Georgia Mapping in R

You can download session 9 files for constructing the population pyramids of Georgia here: https://github.com/rladies/meetup-presentations_tbilisi and specify your working directory with setwd(“/Users/mydomain/myfolder/”)

#set working directory
mypath<-"/Users/DrSpengler/The rectification of the Vuldrini/"
#upload shape files
georgia <- readOGR("./GEO_adm/","GEO_adm0")
## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_adm/", layer: "GEO_adm0"
## with 1 features
## It has 70 fields
# plot(georgia, lwd=1.5)

georgia1 <- readOGR("./GEO_adm/","GEO_adm1")
## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_adm/", layer: "GEO_adm1"
## with 12 features
## It has 16 fields
# plot(georgia1)

georgia2 <- readOGR("./GEO_adm/","GEO_adm2")
## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_adm/", layer: "GEO_adm2"
## with 69 features
## It has 18 fields
# plot(georgia2)

gwat <- readOGR("./GEO_wat/" , "GEO_water_lines_dcw")
## OGR data source with driver: ESRI Shapefile
## Source: "./GEO_wat/", layer: "GEO_water_lines_dcw"
## with 559 features
## It has 5 fields
# plot(gwat)

gpop <- raster("./GEO_pop/geo_pop.grd")
# plot(gpop)

galt <- raster("./GEO_msk_alt/GEO_msk_alt.grd")
# plot(galt)
 plot(georgia, lwd=1.5) #n1

map1

 plot(georgia1, lwd=1.5) #n2

map2

 plot(georgia2, lwd=1.5) #n3

map3

 plot(georgia, lwd=1.5) #n4
 plot(gwat, lwd=1.5, col="blue", add=T) #n4

map4

 plot(gpop) #n5
 plot(georgia, lwd=1.5,  add=T) #n5

map5

 plot(galt, lwd=1.5) #n6

map6

Plot neighbouring countries

tur <- readOGR("./TUR_adm" , "TUR_adm0")
## OGR data source with driver: ESRI Shapefile
## Source: "./TUR_adm", layer: "TUR_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1
arm <- readOGR("./ARM_adm" , "ARM_adm0")
## OGR data source with driver: ESRI Shapefile
## Source: "./ARM_adm", layer: "ARM_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1
rus <- readOGR("./RUS_adm" , "RUS_adm0")
## OGR data source with driver: ESRI Shapefile
## Source: "./RUS_adm", layer: "RUS_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1
aze <- readOGR("./AZE_adm" , "AZE_adm0")
## OGR data source with driver: ESRI Shapefile
## Source: "./AZE_adm", layer: "AZE_adm0"
## with 1 features
## It has 70 fields
## Integer64 fields read as strings:  ID_0 OBJECTID_1

plot maps

plot(georgia, lwd=1.5, col="white", bg="lightblue")
plot(georgia1, add=T, lty=2)
plot(tur, add=T, col="white")
plot(arm, add=T, col="white")
plot(rus, add=T, col="white")
plot(aze, add=T, col="white")

map7

add labels for the countries

x.loc <- c(44.32002, 46.35746, 44.40421, 42.18156, 40.71662)
y.loc <- c(43.42472, 40.87209, 40.82228, 40.90945, 41.99276)
nb.lab <- c("Russia", "Azerbaijan", "Armenia", "Turkey", "Black Sea")
plot(georgia, lwd=1.5, col="white", bg="lightblue")
plot(georgia1, add=T, lty=2)
plot(tur, add=T, col="white")
plot(arm, add=T, col="white")
plot(rus, add=T, col="white")
plot(aze, add=T, col="white")
text(x.loc, y.loc, nb.lab)

let’s add everything (or almost everything) together

plot(gwat, col="blue")
# plot(georgia1[1,], lwd=1, col="lightblue", border="black", add=T)
plot(georgia2, lwd=0.5, border="black", lty=3, add=T)
plot(georgia1, border="black", lty=2, add=T)
plot(georgia, lwd=1.5, add=T)

map8

check georgia@data

head(georgia1)
##   ID_0 ISO  NAME_0 ID_1       NAME_1 VARNAME_1 NL_NAME_1 HASC_1 CC_1
## 0   81 GEO Georgia 1034     Abkhazia   Sokhumi      <NA>  GE.AB <NA>
## 1   81 GEO Georgia 1035       Ajaria    Batumi      <NA>  GE.AJ <NA>
## 2   81 GEO Georgia 1036        Guria  Ozurgeti      <NA>  GE.GU <NA>
## 3   81 GEO Georgia 1037      Imereti   Kutaisi      <NA>  GE.IM <NA>
## 4   81 GEO Georgia 1038      Kakheti    Telavi      <NA>  GE.KA <NA>
## 5   81 GEO Georgia 1039 Kvemo Kartli   Rustavi      <NA>  GE.KK <NA>
##                   TYPE_1           ENGTYPE_1 VALIDFR_1 VALIDTO_1 REMARKS_1
## 0 Avtonomiuri Respublika Autonomous Republic      1994   Present      <NA>
## 1 Avtonomiuri Respublika Autonomous Republic      1994   Present      <NA>
## 2                 Region              Region      1994   Present      <NA>
## 3                 Region              Region      1994   Present      <NA>
## 4                 Region              Region      1994   Present      <NA>
## 5                 Region              Region      1994   Present      <NA>
##   Shape_Leng Shape_Area
## 0   6.643211  0.9744622
## 1   3.055014  0.3074264
## 2   2.880653  0.2092665
## 3   4.214567  0.6783179
## 4   6.820519  1.2485036
## 5   5.219352  0.6807876

print labels on the map

labels for admin 2

coords2<- coordinates(georgia2[2:6,])
admin2 <- c(as.character(georgia2$NAME_2[1:5]))
admin2
## [1] "Gagra"      "Gali"       "Gudauta"    "Gulripshi"  "Ochamchire"

Upload data from World Bank

dt <- read.csv("/Users/ac1y15/Google Drive/blog/RLadies_Georgia_files/Session_3/Data_Extract_From_Subnational_Malnutrition/3f075abc-c51c-40c5-afb1-f8fbcfa30f23_Data.csv", header=T)
dt.1 <- subset(dt, dt$type==1&dt$select==1)

head(dt.1)
##            Admin.Region.Name select order
## 6                                 1     1
## 7  Georgia, Adjara Aut. Rep.      1     2
## 16            Georgia, Guria      1     3
## 26          Georgia, Imereti      1     4
## 31          Georgia, Kakheti      1     5
## 36     Georgia, Kvemo Kartli      1     6
##                         Admin.Region.Code type
## 6                                            1
## 7  GEO_Adjara_Aut._Rep._GE.AR_1297_GEO002    1
## 16            GEO_Guria_GE.GU_1298_GEO003    1
## 26          GEO_Imereti_GE.IM_1299_GEO004    1
## 31          GEO_Kakheti_GE.KA_1300_GEO005    1
## 36     GEO_Kvemo_Kartli_GE.KK_1301_GEO006    1
##                                                            Series.Name
## 6
## 7  Prevalence of overweight, weight for height (% of children under 5)
## 16 Prevalence of overweight, weight for height (% of children under 5)
## 26 Prevalence of overweight, weight for height (% of children under 5)
## 31 Prevalence of overweight, weight for height (% of children under 5)
## 36 Prevalence of overweight, weight for height (% of children under 5)
##          Series.Code YR2000 YR2005 YR2009
## 6                        NA     NA     NA
## 7  SN.SH.STA.OWGH.ZS     NA   28.1     NA
## 16 SN.SH.STA.OWGH.ZS     NA    7.9     NA
## 26 SN.SH.STA.OWGH.ZS    9.9   21.5     NA
## 31 SN.SH.STA.OWGH.ZS    7.0   19.6   13.2
## 36 SN.SH.STA.OWGH.ZS    9.5   28.2   19.1

Map the prevalence overweight w/h

library(classInt)
nclassint <- 3 #number of colors to be used in the palette
cat <- classIntervals(dt.1$YR2005, nclassint,style = "quantile") #style refers to how the breaks are created
colpal <- brewer.pal(nclassint,"Greens") #sequential
color.palette <- findColours(cat,colpal)
is.na(color.palette)
##  [1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
## [12] FALSE
bins <- cat$brks
lb <- length(bins)

color.palette[c(1, 10)] <- "gray"
value.vec <- c(round(bins[-length(bins)],2))
value.vec.tail <- c(round(bins[-1],2))

Plot and SAVE map:

plot(georgia1, col=color.palette, border=T, main="Prevalence of overweight, \nweight for height (% of children under 5)")
legend("topright",fill=c("gray", "#E5F5E0", "#A1D99B", "#31A354"),legend=c("NA",paste(value.vec,":",value.vec.tail)),cex=1.1, bg="white", bty = "n")
# map.scale(41, 41, 2, "km", 2, 100)
map.scale(x=40.1, y=41.2, relwidth=0.1 , metric=T, ratio=F, cex=0.8)
SpatialPolygonsRescale(layout.north.arrow(2), offset= c(40.1, 41.6), scale = 0.5, plot.grid=F)

map12

BAR CHART: a ggplot balance plot (2)

Merchandise trade balance plot in ggplot2

BAR CHART+LINE

Graph 2: Merchandise trade balance

You can find the data for this plot here or alternatively here is the dput data for balance:

structure(list(variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Merchandize Trade Balance", class = "factor"),
type = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "Balance", class = "factor"), year = c(2013L,
2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L, 2013L,
2013L, 2013L, 2013L), value = c(-0.5, -1.5, -0.1, -0.4, -0.2,
0, 0.1, -0.1, -0.6, -0.2, -0.2, -1.3, 0), geo = structure(c(2L,
4L, 7L, 9L, 1L, 6L, 12L, 5L, 3L, 11L, 10L, 13L, 8L), .Label = c("CIS",
"Dev. Asia Pacific", "Eastern Asia", "Europe", "Latin Am. And Carr.",
"North Africa", "North America", "Oceania", "South Eastern Europe",
"South-Eastern Asia", "Southern Asia", "Sub-Saharan Africa",
"Western Asia"), class = "factor")), .Names = c("variable",
"type", "year", "value", "geo"), class = "data.frame", row.names = c(NA,
-13L))
library(dplyr) #to manipulate the dataset
library(ggplot2) #plotting
mer.bal <- mydt %>%
filter(variable == "Merchandize Trade Balance")

base <- mer.bal %>%
filter(type != "Balance") %>%
mutate(
value = ifelse(type == "Exports", value, -value)
)
balance <- mer.bal %>%
filter(type == "Balance")

ggplot(balance, aes(x = geo, y = value, fill=factor(type))) +
geom_bar(data = base %>%
filter(type=="Exports"), aes(col=type), stat = "identity") +
geom_bar(data = base %>%
filter(type=="Imports"), aes(col=type), stat = "identity") +
geom_bar(data = balance, aes(col=type), stat = "identity", width=.2) +
ggtitle(expression(atop("Merchandise trade balance", atop(italic("(Bln US$ by MDG Regions in 2013)"), "")))) +
theme_bw()+
theme(axis.text.x = element_text(size=8, color="black"),
axis.text.y = element_text(size=8, color="black"),
legend.text=element_text(size=10),
plot.title = element_text(size = 20, face = "bold", colour = "black", vjust = -1))+
scale_fill_manual(values = c(Exports = "#0072B2", Imports = "#56B4E9", Balance="red"), name="") +
scale_colour_manual(values = c(Exports = "#0072B2", Imports = "#56B4E9", Balance="red"), name="") +
coord_flip()+
labs(x = "", y = "")

graph3

DONUT CHART in ggplot2

 DONUT CHART

I personally don’t like pie charts that much, I prefer donut charts, they take up less space and the center can be used for extra annotations. In ggplot2 to get the “Donut” you design a bar chart (geom_bar) and then just bend it (coord_polar) at the extremities to get a donut.

To reproduce the chart below, you can download the data from the RLadies Tbilisi github webpage, Session 9 on Plotting.

Alternatively here’s the dput(-ted) data:

structure(list(X = 1:3, variable = structure(c(1L, 1L, 1L), .Label = "Export of Services", class = "factor"), type = structure(c(3L, 2L, 1L), .Label = c("Remaining", "Transportation", "Travel"), class = "factor"), year = c(2012L, 2012L, 2012L ), value = c(55.5, 33.4, 11.1), geo = c(NA, NA, NA), pos = c(27.75, 72.2, 94.45)), .Names = c("X", "variable", "type", "year", "value", "geo", "pos"), class = "data.frame", row.names = c(NA, -3L))

Exports of services by EBOPS category

#set the working directory
setwd("/Users/DrVenkman/The Gatekeepers Folder/")

require(dplyr) #data manipulation
require(tidyr) #data manipulation, wide to long format
require(ggplot2) #ggplot package for plotting

exp.ser <- mydt %>%
filter(variable == "Export of Services")

exp.ser <- exp.ser %>% group_by(year) %>% mutate(pos = cumsum(value)- value/2)

p <- ggplot(exp.ser, aes(x=2, y=value, fill=type))+
geom_bar(stat="identity")+
geom_text( aes(label = value, y=pos), size=10, fontface="bold")+
xlim(0.5, 2.5) +
coord_polar(theta = "y")+
labs(x=NULL, y=NULL)+
labs(fill="") +
scale_fill_manual(values = c(Remaining = "blue", Transportation = "#E69F00", Travel= "#D55E00"), name="")+
ggtitle("Exports of services by EBOPS category, 2013")+
theme_bw()+
theme(plot.title = element_text(face="bold",family=c("sans"),size=15),
legend.text=element_text(size=10),
axis.ticks=element_blank(),
axis.text=element_blank(),
axis.title=element_blank(),
panel.grid=element_blank(),
panel.border=element_blank())

p

graph2

 giphy