Geographical county level heat map - r

Question

I frequently need to create geographical heat maps in R. Currently, I have been doing it in a licensed version of Tableau in my office computer which does a superb job. But I need to learn how to do it when I'm out of office. The data is sometimes confidential, so I cannot use Tableau public over the internet. I looked but could not find any solution that produces the result I need.
The data consists of names of districts in the state of Jharkhand, India along with child population in age group 6 to 14 in thousands. In Tableau, I merely have to set the DISTNAME column to "Geographical Role" at "County" level and it pulls the map of the state along with district boundaries from the internet (OpenStreetMap) and produces a heat map like this which is the result I expect from R, if possible:
The data is:
geo_data <- structure(list(DISTNAME = c("BOKARO", "CHATRA", "DEOGHAR", "DHANBAD",
"DUMKA", "GARHWA", "GIRIDIH", "GODDA", "GUMLA", "HAZARIBAGH",
"JAMTARA", "KHUNTI", "KODARMA", "LATEHAR", "LOHARDAGA", "PAKUR",
"PALAMU", "PASHCHIMI SINGHBHUM", "PURBI SINGHBHUM", "RAMGARH",
"RANCHI", "SAHIBGANJ", "SARAIKELA-KHARSAWAN", "SIMDEGA"), POP = c(521.5,
196.5, 323.8, 445.5, 123, 373.9, 357.6, 248.2, 212.4, 686.7,
626.7, 383.6, 391.9, 141, 436.1, 454.6, 301.3, 325.5, 193.7,
238.3, 208.7, 587.4, 130.1, 268)), .Names = c("DISTNAME", "POP"
), row.names = c(NA, 24L), class = "data.frame")
And looks like:
DISTNAME POP
1 BOKARO 521.5
2 CHATRA 196.5
3 DEOGHAR 323.8
4 DHANBAD 445.5
5 DUMKA 123.0
6 GARHWA 373.9
7 GIRIDIH 357.6
8 GODDA 248.2
9 GUMLA 212.4
10 HAZARIBAGH 686.7
11 JAMTARA 626.7
12 KHUNTI 383.6
13 KODARMA 391.9
14 LATEHAR 141.0
15 LOHARDAGA 436.1
16 PAKUR 454.6
17 PALAMU 301.3
18 PASHCHIMI SINGHBHUM 325.5
19 PURBI SINGHBHUM 193.7
20 RAMGARH 238.3
21 RANCHI 208.7
22 SAHIBGANJ 587.4
23 SARAIKELA-KHARSAWAN 130.1
24 SIMDEGA 268.0

Answer 1

You'll need SHP file, which can be found using getData(). Full working code:
library(tidyverse)
library(broom)
library(rgdal)
Your geo data
geo_data <- structure(list(DISTNAME = c("BOKARO", "CHATRA", "DEOGHAR", "DHANBAD", "DUMKA", "GARHWA", "GIRIDIH", "GODDA", "GUMLA", "HAZARIBAGH", "JAMTARA", "KHUNTI", "KODARMA", "LATEHAR", "LOHARDAGA", "PAKUR", "PALAMU", "PASHCHIMI SINGHBHUM", "PURBI SINGHBHUM", "RAMGARH", "RANCHI", "SAHIBGANJ", "SARAIKELA-KHARSAWAN", "SIMDEGA"),
POP = c(521.5, 196.5, 323.8, 445.5, 123, 373.9, 357.6, 248.2, 212.4, 686.7, 626.7, 383.6, 391.9, 141, 436.1, 454.6, 301.3, 325.5, 193.7, 238.3, 208.7, 587.4, 130.1, 268)),
.Names = c("DISTNAME", "POP"),
row.names = c(NA, 24L),
class = "data.frame")
get the map
library(raster)
IN2 <- getData('GADM', country='IND', level=2)
IN2 <- spTransform(IN2, CRS("+init=epsg:4326"))
IN2_map <- tidy(IN2, region = "NAME_2")
id in geo_data to lower
geo_data$DISTNAME <- tolower(geo_data$DISTNAME)
IN2_map %>%
mutate(id = tolower(id)) %>%
left_join(geo_data, by = c("id" = "DISTNAME")) %>%
ggplot() +
geom_polygon(aes(long, lat, group=group, fill = POP), color = "black")

Answer 2

In the solution below I've used map shapefiles downloaded from: http://projects.datameet.org/maps/districts/
Edit: Later I also tried Jharkhand map extracted from http://gadm.org/country which shows slight differences in district boundaries. It matches better with other political maps of the state available on the internet.
Here's my solution:
library(tmap)
library(tmaptools)
geo_data <- data.frame(
DISTNAME = c("BOKARO", "CHATRA", "DEOGHAR", "DHANBAD", "DUMKA", "GARHWA", "GIRIDIH", "GODDA", "GUMLA", "HAZARIBAGH", "JAMTARA", "KHUNTI", "KODARMA", "LATEHAR", "LOHARDAGA", "PAKUR", "PALAMU", "PASHCHIMI SINGHBHUM", "PURBI SINGHBHUM", "RAMGARH", "RANCHI", "SAHIBGANJ", "SARAIKELA-KHARSAWAN", "SIMDEGA"),
POP = c(521.5, 196.5, 323.8, 445.5, 123, 373.9, 357.6, 248.2, 212.4, 686.7, 626.7, 383.6, 391.9, 141, 436.1, 454.6, 301.3, 325.5, 193.7, 238.3, 208.7, 587.4, 130.1, 268))
# the path to shape file
shp_file <- "H:/Mapping/maps-master/Districts/Census_2011/2011_Dist.shp"
india <- read_shape(shp_file, as.sf = TRUE, stringsAsFactors = FALSE)
india$DISTRICT <- toupper(india$DISTRICT)
jharkhand <- india[india$ST_NM =="Jharkhand", ]
jharkhand_pop <- merge(x = jharkhand,
y = geo_data,
by.x = "DISTRICT",
by.y = "DISTNAME")
#tmap_mode(mode = "plot") # static
tmap_mode(mode = "view") # interactive
qtm(jharkhand_pop, fill = "POP",
text = "DISTRICT",
text.size=.9)
The static map (plot mode) is very good but the interactive map (view mode) is super awesome. It gives the option to pull additional map information from three different sources from the internet.
A big thanks to the creators of tmap and tmaptools packages. This method is far superior to many comparatively longer and awkward solutions that can be found on the internet.
If we want more customization:
tm_shape(jharkhand_pop) +
tm_polygons() +
tm_shape(jharkhand_pop) +
tm_borders() +
tm_fill("POP",
palette = get_brewer_pal("YlOrRd", n = 20),
n = 20,
legend.show = F,
style = "order") + # "cont" or "order" for continuous variable
tm_text("DISTRICT", size = .7, ymod = .1) +
tm_shape(jharkhand_pop) +
tm_text("POP", size = .7, ymod = -.2)
we get the following in plot mode: