sp librarymaps did), but the methods and classes for plot data provided by other sources.sp package documentation.First, the sp library should be downloaded and loaded.
if (!"sp" %in% installed.packages()) install.packages("sp")
library("sp")
This time, we will use the spatial data provided by GADM, a free spatial database of the location of the world's administrative areas (or administrative boundaries). The site provides map information as native R objects that can be plotted directly with the use of the sp library.
Browse the data by country here and select the R(SpatialPolygonsDataFrame) format.
load(url("http://gadm.org/data/rda/ESP_adm1.RData")) # Creates and stores in memory an object called ´gadm´ which includes the boundary data of spanish administrative regions.
adm0 means the country w/o interior boundaries
adm1 means 'Comunidades Autónomas'
adm2 means 'Provincias'
adm3 means 'Comarcas'
adm4 means 'Municipios'
esp1 <- gadm # we can swap the `gadm` name with the more descriptive `esp1`.
# I don't know why, but it is not possible to do this directly
esp1 <- load(url("http://gadm.org/data/rda/ESP_adm1.RData"))
str(esp1)
## chr "gadm"
# While
class(gadm)
## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"
require(RColorBrewer)
spplot(esp1, "Shape_Area") # Shape_Area is one of the `esp1` object variables, so it fills the map basing on this variable.
We could use another variable. E.g. to plot the map based on the NAME_1 variable (the administrative regions' names):
esp1$NAME_1 <- as.factor(iconv(as.character(esp1$NAME_1), "latin1", "UTF-8")) # Convert the encoding to UTF-8 in order to avoid the problems with 'tildes' and 'eñes'.
spplot(esp1, "NAME_1", col.regions = colorRampPalette(brewer.pal(12, "Set3"))(18),
col = "white") # Plot the 'NAME_1' form the 'esp1' object.
# Set the 'col.regions' argument to change the regions colors. And
# the'col' argument to choose the lines' colors.
It is also possible to attach a new variable in order to plot it.
E.g. Spanish population density by region.
Download the file densityCA.csv that is already cleaned and transformed to match with the esp1 format
fileURL <- "https://dl.dropboxusercontent.com/s/bc5w00p9iw4na4i/densityCA.csv?token_hash=AAGYE7pXXUWHDNjcJ70imi764P8SYfYNEtLDgsJHU7-fYg&dl=1"
download.file(fileURL, destfile = "./densityCA.csv", method = "curl")
densityCA <- read.csv("densityCA.csv", header = TRUE)
densityCA
## Region Density
## 1 andalucía 96.46
## 2 aragón 28.28
## 3 cantabria 111.49
## 4 castilla y león 27.02
## 5 castilla-la mancha 26.70
## 6 cataluña 235.92
## 7 ceuta y melilla 5168.86
## 8 comunidad de madrid 809.52
## 9 comunidad foral de navarra 62.04
## 10 comunidad valenciana 220.57
## 11 extremadura 26.62
## 12 galicia 94.05
## 13 islas baleares 224.26
## 14 islas canarias 284.46
## 15 la rioja 64.14
## 16 país vasco 303.32
## 17 principado de asturias 101.61
## 18 región de murcia 130.32
Attach the Density variable to esp1
esp1$Region <- tolower(esp1$NAME_1) # Create a temporary variable with the 'NAME_1' variable in lower letters in order to merge both data frames.
esp1@data <- merge(esp1@data, densityCA)
esp1$Region <- NULL # Remove the temporary variable
Plot the map 'esp1' based on the 'Density' variable
spplot(esp1, "Density", col.regions = colorRampPalette(brewer.pal(9, "YlGnBu"))(18),
col = "#081D58", main = "Spanish population density by CC.AA. (2012)")
As the 'Ceuta y Melila' density is very high, it´s hard to see the diferences between regions.
We can set the density for this regions to 0 in order to better appreciate the diferences.
esp1$Density[which(esp1$NAME_1 == "Ceuta y Melilla")] <- 0
spplot(esp1, "Density", col.regions = colorRampPalette(brewer.pal(9, "YlGnBu"))(18),
col = "#081D58", main = "Spanish population density by CC.AA. (2012)")
