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

Plot maps with base mapping tools and ggmap in R

Plot maps with ‘base’ mapping tools in R

Understanding what kind of data you have (polygons or points?) and what you want to map is pivotal to start your mapping.

  1. First you need a shapefile of the area you want to plot, such as metropolitan France. There are various resources where to get them from: DIVA-GIS and EUROSTAT are those that I use the most. It’s always important to have a .prj file included, as your final map ‘should’ be projecte. I say “should” as sometimes it is just not possible, especially if you work with historical maps.
  2. Upload libraries

Load and prepare data

setwd(paste(mypath))
fr.prj <- readOGR(".", "FRA_adm2")
## OGR data source with driver: ESRI Shapefile
## Source: ".", layer: "FRA_adm2"
## with 96 features
## It has 18 fields
## NOTE: rgdal::checkCRSArgs: no proj_defs.dat in PROJ.4 shared files
map(fr.prj)
rplot
## Warning in SpatialPolygons2map(database, namefield = namefield): database
## does not (uniquely) contain the field 'name'.

head(fr.prj@data)
##   ID_0 ISO NAME_0 ID_1    NAME_1  ID_2         NAME_2   VARNAME_2
## 0   76 FRA France  989    Alsace 13755       Bas-Rhin  Unterelsaá
## 1   76 FRA France  989    Alsace 13756      Haut-Rhin   Oberelsaá
## 2   76 FRA France  990 Aquitaine 13757       Dordogne        <NA>
## 3   76 FRA France  990 Aquitaine 13758        Gironde Bec-D'Ambes
## 4   76 FRA France  990 Aquitaine 13759         Landes      Landas
## 5   76 FRA France  990 Aquitaine 13760 Lot-Et-Garonne        <NA>
##   NL_NAME_2 HASC_2 CC_2      TYPE_2  ENGTYPE_2 VALIDFR_2 VALIDTO_2
## 0      <NA>  FR.BR <NA> Département Department  17900226   Unknown
## 1      <NA>  FR.HR <NA> Département Department  17900226   Unknown
## 2      <NA>  FR.DD <NA> Département Department  17900226   Unknown
## 3      <NA>  FR.GI <NA> Département Department  17900226   Unknown
## 4      <NA>  FR.LD <NA> Département Department  17900226   Unknown
## 5      <NA>  FR.LG <NA> Département Department  17900226   Unknown
##   REMARKS_2 Shape_Leng Shape_Area
## 0      <NA>   4.538735  0.5840273
## 1      <NA>   3.214178  0.4198797
## 2      <NA>   5.012795  1.0389622
## 3      <NA>   9.200047  1.1489822
## 4      <NA>   5.531231  1.0372815
## 5      <NA>   4.489830  0.6062017
# load or create data
set.seed(100)
myvar <- rnorm(1:96)
# manipulate data for the plot
france.geodata  <- data.frame(id=rownames(fr.prj@data), mapvariable=myvar)
head(france.geodata)
##   id mapvariable
## 1  0  1.12200636
## 2  1  0.05912043
## 3  2 -1.05873510
## 4  3 -1.31513865
## 5  4  0.32392954
## 6  5  0.09152878

Use ggmap

# fortify prepares the shape data for ggplot
france.dataframe <- fortify(fr.prj) # convert to data frame for ggplot
## Regions defined for each Polygons
head(france.dataframe)
##       long      lat order  hole piece id group
## 1 7.847912 49.04728     1 FALSE     1  0   0.1
## 2 7.844539 49.04495     2 FALSE     1  0   0.1
## 3 7.852439 49.04510     3 FALSE     1  0   0.1
## 4 7.854333 49.04419     4 FALSE     1  0   0.1
## 5 7.855955 49.04431     5 FALSE     1  0   0.1
## 6 7.856299 49.03776     6 FALSE     1  0   0.1
#now combine the values by id values in both dataframes
france.dat <- join(france.geodata, france.dataframe, by="id")
head(france.dat)
##   id mapvariable     long      lat order  hole piece group
## 1  0    1.122006 7.847912 49.04728     1 FALSE     1   0.1
## 2  0    1.122006 7.844539 49.04495     2 FALSE     1   0.1
## 3  0    1.122006 7.852439 49.04510     3 FALSE     1   0.1
## 4  0    1.122006 7.854333 49.04419     4 FALSE     1   0.1
## 5  0    1.122006 7.855955 49.04431     5 FALSE     1   0.1
## 6  0    1.122006 7.856299 49.03776     6 FALSE     1   0.1
# Plot 3
p <- ggplot(data=france.dat, aes(x=long, y=lat, group=group))
p <- p + geom_polygon(aes(fill=mapvariable)) +
       geom_path(color="white",size=0.1) +
       coord_equal() +
       scale_fill_gradient(low = "#ffffcc", high = "#ff4444") +
       labs(title="Our map",fill="My variable")
# plot the map
p

image-22-02-2017-at-12-11

Use plot basic

nclassint <- 5 #number of colors to be used in the palette
cat <- classIntervals(myvar, nclassint,style = "jenks") #style refers to how the breaks are created
colpal <- brewer.pal(nclassint,"RdBu")
color <- findColours(cat,rev(colpal)) #sequential
bins <- cat$brks
lb <- length(bins)
plot(fr.prj, col=color,border=T)
legend("bottomleft",fill=rev(colpal),legend=paste(round(bins[-length(bins)],1),":",round(bins[-1],1)),cex=1, bg="white")

image-22-02-2017-at-12-23-copy

Find color breaks for mapping (fast)

I’ve stumbled upon a little trick to compute jenks breaks faster than with the classInt package, just be sure to use n+1 instead of n as the breaks are computed a little bit differently. That is to say, if you want 5 breaks, n=6, no biggie there.

For more on the Bayesian Analysis of Macroevolutionary Mixtures see BAMMtools library

install.packages("BAMMtools")
library(BAMMtools)
system.time(getJenksBreaks(mydata$myvar, 6))
> user system elapsed
> 0.970 0.001 0.971

On the other hand this takes way more time with large datasets
library(classInt)
system.time(classIntervals(mydata$myvar, n=5, style="jenks"))
> Timing stopped at: 1081.894 1.345 1083.511

A ggmap of 2015 Israeli elections by city

IL_el_percThe recent Israeli elections are a reminder of how Demography and Space play a crucial role in the outcome of the 20th Knesset. For more insight, read the full Demotrends blog post by Ashira Menashe-Oren the demographics of the Israeli electorate here. The map has been done using ggmap and ggplot, two simple mapping tools I really like. If you are interested in the code, below you can find the relative syntax and data.

To start upload the libraries:

library(maptools) #reads the shape file

library(ggmap)

library(ggplot2)

Download the shape file (I normally use Diva-GIS website) and read it:

map.ogr<- readOGR(".","ISR_adm1")

Data set:

df <- structure(list(lon = c(35.148529, 35.303546, 34.753934, 34.781768,34.989571, 34.824785, 34.808871, 34.883879, 34.844675, 34.90761, 35.010397, 34.871326, 35.21371, 34.655314, 34.887762, 34.792501, 34.574252, 34.791462, 34.748019, 34.787384, 34.853196, 34.811272, 34.919652, 34.888075, 35.098051, 35.119773, 34.872938, 34.835226, 34.988099, 35.002462), lat = c(32.517127, 32.699635, 31.394548, 32.0853, 32.794046, 32.068424, 32.072176, 32.149961, 32.162413, 32.178195, 31.890267, 32.184781, 31.768319, 31.804381, 32.084041, 31.973001, 31.668789, 31.252973, 32.013186, 32.015833, 32.321458, 31.892773, 32.434046, 31.951014, 33.008536, 32.809144, 31.931566,32.084932, 31.747041, 31.90912), City = structure(c(30L, 19L,24L, 29L, 9L, 25L, 7L, 11L, 10L, 14L, 16L, 23L, 13L, 1L, 21L,28L, 2L, 4L, 3L, 12L, 20L, 27L, 8L, 15L, 18L, 22L, 26L, 6L, 5L, 17L), .Label = c("Ashdod", "Ashkelon", "Bat yam", "Beersheva",  "Beit  Shemesh", "Bnei brak", "Giv'atayim", "Hadera", "Haifa",  "Herzliyya", "Hod HaSharon", "Holon", "Jerusalem", "Kefar Sava",  "Lod", "Modi'in - Makkabbim - Re'ut", "Modi'in Illit", "Nahariyya", "Nazareth ", "Netanya", "Petach Tikva", "Qiryat Atta", "Ra'annana",  "Rahat", "Ramat gan", "Ramla", "Rehovot", "Rishon", "Tel-Aviv",  "Umm Al-Fahm"), class = "factor"), most.votes = c(96.28, 91.41,  87.62, 34.03, 24.98, 30.93, 40.1, 38.77, 34.2, 34.66, 28.95,  32.75, 23.9, 30.96, 27.87, 29.78, 39.31, 37.17, 32.88, 30.86,  33.14, 26.95, 31.77, 32.22, 34.25, 35.01, 39.1, 57.56, 27.89,  71.63), party = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L), .Label = c("joint list", "labour", "likud", "yahadut hatora"), class = "factor")), .Names = c("lon", "lat",  "City", "most.votes", "party"), class = "data.frame", row.names = c(NA,  -30L))

get the map using “get_map"

gmap <- get_map(location=c(34.2,29.4,36,33.5),zoom=7,source="stamen",maptype="watercolor")

and plot the map:

ggmap(gmap)+

geom_polygon(aes(x = long, y = lat, group=id), data = map.ogr, color ="blue", fill ="white", alpha = .8, size = .4)+

geom_point(aes(x=lon,y=lat,color=party,size=most.votes),data=df)+ scale_colour_discrete("Coalition", labels = c("Joint List", "Labour","Likud","United Torah Judaism"), breaks = c("joint list", "labour","likud","yahadut hatora")) + scale_size_continuous("Coalition", labels = c("Joint List", "Labour","Likud","United Torah Judaism"), breaks = c("joint list", "labour","likud","yahadut hatora"), range=c(10,15), guide = FALSE)+ theme(axis.text=element_text(size=18), plot.title=element_text(size=rel(3)), legend.key = element_rect(fill = "white"), legend.background =element_rect("white"), legend.text = element_text(size = 25), legend.title = element_text(size = 25))+ guides(colour = guide_legend(override.aes = list(size=8)))+ labs(x="",y="")

IL_el_perc_city_names_color If you want to add city names you can use the “annotate” option, adding the code below after guides(...)+. I have modified the coordinates to avoid overlapping of labels and colored names to match the color of the winner party.

annotate("text",x=c(35.14853+ 0.2,35.21371+0.15,35.00246+ 0.15,34.79146+0.15, 34.98957-0.08,34.78177-0.14), y=c(32.51713,31.76832,31.90912,31.25297, 32.79405,32.08530),size=5,font=3, label=c("Umm Al-Fahm","Jerusalem","Modin  Illit","Beersheva","Haifa","Tel Aviv"), color=c("darkred","blue4","deeppink4", "blue4","springgreen4","green4"))+

For beginners I highly recommend ggplot2 mailing list, a great and shame-free place to learn.