Bangladesh geofacet plot: Female population projections by upazilas

BGD_plot

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

See you at IUSSP to talk about the fantastic work we do at WorldPop! (plus Demotrends and R-Ladies)

As IUSSP is approaching, I’m looking forward to talk more about fine grid scale mapping research at WorldPop (University of Southampton) and Flowminder.

I will present my research on  the 30th of October in session 5 at 8:30am Integrating spatial and statistical methods in demographic research, meeting room 1.41 and 1.42.

Prof. Andy Tatem will host a side meeting Geospatial Demography: Combining Satellite, Survey, Census and Cellphone Data to Provide Small-area Estimates on the 29 October 2017, 8:30-16:00.

and  will be contributing to the Cape Town R-Ladies chapter Saturday 4 November (details here) with a talk on R-Ladies and data visualization in ggplot2. Come talk to us and become an R-Lady, we are looking forward to sharing our experiences.

A few Demotrend(ers) will also be presenting at IUSSP, come talk to us 🙂

See you in Cape Town!

 

Global and Local Measures of Spatial Autocorrelation

This post aims at being a summary of the available techniques to investigate spatial autocorrelation for the social sciences, rather than presenting the theory behind spatial autocorrelation. For that there are great books available on line, like Anselin’s, Le Page, and Bivand just to cite a few.

The techniques presented here work for a spatial polygons data frame. The difference between spatial points and polygons data frames is not that big, the idea is the same and most of what I am doing here can be applied to data points.

Why do we look at spatial autocorrelation at all? Spatial autocorrelation leads to biased results in regressions, this is the reason why we want to compute Moran’s I and why we include spatial autocorrelation if its measurement proves to be significative and non-random.

Spatial autocorrelation can be investigated globally or locally. “Globally”, implies that the measure you’re going to obtain refers to the dataset as a whole, whether it is a whole country, continent or region. “Locally”, means that you are taking into consideration each and every polygon and getting a measure for each one of them.

We start by uploading the data, projecting them (important when considering the distance based measures -earth is not flat, whatever they may say!), and construct neighborhood relations (in this case Queen and Rook, but could be any other). For more detail see this post on how to construct neighbor relations.

library(maptools)
library(spdep)
NC= readShapePoly(system.file("shapes/sids.shp", package="maptools")[1], IDvar="FIPSNO", proj4string=CRS("+proj=longlat +ellps=clrk66"))
nb.FOQ = poly2nb(NC, queen=TRUE, row.names=NC$FIPSNO)
nb.RK = poly2nb(NC, queen=FALSE, row.names=NC$FIPSNO)

Global measures of spatial autocorrelation

There are two main measures of global spatial autocorrelation: Moran’s I and Geary’s C. Moran’s I is the most used in my experience, but both work are perfectly acceptable.
Moran’s I ranges between -1 (strong negative spatial autocorrelation with a dispersed pattern) and 1 (strong positive spatial autocorrelation with a clustered pattern) with 0 being the absence of spatial autocorrelation.
Geary’s C ranges between 0 and 2, with positive spatial autocorrelation ranging from 0 to 1 and negative spatial autocorrelation between 1 and 2.
Of course being these inferential measures, if the p-value is non significant we cannot exclude that the patterns could be random(!)

In this case Moran’s I is positive and significant, the z-score (not provided by moran.test) is positive implying spatial clusters, so we can reject the null hypothesis.

library(spdep)
nwb <- NC$NWBIR74
moran.test(nwb, listw = nb2listw(nb.RK))
##
##  Moran I test under randomisation
##
## data:  nwb
## weights: nb2listw(nb.RK)
##
## Moran I statistic standard deviate = 3.0787, p-value = 0.001039
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic       Expectation          Variance
##       0.185965551      -0.010101010       0.004055701
geary.test(nwb, listw = nb2listw(nb.RK))
##
##  Geary C test under randomisation
##
## data:  nwb
## weights: nb2listw(nb.RK)
##
## Geary C statistic standard deviate = 2.0324, p-value = 0.02106
## alternative hypothesis: Expectation greater than statistic
## sample estimates:
## Geary C statistic       Expectation          Variance
##        0.83274888        1.00000000        0.00677185

if you have polygons with no neighbors remember to specify zero.policy=NULL

moran.plot(nwb, listw = nb2listw(nb.RK))

moran plot

moran.mc(nwb, listw = nb2listw(nb.RK), nsim=100)
##
##  Monte-Carlo simulation of Moran I
##
## data:  nwb
## weights: nb2listw(nb.RK)
## number of simulations + 1: 101
##
## statistic = 0.18597, observed rank = 100, p-value = 0.009901
## alternative hypothesis: greater

permutation Moran_s I

plot(moran.mc(nwb, listw = nb2listw(nb.RK), nsim=100))


Same thing can be done for Geary’s C:

geary.mc(nwb, listw = nb2listw(nb.RK), nsim=100)
##
##  Monte-Carlo simulation of Geary C
##
## data:  nwb
## weights: nb2listw(nb.RK)
## number of simulations + 1: 101
##
## statistic = 0.83275, observed rank = 5, p-value = 0.0495
## alternative hypothesis: greater
plot(geary.mc(nwb, listw = nb2listw(nb.RK), nsim=100))

Local measures of spatial autocorrelation

Local Moran and Local G

locm <- localmoran(nwb, listw = nb2listw(nb.RK))
locG <- localG(nwb, listw = nb2listw(nb.RK))

Get the neighbor matrix into a listwise format with listw: there’s two options here, row-standardized weights matrix style = "W" creates proportional weights when polygons have an unequal number of neighbors, balancing out observations with few neighbors. Binary weights style = "B" upweight observations with many neighbors.

library(classInt)
library(dplyr)
myvar <- NC$NWBIR74
nb <- nb.RK
# Define weight style
ws <- c("W")

# Define significance for the maps
significance <- 0.05
plot.only.significant <- TRUE

# Transform the neigh mtx into a listwise object
listw <- nb2listw(nb, style=ws)

# Create the lagged variable
lagvar <- lag.listw(listw, myvar)

# get the mean of each
m.myvar <- mean(myvar)
m.lagvar <- mean(lagvar)

The next step is to derive the quadrants and set the coloring scheme. I like to color the border of each polygon with the color of their local moran score, regardless of their pvalue, and then fill only the significant ones.

n <- length(NC)
#
vec <- c(1:n)
vec <- ifelse(locm[,5] < significance, 1,0)

# Derive quadrants
q <- c(1:n) for (i in 1:n) {   if (myvar[[i]]>=m.myvar & lagvar[[i]]>=m.lagvar) q[i] <- 1
  if (myvar[[i]]<m.myvar & lagvar[[i]]<m.lagvar) q[i] <- 2
  if (myvar[[i]]<m.myvar & lagvar[[i]]>=m.lagvar) q[i] <- 3   if (myvar[[i]]>=m.myvar & lagvar[[i]]<m.lagvar) q[i] <- 4
}

# set coloring scheme
q.all <- q
colors <- c(1:n)
for (i in 1:n) {
  if (q.all[i]==1) colors[i] <- "red"
  if (q.all[i]==2) colors[i] <- "blue"
  if (q.all[i]==3) colors[i] <- "lightblue"
  if (q.all[i]==4) colors[i] <- "pink"
  if (q.all[i]==0) colors[i] <- "white"   if (q.all[i]>4) colors[i] <- "white"
}

# Mark all non-significant regions white
locm.dt <- q*vec
colors1 <- colors
for (i in 1:n)
{
  if ( !(is.na (locm.dt[i])) )  {
  if (locm.dt[i]==0) colors1[i] <- "white"
}
}
colors2 <- colors
colors2 <- paste(colors2,vec)
pos = list()
for (i in 1:n) {
  pos[[i]] <- c(which(NC$NWBIR74==colors2["blue 0"]))
}

blue0 <- which(colors2=="blue 0")
red0 <- which(colors2=="red 0")
lightblue0 <- which(colors2=="lightblue 0")
pink0 <- which(colors2=="pink 0")
lb <- 6
labels=c("High-High", "High-Low", "Low-High", "Low-Low")

# plot the map
if (plot.only.significant==TRUE) plot(NC, col=colors1,border=F) else
  plot(NC, col=colors,border=F)
plot(NC[blue0,],border="blue",lwd=0.5,add=T)
plot(NC[lightblue0,],border="lightblue",add=T,lwd=0.5)
plot(NC[red0,],border="red",add=T,lwd=0.5)
plot(NC[pink0,],border="pink",add=T,lwd=0.5)
legend("bottomleft", legend = labels, fill = c("red", "pink", "lightblue", "blue"), bty = "n")

lisa map
Local G gives back z-scores values and indicate the posibility of a local cluster of high values of the variable being analysed, very low values indicate a similar cluster of low values.

library(RColorBrewer)

nclassint <- 3
colpal <- brewer.pal(nclassint,"PiYG")
cat <- classIntervals(locG, nclassint, style = "jenks", na.ignore=T)
color.z <- findColours(cat, colpal)

plot(NC, col= color.z, border=T)

Rplot

# color only significant polygons
plot(NC, border=T)
plot(NC[which(vec==1),], col=color.z[which(vec==1)], border=T, add=T)

lisa map2

Geofacet: Bangladesh 64 districts education

Geofacet example using World Bank data on Bangladesh education attainment

  1. set up grids
  2. upload data, source: World Bank
  3. plot and save

1. Grid for Bangladesh districts:

library(tidyverse)
library(geofacet)
library(ggthemes)
options(scipen = 99)
mygrid <- data.frame(
row = c(1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11),
col = c(3, 2, 3, 4, 3, 4, 5, 3, 4, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 2, 3, 4, 5, 6, 7, 1, 2, 3, 4, 5, 6, 7, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 9),
code = c("BG01","BG02","BG03","BG04","BG05","BG06","BG07","BG08","BG09","BG10","BG11","BG12","BG13","BG14","BG15","BG16","BG17","BG18","BG19","BG20","BG21","BG22","BG23","BG24","BG25","BG26","BG27","BG28","BG29","BG30","BG31","BG32","BG33","BG34","BG35","BG36","BG37","BG38","BG39","BG40","BG41","BG42","BG43","BG44","BG45","BG46","BG47","BG48","BG49","BG50","BG51","BG52","BG53","BG54","BG55","BG56","BG57","BG58","BG59","BG60","BG61","BG62","BG63", "BG64"),
name = c("Panchagar","Takurgaong","Nilphamar","Lamonirhat","Dinajpur","Rangpur","Kurigram","Jaipurat","Gaibandha","Naogaon","Bogra","Jamalpur","Sherpar","Mymensingh","Netrokona","Suramganj","Sylhet","Chapai","Rajshani","Nator","Sirajganj","Tangail","Gazipur","Kishoreganj","Habiganj","Moulvibazar","Kushtia","Pabna","Dhaka","Nardiaganj","Narsingdi","Brahmanbaria","Meherpur","Jhenaidah","Magura","Rajbari","Manikganj","Munshiganj","Comilla","Khagrachhari","Rangramati","Chuadanga","Jessore","Gopalganj","Faridpur","Madanipur","Shariyapur","Chandpur","Feni","Chittagong","Badanbari","Narail","Pirojpur","Barisal","Jhalkati","Laksimipur","Noakhali","Satkhira","Khulna","Bagerhat","Borguna","Patuakhali","Bhola","Cox's Bazar"),
stringsAsFactors = FALSE
)

Preview the grid:

geofacet::grid_preview(mygrid)

Screen Shot 2017-07-23 at 16.17.02.png

2. Data from World Bank

dt3<-structure(list(ordinal = c(58L, 58L, 58L, 58L, 58L, 58L, 58L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 34L, 56L, 57L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 53L, 55L, 59L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 47L, 54L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 22L, 27L, 30L, 33L, 45L, 48L, 63L, 64L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 20L, 23L, 46L, 49L, 51L, 52L, 60L, 61L, 62L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 8L, 18L, 28L, 29L, 40L, 50L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 11L, 25L, 26L, 31L, 38L, 41L, 42L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 9L, 13L, 14L, 17L, 19L, 21L, 24L, 32L, 36L, 37L, 2L, 4L,6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 2L, 4L, 6L, 7L, 10L, 15L, 16L, 43L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L,
5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L, 1L, 3L, 5L, 12L, 35L, 39L, 44L), Division.Name = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 3L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 5L, 6L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 3L, 3L, 5L, 5L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 3L, 3L, 5L, 5L, 5L, 5L, 6L, 7L, 7L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 5L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 4L, 4L, 4L), .Label = c("BARISAL", "CHITTAGONG", "DHAKA", "KHULNA", "RAJSHAHI", "RANGPUR", "SYLHET"
), class = "factor"), Zila.Name = structure(c(50L, 50L, 50L, 50L, 50L, 50L, 50L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 63L, 33L, 47L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 15L, 30L, 56L, 26L, 18L, 26L, 18L, 26L, 18L,
26L, 18L, 26L, 18L, 26L, 18L, 26L, 18L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 22L, 40L, 46L, 59L, 6L, 41L, 61L, 62L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 19L, 29L, 9L, 45L, 54L, 60L, 64L, 21L, 37L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 12L, 36L, 39L, 53L,
25L, 35L, 38L, 12L, 36L, 39L, 53L, 25L, 35L, 38L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 8L, 17L, 27L, 55L, 16L, 20L, 34L, 58L, 11L, 23L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 4L, 24L, 52L, 2L, 10L, 32L, 48L, 42L, 3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L, 3L, 5L, 51L, 13L, 1L, 28L, 57L), .Label = c("BAGERHAT", "BANDARBAN", "BARGUNA", "BARISAL",
"BHOLA", "BOGRA", "BRAHMANBARIA", "CHANDPUR", "CHAPAI NABABGANJ", "CHITTAGONG", "CHUADANGA", "COMILLA", "COX'S BAZAR", "DHAKA", "DINAJPUR", "FARIDPUR", "FENI", "GAIBANDHA", "GAZIPUR", "GOPALGANJ", "HABIGANJ", "JAMALPUR", "JESSORE", "JHALOKATI", "JHENAIDAH", "JOYPURHAT", "KHAGRACHHARI", "KHULNA", "KISHOREGANJ", "KURIGRAM", "KUSHTIA", "LAKSHMIPUR", "LALMONIRHAT", "MADARIPUR", "MAGURA",
"MANIKGANJ", "MAULVIBAZAR", "MEHERPUR", "MUNSHIGANJ", "MYMENSINGH", "NAOGAON", "NARAIL", "NARAYANGANJ", "NARSINGDI", "NATORE", "NETRAKONA", "NILPHAMARI", "NOAKHALI", "PABNA", "PANCHAGARH", "PATUAKHALI", "PIROJPUR", "RAJBARI", "RAJSHAHI", "RANGAMATI", "RANGPUR", "SATKHIRA", "SHARIATPUR", "SHERPUR", "SIRAJGANJ", "SUNAMGANJ", "SYLHET", "TANGAIL", "THAKURGAON"), class = "factor"), name = structure(c(50L, 50L, 50L, 50L, 50L, 50L, 50L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L, 47L, 64L, 33L,
47L, 15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L, 15L, 30L, 55L, 22L, 18L, 22L, 18L, 22L, 18L, 22L, 18L, 22L, 18L, 22L, 18L, 22L, 18L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 23L, 40L, 46L, 59L, 5L, 41L, 61L, 62L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L,
63L, 21L, 38L, 19L, 29L, 9L, 45L, 54L, 60L, 63L, 21L, 38L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 7L, 14L, 43L, 44L, 31L, 49L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 12L, 36L, 39L, 53L, 26L, 35L, 37L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L,
8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 8L, 17L, 27L, 56L, 16L, 20L, 34L, 58L, 11L, 24L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L,
1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 3L, 25L, 52L, 1L, 10L, 32L, 48L, 42L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L, 6L, 4L, 51L, 13L, 2L, 28L, 57L), .Label = c("Badanbari", "Bagerhat", "Barisal", "Bhola", "Bogra", "Borguna", "Brahmanbaria", "Chandpur", "Chapai", "Chittagong", "Chuadanga", "Comilla", "Cox's Bazar", "Dhaka", "Dinajpur", "Faridpur", "Feni", "Gaibandha", "Gazipur", "Gopalganj", "Habiganj", "Jaipurat", "Jamalpur", "Jessore", "Jhalkati", "Jhenaidah", "Khagrachhari", "Khulna", "Kishoreganj", "Kurigram", "Kushtia", "Laksimipur", "Lamonirhat", "Madanipur", "Magura", "Manikganj", "Meherpur", "Moulvibazar", "Munshiganj", "Mymensingh", "Naogaon", "Narail",
"Nardiaganj", "Narsingdi", "Nator", "Netrokona", "Nilphamar", "Noakhali", "Pabna", "Panchagar", "Patuakhali", "Pirojpur", "Rajbari", "Rajshani", "Rangpur", "Rangramati", "Satkhira", "Shariyapur", "Sherpar", "Sirajganj", "Suramganj", "Sylhet", "Takurgaong", "Tangail"), class = "factor"), row = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L), col = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 5L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 4L, 6L, 7L, 5L, 3L, 2L, 8L, 9L, 6L, 7L, 1L, 3L,
2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 6L, 7L, 1L, 3L, 2L, 4L, 5L, 8L, 9L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L,
6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 4L, 5L, 6L, 2L, 3L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 5L, 6L, 4L, 2L, 3L, 1L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 7L, 8L, 9L, 10L, 4L, 3L, 5L, 6L, 1L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 4L, 5L, 3L, 10L, 9L, 6L, 7L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L, 5L, 7L, 6L, 9L, 4L, 3L, 2L), code = structure(c(12L, 12L, 12L, 12L, 12L, 12L, 12L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 4L, 6L, 5L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 7L, 13L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 10L, 15L, 16L, 14L, 9L, 8L, 11L, 17L, 23L, 24L, 18L, 20L, 19L, 21L,22L,25L,26L,23L,24L,18L,20L,19L,21L,22L,25L,26L,23L,24L,
18L,20L,19L,21L,22L,25L,26L,23L,24L,18L,20L,19L,21L, 22L,25L,26L,23L, 24L, 18L, 20L, 19L, 21L, 22L, 25L, 26L, 23L, 24L, 18L, 20L, 19L, 21L, 22L, 25L, 26L, 23L, 24L, 18L, 20L, 19L, 21L, 22L, 25L, 26L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 32L, 29L, 30L, 31L, 27L, 28L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 39L, 37L, 38L, 36L, 34L, 35L, 33L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L, 49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 48L,
49L, 40L, 41L, 45L, 44L, 46L, 47L, 42L, 43L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 54L, 55L, 53L, 51L, 50L, 56L, 57L, 52L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L, 61L, 63L, 62L, 64L, 60L, 59L, 58L), .Label = c(" BG02", " BG03", " BG04", " BG05", " BG06", " BG07", " BG08", " BG10", " BG11", " BG12", " BG16", "BG01", "BG09", "BG13", "BG14", "BG15", "BG17", "BG18", "BG19", "BG20", "BG21", "BG22", "BG23", "BG24", "BG25", "BG26", "BG27", "BG28", "BG29", "BG30", "BG31", "BG32", "BG33", "BG34", "BG35", "BG36", "BG37", "BG38", "BG39", "BG40", "BG41", "BG42", "BG43", "BG44", "BG45", "BG46", "BG47", "BG48", "BG49", "BG50", "BG51", "BG52", "BG53", "BG54", "BG55", "BG56", "BG57", "BG58", "BG59", "BG60", "BG61", "BG62", "BG63", "BG64"), class = "factor"),
type = structure(c(1L, 2L, 3L, 4L, 1L, 3L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 2L, 2L,
3L, 3L, 4L, 4L, 1L, 1L, 3L, 3L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("lessthanPrimary", "Primary", "Secondary", "University"), class = "factor"), mean = c(0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L),
    value = c(53.9, 33.2, 10.3, 2.7, 50.3, 12.8, 3.8, 57.2, 59.5, 61.3, 29.1, 29, 26.1, 11, 9.3, 10.1, 2.6, 2.3, 2.5, 50.3,50.3, 50.3, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 50.4, 63.5,     56.7, 33.1, 24.8, 27.4, 13.1, 9.4, 12.3, 3.4, 2.3, 3.6, 50.3,     50.3, 50.3, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 50, 62.9, 34.2,     23.9, 12.7, 10.7, 3.1, 2.5, 50.3, 50.3, 12.8, 12.8, 3.8,     3.8, 67.4, 58.1, 64.2, 66.9, 53.8, 53.7, 68.7, 50.5, 21.3,     29.1, 26.2, 23, 30.1, 33.9, 25, 34.6, 9.1, 10.2, 7.7, 8.3,     13, 10.1, 5.2, 11.7, 2.3, 2.6, 1.9, 1.8, 3.1, 2.3, 1.1, 3.2, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8,     12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 35.4, 62.1, 59.7, 54.4, 48.4, 60.9, 55, 61.6, 53.9, 43.3, 28.1, 29.2, 32, 33, 27.2, 30.4, 30.2, 35.6, 17.5, 8.1, 8.7, 10.7, 13.5, 9.7, 11.3, 6.9, 8.7, 3.8, 1.8, 2.3, 2.8, 5.1, 2.2, 3.2, 1.3, 1.8, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8,     12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8,     3.8, 56.8, 28.8, 42.7, 51.7, 57, 56.7, 32.1, 34.8, 39.1,     34.6, 28.6, 29.1, 9.4, 23.3, 14.8, 11.3, 11.5, 11.3, 1.7,     13.1, 3.5, 2.4, 2.9, 2.8, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3,     12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 47.3, 56.1, 49, 56.7, 54.3, 53, 59.8, 36.1, 31.8, 38.4,     31, 33.1, 33.3, 29.9, 13.9, 10, 10.8, 10, 10.1, 11.2, 8.2,     2.7, 2.1, 1.8, 2.4, 2.5, 2.6, 2, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 44.7, 38.8, 59.3, 57, 54.5, 46.4, 56, 58.9, 55, 46.5, 40.1, 41.1, 29.5, 29, 32.8, 40, 32.6, 32.3, 34.3, 36.5, 12.9, 17, 9.3, 11.5, 10, 11, 9.2, 7.3, 8.9, 13.4, 2.3, 3.1, 1.9, 2.5, 2.6, 2.6, 2.1, 1.5, 1.8, 3.5, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8,  12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 39.8, 33.8, 36.6, 70, 38.9, 52.9, 47.6, 49.4, 41.2, 46, 45.9, 20.9, 36.3, 34.8, 38.2, 36.8, 15.2, 17.1, 14.4, 7.6, 19.1, 10.4, 12, 11.3, 3.8, 3.1, 3, 1.5, 5.8, 2, 2.2, 2.5, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8,  12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 43.6, 60.6, 48.1, 62.3, 42.8, 41.1, 51.3, 42.9, 28.6, 37.8, 27.1, 41.4, 36.7, 35.1, 11.1, 8.8, 11.7, 8.6, 12.8, 16.8, 10.9, 2.3, 2.1, 2.4, 2, 3, 5.4, 2.7, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 50.3, 12.8, 12.8, 12.8,    12.8, 12.8, 12.8, 12.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8    )), .Names = c("ordinal", "Division.Name", "Zila.Name", "name", "row", "col", "code", "type", "mean", "value"), class = "data.frame", row.names = c(NA, -448L))

3. Plot and save:

library(ggplot2)
p <- ggplot(dt3 , aes(type, value, fill=type))+
geom_col(position = position_dodge())+
scale_fill_manual(values = c("#7fc97f", "#beaed4","#fdc086","#ffff99"))+
themebw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank()),
plot.title=element_text(size=20, face="bold"))+
facet_geo(~name, grid = mygrid) +
labs(title = "Educational attainment in Bangladesh", fill="Edu attainment%",
caption = "By Aledemogr    Source: World Bank"
)
# ggsave("bgd1.png",height = 17, width = 10)</code>

 

bgd1

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

How to get good maps in R and avoid the expensive softwares

How to convey as much information as possible in a clear and simple way? Producing maps for social sciences is not difficult, there are a plethora of softwares that can help us. But there are a few issues to consider when choosing your to go program:
(1) Do I want to do all my analysis in one (or more) program(s) and then switch to another one to make those maps?
(2) Are those programs freely accessible to me?
1. Using more than one softwares usually implies spending time to learn different syntax:  why do your analysis in (insert name here ____) and then plot in R when you can do everything in R?
2. The availability of mapping softwares is no trivial issue. Not all researchers have powerful computers, not all institutes have bottomless funds to buy licences, and sometimes having the possibility to map on your laptop while bingeing on Netflix is way nicer than waiting for the one computer with the one licence.

Probably the best and most elegant mapping tool available to Geographers is ArcGIS (to my knowledge, but again, I use R and own a Mac), however it does not come for free. What to do? Well, R is a very good alternative, you can produce elegant maps, customizable to the very last detail. The only drawback I have encountered is the time you would spend to get the first map, but then you would have the syntax and any other map would be pretty quick to plot, and you can always for loop all graphics (although I do not recommend it). Moreover, R runs on your Mac (and Linux), it allows for way more control over features, and has great color palettes (see here and here).

Here are some useful libraries:
library(maps) #for creating geographical maps
library(RColorBrewer) #contains color palettes
library(classInt) #defines the class intervals for the color palettes
library(maptools) #tools for handling spatial objects
library(raster) #tools to deal with raster maps
library(ggplot2) #to create maps, quick and painless

Some stuff to keep in mind:
(1) add a scale with scale.map (or a nice  scalebar);
(2) it is sometimes required to add a north arrow, you can find many versions for that (see this document on page 4 for  examples, I use the same with no labels);
(3) locator() is a very useful tool to get the coordinates when adding labels, arrows, scales and so on.

Part 1: get a plain map.

Below is a very simple example produced using EUROSTAT shape files for world countries (world) and DIVA-GIS for Spain at NUTS3 level (spain). In this map I have removed the Canary Islands, but you can always cut it and paste it in the map using either par(fig=c(…)) or par(fin(…)), inset, or something more elaborated with layout, and framing it using box() or rectangle.

world is the shapefile for the whole world, where I select the neighboring countries I want to appear in the map, in this case Spain, France, Portugal, France, Morocco, and Algeria.
spain is the Spain NUTS3 shapefile where I remove the Canary Islands (45)


plot(spain[-c(45),], border=F) #this first line does not plot anything, it just centers my graph on Spain, the -c(45) removes the Canary Islands
plot(world[c(6, 67,74, 132, 177),], border="lightblue",add=T, col="beige") #plotting the countries appearing in the map
plot(spain[-c(45),], border="brown", lwd=0.2, add=T, col="lightblue") #plot spain, removing the Canary Islands
map.scale(3,35.81324, ratio=F, cex=0.7, relwidth=0.1) # scale map
map.axes(cex.axis=0.9)
northarrow(c(4.8,42.9),0.7, cex=0.8)

Spain

Part 2: Add labels

Using the function shadow text to avoid labels overlapping.

coords<- coordinates(spain) # get goordinates of the centroids, it's where you center your labels
# p.names is a data frame containing the coordinates and all the names of the provinces (remember to get rid of those you don't want to use if using only a selection). Usually you can find the names in the shapefile, but I didn't have them.
shadowtext(p.names[,1],p.names[,2], label=paste(p.names[,3]), cex=0.7,col="black", bg="white",r=0.1)

Spain3