I am a newbie in using tmap package to produce maps!. So this might be a simple problem to solve. I am using cartogram_dorling and cartogram_ncont to Plot a map of Africa representing Life expectancy. I think the problem arises since this values are 'small' and maybe the range (26.962, 57.964) is small. So when representing this on the map, polygons are shrunk such that they are too small and not visible. For the case of cartogram_dorling they appear as circles and the shape of African is not maintained.
I import a shapefile and since I have life expectancy in a separate file I then append it as a column on the spatial polygon dataframe. I the proceed with mapping. Below is my code. I am sorry this code might not reproducible i am just attaching for display together with my outputs. Any quick solution to this?. Thanks.
world <- st_read("TM_WORLD_BORDERS-0.3/TM_WORLD_BORDERS-0.3.shp") africa <- world[world$REGION == 2, ]
I am a newbie in using tmap package to produce maps!. So this might be a simple problem to solve. I am using
cartogram_dorlingandcartogram_ncontto Plot a map of Africa representing Life expectancy. I think the problem arises since this values are 'small' and maybe the range (26.962, 57.964)is small. So when representing this on the map, polygons are shrunk such that they are too small and not visible. For the case ofcartogram_dorlingthey appear as circles and the shape of African is not maintained.I import a shapefile and since I have life expectancy in a separate file I then append it as a column on the spatial polygon dataframe. I the proceed with mapping. Below is my code. I am sorry this code might not reproducible i am just attaching for display together with my outputs. Any quick solution to this?. Thanks.
world <- st_read("TM_WORLD_BORDERS-0.3/TM_WORLD_BORDERS-0.3.shp") africa <- world[world$REGION == 2, ]Africa_life_Expectancy <- read_excel("Africa_Population.xlsx",sheet = "Sheet6")africa <- africa[order(africa$NAME),]africa <- as_Spatial(africa)Africa_life_Expectancy <- Africa_life_Expectancy[order(Africa_life_Expectancy$Region),]africa$Life_Expectancy_1950_1955 <- Africa_life_Expectancy$1950-1955africa <- spTransform(africa, CRS("+init=epsg:3395"))africa_cont <- cartogram_ncont(africa, 'Life_Expectancy_1950_1955 ', inplace = TRUE)tm_shape(africa_cont) + tm_polygons('Life_Expectancy_1950_1955 ', style = "jenks", area = 'AREA') + tm_layout(frame = FALSE, legend.position = c("left","top"), legend.title.size = 2, legend.text.size = 0.6, legend.bg.color = "white", legend.outside=TRUE )Here is the output:
africa_cont <- cartogram_dorling(africa, 'Life_Expectancy_1950_1955 ')tm_shape(africa_cont) + tm_polygons('Life_Expectancy_1950_1955 ', style = "jenks") + tm_layout(frame = FALSE, legend.position = c("left","top"), legend.title.size = 1, legend.text.size = 0.6, legend.bg.color = "white", legend.outside=TRUE )Ouput:
Using
cartogram_contseems to be better. But the Mean size error for iteration is very high.Mean size error for iteration 1: 47.7556381408357Mean size error for iteration 2: 40.5957713606489Mean size error for iteration 3: 34.4636034987029I am aware increasing the number of iterations reduces the error.
africa_cont <- cartogram_cont(africa, 'Life_Expectancy_1950_1955 ', itermax = 3)tm_shape(africa_cont) + tm_polygons('Life_Expectancy_1950_1955 ', style = "jenks") + tm_layout(frame = TRUE, legend.position = c("left","top"), legend.title.size = 1, legend.text.size = 0.6, legend.bg.color = "white", legend.outside=TRUE )+ tm_scale_bar()