Explaining the density of Esperanto speakers with language and politics

esperanto correlations.Rmd

---
title: "Esperanto correlations"
output: html_notebook
---

From the blog post Frequencies of Esperanto Speakers in all countries I have the main variable.

```{r}
prefix='../../Esperanto/L/language failes/'
lf=list.files(prefix)
esperanto_file='../../Linguistic differences/asjp-contribution-ESPERANTO.csv'
lf=c(lf,esperanto_file)
res=matrix(0,nrow=100, ncol=length(lf))
cnames=rep("", length(lf))#names of the languages
for(i in 1:length(lf)){
  l=lf[i]
  
  #still haven't learnt regular expresions:
  name=strsplit( strsplit(l, "-")[[1]][3],  ".", fixed=T)[[1]][1]
  cnames[i]=name
  
  #load file and extrat relevant information.
  ad=read.csv(paste0(prefix,l))
  res[as.numeric(ad$Parameter_ID),i]=as.character(ad$Value)
}
res=data.frame(res)
colnames(res) <- cnames
```

```{r}
library(stringdist)

#this will be th LDN distance matrix in the nd
resulting_matrix=matrix(0, nrow=ncol(res), ncol=ncol(res))

#this is the denominator in the average.
counter_matrix=resulting_matrix

#function that calculates the longest word for each entry in the outer product v1%o%v2
len_f=function(v1,v2){
  res=matrix(0, nrow=length(v1),ncol=length(v2))
  for(i in 1:length(v1)){
    for(j in 1:length(v2)){
      res[i,j]=max(nchar(v1[i]), nchar(v2[j]))
    }
  }
  return(res)
}

#calculates the sum and denominator in the average cummulatively for all entries.
for(i in 1:nrow(res)){
  
  #x is the translation of a certain term to all languages
  x=as.character(unlist(res[i,]))
  
  #calculates levenshtein differences and rem the missing values 
  to_add=stringdistmatrix(x,x, method="lv")*(x!="0")%o%(x!="0")
  
  #the len_f(x,x) term is the N in LDN.
  counter_matrix=counter_matrix+len_f(x,x)*(to_add>0)
  resulting_matrix=resulting_matrix+to_add
  cat(i)
}

#the average is calculated.
resulting_matrix[counter_matrix>0]=resulting_matrix[counter_matrix>0]/counter_matrix[counter_matrix>0]
rownames(resulting_matrix) <- cnames
colnames(resulting_matrix) <- cnames
```

```{r}
linguistic_differences=resulting_matrix[nrow(resulting_matrix),]
write.csv(cbind(rownames(resulting_matrix), linguistic_differences), file='esperanto_difs.csv', quote=F, row.names = F)
```

```{r}
language_proportions=read.csv('languages.txt', fill=T, header=F,
                              col.names=c('number','country','L1',
                                          'P1','L2','P2','L3','P3',
                                          'L4','P4','L5','P5','L6','P6'),
                              stringsAsFactors = F
                              )
row_to_number=function(x){
  res=0
  total_prob=0
  for(i in 1:6){
    if(!is.na(x[i*2])){
      #print(i)
      #print(res)
      #print(total_prob)
      p=as.numeric(x[i*2])
      #print(p)
      #print(as.character(x[(i-1)*2+1]))
      #print(linguistic_differences[as.character(x[(i-1)*2+1])])
      total_prob=total_prob+p
      res=res+as.numeric(linguistic_differences[as.character(x[(i-1)*2+1])])*p
    }
  }
  res=res/total_prob
  return(res)
}
country_to_esperanto=apply(language_proportions[,3:12],1,row_to_number)
names(country_to_esperanto) <- language_proportions[,2]
```

```{r}
esperanto_numbers=read.csv('uploadable.csv', header=F)
country_codes=as.character(esperanto_numbers$V1)
rplace=function(x, old_val, new_val){
  index=which(x==old_val)
  x[index]=new_val
  return(x)
}
esperanto_rates=esperanto_numbers$V2
df=data.frame(esperanto=esperanto_rates^2)
rownames(df) <- country_codes
```

From http://data.worldbank.org I get GDP data and population size and internet usage

```{r}
pop=read.csv('pop.csv', skip = 4)
rownames(pop) <- pop$Country.Code
df$pop=pop[rownames(df), 'X2015']

gdp=read.csv('gdp.csv', skip=4)
rownames(gdp) <- gdp$Country.Code
df$GDP=gdp[rownames(df), 'X2015']
df$GDPcapita=df$GDP/df$pop

www=read.csv('www.csv',skip=4)
rownames(www) <- www$Country.Code
df$www=www[rownames(df), 'X2015']
```

```{r}
ujo=read.table('esperantujodirectory2.txt', header=T)
df$continent=ujo[rownames(df), 'continent']
```


#unused variable
```{r}
prof=read.csv('english_profeciency.txt', sep='\t', header=F)
prof=prof[substr(as.character(prof$V1),1,1)!='-',]
rownames(prof) <- prof$V1
df$prof=prof[rownames(df),'V4']
```

```{r}
div=read.csv('Linguistic Diversity.txt', sep='\t', header=T, comment.char = '#')
div=div[substr(as.character(div$ï..CODE),1,1)!='-',]
rownames(div) <- div$ï..CODE
df$div=as.numeric(as.character(div[rownames(df),'Index']))
```


Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).

```{r}
df$ling_dist=country_to_esperanto[rownames(df)]
```

```{r}
df_tmp=read.csv('goodcountryrankings.txt', header=T)
df_cop=df_tmp[rownames(df), colnames(df_tmp)[-1]]
df=cbind(df,df_cop)
```

```{r}
df$europe=(df$continent=='E')
```

```{r}
#this has profound effects (meaning that it introduces a bias.)
#library('DMwR')
#df2=knnImputation(df[, !names(df) %in% "esperanto"])
#df2$esperanto=df$esperanto
#df=df2
```


```{r}
l=lm(log(esperanto) ~ div+ling_dist+www+GDPcapita+continent+science+culture+peace+order+climate+equality+health, data=df[1:(nrow(df)-1),])
summary(l)
plot(l)
```
```{r}
library('car')
vif(l)
```
```{r}
plot(df$ling_dist, log(df$esperanto))
```

```{r}
h=summary(l)
tvals=h$coefficients[-c(5:7),3]
signed_tvals=tvals[-1]*c(c(1,-1,1,1), rep(-1,7))
midpoints=barplot(signed_tvals, xaxt="n", ylab='T statistic',ylim=c(-2.3,2.3), main='Joint analysis')
labs <- c('Lang. div.','Eo. simil.', 'Internet','GDP per c', 'Science','Culture','Peace','Order','Climate','Equality','Health')
text(cex=1, x=midpoints, y=-1.1*sign(signed_tvals), labs, xpd=TRUE, srt=90)
abline(h=qt(0.975,125), col='red')
abline(h=-qt(0.975,125), col='red')
```

```{r}
tr=c("div","ling_dist","www"  ,"GDPcapita" , "science",   "culture","peace" ,    "order" ,    "climate",   "equality" , "health")
sgn=c(1,-1,1,1,rep(-1,7))
res=rep(0,11)
for(n in 1:length(tr)){
  lmarg=lm(as.formula(paste("log(esperanto) ~ ", tr[n])), data=df[2:(nrow(df)-1),])
  res[n]=summary(lmarg)$coefficients[2,3]*sgn[n]
}
midpoints=barplot(res, xaxt="n", ylab='T statistic',ylim=c(-15,15), main='Marginal analyses')
labs <- c('Lang. div.','Eo. simil.', 'Internet','GDP per c', 'Science','Culture','Peace','Order','Climate','Equality','Health')
text(cex=1, x=midpoints, y=-6.1*sign(res), labs, xpd=TRUE, srt=90)
abline(h=qt(0.975,125), col='red')
abline(h=-qt(0.975,125), col='red')
```

```{r}
l=lm(log(esperanto) ~ div+ling_dist+www+GDPcapita+continent+science+culture+peace+order+climate+equality+health, data=df[1:(nrow(df)-1),], na.action=na.exclude)
nams=names(l$fitted.values)
expected=l$fitted.values
observed=na.omit(log(df[nams, 'esperanto'][1:(nrow(df)-1)]))
residuals=observed-expected
limits=quantile(residuals, p=c(0.08,0.94))
bad=which(residuals<limits[1])
good=which(residuals>limits[2])

plot(expected, observed, pch=21, main='Model prediction',
     xlab='log model density ', ylab='log observed density')
points(expected[bad], observed[bad], bg='red', pch=21)
points(expected[good], observed[good], bg='green',pch=21)
abline(0,1, col='red')
```
```{r}
bads=cbind(exp(l$fitted.values[nams[bad]])*df[nams[bad],'pop'], df[nams[bad],'esperanto']*df[nams[bad],'pop'])/1000000

goods=cbind(exp(l$fitted.values[nams[good]])*df[nams[good],'pop'], df[nams[good],'esperanto']*df[nams[good],'pop'])/1000000

library(xtable)
print(xtable(bads, digits=1), type='html')
print(xtable(goods, digits=1), type='html')
```

```{r}
print(l$coefficients)
print(df['GBR',])
cofs=l$coefficients[c(1,2,3,4,5,6,9,10,11,12,13,14,15)]
adr=c(1,df['GBR',c('div','ling_dist','www','GDPcapita','continent','science','culture','peace','order','climate','equality','health')])
dfex=na.exclude(df[,c('div','ling_dist','www','GDPcapita','continent','science','culture','peace','order','climate','equality','health')])
fit_value=function(x){
  continent=x[5]
  y=x
  y[5]=0
  contrib=0
  if(continent=='E'){
    contrib=l$coefficients[6]
  }
  if(continent=='F'){
    contrib=l$coefficients[7]
  }
  if(continent=='M'){
    contrib=l$coefficients[8]
  }
  f=as.numeric(y)*as.numeric(cofs[-1])
  f[5]=contrib
  return(f)
}
a=apply(apply(dfex, 1,fit_value),1,mean)
b=exp(fit_value(dfex['GBR',])-a)
base=exp(sum(a)+cofs[1])
test=prod(b)*base*df['GBR','pop']/1000000
print(b)
print(base*df['GBR','pop']/1000000)
```

esperanto_difs.csv

esperantujodirectory2.txt

continent name ujo pop uea lernu www pas edu nat
AFG A afghanistan 0 27.7 1 48 - 0 0 0
ALB E albanien 0 2.8 14 64 0.6 1 5 33
DZA F algeriet 1 39.2 6 503 0.15 1 8 0
AND E andorra - 0.078 0 167 - 0 8 0
AGO F angola 1 21.5 2 42 0.19 0 0 -
ARG M argentino 27 41.5 29 1965 0.60 16 61 140
ARM E armenien 0 3.0 12 92 - 0 1 27
AUS A australio 23 23.1 40 2568 0.83 16 52 130
AUT E austria 4 8.6 44 780 0.81 8 23 79
AZE E azerbadj 1 9.8 1 112 - 0 2 0
BHS M bahamas 1 0.378 1 15 - 0 0 0
BGD A bangladesh 0 156.6 2 49 0.39 0 1 0
BLR E belarus 5 9.5 12 425 0.54 5 6 0
BEL E belgien 16 11.3 143 1695 0.82 37 106 360
BLZ M belize 0 0.380 1 14 - 0 0 0 
BEN F benin 0 10.6 14 69 - 6 6 500
BTN A bhutan 0 0.8 0 8 - 0 0 0
BOL M bolivia 1 11.0 2 330 - 0 3 0
BIH E bosnien 1 3.8 13 175 0.72 3 7 300
BWA F botswana 0 2.2 0 8 - 0 0 0
BRA M brasilien 299 200.4 382 16845 0.60 137 577 385
BRN A brunei 0 0.417 0 13 - 0 0 0
BGR E bulgarien 7 7.2 45 622 0.53 11 10 140
BFA F burkina 1 19 0 15 - 0 0 0
BDI F burundi 0 10.1 15 41 - 3 9 95
KHM A cambodja 0 15.6 2 26 - 1 1 -
CMR F cameroun 0 22.7 4 35 - 0 1 -
CAN M canada 27 35.2 67 3982 0.86 25 63 115
CAF F centralafr 0 5.0 0 26 - 2 0 0
CHL M chile 7 17.6 10 1541 0.67 4 11 -
COL M colombia 13 47.12 24 3307 0.52 13 42 -
COG F congoBraz 0 4.7 3 13 - 0 0 0
CUB M cuba 2 11.3 35 152 0.26 10 13 350
CYP E cypern 0 0.8 2 64 - 0 2 -
DNK E danmark 9 5.7 86 995 0.95 16 22 140
DJI F djibouti 0 0.9 0 8 - 0 0 0
DOM M domrep 2 10.4 3 551 0.52 1 0 0
EGY F egypt 1 82.1 1 364 0.5 1 0 0
ECU M equador 2 15.7 4 642 0.4 1 6 0
GNQ F equatorialguinea 0 1.2 0 5 - 0 0 0
ERI F eritrea 0 5.3 0 7 - 0 0 0
EST E estland 2 1.3 17 151 0.8 2 2 100
TLS A etimor 1 1.2 4 11 - 0 0 0
ETH F etiopien 0 101.9 1 43 - 0 0 0
FJI A fiji - 0.9 0 8 - 0 0 0
PHL A filip 12 98.4 6 584 0.44 1 3 0
FIN E finland 6 5.5 97 1105 0.92 12 35 300
FRA E france 86 66.4 734 7061 0.89 234 637 650
GAB F gabon 0 1.8 0 19 - 0 0 0
GMB F gambia 0 1.9 0 7 - 0 0 0
GEO E georgien 0 3.7 3 71 - 2 2 0
GHA F ghana 0 28.3 5 60 - 3 2 -
GRC E greece 1 10.8 11 383 0.6 2 5 65
GRL E greenland 0 0.056 0 7 - 0 0 0
GTM M guatem 3 15.5 2 438 0.20 1 3 -
GIN F guinea 0 12.9 0 10 - 0 0 0
GNB F guineabissau 0 1.5 0 7 - 0 0 0
GUY M guyana 0 0.746 0 3 - 0 0 0
HTI M haiti 1 11.1 0 48 - 0 0 0
NLD E holland 25 17 205 2182 0.94 32 106 235
HND M honduras 2 8.1 2 199 0.18 0 1 0
HKG A hongkong - 7.4 2 1048 - 1 3 -
ISL E iceland 1 0.323 14 92 0.97 1 2 50
IND A indien 3 1252 14 1371 0.35 4 7 150
IDN A indon 8 249.9 9 1136 0.35 0 15 0
IRQ A irak 2 36.8 0 53 - 0 0 0
IRN A iran 57 77.5 60 1781 0.31 17 39 40
IRL E irland 5 4.6 10 627 0.78 2 2 63
ISR A israel 6 8.1 64 1812 0.71 3 15 160
ITA E italien 29 59.8 171 2997 0.58 27 151 910
CIV A ivory 0 22.7 1 53 - 0 0 100
JAM M jamaica 0 2.7 0 27 - 0 0 0
JPN A japan 10 127.3 378 1399 0.9 11 62 1298
JOR A jordan 1 9.8 0 68 - 0 0 0
KAZ A kasak 2 17.0 8 286 0.54 0 10 0
CPV F kapverde - 0.531 0 23 - 0 0 0
KEN F kenya 0 44.3 3 65 0.39 1 0 -
CHN A kina 17 1357 135 8273 0.52 38 91 1144
KGZ A kirigistan 1 6.1 1 37 - 0 2 0
COD F KongoDR 2 67.5 16 118 0.022 3 18 456
KOR A korea 4 50.2 87 1520 0.85 11 44 250
CRI M kostariko 4 4.9 8 636 0.46 3 7 34
HRV E kroatien 4 4.2 32 409 0.67 3 16 316
KWT A kuwait 0 4.2 0 46 - 0 0 0
LAO A laos 0 6.5 0 17 - 0 0 0
LSO F lesotho 0 1.9 0 1 - 0 0 0
LVA E letland 1 2.0 18 160 0.75 1 5 102
LBN A libanon 1 6.0 1 88 - 0 0 0
LBR F liberia 0 4.1 0 8 - 0 0 0
LBY F libya 0 6.4 0 21 - 0 0 0
LIE E liechtenstein - 0.037 0 8 - 0 0 0
LTU E litauen 8 3.0 43 5127 0.68 13 32 960
LUX E luxemborg 2 0.57 17 86 0.94 4 5 104
MDG F madagaskar 1 22.9 6 66 0.61 3 4 70
MKD E makedonien 1 2.1 4 48 - 2 2 45
MWI F malawi 0 16.8 2 7 - 0 3 0
MYS A malaysia 2 31.9 0 662 - 1 5 0
MLD A maldives - 0.344 0 4 - 0 0 0
MLI F mali 0 18.3 1 21 - 0 1 -
MTA E malta - 0.425 3 28 0.69 0 1 3
MAR F maroko 1 33.0 5 1096 0.56 0 4 0
MRT F mauretania 0 3.7 0 16 - 0 0 0
MAU F mauritius - 1.3 0 23 - 0 0 0
MEX M mexico 40 122.3 26 5162 0.51 19 67 145
MDA E moldova 0 3.6 1 102 - 1 3 0
MNG A mongoliet 2 2.8 19 58 0.16 3 2 50
MNE E montenegro 0 0.621 1 11 - 0 0 -
MOZ F mozambique 0 26.4 0 14 - 0 0 0
MMR A myanmar 1 54.6 1 25 - 0 1 0
NAM F namibia 0 2.3 0 21 - 0 0 0
NPL A nepal 7 31.0 26 115 - 2 4 147
NCL A newkaledonia 0 0.268 2 46 - 1 6 -
NZL A newzealand 8 4.5 21 577 0.83 5 6 46
NIC M nicaragua 1 6.1 19 295 0.16 1 8 -
NER F niger 0 20.7 3 25 - 1 1 -
NGA F nigeria 1 173.6 10 77 0.46 2 2 142
NOR E norge 7 5.2 36 826 0.95 14 14 144
OMN A oman 0 4.6 0 7 - 0 0 0
PAK A pakistano 5 182.1 40 195 0.18 29 2 450
PAN M panamo 0 3.9 0 214 0.43 0 1 0
PNG A papua 0 8.2 0 0 - 0 0 0
PRY M paraguay 0 6.8 0 147 0.37 0 1 0
PER M peru 6 30.4 9 1815 0.39 2 13 -
POL E polen 48 38.5 162 4556 0.76 62 197 620
PRT E portugal 5 10.3 11 1025 - 2 17 95
PRI M puertorico 3 3.5 1 288 - 0 2 0
QAT A qatar 0 2.6 0 37 - 0 1 0
ROU E romania 5 19.8 35 422 0.5 3 17 51
RUS E rusland 43 144.0 107 7343 0.71 76 193 148
RWA F rwanda 0 11.6 4 21 - 0 0 0
SLB A salomon 0 0.642 0 3 - 0 0 0
SLV M salvadoro 1 6.3 1 218 0.23 3 2 0
SAU A saudi 0 28.8 0 163 0.61 0 0 0
CHE E schweiz 9 8.1 85 935 0.87 29 44 170
SEN F senegal 0 14.8 4 129 - 6 12 -
SRB E serbien 5 7.1 28 234 0.72 5 15 -
SLE F sierraleone 0 7.1 0 10 - 0 0 0
SVK E slovakia 2 5.4 30 1418 0.78 5 28 80 
SVN E slovenien 0 2.1 14 233 - 1 18 106
SOM F somalia 0 11.1 0 3 - 0 0 0
ESP E spanien 60 46.4 131 5876 0.84 47 207 285
LKA A srilanka 0 21.2 3 31 - 0 0 -
SSD F ssudan 0 12.1 0 1 - 0 0 0
ZAF F sudafriko 10 53.0 5 288 0.49 6 2 54
SDN F sudan 0 41.2 0 35 - 0 1 0
SUR M surinam 0 0.541 0 7 - 0 0 0
SWE E sverige 19 9.9 103 1609 0.95 30 46 275
SWZ F swaziland 0 1.3 1 6 - 1 1 0
SYR A syria 1 18.6 0 82 - 0 1 0
TJK A tadjstikistan 1 8.6 3 14 - 1 3 25
TWN A taiwan 5 23.5 10 1771 0.8 3 10 -
TZA F tanzania 1 55.2 9 23 - 1 1 -
TCD F tchad 0 14.5 3 2 - 0 0 0
THA A thailand 3 67.0 5 277 0.29 3 3 0
CZE E tjekkiet 11 10.5 86 1724 0.74 23 64 105
TGO F togo 0 7.1 28 81 - 6 14 300
TTO M trinidad 0 1.4 0 28 - 0 0 0
TUN F tunesien 0 11.2 5 241 - 0 0 0
TKM A turkmenistan 0 4.8 0 5 - 0 0 0
TUR A tyrkiet 13 74.9 14 1193 0.46 0 14 0
DEU E tyskland 76 81.5 480 6684 0.88 109 226 1086
ARE A uae 2 9.9 1 173 - 2 2 0
UGA F uganda 0 36.9 6 18 - 0 1 -
GBR E uk 55 65.1 119 5484 0.82 28 90 385 
UKR E ukraine 13 42.9 47 2042 0.62 27 38 160
HUN E ungarn 39 9.8 80 7950 0.73 35 234 120
URY M uruguay 2 3.4 13 278 0.58 2 4 30
USA M usa 210 318.9 245 30152 0.88 76 354 700
UZB A usbekistan 0 31.6 2 72 - 1 1 0
VUT A vanuatu 0 0.277 0 2 - 1 0 0
VEN M venezuela 16 30.4 7 1156 0.55 4 16 32
VNM A vietnam 5 89.7 63 1287 0.44 0 30 200
YEM A yemen 0 27.5 0 28 - 0 0 0
ZMB F zambia 0 15.9 0 15 - 1 2 0
ZWE F zimbabwe 0 14.2 1 30 - 1 1 0

gdp.csv

"Data Source","World Development Indicators",

"Last Updated Date","2017-02-01",

"Country Name","Country Code","Indicator Name","Indicator Code","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016",
"Aruba","ABW","GDP (current US$)","NY.GDP.MKTP.CD","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1330167597.76536","1320670391.06145","1379888268.15642","1531843575.41899","1665363128.49162","1722798882.68156","1873452513.96648","1920262569.8324","1941094972.06704","2021301675.97765","2228279329.60894","2331005586.59218","2421474860.3352","2623726256.98324","2791960893.85475","2498932960.89385","2467703910.61453","2584463687.15084","","","","","",
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goodcountryrankings.txt

country,science,culture,peace,order,climate,equality,health
ALB,Albania,121,51,148,118,87,49,126
DZA,Algeria,105,135,121,117,98,134,143
AGO,Angola,161,149,128,144,36,96,144
ARG,Argentina,67,57,30,39,86,116,100
ARM,Armenia,48,124,95,111,140,44,69
AUS,Australia,17,43,33,9,54,66,7
AUT,Austria,2,4,71,1,23,18,23
AZE,Azerbaijan,120,142,113,91,49,119,44
BHS,Bahamas,125,46,68,139,37,40,153
BGD,Bangladesh,119,138,59,61,132,123,94
BLR,Belarus,37,94,97,75,69,148,52
BEL,Belgium,12,1,105,18,30,6,9
BLZ,Belize,108,83,54,107,139,154,151
BEN,Benin,85,99,14,105,162,32,111
BOL,Bolivia,151,128,32,54,131,135,148
BIH,Bosnia and Herzegovina,25,66,151,43,109,70,128
BWA,Botswana,147,62,117,106,104,141,62
BRA,Brazil,98,63,37,42,33,158,32
BRN,Brunei Darussalam,140,86,11,127,79,127,123
BGR,Bulgaria,22,29,43,45,42,82,81
BFA,Burkina Faso,89,116,6,109,154,53,115
BDI,Burundi,61,158,38,153,152,133,133
KHM,Cambodia,154,125,144,93,123,62,103
CMR,Cameroon,87,137,28,79,136,43,84
CAN,Canada,21,22,36,8,29,24,3
CAF,Central African Republic,69,159,162,141,134,140,162
TCD,Chad,143,134,86,146,115,146,150
CHL,Chile,64,101,15,24,40,19,66
CHN,China,60,102,16,97,121,118,27
COL,Colombia,65,98,57,122,58,37,124
COG,Congo,158,97,149,120,52,98,122
CRI,Costa Rica,79,47,40,23,85,47,101
HRV,Croatia,33,35,106,90,16,95,51
CYP,Cyprus,5,34,73,13,6,28,55
CZE,Czech Republic,3,8,107,34,48,152,38
CIV,Côte d'Ivoire,115,136,88,150,82,113,95
COD,Democratic Republic of the Congo,130,163,116,152,127,76,97
DNK,Denmark,4,2,49,3,19,3,6
DOM,Dominican Republic,131,118,126,37,72,77,56
ECU,Ecuador,138,91,13,51,114,109,79
EGY,Egypt,52,100,2,98,118,69,45
SLV,El Salvador,136,65,101,149,128,87,73
GNQ,Equatorial Guinea,162,152,159,161,65,161,163
EST,Estonia,32,6,56,67,89,129,42
FJI,Fiji,59,76,85,162,100,73,31
FIN,Finland,7,16,65,12,26,4,4
FRA,France,13,20,47,15,8,20,18
GAB,Gabon,150,148,157,73,53,83,138
GEO,Georgia,81,90,62,112,84,11,35
DEU,Germany,11,7,44,2,17,30,13
GHA,Ghana,76,52,12,71,116,79,156
GRC,Greece,35,42,133,32,11,65,53
GTM,Guatemala,106,73,10,102,156,117,57
GIN,Guinea,91,120,35,104,159,94,161
GNB,Guinea-Bissau,68,126,147,81,145,57,145
GUY,Guyana,137,55,75,154,120,85,41
HTI,Haiti,135,89,123,159,113,120,159
HND,Honduras,129,107,98,96,158,84,137
HUN,Hungary,9,17,60,50,39,63,39
ISL,Iceland,19,37,112,28,1,108,46
IND,India,62,80,27,100,106,124,37
IDN,Indonesia,160,111,19,62,138,35,70
IRN,Iran,118,157,109,64,94,157,74
IRQ,Iraq,163,160,150,156,73,149,98
IRL,Ireland,18,13,51,17,13,23,17
ISR,Israel,39,74,122,68,10,74,54
ITA,Italy,40,24,74,16,5,42,20
JAM,Jamaica,72,81,48,76,108,54,88
JPN,Japan,50,40,22,38,18,60,16
JOR,Jordan,49,141,21,110,130,41,26
KAZ,Kazakhstan,148,133,26,143,129,114,40
KEN,Kenya,56,129,70,57,144,110,68
KWT,Kuwait,132,71,118,136,68,71,59
KGZ,Kyrgyzstan,94,115,76,101,101,156,76
LAO,Laos,73,144,140,157,71,142,106
LVA,Latvia,29,21,90,58,43,125,63
LBN,Lebanon,66,61,153,84,93,22,89
LSO,Lesotho,122,109,80,78,119,144,93
LBR,Liberia,70,156,158,35,161,16,118
LBY,Libya,156,161,161,130,147,153,146
LTU,Lithuania,24,33,141,51,32,100,71
LUX,Luxembourg,43,12,100,5,35,14,15
MDG,Madagascar,103,95,17,148,141,102,160
MWI,Malawi,111,153,91,76,107,59,125
MYS,Malaysia,53,44,46,70,153,9,61
MLI,Mali,139,96,124,82,133,106,142
MRT,Mauritania,157,143,163,129,160,105,109
MEX,Mexico,83,85,84,69,90,36,92
MDA,Moldova,42,45,4,66,67,88,72
MNG,Mongolia,133,154,23,137,142,75,64
MNE,Montenegro,58,54,134,63,81,27,78
MAR,Morocco,82,132,41,125,103,33,87
MOZ,Mozambique,116,93,120,95,124,115,130
NAM,Namibia,77,59,103,114,102,112,48
NLD,Netherlands,15,5,42,4,15,10,10
NZL,New Zealand,6,28,31,19,31,26,14
NIC,Nicaragua,126,114,127,108,148,81,82
NER,Niger,110,121,7,128,151,92,158
NGA,Nigeria,145,151,25,115,88,111,127
NOR,Norway,41,36,58,6,2,17,8
OMN,Oman,144,127,50,88,78,68,77
PAK,Pakistan,102,150,110,87,126,90,49
PAN,Panama,92,68,83,25,125,45,75
PNG,Papua New Guinea,152,112,78,85,96,132,108
PRY,Paraguay,159,105,39,31,117,163,43
PER,Peru,112,103,96,86,77,64,114
PHL,Philippines,146,108,102,55,61,29,85
POL,Poland,31,19,53,30,46,89,50
PRT,Portugal,46,18,131,40,4,12,25
QAT,Qatar,153,110,69,65,51,58,33
KOR,Republic of Korea,30,32,92,29,83,72,28
MCD,Macedonia,27,70,125,59,62,93,119
ROU,Romania,51,27,29,47,28,138,80
RUS,Russian Federation,47,82,104,132,63,143,29
RWA,Rwanda,114,162,55,151,47,122,65
SAU,Saudi Arabia,100,147,82,134,112,39,11
SEN,Senegal,101,119,115,116,157,25,112
SRB,Serbia,28,38,114,48,105,34,104
SLE,Sierra Leone,123,145,93,56,137,97,99
SVK,Slovakia,45,15,77,46,24,99,102
SVN,Slovenia,16,14,152,20,12,104,21
ZAF,South Africa,26,79,1,33,143,160,36
ESP,Spain,34,23,94,22,21,48,19
LKASri Lanka,113,131,24,89,91,50,110
SUR,Suriname,142,113,143,135,110,151,155
SWZ,Swaziland,95,72,155,99,59,139,58
SWE,Sweden,8,3,52,11,7,1,1
CHE,Switzerland,10,31,61,10,3,2,5
SYR,Syria,128,146,160,94,135,131,132
TZA,Tanzania,141,123,5,83,74,121,140
THA,Thailand,80,77,34,21,155,80,30
TGO,Togo,109,106,9,140,163,8,135
TTO,Trinidad and Tobago,84,87,81,92,99,21,152
TUN,Tunisia,78,84,8,41,55,52,120
TUR,Turkey,55,58,72,126,60,103,22
UGA,Uganda,75,130,79,60,70,136,134
UKR,Ukraine,14,75,137,49,95,126,96
ARE,United Arab Emirates,96,56,154,80,64,51,24
GBR,United Kingdom,1,11,64,14,22,5,2
USA,United States of America,20,53,66,26,34,46,12
URY,Uruguay,127,78,3,44,9,128,91
VUT,Vanuatu,90,66,136,138,20,147,131
VEN,Venezuela,149,139,111,74,97,162,136
VNM,Viet Nam,86,88,87,145,146,67,47
YEM,Yemen,97,155,135,124,149,86,83
ZMB,Zambia,117,140,67,53,45,150,107
ZWE,Zimbabwe,88,117,108,142,150,78,90

languages.txt

1,AFG,EASTERN_FARSI,0.5,NORTHERN_PASHTO,0.35,UZBEK,0.11
2,AGO,PORTUGUESE,0.712,UMBUNDU,0.23,KOONGO,0.082
3,ALB,ALBANIAN,0.988,GREEK,0.005
4,AND,CATALAN,1.0
5,ARE,STANDARD_ARABIC,1.0
6,ARG,SPANISH,1.0
7,ARM,WESTERN_ARMENIAN,0.979,KURDISH_CENTRAL,0.01
8,AUS,ENGLISH,0.768,MANDARIN,0.016
9,AUT,STANDARD_GERMAN,0.886,TURKISH,0.023
10,AZE,AZERBAIJANI_NORTH,0.925,RUSSIAN,0.015
11,BDI,KIRUNDI,1.0
12,BEL,DUTCH,0.6,FRENCH,0.4
13,BEN,FRENCH,0.35,FONGBE,0.24
14,BFA,MOORE,0.4,FRENCH,0.15,FULFULDE,0.0836
15,BGD,BENGALI,0.988
16,BGR,BULGARIAN,0.768,TURKISH,0.08,ROMA,0.044
17,BHS,ENGLISH,1.0
18,BIH,BOSNIAN,0.529,SERBOCROATIAN,0.308,CROATIAN,0.146
19,BLR,RUSSIAN,0.702,BELARUSIAN,0.234
20,BLZ,ENGLISH,0.278,SPANISH,0.2502,BELIZE_KRIOL_ENGLISH,0.197,PROTO_MAYAN,0.046
21,BOL,SPANISH,0.607,PROTO_QUECHUA,0.212,CENTRAL_AYMARA,0.146
22,BRA,PORTUGUESE,1.0
23,BRN,MALAY,0.657,MANDARIN,0.103
24,BTN,TSHANGLA,0.28,DZONGKHA,0.24,NEPALI,0.22
25,BWA,TSWANA,0.782,KALANGA,0.079
26,CAF,SANGO,0.92
27,CAN,ENGLISH,0.588,FRENCH,0.216
28,CHE,STANDARD_GERMAN,0.637,FRENCH,0.204,ITALIAN,0.065,SERBOCROATIAN,0.015
29,CHL,SPANISH,0.77,MAPUDUNGUN_3,0.05
30,CHN,MANDARIN,0.662,AMOY_MINNAN_CHINESE,0.062,SUZHOU_WU,0.061
31,CIV,BAULE,0.25,SENUFO_CEBAARA,0.25,YAKOUBA,0.25,WE_SOUTHERN,0.25
32,CMR,FRENCH,0.18,BULU,0.2,TIKAR_AKUEN,0.2,BANGANDU,0.2
33,COD,FRENCH,0.31,KITUBA,0.15,LINGALA,0.15,SWAHILI,0.15,L31a_LUBA_KASAYI,0.15
34,COG,KITUBA,0.32,NGUNGWEL,0.124,TUMBUKA_ZAMBIE,0.112 
35,COL,SPANISH,1.0
36,CPV,CAPE_VERDEAN_CREOLE,1.0
37,CRI,SPANISH,1.0
38,CUB,SPANISH,1.0
39,CYP,GREEK,0.809
40,CZE,CZECH,0.949,SLOVAK,0.02
41,DEU,STANDARD_GERMAN,0.95,TURKISH,0.018
42,DJI,SOMALI,0.619,AFAR,0.36,STANDARD_ARABIC,0.07
43,DNK,DANISH,1.0
44,DOM,SPANISH,1.0
45,DZA,STANDARD_ARABIC,0.72,SIWA_BERBER,0.274
46,ECU,SPANISH,0.85
47,EGY,STANDARD_ARABIC,1.0
48,ERI,TIGRINYA,0.5,TIGRE,0.25
49,ESP,SPANISH,0.74,CATALAN,0.17,GALICIAN,0.07
50,EST,ESTONIAN,0.673,RUSSIAN,0.297
51,ETH,AMHARIC,0.327,W_OROMO,0.316,TIGRIGNA,0.061,SOMALI,0.06
52,FIN,FINNISH,0.927,SWEDISH,0.055
53,FJI,FIJIAN,0.54
54,FRA,FRENCH,1.0
55,GAB,FANG,0.3,MBEDE,0.3,B41_SHIRA,0.3,FRENCH,0.05
56,GBR,ENGLISH,0.98,WELSH,0.012
57,GEO,GEORGIAN,0.71,RUSSIAN,0.09,WESTERN_ARMENIAN,0.07
58,GHA,TWI_ASANTE,0.148,EWE_ADANGBE,0.127,TWI_FANTE,0.099,BORONG,0.046
59,GIN,FULA,0.4,MALINKE,0.3,SUSU,0.2
60,GMB,MANDINKA,0.38,FULA,0.212,WOLOF,0.18
61,GNB,CAPE_VERDEAN_CREOLE,0.44,PORTUGUESE,0.11
62,GNQ,SPANISH,0.676
63,GRC,GREEK,0.99
64,GRL,WEST_GREENLANDIC,0.87,DANISH,0.12
65,GTM,SPANISH,0.6,KICHE,0.064
66,GUY,ENGLISH,1.0
67,HKG,CANTONESE,0.908,ENGLISH,0.028
68,HND,SPANISH,1.0
69,HRV,CROATIAN,0.961
70,HTI,HAITIAN_CREOLE,0.9
71,HUN,HUNGARIAN,0.936
72,IDN,MALANG,0.337,INDONESIAN,0.172,SUNDANESE,0.136
73,IND,HINDI,0.41,BENGALI,0.081,TELUGU,0.072,MARATHI,0.07,TAMIL,0.059,URDU,0.05
74,IRL,ENGLISH,0.95,IRISH_GAELIC,0.0234
75,IRN,PERSIAN,0.58,OLD_TURKIC,0.26,KURDISH_CENTRAL,0.09
76,IRQ,STANDARD_ARABIC,0.6,KURDISH_CENTRAL,0.2,TURKMEN,0.05
77,ISL,ICELANDIC,1.0
78,ISR,HEBREW,0.49,STANDARD_ARABIC,0.18,RUSSIAN,0.15
79,ITA,ITALIAN,1.0
80,JAM,ENGLISH,1.0
81,JOR,STANDARD_ARABIC,1.0
82,JPN,TOKYO_JAPANESE,1.0
83,KAZ,KAZAKH,0.644
84,KEN,GIKUYU,0.18,J321_MARAMA,0.117,LUO,0.11
85,KGZ,KYRGYZ,0.647,UZBEK,0.136,RUSSIAN,0.125
86,KHM,KHMER,0.95
87,KOR,KOREAN,1.0
88,KWT,STANDARD_ARABIC,1.0
89,LAO,LAO,0.55,KHMU,0.11,HMONG_DAW,0.05
90,LBN,STANDARD_ARABIC,0.9
91,LBR,MANDINKA,0.2,
92,LBY,STANDARD_ARABIC,0.8,SIWA_BERBER,0.16
93,LIE,STANDARD_GERMAN,1.0
94,LKA,SINHALA,0.74,TAMIL,0.18
95,LSO,SOTHO_SOUTHERN,0.9,ZULU,0.5
96,LTU,LITHUANIAN,0.82,RUSSIAN,0.08,POLISH,0.05
97,LUX,LUXEMBOURGISH,0.52,FRENCH,0.16
98,LVA,LATVIAN,0.582,RUSSIAN,0.375
99,MAR,STANDARD_ARABIC,0.4,SIWA_BERBER,0.4
100,MAU,MAURITIAN,0.857,BHOJPURI,0.053,FRENCH,0.041
101,MDA,ROMANIAN,0.758,RUSSIAN,0.16
102,MDG,MALAGASY_PLATEAU_SIHANAKA_AMBATONDRAZAKA,0.697,FRENCH,0.2
103,MEX,SPANISH,0.927,CLASSICAL_NAHUATL,0.014
104,MKD,MACEDONIAN,0.665,ALBANIAN,0.251,TURKISH,0.035
105,MLD,MALDIVIAN,1.0
106,MLI,BAMBARA,0.8
107,MMR,YANGON_BURMESE,0.66,SHAN,0.06,PROTO_KAREN,0.05
108,MNE,SERBOCROATIAN,0.779,BOSNIAN,0.053,ALBANIAN,0.053
109,MNG,MONGOLIAN,0.95
110,MOZ,MAKWA_ALUA,0.253,PORTUGUESE,0.107,TSONGA,0.103,SENA,0.075,LOMWE,0.07
111,MRT,STANDARD_ARABIC,0.8,SIWA_BERBER,0.1
112,MTA,MALTESE,0.902,ENGLISH,0.06
113,MWI,NYANJA,0.8,YAO_1,0.101
114,MYS,MALAY,0.67,
115,NAM,KWANYAMA,0.48,NAMA,0.11,AFRIKAANS,0.11,KWANGALI,0.1,HERERO,0.1
116,NCL,FRENCH,1.0
117,NER,HAUSA,0.497,ZARMA,0.255,TAMASHEQ,0.084
118,NGA,HAUSA,0.29,YORUBA,0.21,ECHIE,0.18
119,NIC,SPANISH,0.71,MISKITO,0.025
120,NLD,DUTCH,0.9,EASTERN_FRISIAN
121,NOR,NORWEGIAN_BOKMAAL,0.98
122,NPL,NEPALI,0.478,MAITHILI,0.121,BHOJPURI,0.074
123,NZL,ENGLISH,0.912,MAORI,0.039
124,OMN,STANDARD_ARABIC,1.0
125,PAK,PUNJABI_MAJHI,0.58,NORTHERN_PASHTO,0.08,URDU,0.0
126,PAN,SPANISH,0.93
127,PER,SPANISH,0.841,PROTO_QUECHUA,0.13
128,PHL,PROTO_PHILIPPINE,1.0
129,PNG,TOK_PISIN,1.0
130,POL,POLISH,0.978
131,PRI,SPANISH,0.945,ENGLISH,0.053
132,PRT,PORTUGUESE,1.0
133,PRY,GUARANI,0.51,SPANISH,0.49
134,QAT,STANDARD_ARABIC,1.0
135,ROU,ROMANIAN,0.91,HUNGARIAN,0.067
136,RUS,RUSSIAN,1.0
137,RWA,KINYARWANDA,1.0
138,SAU,STANDARD_ARABIC,1.0
139,SDN,STANDARD_ARABIC,1.0
140,SEN,WOLOF,0.8
141,SLB,PIJIN,1.0
142,SLE,THEMNE, 0.375,MENDE,0.295
143,SLV,SPANISH,1.0
144,SOM,SOMALI,1.0
145,SRB,SERBOCROATIAN,0.883,HUNGARIAN,0.038
146,SSD,DINKA_SOUTHEASTERN,0.481,NUER,0.254,BARI_SUDAN,0.144,ZANDE,0.12
147,SUR,DUTCH,0.6,SRANAN_TONGO,0.2
148,SVK,SLOVAK,0.839,HUNGARIAN,0.107
149,SVN,SLOVENIAN,0.911,SERBOCROATIAN,0.045
150,SWE,SWEDISH,1.0
151,SWZ,SWAZI,0.95
152,SYR,STANDARD_ARABIC,1.0
153,TCD,STANDARD_ARABIC,1.0
154,TGO,EWE_ADANGBE,0.4,FRENCH,0.3,KABIYE,0.16,
155,THA,THAI,0.3,LAO,0.24
156,TJK,TAJIK,1.0
157,TKM,TURKMEN,0.72,RUSSIAN,0.12,UZBEK,0.09
158,TLS,TETUN_DILI,0.36,MAMBAI,0.129,MAKASAE,0.097
159,TTO,ENGLISH,1.0
160,TUN,STANDARD_ARABIC,1.0
161,TUR,TURKMEN,0.85,UZBEK,0.05,RUSSIAN,0.04
162,TWN,AMOY_MINNAN_CHINESE,1.0
163,TZA,MAKONDE,0.33,DATOOGA_DIALECT_2,0.33,MACHAME,0.33
164,UGA,LUGANDA,0.5,KARIMOJONG,0.5
165,UKR,UKRAINIAN,0.778,RUSSIAN,0.173
166,URY,SPANISH,1.0
167,USA,ENGLISH,0.8,SPANISH,0.124
168,UZB,UZBEK,0.8,RUSSIAN,0.055,TAJIK,0.5
169,VEN,SPANISH,0.9
170,VNM,VIETNAMESE,0.857
171,VUT,VALPEI,0.667,BISLAMA,0.337
172,YEM,STANDARD_ARABIC,1.0
173,ZAF,ZULU,0.227,XHOSA,0.16,AFRIKAANS,0.135,ENGLISH,0.096
174,ZMB,BEMBE,0.334,NYANJA,0.147,M64_TONGA_1,0.114,LOZI_2,0.055
175,ZWE,SHONA,0.7,ZIMBABWE_NDEBELE,0.2

Linguistic Diversity.txt

CODE	Country	Count	Percent	Established	Immigrant	Total	Mean	Median	Index	Coverage
AFG	Afghanistan	42	0.59	41	1	22,964,800	560,117	8,000	0.790	98%
ALB	Albania	12	0.17	8	4	2,801,786	280,179	4,220	0.503	83%
DZA	Algeria	21	0.30	18	3	33,135,600	1,743,979	40,000	0.360	90%
-	American Samoa	7	0.10	2	5	55,910	9,318	25,800	0.210	86%
AND	Andorra	5	0.07	4	1	74,270	14,854	19,650	0.671	100%
AGO	Angola	40	0.56	40	0	23,511,670	602,863	43,900	0.748	98%
-	Anguilla	2	0.03	2	0	12,450	6,225	6,225	0.141	100%
-	Antigua and Barbuda	5	0.07	2	3	135,000	33,750	66,500	0.515	80%
ARG	Argentina	38	0.54	24	14	44,146,270	1,337,766	8,410	0.165	87%
ARM	Armenia	13	0.18	7	6	3,041,450	276,495	11,135	0.053	85%
-	Aruba	6	0.08	4	2	81,090	20,272	9,000	0.429	67%
AUS	Australia	259	3.65	215	44	21,607,217	87,479	30	0.274	95%
AUT	Austria	29	0.41	13	16	8,961,530	426,740	24,900	0.234	72%
AZE	Azerbaijan	37	0.52	18	19	9,246,510	264,186	20,000	0.202	95%
BHS	Bahamas	5	0.07	3	2	576,280	144,070	284,500	0.509	80%
-	Bahrain	14	0.20	6	8	724,300	55,715	80,000	0.658	93%
BGD	Bangladesh	46	0.65	41	5	193,817,420	4,404,941	15,500	0.318	96%
-	Barbados	3	0.04	3	0	518,290	172,763	256,000	0.500	100%
BLR	Belarus	11	0.15	4	7	9,170,400	833,673	1,230,500	0.411	100%
BEL	Belgium	36	0.51	11	25	12,807,200	457,400	200,000	0.700	78%
BLZ	Belize	12	0.17	8	4	541,160	45,097	14,100	0.721	100%
BEN	Benin	56	0.79	55	1	6,635,390	122,878	60,500	0.933	96%
-	Bermuda	3	0.04	2	1	65,610	32,805	63,000	0.076	67%
BTN	Bhutan	31	0.44	23	8	639,800	25,592	8,000	0.827	81%
BOL	Bolivia	45	0.63	43	2	7,335,305	170,588	400	0.565	96%
BIH	Bosnia and Herzegovina	11	0.15	5	6	2,699,500	269,950	383,000	0.694	91%
BWA	Botswana	38	0.54	31	7	2,213,660	63,247	5,950	0.397	92%
BRA	Brazil	227	3.20	216	11	204,400,751	959,628	240	0.099	94%
-	British Indian Ocean Territory	1	0.01	1	0	3,500	3,500	3,500	0.000	100%
-	British Virgin Islands	2	0.03	2	0	39,700	19,850	19,850	0.500	100%
BRN	Brunei	17	0.24	15	2	459,685	28,730	8,150	0.570	94%
BGR	Bulgaria	25	0.35	11	14	8,016,320	364,378	10,500	0.226	88%
BFA	Burkina Faso	71	1.00	71	0	12,472,930	183,425	20,500	0.721	96%
BDI	Burundi	4	0.06	3	1	10,036,100	2,509,025	9,600	0.007	100%
KHM	Cambodia	28	0.39	27	1	15,826,130	608,697	6,220	0.101	93%
CMR	Cameroon	280	3.94	279	1	10,166,051	37,375	9,500	0.974	97%
CAN	Canada	164	2.31	94	70	32,967,314	207,342	360	0.603	97%
-	Cape Verde Islands	4	0.06	2	2	492,000	492,000	492,000	?	25%
-	Caribbean Netherlands	6	0.08	5	1	18,540	3,708	3,390	0.707	83%
-	Cayman Islands	4	0.06	1	3	74,840	18,710	50,000	0.475	100%
CAF	Central African Republic	83	1.17	72	11	3,488,143	45,301	17,350	0.959	93%
TCD	Chad	132	1.86	131	1	8,638,559	71,988	13,150	0.933	91%
CHL	Chile	17	0.24	11	6	17,728,013	1,772,801	10,500	0.036	59%
CHN	China	301	4.24	299	2	1,298,861,948	4,448,157	27,700	0.521	97%
-	China–Hong Kong	10	0.14	7	3	7,323,700	813,744	89,800	0.205	90%
-	China–Macao	8	0.11	6	2	862,790	107,849	19,620	0.253	100%
TWN	China–Taiwan	28	0.39	22	6	22,093,700	920,571	6,620	0.489	86%
COL	Colombia	86	1.21	82	4	47,059,630	580,983	1,300	0.019	94%
-	Comoros	6	0.08	6	0	764,500	152,900	40,000	0.551	83%
COG	Congo	65	0.92	62	3	3,691,260	59,536	14,400	0.821	95%
-	Cook Islands	5	0.07	5	0	15,000	3,000	680	0.232	100%
CRI	Costa Rica	12	0.17	11	1	4,523,430	452,343	2,840	0.036	83%
HRV	Croatia	25	0.35	15	10	4,433,610	192,766	10,200	0.102	92%
CUB	Cuba	5	0.07	3	2	11,203,500	3,734,500	5,600,000	0.001	60%
-	Curacao	6	0.08	5	1	136,910	27,382	8,550	0.285	83%
CYP	Cyprus	15	0.21	4	11	1,531,180	117,783	154,880	0.444	87%
CZE	Czechia	26	0.37	10	16	10,794,890	415,188	10,450	0.072	100%
CIV	Côte d’Ivoire	99	1.39	84	15	12,308,290	146,527	25,200	0.900	85%
COD	Democratic Republic of the Congo	212	2.99	210	2	39,893,030	199,465	26,000	0.948	94%
DNK	Denmark	23	0.32	4	19	5,637,000	296,684	16,450	0.089	83%
DJI	Djibouti	10	0.14	5	5	731,900	104,557	162,000	0.504	70%
-	Dominica	5	0.07	3	2	52,800	17,600	10,000	0.313	60%
DOM	Dominican Republic	9	0.13	4	5	9,433,500	1,572,250	159,000	0.040	67%
TLS	East Timor	20	0.28	20	0	1,040,006	52,000	16,700	0.819	100%
ECU	Ecuador	25	0.35	24	1	16,279,895	651,196	11,695	0.182	100%
EGY	Egypt	23	0.32	15	8	94,879,800	4,993,674	752,000	0.512	83%
SLV	El Salvador	5	0.07	5	0	6,278,000	2,092,667	7,500	0.003	60%
GNQ	Equatorial Guinea	17	0.24	14	3	699,039	58,253	7,500	0.284	71%
ERI	Eritrea	20	0.28	15	5	6,465,200	461,800	164,500	0.672	70%
EST	Estonia	20	0.28	7	13	1,530,870	85,048	44,250	0.473	90%
ETH	Ethiopia	90	1.27	88	2	68,260,250	784,601	48,000	0.862	97%
-	Faroe Islands	2	0.03	2	0	48,000	48,000	48,000	?	50%
FJI	Fiji	21	0.30	10	11	842,340	64,795	7,700	0.632	62%
FIN	Finland	37	0.52	12	25	5,611,580	151,664	7,585	0.172	100%
FRA	France	41	0.58	21	20	69,428,400	1,827,063	118,000	0.252	93%
-	French Guiana	16	0.23	13	3	79,650	7,241	925	0.549	69%
-	French Polynesia	10	0.14	9	1	257,960	25,796	3,000	0.463	100%
GAB	Gabon	43	0.61	43	0	1,074,960	25,594	4,000	0.846	98%
GMB	Gambia	24	0.34	11	13	1,314,820	69,201	43,650	0.776	79%
GEO	Georgia	27	0.38	19	8	4,882,620	187,793	17,300	0.550	96%
DEU	Germany	92	1.30	24	68	86,065,300	1,212,187	80,000	0.336	77%
CHA	Ghana	87	1.23	81	6	27,661,206	359,236	36,000	0.858	89%
-	Gibraltar	3	0.04	2	1	45,200	15,067	21,200	0.511	100%
GRE	Greece	28	0.39	16	12	11,435,480	519,795	9,700	0.123	79%
GRL	Greenland	3	0.04	3	0	56,200	28,100	28,100	0.196	67%
-	Grenada	4	0.06	4	0	92,250	30,750	2,300	0.064	75%
-	Guadeloupe	4	0.06	3	1	449,300	149,767	218,650	0.083	75%
-	Guam	9	0.13	2	7	191,000	27,286	52,150	0.736	78%
GTM	Guatemala	26	0.37	26	0	14,519,872	558,457	32,500	0.518	100%
-	Guernsey	4	0.06	3	1	62,720	31,360	31,360	0.007	50%
GIN	Guinea	38	0.54	36	2	8,151,300	254,728	23,000	0.748	84%
GNB	Guinea-Bissau	27	0.38	23	4	1,653,490	75,159	8,520	0.859	81%
GUY	Guyana	19	0.27	17	2	1,319,093	101,469	1,215	0.514	68%
HTI	Haiti	4	0.06	4	0	6,960,600	2,320,200	600	0.000	75%
HND	Honduras	12	0.17	10	2	8,142,850	740,259	990	0.039	92%
HUN	Hungary	21	0.30	17	4	10,007,110	588,654	3,710	0.033	81%
ISL	Iceland	3	0.04	2	1	301,000	100,333	150,150	0.007	100%
IND	India	454	6.40	448	6	1,078,672,781	2,508,541	34,400	0.914	95%
IDN	Indonesia	709	9.99	707	2	221,455,810	314,568	3,500	0.816	99%
IRN	Iran	81	1.14	78	3	85,993,020	1,433,217	81,000	0.638	74%
IRQ	Iraq	27	0.38	24	3	33,742,620	1,467,070	110,000	0.761	85%
IRL	Ireland	13	0.18	5	8	4,548,940	413,540	21,000	0.118	85%
-	Isle of Man	2	0.03	2	0	85,000	42,500	42,500	0.000	100%
ISR	Israel	52	0.73	35	17	8,768,485	190,619	42,500	0.719	88%
ITA	Italy	44	0.62	34	10	79,507,930	1,939,218	100,000	0.460	93%
JAM	Jamaica	8	0.11	4	4	2,692,540	448,757	7,500	0.017	75%
JPN	Japan	18	0.25	15	3	129,302,863	9,235,919	5,050	0.035	78%
-	Jersey	4	0.06	3	1	64,100	21,367	30,600	0.136	75%
JOR	Jordan	14	0.20	10	4	5,227,000	522,700	65,300	0.498	71%
KAZ	Kazakhstan	47	0.66	14	33	15,308,820	356,019	90,000	0.514	91%
KEN	Kenya	72	1.01	67	5	37,110,868	545,748	178,000	0.927	94%
-	Kiribati	3	0.04	2	1	103,990	34,663	51,745	0.019	100%
KWT	Kuwait	7	0.10	3	4	1,749,400	349,880	507,200	0.605	71%
KGZ	Kyrgyzstan	33	0.46	6	27	5,825,990	215,777	290,650	0.459	82%
LAO	Laos	87	1.23	81	6	5,773,774	70,412	7,200	0.697	94%
LVA	Latvia	16	0.23	7	9	2,363,930	168,852	8,000	0.526	88%
LBN	Lebanon	10	0.14	7	3	4,680,570	585,071	218,000	0.198	80%
LSO	Lesotho	5	0.07	5	0	2,214,000	553,500	47,000	0.091	80%
LBR	Liberia	34	0.48	31	3	4,290,730	134,085	85,600	0.917	94%
LBY	Libya	30	0.42	10	20	6,186,770	229,140	17,000	0.538	90%
-	Liechtenstein	5	0.07	4	1	35,910	11,970	17,750	0.092	60%
LTU	Lithuania	15	0.21	10	5	3,702,375	336,580	8,000	0.404	73%
LUX	Luxembourg	16	0.23	4	12	460,780	28,799	66,100	0.622	100%
MKD	Macedonia	13	0.18	11	2	2,080,670	208,067	24,900	0.495	77%
MDG	Madagascar	20	0.28	14	6	18,266,000	1,014,778	1,130,000	0.789	90%
MWI	Malawi	23	0.32	16	7	13,032,000	766,588	174,500	0.692	74%
MYS	Malaysia	145	2.04	134	11	22,083,975	210,324	3,400	0.735	72%
MLD	Maldives	2	0.03	2	0	331,000	331,000	331,000	?	50%
MLI	Mali	72	1.01	68	4	12,882,390	189,447	25,000	0.873	94%
MTA	Malta	7	0.10	3	4	457,170	65,310	16,200	0.255	100%
-	Marshall Islands	2	0.03	2	0	65,700	32,850	32,850	0.457	100%
-	Martinique	4	0.06	3	1	427,330	142,443	213,500	0.043	75%
MRT	Mauritania	8	0.11	7	1	3,589,900	512,843	97,800	0.228	88%
MAU	Mauritius	14	0.20	7	7	1,234,660	94,974	28,550	0.216	93%
-	Mayotte	5	0.07	3	2	185,000	46,250	41,700	0.438	80%
MEX	Mexico	290	4.09	285	5	119,501,413	416,381	4,725	0.106	99%
-	Micronesia	19	0.27	18	1	115,550	6,419	2,065	0.751	95%
MDA	Moldova	14	0.20	10	4	3,363,260	480,466	162,000	0.389	50%
-	Monaco	4	0.06	4	0	31,700	7,925	5,350	0.652	100%
MNG	Mongolia	14	0.20	12	2	2,875,100	359,388	31,000	0.179	57%
MNE	Montenegro	9	0.13	5	4	588,140	84,020	32,700	0.244	78%
-	Montserrat	2	0.03	2	0	3,920	1,960	1,960	0.050	100%
MAR	Morocco	14	0.20	14	0	27,713,920	2,519,447	80,000	0.461	79%
MOZ	Mozambique	46	0.65	43	3	20,291,700	450,927	197,500	0.926	98%
MMR	Myanmar	118	1.66	118	0	46,676,605	398,945	15,000	0.522	99%
NAM	Namibia	32	0.45	27	5	2,748,410	88,658	12,000	0.779	97%
-	Nauru	9	0.13	3	6	13,310	2,218	5,005	0.487	67%
NPL	Nepal	124	1.75	120	4	26,642,691	220,188	7,780	0.755	98%
NLD	Netherlands	45	0.63	15	30	20,448,000	639,000	220,000	0.405	71%
NCL	New Caledonia	40	0.56	38	2	143,010	3,972	950	0.750	90%
NZL	New Zealand	28	0.39	4	24	4,543,920	174,766	148,000	0.291	93%
NIC	Nicaragua	11	0.15	11	0	5,454,490	495,863	740	0.052	100%
NER	Niger	22	0.31	22	0	14,000,900	666,710	30,000	0.571	95%
NGA	Nigeria	527	7.42	520	7	103,910,005	222,030	13,800	0.890	89%
-	Niue	3	0.04	2	1	1,490	745	745	0.192	67%
-	Norfolk Island	2	0.03	2	0	2,110	1,055	1,055	0.325	100%
-	North Korea	1	0.01	1	0	23,300,000	23,300,000	23,300,000	0.000	100%
-	Northern Mariana Islands	7	0.10	4	3	42,180	6,026	4,620	0.719	100%
NOR	Norway	21	0.30	11	10	5,261,260	350,751	1,500	0.067	71%
OMN	Oman	23	0.32	17	6	3,646,500	182,325	30,000	0.698	87%
PAK	Pakistan	79	1.11	73	6	198,899,690	2,762,496	56,500	0.752	91%
-	Palau	6	0.08	4	2	19,992	3,332	1,810	0.470	100%
-	Palestine	10	0.14	6	4	3,686,800	409,644	1,400	0.303	90%
PAN	Panama	19	0.27	15	4	3,432,843	264,065	12,400	0.287	68%
PNG	Papua New Guinea	840	11.83	840	0	4,171,079	4,989	1,270	0.988	100%
PRY	Paraguay	27	0.38	23	4	6,622,608	254,716	2,060	0.215	96%
PER	Peru	94	1.32	93	1	29,982,662	325,898	4,210	0.339	98%
PHL	Philippines	191	2.69	183	8	72,034,715	393,632	20,000	0.842	96%
-	Pitcairn	2	0.03	2	0	36	36	36	?	50%
POL	Poland	27	0.38	21	6	38,365,810	1,534,632	12,000	0.050	93%
PRT	Portugal	18	0.25	9	9	10,346,400	574,800	15,000	0.066	100%
PRI	Puerto Rico	12	0.17	4	8	3,601,200	360,120	100,000	0.060	83%
QAT	Qatar	14	0.20	3	11	1,583,200	131,933	320,500	0.825	86%
ROU	Romania	29	0.41	23	6	21,670,430	1,031,925	19,400	0.153	72%
RUS	Russian Federation	155	2.18	108	47	140,654,350	937,696	5,100	0.283	97%
RWA	Rwanda	5	0.07	3	2	11,118,740	2,779,685	5,301,515	0.089	80%
-	Réunion	8	0.11	3	5	651,200	93,029	310,500	0.452	88%
-	Saint Barthélemy	3	0.04	3	0	7,850	2,617	1,000	0.244	100%
-	Saint Kitts and Nevis	3	0.04	3	0	39,240	13,080	200	0.012	100%
-	Saint Lucia	4	0.06	3	1	159,600	79,800	79,800	0.020	50%
-	Saint Martin	5	0.07	3	2	28,500	5,700	5,000	0.689	100%
-	Saint Pierre and Miquelon	3	0.04	2	1	6,790	2,263	3,295	0.110	100%
-	Saint Vincent and the Grenadines	4	0.06	3	1	138,700	34,675	400	0.010	100%
-	Samoa	3	0.04	2	1	187,000	62,333	93,400	0.174	100%
-	San Marino	2	0.03	2	0	25,000	12,500	12,500	0.000	100%
SAU	Saudi Arabia	24	0.34	11	13	18,723,220	851,055	250,000	0.427	92%
SEN	Senegal	47	0.66	38	9	13,974,680	303,797	20,000	0.778	98%
SRB	Serbia	23	0.32	16	7	7,208,080	360,404	31,000	0.224	87%
-	Seychelles	3	0.04	3	0	85,420	28,473	4,590	0.116	100%
SLE	Sierra Leone	26	0.37	24	2	6,977,150	290,715	138,000	0.841	92%
-	Singapore	31	0.44	24	7	5,677,330	210,271	14,100	0.761	87%
-	Sint Maarten	9	0.13	4	5	39,800	4,422	6,000	0.816	100%
SVK	Slovakia	15	0.21	10	5	5,506,240	458,853	15,000	0.247	80%
SVN	Slovenia	15	0.21	9	6	2,094,660	190,424	5,505	0.167	73%
SLB	Solomon Islands	73	1.03	73	0	380,859	5,290	2,735	0.968	99%
SOM	Somalia	14	0.20	13	1	9,938,659	828,222	20,000	0.350	86%
ZAF	South Africa	42	0.59	30	12	51,004,892	1,416,803	133,500	0.871	86%
KOR	South Korea	5	0.07	3	2	48,649,600	12,162,400	24,202,500	0.010	80%
SSD	South Sudan	69	0.97	69	0	4,444,650	79,369	26,000	0.929	81%
ESP	Spain	24	0.34	15	9	45,419,910	2,523,328	42,500	0.276	75%
LKA	Sri Lanka	12	0.17	7	5	19,983,720	1,998,372	46,000	0.446	83%
SDN	Sudan	77	1.08	76	1	32,195,500	473,463	13,000	0.307	88%
SUR	Suriname	27	0.38	21	6	547,035	21,881	1,650	0.709	93%
SWZ	Swaziland	7	0.10	5	2	1,105,900	157,986	19,000	0.209	100%
SWE	Sweden	36	0.51	14	22	10,056,090	295,767	3,200	0.226	94%
CHE	Switzerland	25	0.35	12	13	8,772,330	438,616	30,500	0.683	80%
SYR	Syria	24	0.34	18	6	23,785,610	1,081,164	76,000	0.363	92%
-	São Tomé e Príncipe	7	0.10	4	3	120,090	17,156	7,540	0.630	100%
TJK	Tajikistan	33	0.46	13	20	7,580,210	303,208	27,500	0.276	76%
TZA	Tanzania	126	1.77	125	1	48,071,890	400,599	86,000	0.871	95%
THA	Thailand	83	1.17	70	13	52,892,781	734,622	13,800	0.752	87%
TGO	Togo	46	0.65	44	2	4,889,380	113,707	34,300	0.905	93%
-	Tokelau	5	0.07	2	3	2,421	484	870	0.679	100%
-	Tonga	4	0.06	3	1	105,550	26,388	1,020	0.029	100%
TTO	Trinidad and Tobago	9	0.13	7	2	2,632,500	329,062	20,000	0.599	89%
TUN	Tunisia	13	0.18	6	7	11,532,500	961,042	21,200	0.122	92%
TUR	Turkey	52	0.73	39	13	82,840,960	2,124,127	66,000	0.345	75%
TKM	Turkmenistan	31	0.44	6	25	5,300,460	176,682	256,850	0.457	97%
-	Turks and Caicos Islands	3	0.04	2	1	11,620	5,810	5,810	0.146	67%
-	Tuvalu	3	0.04	3	0	10,100	5,050	5,050	0.020	67%
-	U.S. Virgin Islands	6	0.08	3	3	167,410	33,482	52,300	0.550	83%
UGA	Uganda	45	0.63	43	2	33,690,850	765,701	303,000	0.929	98%
UKR	Ukraine	40	0.56	22	18	49,826,886	1,346,673	75,600	0.489	92%
ARE	United Arab Emirates	34	0.48	7	27	5,651,610	235,484	407,000	0.707	71%
GBR	United Kingdom	84	1.18	11	73	61,536,550	809,691	57,400	0.154	90%
USA	United States	347	4.89	220	127	316,386,398	944,437	25	0.333	97%
URY	Uruguay	14	0.20	4	10	3,469,050	385,450	27,000	0.111	64%
UZB	Uzbekistan	38	0.54	10	28	30,973,900	910,997	378,000	0.466	89%
VUT	Vanuatu	117	1.65	111	6	192,536	1,674	680	0.973	98%
-	Vatican State	2	0.03	2	0	330	165	165	0.000	100%
VEN	Venezuela	51	0.72	44	7	29,699,865	645,649	705	0.040	90%
VNM	Viet Nam	111	1.56	109	2	76,935,380	761,736	13,500	0.267	91%
-	Wallis and Futuna	3	0.04	3	0	12,960	4,320	3,900	0.548	100%
YEM	Yemen	14	0.20	11	3	15,762,900	1,432,991	59,950	0.581	79%
ZMB	Zambia	56	0.79	46	10	11,433,470	238,197	32,850	0.830	86%
ZWE	Zimbabwe	23	0.32	21	2	11,688,440	649,358	145,000	0.597	78%

pop.csv

"Data Source","World Development Indicators",

"Last Updated Date","2017-02-01",

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"Last Updated Date","2017-02-01",

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