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Update github readme [SKIP CI]
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@diegovalle
diegovalle committed Feb 3, 2021
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Expand Up @@ -6,5 +6,6 @@ R/saveshps.R
docs/*
/index.html
/index.md
/index_files
data-raw/simpl/
AGEEML_20211271312276.csv.xz
22 changes: 11 additions & 11 deletions README.Rmd
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Expand Up @@ -44,9 +44,9 @@ devtools::install_github('diegovalle/mxmaps')
```{r}
library(mxmaps)
data("df_mxstate")
df_mxstate$value <- df_mxstate$pop
mxstate_choropleth(df_mxstate,
data("df_mxstate_2020")
df_mxstate_2020$value <- df_mxstate_2020$pop
mxstate_choropleth(df_mxstate_2020,
title = "Total population, by state")
```

Expand All @@ -55,20 +55,20 @@ mxstate_choropleth(df_mxstate,
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) and municipios ("01001", "01002", etc) either as a string 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, two example data.frames, `df_mxstate` and `df_mxmunicipio`, are provided with demographic variables from the Encuesta Intercensal 2015.

```{r}
data("df_mxstate")
knitr::kable(head(df_mxstate))
data("df_mxmunicipio")
knitr::kable(head(df_mxmunicipio))
data("df_mxstate_2020")
knitr::kable(head(df_mxstate_2020))
data("df_mxmunicipio_2020")
knitr::kable(head(df_mxmunicipio_2020))
```

## Municipios

Here's another example showing Mexican municipios (similar to counties):

```{r}
data("df_mxmunicipio")
df_mxmunicipio$value <- df_mxmunicipio$indigenous / df_mxmunicipio$pop
mxmunicipio_choropleth(df_mxmunicipio, num_colors = 1,
title = "Percentage of the population that self-identifies as indigenous")
data("df_mxmunicipio_2020")
df_mxmunicipio_2020$value <- df_mxmunicipio_2020$indigenous_language / df_mxmunicipio_2020$pop
mxmunicipio_choropleth(df_mxmunicipio_2020, num_colors = 1,
title = "Percentage of the population that identifies as indigenous")
```
56 changes: 28 additions & 28 deletions README.md
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@@ -1,7 +1,7 @@
Mexico Choropleths
================
Diego Valle-Jones
November 05, 2020
February 03, 2021

- [What does it do?](#what-does-it-do)
- [Installation](#installation)
Expand Down Expand Up @@ -48,9 +48,9 @@ devtools::install_github('diegovalle/mxmaps')
``` r
library(mxmaps)

data("df_mxstate")
df_mxstate$value <- df_mxstate$pop
mxstate_choropleth(df_mxstate,
data("df_mxstate_2020")
df_mxstate_2020$value <- df_mxstate_2020$pop
mxstate_choropleth(df_mxstate_2020,
title = "Total population, by state")
```

Expand All @@ -69,42 +69,42 @@ specification. Also, two example data.frames, `df_mxstate` and
Encuesta Intercensal 2015.

``` r
data("df_mxstate")
knitr::kable(head(df_mxstate))
data("df_mxstate_2020")
knitr::kable(head(df_mxstate_2020))
```

| region | state\_name | state\_name\_official | state\_abbr | state\_abbr\_official | pop | pop\_male | pop\_female | afromexican | part\_afromexican | indigenous | part\_indigenous |
| :----- | :------------------ | :-------------------- | :---------- | :-------------------- | ------: | --------: | ----------: | ----------: | ----------------: | ---------: | ---------------: |
| 01 | Aguascalientes | Aguascalientes | AGS | Ags. | 1312544 | 640091 | 672453 | 653 | 4559 | 153395 | 18716 |
| 02 | Baja California | Baja California | BC | BC | 3315766 | 1650341 | 1665425 | 7445 | 10432 | 283055 | 38391 |
| 03 | Baja California Sur | Baja California Sur | BCS | BCS | 712029 | 359137 | 352892 | 11032 | 5132 | 103034 | 11728 |
| 04 | Campeche | Campeche | CAMP | Camp. | 899931 | 441276 | 458655 | 3554 | 6833 | 400811 | 13140 |
| 05 | Coahuila | Coahuila de Zaragoza | COAH | Coah. | 2954915 | 1462612 | 1492303 | 2761 | 8137 | 204890 | 28588 |
| 06 | Colima | Colima | COL | Col. | 711235 | 350791 | 360444 | 762 | 3314 | 145297 | 12373 |
| 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 |

``` r
data("df_mxmunicipio")
knitr::kable(head(df_mxmunicipio))
data("df_mxmunicipio_2020")
knitr::kable(head(df_mxmunicipio_2020))
```

| state\_code | municipio\_code | region | state\_name | state\_name\_official | state\_abbr | state\_abbr\_official | municipio\_name | pop | pop\_male | pop\_female | afromexican | part\_afromexican | indigenous | part\_indigenous | metro\_area | long | lat |
| :---------- | :-------------- | :----- | :------------- | :-------------------- | :---------- | :-------------------- | :------------------ | -----: | --------: | ----------: | ----------: | ----------------: | ---------: | ---------------: | :------------- | ---------: | -------: |
| 01 | 001 | 01001 | Aguascalientes | Aguascalientes | AGS | Ags. | Aguascalientes | 877190 | 425731 | 451459 | 532 | 2791 | 104125 | 14209 | Aguascalientes | \-102.2960 | 21.87982 |
| 01 | 002 | 01002 | Aguascalientes | Aguascalientes | AGS | Ags. | Asientos | 46464 | 22745 | 23719 | 3 | 130 | 1691 | 92 | NA | \-102.0893 | 22.23832 |
| 01 | 003 | 01003 | Aguascalientes | Aguascalientes | AGS | Ags. | Calvillo | 56048 | 27298 | 28750 | 10 | 167 | 7358 | 2223 | NA | \-102.7188 | 21.84691 |
| 01 | 004 | 01004 | Aguascalientes | Aguascalientes | AGS | Ags. | Cosío | 15577 | 7552 | 8025 | 0 | 67 | 2213 | 191 | NA | \-102.3000 | 22.36641 |
| 01 | 005 | 01005 | Aguascalientes | Aguascalientes | AGS | Ags. | Jesús María | 120405 | 60135 | 60270 | 32 | 219 | 8679 | 649 | Aguascalientes | \-102.3434 | 21.96127 |
| 01 | 006 | 01006 | Aguascalientes | Aguascalientes | AGS | Ags. | Pabellón de Arteaga | 46473 | 22490 | 23983 | 3 | 74 | 6232 | 251 | NA | \-102.2765 | 22.14920 |
| 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 | \-102.2960 | 21.87982 |
| 01 | 002 | 01002 | Aguascalientes | Aguascalientes | AGS | Ags. | Asientos | 2020 | 51536 | 25261 | 26275 | 225 | 22 | NA | \-102.0893 | 22.23832 |
| 01 | 003 | 01003 | Aguascalientes | Aguascalientes | AGS | Ags. | Calvillo | 2020 | 58250 | 28563 | 29687 | 266 | 76 | NA | \-102.7188 | 21.84691 |
| 01 | 004 | 01004 | Aguascalientes | Aguascalientes | AGS | Ags. | Cosío | 2020 | 17000 | 8292 | 8708 | 2155 | 7 | NA | \-102.3000 | 22.36641 |
| 01 | 005 | 01005 | Aguascalientes | Aguascalientes | AGS | Ags. | Jesús María | 2020 | 129929 | 64219 | 65710 | 2543 | 158 | Aguascalientes | \-102.3434 | 21.96127 |
| 01 | 006 | 01006 | Aguascalientes | Aguascalientes | AGS | Ags. | Pabellón de Arteaga | 2020 | 47646 | 23377 | 24269 | 482 | 52 | NA | \-102.2765 | 22.14920 |

## Municipios

Here’s another example showing Mexican municipios (similar to counties):

``` r
data("df_mxmunicipio")
df_mxmunicipio$value <- df_mxmunicipio$indigenous / df_mxmunicipio$pop
mxmunicipio_choropleth(df_mxmunicipio, num_colors = 1,
title = "Percentage of the population that self-identifies as indigenous")
data("df_mxmunicipio_2020")
df_mxmunicipio_2020$value <- df_mxmunicipio_2020$indigenous_language / df_mxmunicipio_2020$pop
mxmunicipio_choropleth(df_mxmunicipio_2020, num_colors = 1,
title = "Percentage of the population that identifies as indigenous")
```

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