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mapsPERU

errero Open Source Love Project-Status:Active LinuxMint CRAN-status Downloads DOI

mapsPERU is a package that provides datasets with information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.

Installation ⏬

Install mapsPERU from CRAN:

install.packages("mapsPERU")

or install mapsPERU with

library(remotes)
install_github("musajajorge/mapsPERU")

Documentation 📚

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

Data dictionary

  • map_REG
Column Type Description
COD_REGION chr Region code
REGION chr Region name
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
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
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
NOMBRE_CAPITAL_LEGAL chr Capital name
REGION_NATURAL chr Natural region
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

Usage 💪

You do not need to install additional packages to use mapsPERU datasets; however, if you want to see the structure of each dataset with str() or dplyr::glimpse() it is advisable to run library(sf) beforehand.

Use departmental dataset in a map with ggplot2

library(mapsPERU)
df <- map_DEP

library(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 the mapsPERU data sets with other categorical or numeric variables that you want to plot.

Use the departmental dataset with centroids in a map with ggplot2

Note that mapsPERU also provides geographic information of the centroids, so you can include the names of the departments as labels.

library(mapsPERU)
df <- map_DEP

library(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="")

Use regional dataset in a map with ggplot2

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 are going to plot the average cost per student (ACPS) in university higher education in the year 2020.

library(mapsPERU)
df <- map_REG

REGION <- c('Amazonas','Áncash','Apurímac','Arequipa','Ayacucho','Cajamarca','Callao',
            'Cusco','Huancavelica','Huánuco','Ica','Junín','La Libertad','Lambayeque',
            'Lima Metropolitana','Lima Provincias','Loreto','Madre de Dios','Moquegua',
            'Pasco','Piura','Puno','San Martín','Tacna','Tumbes','Ucayali')
ACPS <- c(12364,7001,8615,10302,5015,8632,7507,7909,6843,8412,6950,7182,8363,5941,10595, 
          6742,8250,6888,31287,7630,12647,7282,10512,8017,11454,6998)
ACPS <- data.frame(cbind(REGION,ACPS)) 
ACPS$ACPS <- as.numeric(ACPS$ACPS)

library(dplyr)
library(sf)
df <- left_join(df, ACPS, by="REGION")

library(ggplot2)
ggplot(df, aes(geometry=geometry)) +
  geom_sf(aes(fill=ACPS)) +
  scale_fill_gradient (low="mediumblue", high="red3")

Use the regional dataset with centroids in a map with ggplot2

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_REG

df <- 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(data=df, aes(coords_x, coords_y, group=NULL, label=REGION), size=3) +
  labs(x="", y="")

Use the provincial dataset in a map with ggplot2

library(mapsPERU)
df <- map_PROV

library(readxl)
url <- "https://zenodo.org/record/5646444/files/POBLACION_INEI_2021.xlsx?download=1"
destfile <- "POBLACION_INEI_2021.xlsx"
curl::curl_download(url, destfile)
pob <- read_excel(destfile)

library(dplyr)
pob_prov <- pob %>%
  group_by(COD_PROVINCIA) %>%
  summarise(Población = sum(Cantidad))

library(dplyr)
library(sf)
df <- left_join(df, pob_prov, by="COD_PROVINCIA")

df$Categoría <- cut(df$Población, right=F, breaks=c(0,100000,500000,1000000,Inf),
                    labels=c("Menos a 100 mil personas",
                             "Menos de 500 mil personas",
                             "Más de 500 mil personas",
                             "Más de 1 millón de personas"))

colores <- c('#feebe2','#fbb4b9','#f768a1','#ae017e')

library(ggplot2)
ggplot(df, aes(geometry=geometry)) +
  scale_fill_manual(values=colores)+
  geom_sf(aes(fill=Categoría))

Use the district dataset in a map with ggplot2

library(mapsPERU)
df <- map_DIST

library(readxl)
url <- "https://zenodo.org/record/5646444/files/POBLACION_INEI_2021.xlsx?download=1"
destfile <- "POBLACION_INEI_2021.xlsx"
curl::curl_download(url, destfile)
pob <- read_excel(destfile)

library(dplyr)
pob_dist <- pob %>%
  group_by(COD_DISTRITO) %>%
  summarise(Cantidad = sum(Cantidad))

library(dplyr)
library(sf)
df <- left_join(df, pob_dist, by="COD_DISTRITO")

df$Pob_Group <- ifelse(df$Cantidad<1000, "Menos de 1,000",
                       ifelse(df$Cantidad<5000, "Menos de 5,000",
                              ifelse(df$Cantidad<10000, "Menos de 10,000",
                                     ifelse(df$Cantidad<20000, "Menos de 20,000",
                                            "Más de 20,000"))))

df$Pob_Group <- ifelse(is.na(df$Pob_Group)==T, "No disponible", df$Pob_Group)

df$Pob_Group <- factor(df$Pob_Group, levels=c("Menos de 1,000","Menos de 5,000",
                                              "Menos de 10,000","Menos de 20,000",
                                              "Más de 20,000","No disponible"))

colores <- c('#edf8fb','#b3cde3','#8c96c6','#8856a7','#810f7c','#1C2833')

library(ggplot2)
ggplot(df, aes(geometry=geometry)) +
  scale_fill_manual(values=colores)+
  geom_sf(aes(fill=Pob_Group))

Use the natural region information on a district level map.

library(mapsPERU)
df <- map_DIST

df$REGION_NATURAL <- ordered(df$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')