Given the differnt boundary structure of each country, the package provide specific mapping functions and building object functions to link e manipulate data with spatial coordinates.
Datasets
| World population |
WR() |
| European population nuts 2 |
EU() |
| Italy |
IT() |
| USA 208-2016 election results |
US() |
| Tax wedge OCDE countries 2018 |
|
data("popWR")
str(popWR)
## 'data.frame': 269 obs. of 5 variables:
## $ country : Factor w/ 265 levels "","Afghanistan",..: 2 3 4 5 6 7 8 10 11 12 ...
## $ country_code: Factor w/ 265 levels "","ABW","AFG",..: 3 5 60 11 6 4 12 9 10 2 ...
## $ total : num 37172386 2866376 42228429 55465 77006 ...
## $ male : num 19093281 1460043 21332000 NA NA ...
## $ female : num 18079105 1406333 20896429 NA NA ...
data("popEU")
str(popEU)
## 'data.frame': 2252 obs. of 5 variables:
## $ TIME : num 2019 2019 2019 2019 2019 ...
## $ GEO : chr "BE" "BE1" "BE10" "BE100" ...
## $ total : num 11455519 1215290 1215290 1215290 6596233 ...
## $ male : num 5644826 597008 597008 597008 3265134 ...
## $ female: num 5810693 618282 618282 618282 3331099 ...
data("popIT")
str(popIT)
## 'data.frame': 107 obs. of 4 variables:
## $ ID : chr "Roma" "Milano" "Napoli" "Torino" ...
## $ maschi : num 2081239 1576316 1497289 1092504 624201 ...
## $ femmine: num 2260973 1673999 1587601 1167019 641753 ...
## $ totale : num 4342212 3250315 3084890 2259523 1265954 ...
data("popUS")
str(popUS)
## 'data.frame': 52 obs. of 2 variables:
## $ id : chr "Maine" "North Carolina" "Georgia" "Alaska" ...
## $ population: int 1338404 10383620 10519475 737438 4887871 626299 3034392 1805832 3943079 5813568 ...
Load coordinates and check names
Coordinates can be separately downloaded using this specific functions
| World |
loadCoordWR() |
| European Union |
loadCoordEU() |
| Italy |
loadCoordIT() |
| United States of America |
loadCoordUS() |
Coordinates are download from the GitHub repository , which provides .geojson and .RData files with coordinates, which return an object of class sf.
coord_eu <- loadCoordEU(unit = "nuts0")
The unit argument in the load functions, indicates the type of statistical unit, geographical subdivision or level of aggregation.
library(tmap)
tm_shape(coord_eu) + tm_borders()
Returning an object of class sf, we can also use the mapping function available in the other R packages.
Note that, the data are downloaded from an online repository, and then an internet connection should be preferred. Nevertheless, if the use_internet argument set to FALSE, we will get the coordinates locally available in the package.
checkNames functions will return the nomatching names:
checkNamesIT(popIT$ID, unit = "provincia")
GetNames functions returns the names used in the packages for each unit.
head(getNamesEU(unit = "nuts0"))
## country iso2 iso3 country_code nuts0_id nuts0
## 1 Germany DE DEU 276 DE Germany
## 2 Czechia CZ CZE 203 CZ Czechia
## 3 Bulgaria BG BGR 100 BG Bulgaria
## 4 Switzerland CH CHE 756 CH Switzerland
## 5 Albania AL ALB 8 AL Albania
## 6 Austria AT AUT 40 AT Austria
head(getNamesEU(unit = "nuts0", all_levels = FALSE))
## [1] "Germany" "Czechia" "Bulgaria" "Switzerland" "Albania"
## [6] "Austria"
Building a mapping object
The popIT, as showed in the previous section, does not contain any information about the geographical geometries:
str(popIT)
## 'data.frame': 107 obs. of 4 variables:
## $ ID : chr "Roma" "Milano" "Napoli" "Torino" ...
## $ maschi : num 2081239 1576316 1497289 1092504 624201 ...
## $ femmine: num 2260973 1673999 1587601 1167019 641753 ...
## $ totale : num 4342212 3250315 3084890 2259523 1265954 ...
then, the coordinates are added as follows:
it <- IT(data = popIT, unit = "provincia", year = "2018",colID = "ID")
We have to specify the type of statistical unit, the column containing the ids and, if necessary, the year of the subdivision, and the functions will automatically download the coordinates and link them to the data.
str(it,1)
## Classes 'sf', 'IT', 'IT' and 'data.frame': 107 obs. of 11 variables:
## $ ripartizione : chr "Nord-ovest" "Nord-ovest" "Nord-ovest" "Nord-ovest" ...
## $ regione : chr "Piemonte" "Piemonte" "Piemonte" "Piemonte" ...
## $ code_ripartizione: int 1 1 1 1 1 1 1 1 1 1 ...
## $ code_regione : int 1 1 1 1 1 1 2 7 7 7 ...
## $ code_provincia : int 1 2 3 4 5 6 7 8 9 10 ...
## $ ID : chr "torino" "vercelli" "novara" "cuneo" ...
## $ code : chr "TO" "VC" "NO" "CN" ...
## $ maschi : num 1092504 82848 179588 289459 105011 ...
## $ femmine : num 1167019 88063 189430 297639 109627 ...
## $ totale : num 2259523 170911 369018 587098 214638 ...
## $ geometry :sfc_GEOMETRY of length 107; first list element: List of 2
## ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA
## ..- attr(*, "names")= chr [1:10] "ripartizione" "regione" "code_ripartizione" "code_regione" ...
## - attr(*, "unit")= chr "provincia"
## - attr(*, "year")= chr "2018"
## - attr(*, "colID")= chr "ID"
In linking the data and the coordinates, the functions available in this packages will return also the information about larger units
library(tmap)
tm_shape(it) + tm_borders() + tm_fill("totale")
Data can be subsets before mapping
it <- IT(data = popIT, unit = "provincia",
year = "2018",colID = "ID",
subset = ~ I(regione == "Lazio"))
tm_shape(it) + tm_borders()
and add variables
it <- IT(data = popIT, unit = "provincia",
year = "2018",colID = "ID",
add = ~I(maschi/totale) + I(femmine/totale),
new_var_names = c("Male percentage", "Female percentage"),
print = FALSE)
str(it,1)
## Classes 'sf', 'IT', 'IT' and 'data.frame': 107 obs. of 13 variables:
## $ ripartizione : chr "Nord-ovest" "Nord-ovest" "Nord-ovest" "Nord-ovest" ...
## $ regione : chr "Piemonte" "Piemonte" "Piemonte" "Piemonte" ...
## $ code_ripartizione: int 1 1 1 1 1 1 1 1 1 1 ...
## $ code_regione : int 1 1 1 1 1 1 2 7 7 7 ...
## $ code_provincia : int 1 2 3 4 5 6 7 8 9 10 ...
## $ ID : chr "torino" "vercelli" "novara" "cuneo" ...
## $ code : chr "TO" "VC" "NO" "CN" ...
## $ maschi : num 1092504 82848 179588 289459 105011 ...
## $ femmine : num 1167019 88063 189430 297639 109627 ...
## $ totale : num 2259523 170911 369018 587098 214638 ...
## $ Male.percentage : 'AsIs' num 0.483510.... 0.484743.... 0.486664.... 0.493033.... 0.489247.... ...
## $ Female.percentage: 'AsIs' num 0.516489.... 0.515256.... 0.513335.... 0.506966.... 0.510752.... ...
## $ geometry :sfc_GEOMETRY of length 107; first list element: List of 2
## ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "names")= chr [1:12] "ripartizione" "regione" "code_ripartizione" "code_regione" ...
## - attr(*, "unit")= chr "provincia"
## - attr(*, "year")= chr "2018"
## - attr(*, "colID")= chr "ID"
Note that, the matchWith argument indicates the type of names we have to link, for example code number, iso code, and so on
str(popEU)
## 'data.frame': 2252 obs. of 5 variables:
## $ TIME : num 2019 2019 2019 2019 2019 ...
## $ GEO : chr "BE" "BE1" "BE10" "BE100" ...
## $ total : num 11455519 1215290 1215290 1215290 6596233 ...
## $ male : num 5644826 597008 597008 597008 3265134 ...
## $ female: num 5810693 618282 618282 618282 3331099 ...
eu <- EU(data = popEU, unit = "nuts0", colID = "GEO",
matchWith = "id", check.unit.names = FALSE)
Static maps
coord_eu <- loadCoordEU(unit = "nuts0")
mappingEU(data = coord_eu)
Now we look at the distribution of population among European countries
eu <- EU(data = popEU, unit = "nuts0", colID = "GEO",
matchWith = "iso2", check.unit.names = FALSE)
mappingEU(eu, var = "total")
matching with iso2 code.
It is equivalent to use mapping function without building as EU object
mappingEU(data = popEU,unit = "nuts0", colID = "GEO", matchWith = "iso2", var = "total")
The loadCoord functions, as explained in the previous section, automatically return all the bigger statistical unit aggregation starting from the input unit.
eu_aggr <- EU(data = popEU, unit = "nuts1", colID = "GEO",
matchWith = "id", check.unit.names = FALSE,
aggregation_var = "total", aggregation_unit = "nuts0", aggregation_fun = sum)
mappingEU(eu_aggr, var = "total")
or the same as follows
eu <- EU(data = popEU, unit = "nuts1", colID = "GEO",
matchWith = "id", check.unit.names = FALSE)
mappingEU(eu, var = "total", aggregation_unit = "nuts0", aggregation_fun = sum)
We can also provide multiple variables to generate multiple maps
mappingEU(eu, var = c("male","female"))
or a subset
mappingEU(eu, var = "total",
subset = ~I(country == "Spain" | country == "Italy"))
Let’s look at USA example.
data("popUS")
us <- US(data = popUS, unit = "state", matchWith = "name")
mappingUS(us)
mappingUS(us, var = "population")
mappingUS(us, var = "population", add_text = "state_id",
options = mapping.options(nclass = 10, legend.portrait = FALSE))
mappingUS(us, var = "population", options = mapping.options(nclass = 10, legend.portrait = FALSE))
The facets argument returns the small multiples.
mappingUS(us, var = "population", aggregation_unit = "division", facets = "division")
or facets for some geographical unit
mappingUS(us, var = "population", subset = ~I(region == "Northeast"), facets = "id")
In this case it maps all the divisions.
Interactive maps
The interactive map functions work as the static functions and they share the same argument.
eu <- EU(data = popEU, unit = "nuts0", colID = "GEO",
matchWith = "id", check.unit.names = FALSE)
mappingEU(eu, var = "total", type = "interactive")
mappingEU(eu, var = "total", type = "interactive",
subset = ~I(country == "Spain" | country == "Italy"))
or aggregating for countries (“nuts0”)
mappingEU(eu, var = "total", type = "interactive",
aggregation_unit = "nuts0")
Multiple variable will provide a single interactive map with different layers:
mappingEU(eu, var = c("male","female"), type = "interactive")
and also the facets is implemented for interactive maps.
A generic mapping() function
The package also provide a generic function to map data, mapping. This accept object of class sf, WR, EU, IT, and US.
library(dplyr)
data("popIT")
popIT <- popIT
coords <- loadCoordIT(unit = "provincia", year = '2019')
cr <- left_join(coords, popIT, by = c( "provincia" = "ID"))
mapping(cr)
mapping(cr, var = "maschi")
library(sf)
nc = st_read(system.file("shape/nc.shp", package="sf"))
## Reading layer `nc' from data source `/Library/Frameworks/R.framework/Versions/4.0/Resources/library/sf/shape/nc.shp' using driver `ESRI Shapefile'
## Simple feature collection with 100 features and 14 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## geographic CRS: NAD27
class(nc)
## [1] "sf" "data.frame"
mapping(nc)
Layout options
Aesthetic options are controlled by mapping.options() function. General options can be retrieved
single or multiple options may be retrieved
mapping.options("palette.cont")
## [1] "YlGnBu"
mapping.options("legend.position")
## [1] "right" "top"
and we can globally change until a new R session, as follows
mapping.options(legend.position = c("left","bottom"))
mapping.options("legend.position")
## [1] "right" "top"
Options can be changed locally in mapping functions:
map <- mappingEU(eu, var = "total")
map_options <- mappingEU(eu, var = "total",
options = mapping.options(list(legend.position = c("left","bottom"),
title = "EU total population",
map.frame = FALSE,
col.style = "pretty")))
library(tmap)
tmap_arrange(map, map_options)
or globally outside the functions. Original options can be reseted using mapping.options.reset().
Some other plot example
mappingIT(data = it, var = "totale")
mappingIT(data = it, var = c("maschi", "femmine"))
mappingIT(data = it, var = c("maschi", "femmine"), options = mapping.options(palette.cont = c("Greens", "Blues"), legend.position = c("left","bottom")))
mappingIT(data = it, var = "ripartizione")
mappingIT(data = it, var = "totale", add_text = "code")
mappingIT(data = it, var = "totale", type = "interactive")
mappingIT(data = it, var = "totale", type = "interactive",
options = mapping.options(legend.position = c("left","bottom")))
mappingIT(data = it, var = c("maschi", "femmine"), type = "interactive",
options = mapping.options(interactive.control.collapse = FALSE))
mappingIT(data = it, var = c("maschi", "femmine"), type = "interactive",
options = mapping.options(palette.cont = c("BuGn", "BuPu")))
mappingIT(data = it, var = "totale", type = "interactive",
options = mapping.options(col.style = "pretty"))
mappingIT(data = it, var = "totale", type = "interactive",
options = mapping.options(col.style = "equal"))
mappingIT(data = it, var = "totale", type = "interactive",
options = mapping.options(col.style = "equal", nclass = 7))
mappingIT(data = it, var = "ripartizione", type = "interactive")
mappingIT(data = it, var = "totale", type = "interactive", facets = "ripartizione")
Some other aggregation and facets examples:
data(popIT)
it <- IT(data = popIT, unit = "provincia", year = "2018",colID = "ID")
mappingIT(data = it, var = "totale", aggregation_unit = "regione")
mappingIT(data = it, var = "totale",
aggregation_unit = "ripartizione",
aggregation_fun = function(x) sum(x, na.rm = TRUE))
mappingIT(data = it, var = "totale",
aggregation_unit = "regione", facets = "ripartizione")
mappingIT(data = it, var = "totale",
aggregation_unit = "ripartizione",
aggregation_fun = function(x) sum(x, na.rm = TRUE),
type = "interactive")
mappingIT(data = it, var = "totale",
aggregation_unit = "regione", facets = "regione")
data("popEU")
popEU <- popEU
euNuts2 <- EU(data = popEU, colID = "GEO",unit = "nuts2",matchWith = "id")
str(euNuts2,1)
## Classes 'sf', 'EU', 'EU' and 'data.frame': 328 obs. of 16 variables:
## $ GEO : chr "cz05" "cz06" "cz07" "cz08" ...
## $ country : chr "Czechia" "Czechia" "Czechia" "Czechia" ...
## $ iso2 : chr "CZ" "CZ" "CZ" "CZ" ...
## $ iso3 : chr "CZE" "CZE" "CZE" "CZE" ...
## $ country_code: int 203 203 203 203 276 276 276 276 276 276 ...
## $ nuts0_id : chr "CZ" "CZ" "CZ" "CZ" ...
## $ nuts1_id : chr "CZ0" "CZ0" "CZ0" "CZ0" ...
## $ nuts2_id : chr "CZ05" "CZ06" "CZ07" "CZ08" ...
## $ nuts0 : chr "Czechia" "Czechia" "Czechia" "Czechia" ...
## $ nuts1 : chr "Česko" "Česko" "Česko" "Česko" ...
## $ nuts2 : chr "Severovýchod" "Jihovýchod" "Střední Morava" "Moravskoslezsko" ...
## $ TIME : num 2019 2019 2019 2019 2019 ...
## $ total : num 1513693 1696941 1215413 1203299 4143418 ...
## $ male : num 747330 835577 595503 590516 2067094 ...
## $ female : num 766363 861364 619910 612783 2076324 ...
## $ geometry :sfc_GEOMETRY of length 328; first list element: List of 1
## ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "names")= chr [1:15] "GEO" "country" "iso2" "iso3" ...
## - attr(*, "unit")= chr "nuts2"
## - attr(*, "year")= chr "2021"
## - attr(*, "colID")= chr "GEO"
mappingEU(data = euNuts2, var = "total",
aggregation_unit = "nuts0", aggregation_fun = mean)
mappingEU(data = euNuts2, var = "total",
aggregation_unit = "nuts0", aggregation_fun = sum)
mappingEU(data = euNuts2, var = "total", subset = ~I(iso3 == "FRA"),
aggregation_unit = "nuts1", aggregation_fun = sum, facets = "nuts1")
data("popWR")
popWR <- popWR
wr <- WR(data = popWR, colID = "country_code",
matchWith = "iso3_eh", check.unit.names = FALSE,
res = "low")
mappingWR(data = wr, var = "total",
aggregation_unit = "continent",
aggregation_fun = function(x) sum(x, na.rm = TRUE))
mappingWR(data = wr, var = "total",
aggregation_unit = "continent",
aggregation_fun = function(x) sum(x, na.rm = TRUE),
type = "interactive")