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

 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      <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)

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.

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.
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)

## 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

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")

A map of the US election results

Upload libraries:

rm(list = ls(all=T)) #clear workspace
library(dplyr)
library(readr)
library(stringr)
library(tidyr)
library(readxl)
library(classInt)
library(RColorBrewer)
library(maptools) #to read shapefiles

2. Download the data files (note they are not ready for use but need some cleaning as there are more areas in the excel files than polygons in the shape file). I copy here the code as I have used it in my script but it’s available at RPubs thanks to David Robinson.

download.file("http://www2.census.gov/prod2/statcomp/usac/excel/LND01.xls", "LND01.xls")
download.file("http://www2.census.gov/prod2/statcomp/usac/excel/POP01.xls", "POP01.xls")

according to metadata, this is Land Area in 2010 and resident population in 2010:

us_county_area <- read_excel("LND01.xls") 
transmute(CountyCode = as.character(as.integer(STCOU)), Area = LND110210D)

us_county_population <- read_excel("POP01.xls") 
transmute(CountyCode = as.character(as.integer(STCOU)),Population = POP010210D)

3. Adjust data

election_url <- "https://raw.githubusercontent.com/Prooffreader/election_2016_data/master/data/presidential_general_election_2016_by_county.csv"
county_data <- read_csv(election_url) 
group_by(CountyCode = as.character(fips)) 
mutate(TotalVotes = sum(votes)) 
ungroup() 
mutate(name = str_replace(name, ".\\. ", "")) 
filter(name %in% c("Trump", "Clinton", "Johnson", "Stein")) 
transmute(County = str_replace(geo_name, " County", ""),
State = state,
CountyCode = as.character(fips),
Candidate = name,
Percent = vote_pct / 100,
TotalVotes) 
spread(Candidate, Percent, fill = 0) 
inner_join(us_county_population, by = "CountyCode") 
inner_join(us_county_area, by = "CountyCode")

you can save the data into a csv file:

# write_csv(county_data, "county_election_2016.csv")

You can download the cleaned datafile here: data_election_2016_by_county

4. Upload data and shape files

setwd("/Users/...")

dt <- read.csv("new_county_election_2016.csv", header=T)
us <- readShapePoly("./USA_adm/USA_adm2.shp")
us0 <- readShapePoly("./USA_adm/USA_adm0.shp")
us.m <- us[-c(which(us$NAME_1=="Alaska")),] #get rid of Alaska
us.d <- us.m[-c(67:71),]

5. Prepare the color palette(s)

nclassint <- 5 #number of colors to be used in the palette
cat.T <- classIntervals(dt$Trump[-c(67:71)], nclassint,style = "jenks") #style refers to how the breaks are created
colpal.T <- brewer.pal(nclassint,"Reds")
color.T <- findColours(cat.T,colpal.T) #sequential
bins.T <- cat.T$brks
lb.T <- length(bins.T)

5. Plot the maps with map basic…

# pdf("Where are the trump voters.pdf")
# plot(us.d, col=color.T, border=F)
# plot(us0,add=T, lwd=0.1)
# legend("bottomleft",fill=colpal.T,legend=paste(round(bins[-length(bins.T)],1),":",round(bins.T[-1],1)),cex=1, bg="white")
# dev.off()

where-are-the-trump-voters — % Votes for Trump

… or ggplot2

library(ggplot2)
library(scales)
theme_set(theme_bw())

ggplot(county_data, aes(Population / Area, Trump)) +
  geom_point() +
  geom_point(data=county_data[which(county_data$State=="Texas"),], aes(x=Population/Area, y=Trump), colour="red")+
  scale_x_log10() +
  scale_y_continuous(labels = percent_format()) +
  xlab("Population density (ppl / square mile)") +
  ylab("% of votes going to Trump") +
  geom_text(aes(label = County), vjust = 1, hjust = 1, check_overlap = TRUE) +
  geom_smooth(method = "lm") +
  ggtitle("Population density vs Trump voters by county (Texas Counties in red)")

This is the code to plot in red points according to State (in red) and to add red labels to those points. The check_overlap=T avoids overlapping labels.

# ggplot(county_data, aes(Population / Area, Trump)) +
#   geom_point() +
#   geom_point(data=county_data[which(county_data$State=="California"),], aes(x=Population/Area, y=Trump), colour="red")+
#   scale_x_log10() +
#   scale_y_continuous(labels = percent_format()) +
#   xlab("Population density (ppl / square mile)") +
#   ylab("% of votes going to Trump") +
#   geom_text(data=county_data[which(county_data$State=="California"),], aes(label = ifelse(Trump&amp;gt;.5, as.character(dt$County), "" )), color= "red",size=5,vjust = 1, hjust = 1, check_overlap = TRUE) +
#   geom_smooth(method = "lm") +
#   ggtitle("Population density vs Trump voters by county (California in red)")

The marriage market in XIX century Spain. La soltería en España en el siglo XIX

Using data on single men (21-35 years old) and women (16-30 years old) you can map where the unbalances in the marriage market are caused by “excess” of male or female population

Using data on single men (21-35 years old) and women (16-30 years old) you can map where the unbalances in the marriage market are caused by “excess” of male or female population.

A match made in R: checking the order of geographical areas in shape files and in your data frames

Not every shape file is as nice as those provided in libraries. Sometimes we have to deal with historical maps, which have been hand-drawn, re-touched and what not. To work with geo-referenced data it is essential to have a variable in both shape file and dataframe with unique coding that has exactly the same number of areas and the same ordering in both files.

A quick way to check if shapefile and dataframe have the same number of areas:

nrow(df) == length(shape.file$Code)

In the shapefile, one can also select a couple of areas big enough so that they can easily be located, and plot them as “control” areas.
For instance, I want to select the area with code “15078” in the shapefile:
>which(shape.file$Code=="15078",arr.Ind=T) [1] 271
which is the area in the 271-th position (same way shape.file$Code[271] gives the code of area 271).
plot(shape.file) plot(shape.file[c(271,898),],col="red",border="red",add=T)
this is an easy way to locate your “control” area(s).

Ideally, you should have some variable that is identical to the one in the shapefile, a codification of some sort, providing a unique Code, the name of the area or some factors that allow you to locate the area in space.

An easy way to check if both shape file and data frame have the same ordering of geographical areas is to test it:
>code.sh <- cbind(c(1:length(shape.file$Code)),as.vector(shape.file$Code))
>code.df <- cbind(c(1:nrow(df)),df$Code) >code.df==code.sh [,1] [,2] [1,] TRUE TRUE [2,] TRUE TRUE [3,] TRUE TRUE

What if it’s not?
First option: the inelegant solution
Manually change the order of the areas in a csv file according to the exact order they have in the shape file. It’s easy as you can create an ordinal index for the shapefile codes, paste it in excel, and assign it with a vlookup function.
Second option: the smart R match
In R there is a function called match that returns a vector of the positions of first matches of the first argument in its second:
>my.match <- match(df$Code, shape.file$Code)
NB: to use match the two variables providing the code for the areas have to have the very same unique and identical codes, or else funny stuff happens. To check that everything is in its right place, you can plot the two “control” spatial polygons we chose in the beginning, using their position in the dataframe rather than in the shapefile:
>plot(shape.file)
>plot(shape.file[c(which(df$Code=="305"),which(df$Code=="15078")),],col="orange",add=T)

Game of Thrones maps in R…

The map of GOT world with rivers, roads, lakes, the Wall, and main cities:

Neighborhood relations according to Sphere of Influence pretty much coincide with roads and rivers (package spdep):

Paste some images to locate the (surviving) Stark family members, using rasterImage from the png library:

Category: maptools

Georgia Mapping in R

Plot neighbouring countries

plot maps

add labels for the countries

let’s add everything (or almost everything) together

check georgia@data

print labels on the map

labels for admin 2

Upload data from World Bank

Map the prevalence overweight w/h

Plot and SAVE map:

Plot maps with base mapping tools and ggmap in R

Plot maps with ‘base’ mapping tools in R

Load and prepare data

Use ggmap

Use plot basic

A map of the US election results

The marriage market in XIX century Spain. La soltería en España en el siglo XIX

A match made in R: checking the order of geographical areas in shape files and in your data frames

Game of Thrones maps in R…

Mean Age at Childbearing in Spain 2011

Plot neighbouring countries

plot maps

add labels for the countries

let’s add everything (or almost everything) together

check georgia@data

print labels on the map

labels for admin 2

Upload data from World Bank

Map the prevalence overweight w/h

Plot and SAVE map:

Share:

Like this:

Plot maps with ‘base’ mapping tools in R

Load and prepare data

Use ggmap

Use plot basic

Share:

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