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Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru
musajajorge/mapsPERU
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mapsPERU is a package that provides datasets with information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.
InstallmapsPERU fromCRAN:
install.packages("mapsPERU")or installmapsPERU with
library(remotes)install_github("musajajorge/mapsPERU")
The datasets included in this package are:
- map_REG: Geographic information of the regions of Peru
- map_DEP: Geographic information of the departments of Peru
- map_PROV: Geographic information of the provinces of Peru
- map_DIST: Geographic information of the districts of Peru
- map_REG
| Column | Type | Description |
|---|---|---|
| COD_REGION | chr | Region code |
| REGION | chr | Region name |
| POBLACION_2025 | dbl | Projected population of the region in 2025 |
| coords_x | dbl | Longitude of the centroid of the region |
| coords_y | dbl | Latitude of the centroid of the region |
| geometry | MULTIPOLYGON | MULTIPOLYGON Geometric object |
Note: Officially there is no codification for regions, only for departments. Therefore, the codes 150100 for Metropolitan Lima and 159900 for Lima Provinces should be taken as a reference.
- map_DEP
| Column | Type | Description |
|---|---|---|
| COD_DEPARTAMENTO | chr | Department code |
| DEPARTAMENTO | chr | Department name |
| POBLACION_2025 | dbl | Projected population of the department in 2025 |
| coords_x | dbl | Longitude of the centroid of the department |
| coords_y | dbl | Latitude of the centroid of the department |
| geometry | MULTIPOLYGON | MULTIPOLYGON Geometric object |
- map_PROV
| Column | Type | Description |
|---|---|---|
| COD_REGION | chr | Region code |
| COD_DEPARTAMENTO | chr | Department code |
| COD_PROVINCIA | chr | Province code |
| REGION | chr | Region name |
| DEPARTAMENTO | chr | Department name |
| PROVINCIA | chr | Province name |
| POBLACION_2025 | dbl | Projected population of the province in 2025 |
| coords_x | dbl | Longitude of the centroid of the province |
| coords_y | dbl | Latitude of the centroid of the province |
| geometry | MULTIPOLYGON | MULTIPOLYGON Geometric object |
- map_DIST
| Column | Type | Description |
|---|---|---|
| COD_REGION | chr | Region code |
| COD_DEPARTAMENTO | chr | Department code |
| COD_PROVINCIA | chr | Province code |
| COD_DISTRITO | chr | District code |
| REGION | chr | Region name |
| DEPARTAMENTO | chr | Department name |
| PROVINCIA | chr | Province name |
| DISTRITO | chr | District name |
| REGION_NATURAL | chr | Natural region |
| POBLACION_2025 | dbl | Projected population of the district in 2025 |
| coords_x | dbl | Longitude of the centroid of the district |
| coords_y | dbl | Latitude of the centroid of the district |
| geometry | MULTIPOLYGON | MULTIPOLYGON Geometric object |
You do not need to install additional packages to usemapsPERU datasets; however, if you want to see the structure of each dataset withstr() ordplyr::glimpse() it is advisable to runlibrary(sf) beforehand.
library(mapsPERU)df<-map_DEPlibrary(ggplot2)library(sf)ggplot(df, aes(geometry=geometry))+ geom_sf(aes(fill=DEPARTAMENTO))
In this example we are using the name of the departments as a categorical variable in the graph. You can combine themapsPERU data sets with other categorical or numeric variables that you want to plot.
Note thatmapsPERU also provides geographic information of the centroids, so you can include the names of the departments as labels.
library(mapsPERU)df<-map_DEPlibrary(ggplot2)library(sf)ggplot(df, aes(geometry=geometry))+ geom_sf(aes(fill=DEPARTAMENTO))+ geom_text(data=df, aes(coords_x,coords_y,group=NULL,label=DEPARTAMENTO),size=2.5)+ labs(x="",y="")
The centroids dataset not only provides the longitudes and latitudes of each region but also includes the geometry field, which is a multipolygon that will allow us to plot numerical variables on our map.
In this example, we will graph the projected population by region for the year 2025.
library(mapsPERU)df<-map_REGlibrary(ggplot2)library(sf)ggplot(df, aes(geometry=geometry))+ geom_sf(aes(fill=POBLACION_2025/1000000))+ scale_fill_gradient (low="#abd9e9",high="#c51b7d",name="Población 2025 (millones)")
In this example we will show how the use of regional boundaries and centroids datasets facilitates the filtering of specific regions to be displayed on the map.
library(mapsPERU)df<-map_REGdf<-dplyr::filter(df,REGION=="Lima Metropolitana"|REGION=="Lima Provincias"|REGION=="Callao")library(ggplot2)library(sf)ggplot(df, aes(geometry=geometry))+ geom_sf(aes(fill=REGION))+ geom_text(aes(coords_x,coords_y,group=NULL,label=REGION),size=3)+ labs(x="",y="")
library(mapsPERU)df<-map_PROVdf$Categoría<- cut(df$POBLACION_2025,right=F,breaks=c(0,100000,500000,1000000,Inf),labels=c("[ 0 - 100 mil >","[ 100 mil - 500 mil >","[ 500 mil - 1 millón >","[ 1 millón - ∞ >"))colores<- c('#feebe2','#fbb4b9','#f768a1','#ae017e')library(sf)library(ggplot2)ggplot(df, aes(geometry=geometry))+ scale_fill_manual(values=colores)+ geom_sf(aes(fill=Categoría))
library(mapsPERU)df<-map_DISTdf$Categoría<- cut(df$POBLACION_2025,right=F,breaks=c(0,1000,5000,10000,20000,Inf),labels=c("[ 0 - 1000 >","[ 1000 - 5000 >","[ 5000 - 10000 >","[ 10000 - 20000 >","[ 20000 - ∞ >"))colores<- c('#edf8fb','#b3cde3','#8c96c6','#8856a7','#810f7c')library(sf)library(ggplot2)ggplot(df, aes(geometry=geometry))+ scale_fill_manual(values=colores)+ geom_sf(aes(fill=Categoría))
library(mapsPERU)df<-map_DISTdf$REGION_NATURAL<- ordered(df$REGION_NATURAL,levels=c("Costa","Sierra","Selva"))colores<- c('#F1C40F','#D35400','#229954')library(sf)library(ggplot2)ggplot(df, aes(geometry=geometry))+ scale_fill_manual(values=colores)+ geom_sf(aes(fill=REGION_NATURAL))+ labs(fill='Región natural')
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Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru
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