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
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plot(georgia1, lwd=1.5) #n2
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plot(georgia2, lwd=1.5) #n3
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plot(georgia, lwd=1.5) #n4
plot(gwat, lwd=1.5, col="blue", add=T) #n4
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plot(gpop) #n5
plot(georgia, lwd=1.5, add=T) #n5
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plot(galt, lwd=1.5) #n6
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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")
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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)
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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)
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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$NAME_2[1:5]))
admin2
## [1] "Gagra" "Gali" "Gudauta" "Gulripshi" "Ochamchire"
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Upload data from World Bank
dt <- read.csv("/Users/ac1y15/Google Drive/blog/RLadies_Georgia_files/Session_3/Data_Extract_From_Subnational_Malnutrition/3f075abc-c51c-40c5-afb1-f8fbcfa30f23_Data.csv", header=T)
dt.1 <- subset(dt, dt$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)
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