Download and prepare datasets
# Download municipalities geodata
municipalities17 <- get_municipalities(year = 2017)
#> Requesting response from: http://geo.stat.fi/geoserver/wfs?service=WFS&version=1.0.0&request=getFeature&typename=tilastointialueet%3Akunta4500k_2017
#> Warning: Coercing CRS to epsg:3067 (ETRS89 / TM35FIN)
#> Data is licensed under: Attribution 4.0 International (CC BY 4.0)
# Pull municipalities and population data from Statistics Finland
library(pxweb)
pxweb_query_list <- list("Alue 2019"=c("020","005","009","010","016","018","019","035","043","046","047","049","050","051","052","060","061","062","065","069","071","072","074","075","076","077","078","079","081","082","086","111","090","091","097","098","099","102","103","105","106","108","109","139","140","142","143","145","146","153","148","149","151","152","165","167","169","170","171","172","176","177","178","179","181","182","186","202","204","205","208","211","213","214","216","217","218","224","226","230","231","232","233","235","236","239","240","320","241","322","244","245","249","250","256","257","260","261","263","265","271","272","273","275","276","280","284","285","286","287","288","290","291","295","297","300","301","304","305","312","316","317","318","398","399","400","407","402","403","405","408","410","416","417","418","420","421","422","423","425","426","444","430","433","434","435","436","438","440","441","475","478","480","481","483","484","489","491","494","495","498","499","500","503","504","505","508","507","529","531","535","536","538","541","543","545","560","561","562","563","564","309","576","577","578","445","580","581","599","583","854","584","588","592","593","595","598","601","604","607","608","609","611","638","614","615","616","619","620","623","624","625","626","630","631","635","636","678","710","680","681","683","684","686","687","689","691","694","697","698","700","702","704","707","729","732","734","736","790","738","739","740","742","743","746","747","748","791","749","751","753","755","758","759","761","762","765","766","768","771","777","778","781","783","831","832","833","834","837","844","845","846","848","849","850","851","853","857","858","859","886","887","889","890","892","893","895","785","905","908","911","092","915","918","921","922","924","925","927","931","934","935","936","941","946","976","977","980","981","989","992"),
"Tiedot"=c("M411", "M391","M421","M478"),
"Vuosi"=c("2017"))
px_data <- pxweb_get(url = "https://pxnet2.stat.fi/PXWeb/api/v1/fi/Kuntien_avainluvut/2019/kuntien_avainluvut_2019_aikasarja.px",
query = pxweb_query_list)
# Convert to data.frame and wrangle dataset so that every row is municipality
tk_data <- as.data.frame(px_data,
column.name.type = "text",
variable.value.type = "text")
tk_data <- tk_data %>%
rename(name = `Alue 2019`) %>%
mutate(name = as.character(name),
# Paste Tiedot and Vuosi
Tiedot = paste(Tiedot, Vuosi)) %>%
select(-Vuosi) %>%
spread(Tiedot, `Kuntien avainluvut`) %>%
as_tibble()
tk_data <- janitor::clean_names(tk_data)
# Rename variables in english
tk_data <- tk_data %>%
rename(
under_15 = alle_15_vuotiaiden_osuus_vaestosta_percent_2017,
y16_64 = x15_64_vuotiaiden_osuus_vaestosta_percent_2017,
over64 = yli_64_vuotiaiden_osuus_vaestosta_percent_2017,
popu2017 = vakiluku_2017
)
# Join with Statistics Finland attribute data
dat <- left_join(municipalities17, tk_data)
#> Joining, by = "name"
# Join the municipality level data with internal municipality_key_2017-data
finland_popu2017 <- left_join(dat, municipality_key_2017, by = c("name" = "municipality_name_fi"))Calculating color-code to the dataset
Ternary color codes are calculated by Tricolore()-function, which excepts variables which is used for color coding. Save rgb values to population dataset.
tric_dat <- Tricolore(finland_popu2017,
p1 = 'over64',
p2 = 'y16_64',
p3 = 'under_15',
center = NA,
crop = TRUE
)
#> Warning: Ignoring unknown aesthetics: z
finland_popu2017$popu_rgb <- tric_dat$rgbPlot map by ggplot and use scale_fill_identity() option to make sure that each region is color coded by rgb-code.
plot_pop <- ggplot(finland_popu2017) +
geom_sf(aes(fill = popu_rgb, geometry = geom), size = .1) +
scale_fill_identity()
plot_pop
Ternary colors can be adjusted to distinct municipalies differences better.
tric_dat <- Tricolore(finland_popu2017,
p1 = 'over64',
p2 = 'y16_64',
p3 = 'under_15',
center = NA,
crop = TRUE,
spread = 3,
contrast = .4,
lightness = 1,
chroma = 1,
hue = 2/12
)
#> Warning: Ignoring unknown aesthetics: z
finland_popu2017$popu_rgb <- tric_dat$rgbplot_pop <- ggplot(finland_popu2017) +
geom_sf(aes(fill = popu_rgb, geometry = geom), size = .1) +
scale_fill_identity()
plot_pop
Building legend for a plot
Legend for tertinary color is added by ggtern and annotation_custom(). Package ggtern is important to load after ggplot2 package.
library(ggtern)
#> --
#> Remember to cite, run citation(package = 'ggtern') for further info.
#> --
#>
#> Attaching package: 'ggtern'
#> The following objects are masked from 'package:ggplot2':
#>
#> aes, annotate, ggplot, ggplotGrob, ggplot_build, ggplot_gtable,
#> ggsave, layer_data, theme_bw, theme_classic, theme_dark,
#> theme_gray, theme_light, theme_linedraw, theme_minimal, theme_void
plot_pop <- ggplot(finland_popu2017) +
geom_sf(aes(fill = popu_rgb, geometry = geom), size = .1) +
coord_sf(xlim = c(-2e5,7.5e5), ylim = c(66.3e5, 77.7e5), expand = FALSE, datum = NA) +
annotation_custom(
ggplotGrob(tric_dat$key + labs(L = "% Young\n(0-14)", T = " % Working age\n(15-64)", R = "% Elderly\n(65+)")) ,
xmin = -7e5, xmax = 1.7e5, ymin = 68e5, ymax = 77e5
) +
scale_fill_identity() +
theme_void() +
labs(title = "Finnish population by municipalites",
subtitle = "Regional distibution of age groups in 2017.")
#> Coordinate system already present. Adding new coordinate system, which will replace the existing one.
plot_pop
