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

plot(georgia1, lwd=1.5) #n2

plot(georgia2, lwd=1.5) #n3

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

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

plot(galt, lwd=1.5) #n6

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")

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)

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 GE.AB ## 1 81 GEO Georgia 1035 Ajaria Batumi GE.AJ ## 2 81 GEO Georgia 1036 Guria Ozurgeti GE.GU ## 3 81 GEO Georgia 1037 Imereti Kutaisi GE.IM ## 4 81 GEO Georgia 1038 Kakheti Telavi GE.KA ## 5 81 GEO Georgia 1039 Kvemo Kartli Rustavi GE.KK ## TYPE_1 ENGTYPE_1 VALIDFR_1 VALIDTO_1 REMARKS_1 ## 0 Avtonomiuri Respublika Autonomous Republic 1994 Present ## 1 Avtonomiuri Respublika Autonomous Republic 1994 Present ## 2 Region Region 1994 Present ## 3 Region Region 1994 Present ## 4 Region Region 1994 Present ## 5 Region Region 1994 Present ## 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,

weight 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)