0.708] (0.708,0.876] (0.708,0.876] (0.373,0.54]
## Levels: (0.204,0.373] (0.373,0.54] (0.54,0.708] (0.708,0.876]Se genera el mapa con los cuantiles y una paleta de colores:
my_colors <- brewer.pal(5, "Blues")
my_colors <- colorRampPalette(my_colors)(4)
cuantil <- cut(mapa_mex@data$Pobreza,4)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors, bg = "white")
Se agrega la leyenda para identificar los cuantiles:
my_colors <- brewer.pal(5, "Blues")
my_colors <- colorRampPalette(my_colors)(4)
cuantil <- cut(mapa_mex@data$Pobreza,4)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors, bg = "white")
texto4 <- c("20.4%-37.3%", "37.3%-54.0%", "54.0%-70.8%", "70.8%-87.6%")
legend("topright",texto4, cex = .5, bty = "o",
col = c("#BDD7E7", "#6BAED6", "#3182BD", "#08519C"),
pch = 19)
Para hacerlo interactivo:
textoss <- paste(
"Municipio : ",mapa_mex$NOMGEO,"<br/>",
"% Pobreza: ", round(mapa_mex$Pobreza*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0,0.204,0.373,0.54,0.708,0.876,Inf)
colores <- colorBin( palette="Blues", domain=mapa_mex$Pobreza, na.color="transparent", bins=cortess)
leaflet(data=mapa_mex) %>%
addTiles() %>%
addPolygons(label = textoss,fillColor = colores(mapa_mex$Pobreza),
fillOpacity = 0.9)Inciso 2)
Para el 2015, considerando la division (cut) por cuantiles, representar en un mapa la distribucion de la pobreza de los municipios.
datos_coneval_mex$Prop_pob15 <- as.numeric(datos_coneval_mex$Pob_2015)/as.numeric(datos_coneval_mex$Población_2015)
red_mex15 <- datos_coneval_mex[,c(3,16)]
mapa_mex@data$Pobreza15 <- datos_coneval_mex$Prop_pob15
cut(mapa_mex@data$Pobreza15,4)## [1] (0.735,0.893] (0.42,0.577] (0.577,0.735] (0.577,0.735] (0.577,0.735]
## [6] (0.577,0.735] (0.735,0.893] (0.735,0.893] (0.42,0.577] (0.42,0.577]
## [11] (0.577,0.735] (0.577,0.735] (0.262,0.42] (0.577,0.735] (0.735,0.893]
## [16] (0.577,0.735] (0.42,0.577] (0.42,0.577] (0.42,0.577] (0.262,0.42]
## [21] (0.577,0.735] (0.42,0.577] (0.42,0.577] (0.262,0.42] (0.42,0.577]
## [26] (0.577,0.735] (0.262,0.42] (0.42,0.577] (0.42,0.577] (0.577,0.735]
## [31] (0.577,0.735] (0.735,0.893] (0.42,0.577] (0.735,0.893] (0.262,0.42]
## [36] (0.577,0.735] (0.262,0.42] (0.42,0.577] (0.42,0.577] (0.577,0.735]
## [41] (0.735,0.893] (0.577,0.735] (0.577,0.735] (0.42,0.577] (0.42,0.577]
## [46] (0.42,0.577] (0.577,0.735] (0.577,0.735] (0.577,0.735] (0.577,0.735]
## [51] (0.42,0.577] (0.735,0.893] (0.42,0.577] (0.262,0.42] (0.42,0.577]
## [56] (0.735,0.893] (0.262,0.42] (0.262,0.42] (0.42,0.577] (0.42,0.577]
## [61] (0.42,0.577] (0.42,0.577] (0.577,0.735] (0.577,0.735] (0.577,0.735]
## [66] (0.735,0.893] (0.577,0.735] (0.577,0.735] (0.42,0.577] (0.42,0.577]
## [71] (0.42,0.577] (0.262,0.42] (0.262,0.42] (0.735,0.893] (0.42,0.577]
## [76] (0.42,0.577] (0.577,0.735] (0.577,0.735] (0.42,0.577] (0.735,0.893]
## [81] (0.262,0.42] (0.577,0.735] (0.262,0.42] (0.577,0.735] (0.735,0.893]
## [86] (0.735,0.893] (0.577,0.735] (0.577,0.735] (0.42,0.577] (0.577,0.735]
## [91] (0.42,0.577] (0.42,0.577] (0.42,0.577] (0.577,0.735] (0.262,0.42]
## [96] (0.577,0.735] (0.735,0.893] (0.577,0.735] (0.42,0.577] (0.577,0.735]
## [101] (0.577,0.735] (0.42,0.577] (0.42,0.577] (0.262,0.42] (0.735,0.893]
## [106] (0.42,0.577] (0.42,0.577] (0.262,0.42] (0.262,0.42] (0.577,0.735]
## [111] (0.735,0.893] (0.577,0.735] (0.577,0.735] (0.577,0.735] (0.42,0.577]
## [116] (0.577,0.735] (0.735,0.893] (0.42,0.577] (0.735,0.893] (0.42,0.577]
## [121] (0.262,0.42] (0.577,0.735] (0.735,0.893] (0.735,0.893] (0.42,0.577]
## Levels: (0.262,0.42] (0.42,0.577] (0.577,0.735] (0.735,0.893]my_colors <- brewer.pal(5, "Blues")
my_colors <- colorRampPalette(my_colors)(4)
cuantil <- cut(mapa_mex@data$Pobreza15,4)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors, bg = "white")
my_colors <- brewer.pal(5, "Blues")
my_colors <- colorRampPalette(my_colors)(4)
cuantil <- cut(mapa_mex@data$Pobreza15,4)
my_colors <- my_colors[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors, bg = "white")
texto4 <- c("26.2%-42.0%", "42.0%-57.7%", "57.7%-73.5%", "73.5%-89.3%")
legend("topright",texto4, cex = .5, bty = "o",
col = c("#BDD7E7", "#6BAED6", "#3182BD", "#08519C"),
pch = 19)
textoss <- paste(
"Municipio : ",mapa_mex$NOMGEO,"<br/>",
"% Pobreza: ", round(mapa_mex$Pobreza15*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0,0.262,0.42,0.577,0.735,0.893,Inf)
colores <- colorBin( palette="Blues", domain=mapa_mex$Pobreza15, na.color="transparent", bins=cortess)
leaflet(data=mapa_mex) %>%
addTiles() %>%
addPolygons(label = textoss,fillColor = colores(mapa_mex$Pobreza15),
fillOpacity = 0.9)Inciso 3)
Para el 2010, considerando la division (cut) por cuantiles, representar en un mapa la distribucion de la pobreza extrema de los municipios.
datos_coneval_mex$Prop_pob_ext <- as.numeric(datos_coneval_mex$Pob_ext_2010)/as.numeric(datos_coneval_mex$Población_2010)
red_mex_ext <- datos_coneval_mex[,c(3,17)]
mapa_mex@data$Pobreza_ext <- datos_coneval_mex$Prop_pob_ext
cut(mapa_mex@data$Pobreza_ext,4)## [1] (0.144,0.273] (0.0141,0.144] (0.144,0.273] (0.144,0.273] (0.144,0.273]
## [6] (0.0141,0.144] (0.144,0.273] (0.144,0.273] (0.0141,0.144] (0.0141,0.144]
## [11] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.144,0.273]
## [16] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [21] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [26] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [31] (0.0141,0.144] (0.402,0.532] (0.0141,0.144] (0.273,0.402] (0.0141,0.144]
## [36] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.144,0.273]
## [41] (0.273,0.402] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [46] (0.0141,0.144] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [51] (0.0141,0.144] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [56] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [61] (0.0141,0.144] (0.0141,0.144] (0.144,0.273] (0.273,0.402] (0.0141,0.144]
## [66] (0.273,0.402] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [71] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.273,0.402] (0.0141,0.144]
## [76] (0.0141,0.144] (0.144,0.273] (0.144,0.273] (0.0141,0.144] (0.402,0.532]
## [81] (0.0141,0.144] (0.273,0.402] (0.0141,0.144] (0.0141,0.144] (0.273,0.402]
## [86] (0.144,0.273] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [91] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [96] (0.0141,0.144] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144]
## [101] (0.0141,0.144] (0.144,0.273] (0.0141,0.144] (0.0141,0.144] (0.273,0.402]
## [106] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.0141,0.144] (0.144,0.273]
## [111] (0.402,0.532] (0.144,0.273] (0.0141,0.144] (0.144,0.273] (0.0141,0.144]
## [116] (0.144,0.273] (0.402,0.532] (0.144,0.273] (0.273,0.402] (0.0141,0.144]
## [121] (0.0141,0.144] (0.0141,0.144] (0.402,0.532] (0.402,0.532] (0.0141,0.144]
## Levels: (0.0141,0.144] (0.144,0.273] (0.273,0.402] (0.402,0.532]my_colors2 <- brewer.pal(5, "Greens")
my_colors2 <- colorRampPalette(my_colors2)(4)
cuantil <- cut(mapa_mex@data$Pobreza_ext,4)
my_colors2 <- my_colors2[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors2, bg = "white")
my_colors2 <- brewer.pal(5, "Greens")
my_colors2 <- colorRampPalette(my_colors2)(4)
cuantil <- cut(mapa_mex@data$Pobreza_ext,4)
my_colors2 <- my_colors2[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors2, bg = "white")
texto4 <- c("1.41%-14.4%", "14.4%-27.3%", "27.3%-40.2%", "40.2%-53.2%")
legend("topright",texto4, cex = .5, bty = "o",
col = c("#BAE4B3", "#74C476", "#31A354", "#006D2C"),
pch = 19)
textoss <- paste(
"Municipio : ",mapa_mex$NOMGEO,"<br/>",
"% Pobreza extrema: ", round(mapa_mex$Pobreza_ext*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0.0141,0.144,0.273,0.402,0.532,Inf)
colores <- colorBin( palette="Greens", domain=mapa_mex$Pobreza_ext, na.color="transparent", bins=cortess)
leaflet(data=mapa_mex) %>%
addTiles() %>%
addPolygons(label = textoss,fillColor = colores(mapa_mex$Pobreza_ext),
fillOpacity = 0.9)Inciso 4)
Para el 2015, considerando la division (cut) por cuantiles, representar en un mapa la distribucion de la pobreza extrema de los municipios.
datos_coneval_mex$Prop_pob_ext15 <- as.numeric(datos_coneval_mex$Pob_ext_2015)/as.numeric(datos_coneval_mex$Población_2015)
red_mex_ext15 <- datos_coneval_mex[,c(3,18)]
mapa_mex@data$Pobreza_ext15 <- datos_coneval_mex$Prop_pob_ext15
cut(mapa_mex@data$Pobreza_ext15,4)## [1] (0.175,0.256] (0.0147,0.0952] (0.0952,0.175] (0.175,0.256]
## [5] (0.0952,0.175] (0.0147,0.0952] (0.0952,0.175] (0.175,0.256]
## [9] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952]
## [13] (0.0147,0.0952] (0.0952,0.175] (0.0952,0.175] (0.0147,0.0952]
## [17] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952]
## [21] (0.0952,0.175] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952]
## [25] (0.0147,0.0952] (0.0952,0.175] (0.0147,0.0952] (0.0147,0.0952]
## [29] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952] (0.256,0.336]
## [33] (0.0147,0.0952] (0.175,0.256] (0.0147,0.0952] (0.0147,0.0952]
## [37] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952]
## [41] (0.256,0.336] (0.0952,0.175] (0.0147,0.0952] (0.0147,0.0952]
## [45] (0.0952,0.175] (0.0147,0.0952] (0.0952,0.175] (0.0147,0.0952]
## [49] (0.0952,0.175] (0.0147,0.0952] (0.0147,0.0952] (0.175,0.256]
## [53] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952] (0.175,0.256]
## [57] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952]
## [61] (0.0147,0.0952] (0.0147,0.0952] (0.0952,0.175] (0.0952,0.175]
## [65] (0.0147,0.0952] (0.175,0.256] (0.0952,0.175] (0.0952,0.175]
## [69] (0.0147,0.0952] (0.0952,0.175] (0.0147,0.0952] (0.0147,0.0952]
## [73] (0.0147,0.0952] (0.175,0.256] (0.0147,0.0952] (0.0147,0.0952]
## [77] (0.0952,0.175] (0.0952,0.175] (0.0147,0.0952] (0.256,0.336]
## [81] (0.0147,0.0952] (0.175,0.256] (0.0147,0.0952] (0.0147,0.0952]
## [85] (0.175,0.256] (0.175,0.256] (0.175,0.256] (0.0147,0.0952]
## [89] (0.0147,0.0952] (0.0952,0.175] (0.0147,0.0952] (0.0147,0.0952]
## [93] (0.0147,0.0952] (0.0952,0.175] (0.0147,0.0952] (0.0147,0.0952]
## [97] (0.175,0.256] (0.0147,0.0952] (0.0147,0.0952] (0.0952,0.175]
## [101] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952]
## [105] (0.256,0.336] (0.0147,0.0952] (0.0147,0.0952] (0.0147,0.0952]
## [109] (0.0147,0.0952] (0.0147,0.0952] (0.256,0.336] (0.0952,0.175]
## [113] (0.0952,0.175] (0.175,0.256] (0.0147,0.0952] (0.0952,0.175]
## [117] (0.256,0.336] (0.0147,0.0952] (0.175,0.256] (0.0147,0.0952]
## [121] (0.0147,0.0952] (0.0147,0.0952] (0.256,0.336] (0.175,0.256]
## [125] (0.0147,0.0952]
## Levels: (0.0147,0.0952] (0.0952,0.175] (0.175,0.256] (0.256,0.336]my_colors2 <- brewer.pal(5, "Greens")
my_colors2 <- colorRampPalette(my_colors2)(4)
cuantil <- cut(mapa_mex@data$Pobreza_ext15,4)
my_colors2 <- my_colors2[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors2, bg = "white")
my_colors2 <- brewer.pal(5, "Greens")
my_colors2 <- colorRampPalette(my_colors2)(4)
cuantil <- cut(mapa_mex@data$Pobreza_ext15,4)
my_colors2 <- my_colors2[as.numeric(cuantil)]
plot(mapa_mex, col=my_colors2, bg = "white")
texto4 <- c("1.47%-9.52%", "9.52%-17.5%", "17.5%-25.6%", "25.6%-33.6%")
legend("topright",texto4, cex = .5, bty = "o",
col = c("#BAE4B3", "#74C476", "#31A354", "#006D2C"),
pch = 19)
textoss <- paste(
"Municipio : ",mapa_mex$NOMGEO,"<br/>",
"% Pobreza extrema: ", round(mapa_mex$Pobreza_ext15*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0.0147,0.0952,0.175,0.256,0.336, Inf)
colores <- colorBin( palette="Greens", domain=mapa_mex$Pobreza_ext15, na.color="transparent", bins=cortess)
leaflet(data=mapa_mex) %>%
addTiles() %>%
addPolygons(label = textoss,fillColor = colores(mapa_mex$Pobreza_ext15),
fillOpacity = 0.9)Inciso 5)
¿La pobreza extrema se distribuye de manera aleatoria en los municipios? Es decir, ¿hay grupos o zonas especificas del estado elegido donde se concentran porcentajes mayores de la pobreza extrema?
Sí se distribuye de manera aleatoria, cualquier municipio puede obtener un valor que se encuentre entre el 0% y 100% y la pobreza extrena no se encuentra concentrada en una zona específica los municipios con el mayor porcentaje de pobreza extrema se encuentran distribuidos en todo el estado.