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$Pobreza*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0,0.0280,0.0566,0.0849,Inf)
colores <- colorBin( palette="Reds", domain=mapa_mex$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$Pobreza15*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0,0.0280,0.0566,0.0849,Inf)
colores <- colorBin( palette="Reds", domain=mapa_mex$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$Pobreza_ext*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0.0141,0.144,0.273,0.402,Inf)
colores <- colorBin( palette="Greens", domain=mapa_mex$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$Pobreza_ext15*100,2) ) %>% lapply(htmltools::HTML)
cortess <- c(0.0147,0.0952,0.175,0.256,Inf)
colores <- colorBin( palette="Greens", domain=mapa_mex$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?