I am able to produce a cartogram using cartogram::cartogram_cont() But then have difficulty in customising the styling.
I have used broom::tidy() and dplyr::left_join() to fortify the cartogram, but I think perhaps the tidy stage has interfered with the plotOrder. If possible, I will include the output cartograms.
I'm attempting to replicate this type of output, but within my locality. Plesae note that the dataset used for the weighting in cartogram_cont() is not particularly significant, just a proof of concept at this stage:
[R Graph Gallery][1]
[1]: https://www.r-graph-gallery.com/331-basic-cartogram/
Shapefile from: [Lle Shapefile Location][2]
[2]: http://lle.gov.wales/catalogue/item/LocalAuthorities/?lang=en
library(dplyr)
library(leaflet)
library(maptools)
library(cartogram)
library(devtools)
install_github("HanOostdijk/odataR" , build_vignettes = T)
library(odataR)
library(tidyr)
library(rgdal)
library(htmltools)
#Read in shapefile and transform shape
#dsn = folder name, layer = filename but drop the .shp
shapefile <- readOGR(dsn = "Wales Shapefile",
layer = "localauthoritiesPolygon") %>%
#Transform coordinate referencing system
spTransform(CRS("+init=epsg:4326"))
#Next step is to join an interesting dataset to the shapefile using dplyr, then pass this to the cartoram package to render.
#Gone for the teacher sickness dataset from Stats Wales. Noticed it's only up to 2017, wonder if they've stopped collecting.
teacher_sickness_data <- odataR_query('http://open.statswales.gov.wales/dataset/schw0001')
#Check values for join.
categories <- unique(teacher_sickness_data$Area_ItemName_ENG)
categories_shp <- shapefile@data$name_en
categories
categories_shp
#Teacher data has "All Welsh local authorities". Not contained in shapefile so remove.
UA_sickness_data <- teacher_sickness_data[-c(2, 4:6, 8, 9, 11:13, 15:17)] %>%
filter(Area_ItemName_ENG != "All Welsh local authorities")
#Perform join to shapefile
shapefile_1 <- shapefile %>%
merge(UA_sickness_data, by.x = "name_en", by.y = "Area_ItemName_ENG",
duplicateGeoms = TRUE)
#Shiny App will allow choice of inputs to achieve one row per polygon. However, for testing
#functionality with cartograph functions, perform test filtering.
data_filtered <- UA_sickness_data %>%
filter(Year_ItemName_ENG == 2017) %>%
filter(Type_ItemName_ENG == "Full-time") %>%
filter(Variable_ItemName_ENG == "Total days of sick leave")
test_merge <- shapefile %>%
merge(data_filtered, by.x = "name_en", by.y = "Area_ItemName_ENG")
nc_pal <- colorNumeric(palette = "Reds",
domain = log(test_merge@data$Data))
m <-test_merge %>%
leaflet() %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(weight = 1,
color = ~nc_pal(log(Data)),
label = ~name_en,
highlight = highlightOptions(weight = 3,
color = "crimson",
bringToFront = TRUE),
popup = ~ paste0(Variable_ItemName_ENG, "<br/>",
"<b/>",
Data))
m
wales_cart <- cartogram_cont(test_merge, "Data", itermax=5)
plot(wales_cart)
[![Wales_Cartogram][3]][3]
[3]: https://i.stack.imgur.com/2tsMC.png
library(tidyverse)
library(ggmap)
library(broom)
library(rgeos) #used for gBuffer
#Buffer allows to tidy cartogram based on factor of choice.
wales_cart_buffered <- gBuffer(wales_cart, byid=TRUE, width=0)
#tidy cartogram in order to pass to ggplot
spdf_fortified_wales <- tidy(wales_cart_buffered, region = "name_en")
#Now perform a join based on english UA names
spdf_fortified_wales_joined <- spdf_fortified_wales %>%
left_join(. , wales_cart@data, by=c("id"="name_en"))
ggplot() +
geom_polygon(data = spdf_fortified_wales_joined, aes(fill = Data, x = long, y = lat, group = "name_en") , size=0, alpha=0.9) +
coord_map() +
theme_void()
[![incorrect_ggplot][4]][4]
[4]: https://i.stack.imgur.com/as0Z4.png
ggplot() +
geom_polygon(data = spdf_fortified_wales_joined, aes(fill = Data, x = long, y = lat, group = "name_en") , size=0, alpha=0.9) +
coord_map() +
theme_void()
Success Criteria: Polygons are rendered correctly distorted and colour scale reflects weighting variable.
