The data.frame that you provide to the plotting functions must have one column named "region" and one column named "value". The entries for "region" must match the INEGI codes for states ("01", "02", etc) or municipios ("01001", "01002", etc) either as strings with or without a leading "0" or as numerics. The functions str_mxstate and str_mxmunicipio are provided to easily format codes to the INEGI specification. Also, some example data.frames, such as df_mxstate_2020 and df_mxmunicipio_2020, are provided with demographic variables from the 2020 Census.

library(mxmaps)
library(htmlTable)

data("df_mxstate_2020")
htmlTable(head(df_mxstate_2020), rnames = FALSE, col.rgroup = c("none", "#F7F7F7"))
region state_name state_name_official state_abbr state_abbr_official year pop pop_male pop_female afromexican indigenous_language
01 Aguascalientes Aguascalientes AGS Ags. 2020 1425607 696683 728924 22425 2539
02 Baja California Baja California BC BC 2020 3769020 1900589 1868431 64362 49130
03 Baja California Sur Baja California Sur BCS BCS 2020 798447 405879 392568 26330 13581
04 Campeche Campeche CAMP Camp. 2020 928363 456939 471424 19319 91801
05 Coahuila Coahuila de Zaragoza COAH Coah. 2020 3146771 1563669 1583102 45976 5527
06 Colima Colima COL Col. 2020 731391 360622 370769 13574 5210
data("df_mxmunicipio_2020")
htmlTable(head(df_mxmunicipio_2020), rnames = FALSE, col.rgroup = c("none", "#F7F7F7"))
state_code municipio_code region state_name state_name_official state_abbr state_abbr_official municipio_name year pop pop_male pop_female afromexican indigenous_language metro_area long lat
01 001 01001 Aguascalientes Aguascalientes AGS Ags. Aguascalientes 2020 948990 462073 486917 15170 1839 Aguascalientes -102.296047 21.879823
01 002 01002 Aguascalientes Aguascalientes AGS Ags. Asientos 2020 51536 25261 26275 225 22 -102.089275 22.238317
01 003 01003 Aguascalientes Aguascalientes AGS Ags. Calvillo 2020 58250 28563 29687 266 76 -102.718751 21.846907
01 004 01004 Aguascalientes Aguascalientes AGS Ags. Cosío 2020 17000 8292 8708 2155 7 -102.300044 22.366409
01 005 01005 Aguascalientes Aguascalientes AGS Ags. Jesús María 2020 129929 64219 65710 2543 158 Aguascalientes -102.343416 21.961273
01 006 01006 Aguascalientes Aguascalientes AGS Ags. Pabellón de Arteaga 2020 47646 23377 24269 482 52 -102.276542 22.149202

2015 Conteo Intercensal

There are additional data.frames with data from other censuses or conteos intercensales

data("df_mxstate_2015")
htmlTable(head(df_mxstate_2015), rnames = FALSE, col.rgroup = c("none", "#F7F7F7"))
region state_name state_name_official state_abbr state_abbr_official year pop pop_male pop_female afromexican part_afromexican indigenous part_indigenous
01 Aguascalientes Aguascalientes AGS Ags. 2015 1312544 640091 672453 653 4559 153395 18716
02 Baja California Baja California BC BC 2015 3315766 1650341 1665425 7445 10432 283055 38391
03 Baja California Sur Baja California Sur BCS BCS 2015 712029 359137 352892 11032 5132 103034 11728
04 Campeche Campeche CAMP Camp. 2015 899931 441276 458655 3554 6833 400811 13140
05 Coahuila Coahuila de Zaragoza COAH Coah. 2015 2954915 1462612 1492303 2761 8137 204890 28588
06 Colima Colima COL Col. 2015 711235 350791 360444 762 3314 145297 12373
data("df_mxmunicipio_2015")
htmlTable(head(df_mxmunicipio_2015), rnames = FALSE, col.rgroup = c("none", "#F7F7F7"))
state_code municipio_code region state_name state_name_official state_abbr state_abbr_official municipio_name year pop pop_male pop_female afromexican part_afromexican indigenous part_indigenous metro_area long lat
01 001 01001 Aguascalientes Aguascalientes AGS Ags. Aguascalientes 2015 877190 425731 451459 532 2791 104125 14209 Aguascalientes -102.296047 21.879823
01 002 01002 Aguascalientes Aguascalientes AGS Ags. Asientos 2015 46464 22745 23719 3 130 1691 92 -102.089275 22.238317
01 003 01003 Aguascalientes Aguascalientes AGS Ags. Calvillo 2015 56048 27298 28750 10 167 7358 2223 -102.718751 21.846907
01 004 01004 Aguascalientes Aguascalientes AGS Ags. Cosío 2015 15577 7552 8025 0 67 2213 191 -102.300044 22.366409
01 005 01005 Aguascalientes Aguascalientes AGS Ags. Jesús María 2015 120405 60135 60270 32 219 8679 649 Aguascalientes -102.343416 21.961273
01 006 01006 Aguascalientes Aguascalientes AGS Ags. Pabellón de Arteaga 2015 46473 22490 23983 3 74 6232 251 -102.276542 22.149202

Older Censuses

data("df_mxstate_1990_2010")
htmlTable(head(df_mxstate_1990_2010), rnames = FALSE, col.rgroup = c("none", "#F7F7F7"))
region state_name_official year sex pop
01 Aguascalientes 1990 Total 719659
01 Aguascalientes 1995 Total 862720
01 Aguascalientes 2000 Total 944285
01 Aguascalientes 2005 Total 1065416
01 Aguascalientes 2010 Total 1184996
01 Aguascalientes 1990 Male 350218
data("df_mxmunicipio_1990_2010")
htmlTable(head(df_mxmunicipio_1990_2010), rnames = FALSE, col.rgroup = c("none", "#F7F7F7"))
region municipio_name year sex pop
01001 Aguascalientes 1990 Total 506274
01001 Aguascalientes 1995 Total 582827
01001 Aguascalientes 2000 Total 643419
01001 Aguascalientes 2005 Total 723043
01001 Aguascalientes 2010 Total 797010
01001 Aguascalientes 1990 Male 245197

State names and abbreviations come in two versions since normal people always use Veracruz instead of its full official name of Veracruz de Ignacio de la Llave, and it's kind of awkward to use the Chiapas official abbreviation of Chis.

library("geojsonio")

# Convert the topoJSON to spatial object
data(mxstate.topoJSON)
tmpdir <- tempdir()
# have to use RJSONIO or else the topojson isn't valid
write(RJSONIO::toJSON(mxstate.topoJSON), file.path(tmpdir, "state.topojson"))
states <- topojson_read(file.path(tmpdir, "state.topojson"))
## Reading layer `state' from data source `/tmp/diego/RtmpRruVVy/state.topojson' using driver `TopoJSON'
## Simple feature collection with 32 features and 1 field
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: -117.2457 ymin: 14.5321 xmax: -86.72229 ymax: 32.71865
## CRS:            NA

String conversion

str_mxstate(c(32, 5))
## [1] "32" "05"
str_mxmunicipio(c(32, 5), c(9, 18))
## [1] "32009" "05018"
str_mxmunicipio(c(32001, 5009))
## [1] "32001" "05009"