Abandon the rainbows?

Are rainbow palettes really that bad?
If it depended on me, my ideal color palettes would be the color of my favorite toy as a kid: fluo squishy slime, the kind that lights up in the dark. However, not everyone appreciates it, especially scientific journals.
In the end, the presence of colors should be motivated by what’s being represented: a cluster of lines depicting the TFR trends in every country in the world during the past century would not benefit from a 250 colors scale. However, it’s different for maps and heat-maps as the right palette can improve the message, condensing and directing information (see this Lancet paper ).
Rainbow palettes work however rather well (in my opinion) in some instances although they can easily be substituted with less catchy and more printer friendly colors.

I’ll always be a fan of bright colors but I see the point for plotting minimalism. I’ve really enjoyed this article: End of the Rainbow? New Map Scale is More Readable by People Who Are Color Blind

I have downloaded the json file from here and transformed it into a dataframe using the rjson package.
I have used a bunch of color palettes to compare results. Ever since, discovering the viridis palettes, I am a huge fan of the ‘magma’ and ‘inferno’ as their darkest color is a deep black and it’s easier to highlight everything else.

 

Red-yellow-green palette:

Screen Shot 2018-08-14 at 09.48.02

Red-purple palette:

Screen Shot 2018-08-14 at 09.48.13

Rainbow palette:

Screen Shot 2018-08-14 at 09.48.21.png

Magma palette:

Screen Shot 2018-08-14 at 09.50.51.png
Inferno palette:

Screen Shot 2018-08-14 at 09.51.14
Plasma palette:

Screen Shot 2018-08-14 at 09.51.45
Viridis palette:

Screen Shot 2018-08-14 at 09.52.04.png

Cividis palette (from cividis library here):
Screen Shot 2018-08-14 at 09.52.45.png

Greys palette:

Screen Shot 2018-08-14 at 09.53.06

Inverted grey palette:
Screen Shot 2018-08-14 at 09.53.14

 

Here’s the code for the cividis palette plot:

ggplot(dt, aes(order, year))+ geom_tile(aes(fill = temp)) + scale_fill_cividis(na.value = "transparent")+ scale_y_reverse(name='', breaks = c(1876, 1900, 1950, 2000, 2018), labels=c('1876', '1900', '1950', '2000', '2018'))+ scale_x_continuous(name='', breaks = c(30-15, 61-15, 92-15, 122-15), labels=c('June', 'July', 'August', 'September'))+ theme(axis.ticks.x = element_blank())+ geom_vline(xintercept=c(30, 61, 92), linetype = "longdash" ) 

The Spatial side of Demography at EPC…

Come meet me at EPC to discuss Spatial Demography!

Thursday, 7 June 2018, 9:00-10:30 Session 1 Motherhood, Labor Market and Wages: Contextual Determinants of Childbearing in Spain: A Spatial Panel Study

Friday, 8 June 2018, 11:00-12:30 Session 65 Fertility Trends and ProspectsThe Geography of Fertility Rates in Low and Middle-Income Countries: Analysis of Cross-Sectional Surveys from 74 Countries

Kuwait population pyramids: changes between 2000-2020

Plot made in ggplot2 using geofacet, personal elaboration of Census data.

KWT_pyr2000-2020

Here is the the geofacet gris, although maybe not one of the most exciting ones…

mygrid <- data.frame(
row = c(1, 1, 1, 2, 2, 3),
col = c(1, 2, 3, 2, 3, 3),
code = c("KWT1", "KWT2", "KWT3", "KWT4", "KWT5", "KWT6"),
name = c("Al-Jahra Governorate", "Capital (Al Asimah) Governorate", "Hawalli Governorate", "Al-Farwaniya Governorate", "Mubarak Al-Kabeer Governorate", "al Ahmadi Governorate"),
stringsAsFactors = FALSE
)
geofacet::grid_preview(mygrid)

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

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

Geofacet: Nepal 75 districts

Screen Shot 2017-07-20 at 16.07.30row,col,code,name
1,4, NP01, Humla
1,5, NP02, Mugu
1,6, NP03, Dolpa
1,7, NP04, Mustang
1,8, NP05, Manang
2,9, NP06, Gorkha
2,1, NP07, Dharchula
2,2, NP08, Bajhang
2,3, NP09, Bajura
2,4, NP10, Kalikot
2,5, NP11, Jumla
2,6, NP12, Rukum
2,7, NP13, Myagdi
2,8, NP14, Kaski
2,10, NP15, Dhading
2,11, NP16, Rasuwa
2,12, NP17, Sindhupalchowk
2,13, NP18, Dolakha
2,14, NP19, Solukhumbu
2,15, NP20, Sankhuwasabha
2,16, NP21, Taplejung
3,9, NP22, Lamjung
3,1, NP23, Baitadi
3,2, NP24, Doti
3,3, NP25, Achham
3,4, NP26, Dailekh
3,5, NP27, Jajarkot
3,6, NP28, Rolpa
3,7, NP29, Baglung
3,8, NP30, Parbat
3,10, NP31, Nuwakot
3,11, NP32, Kavrepalanchok
3,12, NP33, Kathmandu
3,13, NP34, Okhaldhunga
3,14, NP35, Khotang
3,15, NP36, Bhojpur
3,16, NP37, Dhankuta
3,17, NP38, Tehrathum
4,9, NP39, Tanahun
4,1, NP40, Kanchanpur
4,2, NP41, Dadeldhura
4,3, NP42, Kailali
4,4, NP43, Surkhet
4,5, NP44, Salyan
4,6, NP45, Pyuthan
4,7, NP46, Gulmi
4,8, NP47, Syangja
4,10, NP48, Chitwan
4,11, NP49, Patan
4,12, NP50, Bhaktapur
4,13, NP51, Ramechhap
4,14, NP52, Udayapur
4,15, NP53, Sunsari
4,16, NP54, Panchthar
4,17, NP55, Ilam
5,9, NP56, Nawalparasi
5,4, NP57, Bardiya
5,5, NP58, Banke
5,6, NP59, Dang
5,7, NP60, Argakhanchi
5,8, NP61, Palpa
5,10, NP62, Parsa
5,11, NP63, Makwanpur
5,12, NP64, Sindhuli
5,13, NP65, Dhanussa
5,14, NP66, Siraha
5,15, NP67, Saptari
5,16, NP68, Morang
5,17, NP69, Jhapa
6,7, NP70, Kapilvastu
6,8, NP71, Rupandehi
6,10, NP72, Bara
6,11, NP73, Rahuttahat
6,12, NP74, Sarlahi
6,13, NP75, Mahottari