mapsPERU

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 version 0.1.3 from CRAN:

install.packages("mapsPERU")

Note: Version 0.1.2 does not include geographic information at the district level.

or install mapsPERU version 1.0.0 with

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

Documentation

The datasets included in this package are:

• 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: Geographic information of the regions of Peru

Data dictionary (version 1.0.0)

• 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_DEPARTAMENTO	chr	Province Code
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_DISTRITO	chr	District Code
DEPARTAMENTO	chr	Department name
PROVINCIA	chr	Province name
DISTRITO	chr	District name
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

• 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.

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)
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(ggrepel)
ggplot(df, aes(geometry=geometry)) +
  geom_sf(aes(fill=DEPARTAMENTO)) +
  geom_text_repel(mapping = aes(coords_x, coords_y, label=DEPARTAMENTO), 
                  size=3, min.segment.length=0)+
  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(ggrepel)
ggplot(df, aes(geometry=geometry)) +
  geom_sf(aes(fill=REGION)) +
  geom_text_repel(mapping = aes(coords_x, coords_y, label=REGION), 
                  size=3, min.segment.length=0)+
  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$Pob_Group <- factor(df$Pob_Group, levels=c("Menos de 1,000","Menos de 5,000",
                                              "Menos de 10,000","Menos de 20,000",
                                              "Más de 20,000"))

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

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

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