Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
R
Abstract
You can find the data used in this blog post here: https://github.com/b-rodrigues/elections_lux
This is a follow up to a previous blog post
where I extracted data of the 2018 Luxembourguish elections from Excel Workbooks.
Now that I have the data, I will create a map of Luxembourg by commune, with pie charts of the
results on top of each commune! To do this, I use good ol’ {ggplot2} and another packages
called {scatterpie}. As a bonus, I have added the code to extract the data from the 2013
elections from Excel. You’ll find this code in the appendix at the end of the blog post.
Introduction
Before importing the data for the elections of 2018, let’s install some packages:
install.packages('rgeos', type='source') # Dependency of rgdal
install.packages('rgdal', type='source') # To read in the shapefileThese packages might be very tricky to install on OSX and Linux, but they’re needed to import the
shapefile of the country, which is needed to draw a map. So to make things easier, I have
created an rds object, from the shapefile of Luxembourg, that you can import natively in R without
needing these two packages. But if you want to use them, here is how:
communes <- readOGR("Limadmin_SHP/LIMADM_COMMUNES.shp")By the way, you can download the shapefile for Luxembourg here.
I’ll use my shapefile though (that you can download from the same github repo as the data):
communes_df <- readRDS("commune_shapefile.rds")Here’s how it looks like:
head(communes_df)## long lat order hole piece group id
## 1 91057.65 101536.6 1 FALSE 1 Beaufort.1 Beaufort
## 2 91051.79 101487.3 2 FALSE 1 Beaufort.1 Beaufort
## 3 91043.43 101461.7 3 FALSE 1 Beaufort.1 Beaufort
## 4 91043.37 101449.8 4 FALSE 1 Beaufort.1 Beaufort
## 5 91040.42 101432.1 5 FALSE 1 Beaufort.1 Beaufort
## 6 91035.44 101405.6 6 FALSE 1 Beaufort.1 BeaufortNow let’s load some packages:
library("tidyverse")
library("tidyxl")
library("ggplot2")
library("scatterpie")Ok, now, let’s import the elections results data, which is the output of last week’s blog post:
elections <- read_csv("elections_2018.csv")## Parsed with column specification:
## cols(
## Party = col_character(),
## Year = col_double(),
## Variables = col_character(),
## Values = col_double(),
## locality = col_character(),
## division = col_character()
## )I will only focus on the data at the commune level, and only use the share of votes for each party:
elections_map <- elections %>%
filter(division == "Commune",
Variables == "Pourcentage")Now I need to make sure that the names of the communes are the same between the elections data and the shapefile. Usual suspects are the “Haute-Sûre” and the “Redange-sur-Attert” communes, but let’s take a look:
locality_elections <- unique(elections_map$locality)
locality_shapefile <- unique(communes_df$id)
setdiff(locality_elections, locality_shapefile)## [1] "Lac de la Haute-Sûre" "Redange Attert"Yep, exactly as expected. I’ve had problems with the names of these two communes in the past already. Let’s rename these two communes in the elections data:
elections_map <- elections_map %>%
mutate(commune = case_when(locality == "Lac de la Haute-Sûre" ~ "Lac de la Haute Sûre",
locality == "Redange Attert" ~ "Redange",
TRUE ~ locality))Now, I can select the relevant columns from the shapefile:
communes_df <- communes_df %>%
select(long, lat, commune = id)and from the elections data:
elections_map <- elections_map %>%
select(commune, Party, Variables, Values)Plotting the data on a map
Now, for the type of plot I want to make, using the {scatterpie} package, I need the data to be
in the wide format, not long. For this I will use tidyr::spread():
elections_map <- elections_map %>%
spread(Party, Values)This is how the data looks now:
glimpse(elections_map)## Observations: 102
## Variables: 10
## $ commune <chr> "Beaufort", "Bech", "Beckerich", "Berdorf", "Bertran…
## $ Variables <chr> "Pourcentage", "Pourcentage", "Pourcentage", "Pource…
## $ ADR <dbl> 0.12835106, 0.09848661, 0.08596748, 0.16339234, 0.04…
## $ CSV <dbl> 0.2426239, 0.2945285, 0.3004751, 0.2604552, 0.290278…
## $ `déi gréng` <dbl> 0.15695672, 0.21699651, 0.24072721, 0.15619529, 0.15…
## $ `déi Lénk` <dbl> 0.04043732, 0.03934808, 0.05435776, 0.02295273, 0.04…
## $ DP <dbl> 0.15875393, 0.19394645, 0.12899689, 0.15444466, 0.30…
## $ KPL <dbl> 0.015875393, 0.006519208, 0.004385164, 0.011476366, …
## $ LSAP <dbl> 0.11771754, 0.11455180, 0.08852549, 0.16592103, 0.09…
## $ PIRATEN <dbl> 0.13928411, 0.03562282, 0.09656496, 0.06516242, 0.04…For this to work, I need two datasets; one to draw the map (commune_df) and one to draw the
pie charts over each commune, with the data to draw the charts, but also the position of where I
want the pie charts. For this, I will compute the average of the longitude and latitude, which
should be good enough:
scatterpie_data <- communes_df %>%
group_by(commune) %>%
summarise(long = mean(long),
lat = mean(lat))Now, let’s join the two datasets:
final_data <- left_join(scatterpie_data, elections_map, by = "commune") I have all the ingredients to finally plot the data:
ggplot() +
geom_polygon(data = communes_df, aes(x = long, y = lat, group = commune), colour = "grey", fill = NA) +
geom_scatterpie(data = final_data, aes(x=long, y=lat, group=commune),
cols = c("ADR", "CSV", "déi gréng", "déi Lénk", "DP", "KPL", "LSAP", "PIRATEN")) +
labs(title = "Share of total vote in each commune, 2018 elections") +
theme_void() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(colour = "white"),
plot.background = element_rect("#272b30"),
plot.title = element_text(colour = "white")) +
scale_fill_manual(values = c("ADR" = "#009dd1",
"CSV" = "#ee7d00",
"déi gréng" = "#45902c",
"déi Lénk" = "#e94067",
"DP" = "#002a54",
"KPL" = "#ff0000",
"LSAP" = "#ad3648",
"PIRATEN" = "#ad5ea9"))
Not too bad, but we can’t really read anything from the pie charts. I will now make their size proportional to the number of voters in each commune. For this, I need to go back to the Excel sheets, and look for the right cell:
It will be easy to extract this info. It located in cell “E5”:
elections_raw_2018 <- xlsx_cells("leg-2018-10-14-22-58-09-737.xlsx")
electors_commune <- elections_raw_2018 %>%
filter(!(sheet %in% c("Le Grand-Duché de Luxembourg", "Centre", "Est", "Nord", "Sud", "Sommaire"))) %>%
filter(address == "E5") %>%
select(sheet, numeric) %>%
rename(commune = sheet,
electors = numeric)I can now add this to the data:
final_data <- final_data %>%
full_join(electors_commune) %>%
mutate(log_electors = log(electors) * 200)## Joining, by = "commune"In the last line, I create a new column called log_electors that I then multiply by 200. This
will be useful later.
Now I can add the r argument inside the aes() function on the third line, to make the pie chart
size proportional to the number of electors in that commune:
ggplot() +
geom_polygon(data = communes_df, aes(x = long, y = lat, group = commune), colour = "grey", fill = NA) +
geom_scatterpie(data = final_data, aes(x=long, y=lat, group = commune, r = electors),
cols = c("ADR", "CSV", "déi gréng", "déi Lénk", "DP", "KPL", "LSAP", "PIRATEN")) +
labs(title = "Share of total vote in each commune, 2018 elections") +
theme_void() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(colour = "white"),
plot.background = element_rect("#272b30"),
plot.title = element_text(colour = "white")) +
scale_fill_manual(values = c("ADR" = "#009dd1",
"CSV" = "#ee7d00",
"déi gréng" = "#45902c",
"déi Lénk" = "#182024",
"DP" = "#002a54",
"KPL" = "#ff0000",
"LSAP" = "#ad3648",
"PIRATEN" = "#ad5ea9"))## Warning: Removed 32 rows containing non-finite values (stat_pie).
Ok, that was not a good idea! Perhaps the best option would be to have one map per circonscription. For this, I need the list of communes by circonscription. This is available on Wikipedia. Here are the lists:
centre <- c("Bissen", "Colmar-Berg", "Fischbach", "Heffingen", "Larochette",
"Lintgen", "Lorentzweiler", "Mersch", "Nommern", "Helperknapp", "Bertrange", "Contern",
"Hesperange", "Luxembourg", "Niederanven", "Sandweiler", "Schuttrange", "Steinsel",
"Strassen", "Walferdange", "Weiler-la-Tour")
est <- c("Beaufort", "Bech", "Berdorf", "Consdorf", "Echternach", "Rosport-Mompach", "Waldbillig",
"Betzdorf", "Biwer", "Flaxweiler", "Grevenmacher", "Junglinster", "Manternach", "Mertert",
"Wormeldange","Bous", "Dalheim", "Lenningen", "Mondorf-les-Bains", "Remich", "Schengen",
"Stadtbredimus", "Waldbredimus")
nord <- c("Clervaux", "Parc Hosingen", "Troisvierges", "Weiswampach", "Wincrange", "Bettendorf",
"Bourscheid", "Diekirch", "Erpeldange-sur-Sûre", "Ettelbruck", "Feulen", "Mertzig", "Reisdorf",
"Schieren", "Vallée de l'Ernz", "Beckerich", "Ell", "Grosbous", "Préizerdaul",
"Rambrouch", "Redange", "Saeul", "Useldange", "Vichten", "Wahl", "Putscheid", "Tandel",
"Vianden", "Boulaide", "Esch-sur-Sûre", "Goesdorf", "Kiischpelt", "Lac de la Haute Sûre",
"Wiltz", "Winseler")
sud <- c("Dippach", "Garnich", "Käerjeng", "Kehlen", "Koerich", "Kopstal", "Mamer",
"Habscht", "Steinfort", "Bettembourg", "Differdange", "Dudelange", "Esch-sur-Alzette",
"Frisange", "Kayl", "Leudelange", "Mondercange", "Pétange", "Reckange-sur-Mess", "Roeser",
"Rumelange", "Sanem", "Schifflange")
circonscriptions <- list("centre" = centre, "est" = est,
"nord" = nord, "sud" = sud)Now, I can make one map per circonscription. First, let’s split the data sets by circonscription:
communes_df_by_circonscription <- circonscriptions %>%
map(~filter(communes_df, commune %in% .))
final_data_by_circonscription <- circonscriptions %>%
map(~filter(final_data, commune %in% .))By using pmap(), I can reuse the code to generate the plot to each element of the two lists.
This is nice because I do not need to copy and paste the code 4 times:
pmap(list(x = communes_df_by_circonscription,
y = final_data_by_circonscription,
z = names(communes_df_by_circonscription)),
function(x, y, z){
ggplot() +
geom_polygon(data = x, aes(x = long, y = lat, group = commune),
colour = "grey", fill = NA) +
geom_scatterpie(data = y, aes(x=long, y=lat, group = commune),
cols = c("ADR", "CSV", "déi gréng", "déi Lénk", "DP", "KPL", "LSAP", "PIRATEN")) +
labs(title = paste0("Share of total vote in each commune, 2018 elections for circonscription ", z)) +
theme_void() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(colour = "white"),
plot.background = element_rect("#272b30"),
plot.title = element_text(colour = "white")) +
scale_fill_manual(values = c("ADR" = "#009dd1",
"CSV" = "#ee7d00",
"déi gréng" = "#45902c",
"déi Lénk" = "#182024",
"DP" = "#002a54",
"KPL" = "#ff0000",
"LSAP" = "#ad3648",
"PIRATEN" = "#ad5ea9"))
}
)## $centre
##
## $est
##
## $nord
##
## $sud
I created an anonymous function of three argument, x, y and z. If you are unfamiliar with
pmap(), study the above code closely. If you have questions, do not hesitate to reach out!
The pie charts are still quite small, but if I try to change the size of the pie charts, I’ll have the same problem as before: inside the same circonscription, some communes have really a lot of electors, and some a very small number. Perhaps I can try with the log of the electors?
pmap(list(x = communes_df_by_circonscription,
y = final_data_by_circonscription,
z = names(communes_df_by_circonscription)),
function(x, y, z){
ggplot() +
geom_polygon(data = x, aes(x = long, y = lat, group = commune),
colour = "grey", fill = NA) +
geom_scatterpie(data = y, aes(x=long, y=lat, group = commune, r = log_electors),
cols = c("ADR", "CSV", "déi gréng", "déi Lénk", "DP", "KPL", "LSAP", "PIRATEN")) +
labs(title = paste0("Share of total vote in each commune, 2018 elections for circonscription ", z)) +
theme_void() +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.text = element_text(colour = "white"),
plot.background = element_rect("#272b30"),
plot.title = element_text(colour = "white")) +
scale_fill_manual(values = c("ADR" = "#009dd1",
"CSV" = "#ee7d00",
"déi gréng" = "#45902c",
"déi Lénk" = "#182024",
"DP" = "#002a54",
"KPL" = "#ff0000",
"LSAP" = "#ad3648",
"PIRATEN" = "#ad5ea9"))
}
)## $centre
##
## $est
##
## $nord## Warning: Removed 16 rows containing non-finite values (stat_pie).
##
## $sud
This looks better now!
Conclusion
Having data in a machine readable format is really important. The amount of code I had to write to go from the Excel Workbooks that contained the data to this plots is quite large, but if the data was in a machine readable format to start with, I could have focused on the plots immediately.
The good thing is that I got to practice my skills and discovered {scatterpie}!
Hope you enjoyed! If you found this blog post useful, you might want to follow me on twitter for blog post updates or buy me an espresso.
Appendix
The following lines of code extract the data (from the 2013 elections) from the Excel Workbooks that can be found in Luxembourguish Open Data Portal.
I will not comment them, as they work in a similar way than in the previous blog post where I extracted the data from the 2018 elections. The only difference, is that the sheet with the national level data was totally different, so I did not extract it. The first reason is because I don’t need it for this blog post, the second is because I was lazy. For me, that’s two pretty good reasons not to do something. If you have a question concerning the code below, don’t hesitate to reach out though!
library("tidyverse")
library("tidyxl")
library("brotools")
path <- Sys.glob("content/blog/2013*xlsx")[-5]
elections_raw_2013 <- map(path, xlsx_cells) %>%
map(~filter(., sheet != "Sommaire"))
elections_sheets_2013 <- map(map(path, xlsx_sheet_names), ~`%-l%`(., "Sommaire"))
list_targets <- list("Centre" = seq(9, 32),
"Est" = seq(9, 18),
"Nord" = seq(9, 20),
"Sud" = seq(9, 34))
position_parties_national <- seq(1, 24, by = 3)
extract_party <- function(dataset, starting_col, target_rows){
almost_clean <- dataset %>%
filter(row %in% target_rows) %>%
filter(col %in% c(starting_col, starting_col + 1)) %>%
select(character, numeric) %>%
fill(numeric, .direction = "up") %>%
filter(!is.na(character))
party_name <- almost_clean$character[1]
almost_clean$character[1] <- "Pourcentage"
almost_clean$party <- party_name
colnames(almost_clean) <- c("Variables", "Values", "Party")
almost_clean %>%
mutate(Year = 2013) %>%
select(Party, Year, Variables, Values)
}
# Treat one district
extract_district <- function(dataset, sheets, target_rows, position_parties_national){
list_data_districts <- map(sheets, ~filter(.data = dataset, sheet == .))
elections_districts_2013 <- map(.x = list_data_districts,
~map_df(position_parties_national, extract_party, dataset = .x, target_rows = target_rows))
map2(.y = elections_districts_2013, .x = sheets,
~mutate(.y, locality = .x, division = "Commune", Year = "2013")) %>%
bind_rows()
}
elections_2013 <- pmap_dfr(list(x = elections_raw_2013,
y = elections_sheets_2013,
z = list_targets),
function(x, y, z){
map_dfr(position_parties_national,
~extract_district(dataset = x, sheets = y, target_rows = z, position_parties_national = .))
})
# Correct districts
elections_2013 <- elections_2013 %>%
mutate(division = case_when(locality == "CENTRE" ~ "Electoral district",
locality == "EST" ~ "Electoral district",
locality == "NORD" ~ "Electoral district",
locality == "SUD" ~ "Electoral district",
TRUE ~ division))