India Maps - states, districts, wards, roads, waterworks, chorpleths
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Download the shape files for the boundaries
We would need the shape files which will be used to draw the boundaries which are needed to make a choropleth maps. The following website has various shape files for India. If you do not like the boundary files, then find any shape files you find for the country and use them.
https://www.diva-gis.org/datadown
Here is a direct link https://biogeo.ucdavis.edu/data/diva/adm/IND_adm.zip
Extract the data and we will be mainly interested in the .shp files
Libraries needed
library(ggplot2)
library(sf)
library(rvest)
library(dplyr)
library(viridis)
library(ggrepel)
library(ggthemes)Get the shape files loaded.
How do you get the shape files is described above.
shp0 <- read_sf("E:\\Sidebar Items\\Documents\\r scripts\\India SHP Files\\IND_adm\\IND_adm0.shp")
shp1 <- read_sf("E:\\Sidebar Items\\Documents\\r scripts\\India SHP Files\\IND_adm\\IND_adm1.shp")
shp2 <- read_sf("E:\\Sidebar Items\\Documents\\r scripts\\India SHP Files\\IND_adm\\IND_adm2.shp")
shp3 <- read_sf("E:\\Sidebar Items\\Documents\\r scripts\\India SHP Files\\IND_adm\\IND_adm3.shp")
shp1 <- shp1%>%
dplyr::mutate(NAME_1 = if_else(NAME_1 == 'Uttaranchal'
, 'Uttarakhand'
, NAME_1))
Country Map
Just the boundary of the whole country without any states or any other details. shp0 file just has the country boundary.
# country level map
pl1 <- ggplot(shp0)
pl1 <- pl1 + geom_sf(fill="#E8442A", color ="#F53214")
pl1 <- pl1 + ggthemes::theme_map()
pl1 <- pl1 + labs(title = "India country map")
pl1
# Plot the states of the country shp1 file consists the boundaries of
each state.
# Fill each state in different colour ----
pl2 <- ggplot(shp1)
pl2 <- pl2 + geom_sf(aes(fill = NAME_1))
pl2 <- pl2 + ggthemes::theme_map()
pl2 <- pl2 + theme(legend.position = "none")
pl2 <- pl2 + labs(title = "India States map")
pl2 <- pl2 + scale_fill_viridis_d()
pl2
District boundaries
shp2 file contains the district boundaries.
# Fill each district in different colour ----
pl3 <- ggplot(shp2)
pl3 <- pl3 + geom_sf(aes(fill = NAME_2))
pl3 <- pl3 + ggthemes::theme_map()
pl3 <- pl3 + theme(legend.position = "none")
pl3 <- pl3 + labs(title = "India Districts map")
pl3 <- pl3 + scale_fill_viridis_d(option="magma")
pl3
Wards boundaries
shp3 file contains these Wards boundaries,
pl4 <- ggplot(shp3)
pl4 <- pl4 + geom_sf(aes(fill = NAME_3))
pl4 <- pl4 + ggthemes::theme_map()
pl4 <- pl4 + theme(legend.position = "none")
pl4 <- pl4 + labs(title = "India Wards map")
pl4 <- pl4 + scale_fill_viridis_d(option="magma")
pl4
Selective fill and selective text labels.
In this example we just want to fill the Union Territories and display the names as well.
# Show selected areas ----
pl5 <- ggplot(shp1)
pl5 <- pl5 + geom_sf(aes(fill = (ENGTYPE_1 %in% 'Union Territory'))
, size = .2, alpha = 0.5)
pl5 <- pl5 + geom_sf_text(aes(label = if_else(ENGTYPE_1 %in% 'Union Territory',NAME_1,""))
, size = 3, colour = "red")
pl5 <- pl5 + ggthemes::theme_map()
pl5 <- pl5 + theme(legend.position = "none")
pl5 <- pl5 + scale_fill_manual(values = c("#B3E82A" , "#E8442A"))
pl5 <- pl5 + labs(title = "India Map highlighting the Union Territories")
pl5
Selective filling of states
We can choose which states to fill and then show the labels for them also.
dstates <- c('Uttar Pradesh'
,'Uttarakhand'
, 'Himachal Pradesh'
, 'Gujarat'
,'Goa'
,'Manipur'
,'Punjab'
)
pl6 <- ggplot(shp1)
pl6 <- pl6 + geom_sf(aes(fill = NAME_1 %in% dstates)
, size = .5
, colour = "white")
pl6 <- pl6 + geom_sf_text(data = filter(shp1, NAME_1 %in% dstates)
,aes(label = NAME_1), size = 3)
pl6 <- pl6 + ggthemes::theme_map()
#pl5 <- pl + theme_bw()
pl6 <- pl6 + theme(legend.position = "none")
pl6 <- pl6 + scale_fill_manual(values = c("grey" , "#E8442A"))
pl6 <- pl6 + labs(title = "India Map showing states\nwith elections in 2022")
pl6
Create Choropleth maps
By using some dummy sales data for each of these states we can create a choropleth map.
#Choropleth maps
# fill each state based on the your own data
shp1$sales <- c(7261
, 1810000
, 31282
, 459000
, 717000
, 61110
, 987000
,0
,0
,1430000
, 162000
, 820000
, 766000
, 199000
, 307000
, 343000
, 2770000
, 2730000
, 9209
, 788000
, 5910000
, 59852
, 41906
, 15364
, 23644
, 852000
, 113000
, 588000
, 950000
, 18414
, 2350000
, 603000
, 59321
, 1700000
, 337000
, 1460000
)
options(scipen = 999)
pl7 <- ggplot(shp1)
pl7 <- pl7 + geom_sf(aes(fill = sales), alpha = 0.7)
pl7 <- pl7 + geom_sf_text(aes(label = NAME_1), size = 2, color = "black")
pl7 <- pl7 + ggthemes::theme_map()
pl7 <- pl7 + theme(legend.position = "right")
pl7 <- pl7 + labs(title = "India Choropleth map\nshowing sales in each state")
pl7 <- pl7 + scale_fill_viridis(option="magma")
pl7
# show roads on the map We use the shp1 file to show the states and then
use the roads file to show the roads on the map.
# Roads ----
roads <- read_sf("E:\\Sidebar Items\\Documents\\r scripts\\India SHP Files\\IND_rds\\IND_roads.shp")
pl8 <- ggplot()
pl8 <- pl8 + geom_sf(data = shp1,colour = "blue")
pl8 <- pl8 + geom_sf(data = roads,colour = "red" , alpha = 0.2)
pl8 <- pl8 + ggthemes::theme_map()
pl8 <- pl8 + theme(legend.position = "none")
pl8 <- pl8 + labs(title = "India roads network")
pl8
pl9 <- ggplot()
pl9 <- pl9 + geom_sf(data = shp1, color = "grey")
pl9 <- pl9 + geom_sf(data = roads%>%dplyr::filter(RTT_DESCRI =='Primary Route')
, colour = "red", alpha = 0.5)
pl9 <- pl9 + ggthemes::theme_map()
pl9 <- pl9 + theme(legend.position = "none")
pl9 <- pl9 + labs(title = "India - Primary routes")
pl9
# Water ----
water <- read_sf("E:\\Sidebar Items\\Documents\\r scripts\\India SHP Files\\IND_wat\\IND_water_lines_dcw.shp")
water1 <- water%>%
dplyr::filter(NAM %in% c('INDUS', "NARMADA") )
pl10 <- ggplot()
pl10 <- pl10 + geom_sf(data = shp1, color = "grey")
pl10 <- pl10 + geom_sf(data = water, color = "blue", alpha = 0.5)
pl10 <- pl10 + ggthemes::theme_map()
pl10 <- pl10 + theme(legend.position = "none")
pl10 <- pl10 + labs(title = "India- water channels, canals, rivers")
pl10
url <- "https://www.latlong.net/category/hospitals-102-26.html"
ds <- read_html(url)
# there is a table in the webpage which displays the data
# As this is one table on the web page so we get the first table which is available on the web page
ds <- html_table(html_nodes(ds, "table")[[1]])
library(ggrepel)
pl11 <- ggplot()
pl11 <- pl11 + geom_sf(data = shp1, alpha = 0.5, color = "orange", fill = "orange")
pl11 <- pl11 + geom_point(data = ds
, aes(x = Longitude
, y = Latitude
, color = `Place Name` == 'Fortis Memorial Research Institute, Gurgaon, India'
))
pl11 <- pl11 + ggrepel::geom_text_repel(data = ds, aes(x = Longitude
, y = Latitude
, label= if_else(`Place Name` == 'Fortis Memorial Research Institute, Gurgaon, India'
, `Place Name`,''))
, color = "blue", size = 2)
#pl <- pl + geom_sf_text(data = water1, aes(label = NAM), size = 2, color = "red")
pl11 <- pl11 + ggthemes::theme_map()
pl11 <- pl11 + theme(legend.position = "none")
pl11 <- pl11 + scale_color_manual(values = c("red","blue"))
pl11 <- pl11 + labs(title = "India - some major hospitals")
pl11
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