Create a map of country in R - r

Question

I need to create a map of country(Thailand) based on shapes files (preferably colored)and to add the codes of the provinces (from 10 to 96,77 codes) and the corresponding coefficient from h.сsv(also 77 values) file on the map.
I am trying to show my two codes(maybe,one of them will be better for map):
1st:
library(raster)
library(rasterVis)
library(rgdal)
library(rgeos)
library(dismo)
library(sp)
library(maptools)
library(maps)
library(mapdata)
library(XML)
library(foreign)
library(latticeExtra)
library(shapefiles)
library(RColorBrewer)
library(GISTools)
#library(SDMTools)
library(dplyr)
library(tidyr)
library(tidyverse)
library(lubridate)
## preparing shapefiles
thailand_district <- shapefile("C:/usa/archive/TH_Province2012.shp")
thailand_district
crs(thailand_district)
names(thailand_district)
thailand_district_lonlat<- spTransform(thailand_district, CRS("+proj=longlat +datum=WGS84"))
crs(thailand_district_lonlat)
thailand_district_lonlat_s<-gSimplify(thailand_district_lonlat, tol=0.02, topologyPreserve=TRUE)
district_id<-thailand_district_lonlat$A_CODE
province_id<-thailand_district_lonlat$P_CODE
thailand_prov <- shapefile("C:/usa/archive/TH_Province2012.shp")
thailand_prov
crs(thailand_prov)
thailand_prov_lonlat<- spTransform(thailand_prov, CRS("+proj=longlat +datum=WGS84"))
crs(thailand_prov_lonlat)
thailand_prov_lonlat_s<-gSimplify(thailand_prov_lonlat, tol=0.02, topologyPreserve=TRUE)
## preparing centroids
thailand_district_centroids <- getSpPPolygonsLabptSlots(thailand_district_lonlat)
head(thailand_district_centroids)
district_centroids<- data.frame(province_id,district_id, thailand_district_centroids[,1],thailand_district_centroids[,2])
district_centroids<-read.csv("data.scrub.district.csv")
names(district_centroids) <- c("province_id","district_id","longitude", "latitude")
ex<-district_centroids
coordinates(ex)<- cbind("longitude", "latitude")
plot(ex)
thailand_province_centroids <- getSpPPolygonsLabptSlots(thailand_prov_lonlat)
head(thailand_province_centroids)
## read cases
scrub1<-read_csv("C:/usa/archive/scrub_2003-07_180319.csv")
names(scrub1)
scrub1<-dplyr::select(scrub1,Address, The.day.began.to.get.sick..M.D.Y.)
scrub1 <- dplyr::rename(scrub1,date=The.day.began.to.get.sick..M.D.Y.)
scrub1 <-na.omit(scrub1)
scrub2<-read_csv("C:/usa/archive/scrub_2008-11_180319.csv")
scrub2<-dplyr::select(scrub2,Address, The.day.began.to.get.sick..M.D.Y.)
scrub2 <- dplyr::rename(scrub2,date=The.day.began.to.get.sick..M.D.Y.)
scrub3<-read_csv("C:/usa/archive/scrub_2012-18_180319.csv")
scrub3<-dplyr::select(scrub3,Address, The.day.began.to.get.sick..M.D.Y.)
scrub3 <- dplyr::rename(scrub3,date=The.day.began.to.get.sick..M.D.Y.)
scrub<-dplyr::union(scrub1, scrub2)
scrub<-dplyr::union(scrub, scrub3)
scrub$district_id<-(tamboon_id=substr(scrub$Address, 1,4))
scrub <- dplyr::rename(scrub,village_id=Address)
scrub<-tidyr::drop_na(scrub,village_id)
scrub_district <- dplyr::select(scrub,district_id)
# preparation
scrub$date1 <- as.Date(scrub$date,
format = "%d/%m/%Y")
scrub$year<-lubridate::year(scrub$date1)
scrub$YearMonth<-format(scrub$date1, "%Y-%m")
scrubYear<-scrub %>%
drop_na() %>%
group_by(year) %>%
summarise(scrubcases= n())
scrub$district_id<-as.factor(scrub$district_id)
is.factor(scrub$district_id)
scrubDistrict<-scrub %>%
drop_na() %>%
group_by(district_id) %>%
summarise(scrubcases= n())
district_centroids
district_centroids2<-district_centroids %>%
unite("district_id", province_id,district_id2)
district_centroids2$district_id<-gsub("_", "",district_centroids2$district_id )
scrubdistict_longlat<-dplyr::left_join(district_centroids2,scrubDistrict,
by="district_id")
write_csv(scrubdistict_longlat,"data.scrub.district.csv")
scrubClean<-read_csv("data.scrub.district.csv")
mydata<-dplyr::filter(scrubClean, scrubcases > 0)
ex2<-mydata
coordinates(ex2)<-c("longitude","latitude")
bubble(ex2,"scrubcases")
# map
library(tmap)
library(tmaptools)
proj4string(ex2) <- proj4string(thailand_district_lonlat)
tmaptools::palette_explorer()
# thailand
tm1<-tm_shape(thailand_prov_lonlat_s) +
tm_fill(NA) + tm_borders("black")+
tm_borders("black")+
tm_compass(type = "8star", position = c("right", "top"),size = 2)+
tm_scale_bar(breaks = c(0, 100, 100), size = 0.5, position = c("right", "bottom"))+
tm_style( "beaver")
tm1
tm2<-tm_shape(thailand_district_lonlat_s)+
tm_polygons()+
tm_shape(ex2) +
tm_bubbles("scrubcases",col = "lightblue",scale = 2,
border.col = "black", border.alpha = .5,
contrast=1,
title.size="cases / district")
tm2
library(dplyr)
library(tidyr)
library(tmap)
data(World)
names(World)
mygideon<-read_csv("data.gideon.iso.final.csv") %>%
group_by(iso_a3) %>%
summarise(total.outbreaks=n())
world2<-dplyr::left_join(World,mygideon2,by="iso_a3")
tm_shape(world2) +
tm_polygons("total.outbreaks",
style = "fixed",
breaks = c(1,50,100,250,500,750, 1000, 1500, 2500),
palette="Oranges",
title = "Total outbreaks (1940-2018)", contrast = 1.2,
border.col = "gray30", id = "name", n=6,
legend.hist = TRUE,alpha = 1)+
tm_layout(legend.outside = TRUE)
**Error in data.frame(province_id, district_id, thailand_district_centroids[, :
arguments imply differing number of rows : 0, 77.use coordinates method**
2n code.
library(raster)
library(rasterVis)
library(rgdal)
library(rgeos)
library(dismo)
library(sp)
library(maptools)
library(maps)
library(mapdata)
library(XML)
library(foreign)
library(latticeExtra)
library(shapefiles)
library(RColorBrewer)
library(GISTools)
#library(SDMTools)
library(dplyr)
library(tidyr)
library(tidyverse)
library(rgeos) # to fortify without needing gpclib
library(ggplot2)
library(scales) # for formatting ggplot scales with commas
thamap <- readOGR("C:/usa/archive/TH_Province2012.shp")
thamap
crs(thamap)
thamap_lonlat<- spTransform(thamap, CRS("+proj=longlat +datum=WGS84"))
crs(thamap_lonlat)
thamap_lonlat_s<-gSimplify(thamap_lonlat, tol=0.02, topologyPreserve=TRUE)
thamap.fort <- fortify(thamap)
idList <-thamap#data$PROV_CODE
centroids.df <- as.data.frame(coordinates(thamap))
names(centroids.df) <- c("Longitude", "Latitude")
info <- read.csv("h.csv")
pop.df <- data.frame(idList,info,centroids.df)
ggplot(pop.df, aes(map_id = idList)) + #"id" is col in your df, not in the map object
geom_map(aes(fill = info), colour= "grey", map = thamap.fort) +
expand_limits(x = thamap.fort$long, y = thamap.fort$lat) +
scale_fill_gradient(high = "red", low = "white", guide = "colorbar", labels = comma) +
geom_text(aes(label = id, x = Longitude, y = Latitude)) + #add labels at centroids
coord_equal(xlim = c(-90,-30), ylim = c(-60, 20)) +
labs(x = "Longitude", y = "Latitude", title = "map Thailand") +
theme_bw()
Don't know how to automatically pick scale for object of type function. Defaulting to continuous.
Aesthetics must be valid data columns. Problematic aesthetic(s): label = id.
Did you mistype the name of a data column or forget to add after_stat()?
I would really appreciate it if you could help me to fix my codes a little to create the map.
Could you tell me please also,how is it possible to add the data(77 values) from csv file on the map near the codes of provinces?
Thank you very much for your help

Answer 1

Here's a solution using tmap. The shapefile containing the borders of Thailand (country and provinces) is available from https://data.humdata.org/dataset/thailand-administrative-boundaries. The province codes (10-96) are also included in the dataset in character format and can easily be extracted.
library(tmap)
library(sf)
library(tidyverse)
provinces <- st_read(dsn = "tha_adm_rtsd_itos_20190221_SHP_PART_1/tha_admbnda_adm1_rtsd_20190221.shp") %>%
as.tibble() %>%
separate(ADM1_PCODE, into = c("pcode_text", "pcode_num"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
select(geometry, pcode_num) %>%
st_as_sf()
tm_shape(provinces) +
tm_fill(col = "MAP_COLORS") +
tm_text("pcode_num", size = .5) +
tm_borders(lwd = .7, col = "black")