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Explaining the density of Esperanto speakers with language and politics
---
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)
```
linguistic_differences
AFAR 0.953367875647668
AFRIKAANS 0.781420765027322
ALBANIAN 0.88780487804878
AMHARIC 0.930693069306931
AMOY_MINNAN_CHINESE 0.924107142857143
AZERBAIJANI_NORTH 0.922279792746114
B41_SHIRA 0.883977900552486
BAMBARA 0.881578947368421
BANGANDU 0.890173410404624
BARI_SUDAN 0.894736842105263
BAULE 0.922535211267606
BELARUSIAN 0.862244897959184
BELIZE_KRIOL_ENGLISH 0.694117647058824
BEMBE 0.880681818181818
BENGALI 0.827225130890052
BHOJPURI 0.878787878787879
BISLAMA 0.820987654320988
BORONG 0.855721393034826
BOSNIAN 0.753846153846154
BULGARIAN 0.803030303030303
BULU 0.911242603550296
CANTONESE 0.906779661016949
CAPE_VERDEAN_CREOLE 0.590163934426229
CATALAN 0.729166666666667
CENTRAL_AYMARA 0.917355371900826
CLASSICAL_NAHUATL 0.887179487179487
CROATIAN 0.771573604060914
CZECH 0.807106598984772
DANISH 0.836065573770492
DATOOGA_DIALECT_2 0.938202247191011
DINKA_SOUTHEASTERN 0.918367346938776
DUTCH 0.777173913043478
DZONGKHA 0.905405405405405
EASTERN_FARSI 0.832460732984293
EASTERN_FRISIAN 0.740740740740741
ECHIE 0.909090909090909
ENGLISH 0.733333333333333
ESTONIAN 0.9
EWE_ADANGBE 0.865671641791045
FANG 0.931506849315068
FIJIAN 0.894736842105263
FINNISH 0.941489361702128
FONGBE 0.9
FRENCH 0.703703703703704
FULA 0.916666666666667
FULFULDE 0.935643564356436
GALICIAN 0.6524064171123
GEORGIAN 0.940677966101695
GIKUYU 0.912790697674419
GREEK 0.792079207920792
GUARANI 0.918103448275862
HAITIAN_CREOLE 0.761904761904762
HAUSA 0.845410628019324
HEBREW 0.864583333333333
HERERO 0.891304347826087
HINDI 0.807692307692308
HMONG_DAW 0.92948717948718
HUNGARIAN 0.91304347826087
ICELANDIC 0.848341232227488
INDONESIAN 0.848484848484849
IRISH_GAELIC 0.898058252427185
ITALIAN 0.572916666666667
J321_MARAMA 0.917197452229299
KABIYE 0.918518518518519
KALANGA 0.863636363636364
KARIMOJONG 0.870192307692308
KAZAKH 0.93048128342246
KHMER 0.926724137931034
KHMU 0.905263157894737
KICHE 0.946808510638298
KINYARWANDA 0.878947368421053
KIRUNDI 0.880281690140845
KITUBA 0.908496732026144
KOONGO 0.90625
KOREAN 0.904761904761905
KURDISH_CENTRAL 0.823204419889503
KWANGALI 0.843137254901961
KWANYAMA 0.875776397515528
KYRGYZ 0.861702127659574
L31a_LUBA_KASAYI 0.884393063583815
LAO 0.898477157360406
LATVIAN 0.884422110552764
LINGALA 0.883977900552486
LITHUANIAN 0.848739495798319
LOMWE 0.879781420765027
LOZI_2 0.863905325443787
LUGANDA 0.8743961352657
LUO 0.904255319148936
LUXEMBOURGISH 0.782608695652174
M64_TONGA_1 0.890243902439024
MACEDONIAN 0.8125
MACHAME 0.895061728395062
MAITHILI 0.885496183206107
MAKASAE 0.915151515151515
MAKONDE 0.919431279620853
MAKWA_ALUA 0.87431693989071
MALAGASY_PLATEAU_SIHANAKA_AMBATONDRAZAKA 0.824742268041237
MALANG 0.898395721925134
MALAY 0.8743961352657
MALDIVIAN 0.876811594202899
MALINKE 0.875
MALTESE 0.882926829268293
MAMBAI 0.902173913043478
MANDARIN 0.915178571428571
MANDINKA 0.886010362694301
MAORI 0.877659574468085
MAPUDUNGUN_3 0.914141414141414
MARATHI 0.869109947643979
MAURITIAN 0.764397905759162
MBEDE 0.936507936507937
MBOSHI 0.867052023121387
MENDE 0.913907284768212
MISKITO 0.913333333333333
MONGOLIAN 0.919354838709677
MOORE 0.888888888888889
NAMA 0.877094972067039
NEPALI 0.86046511627907
NGUNGWEL 0.925
NORTHERN_PASHTO 0.878306878306878
NORWEGIAN_BOKMAAL 0.82122905027933
NUER 0.938461538461538
NYANJA 0.870466321243523
OLD_TURKIC 0.942857142857143
PERSIAN 0.832460732984293
PIJIN 0.790575916230366
POLISH 0.869346733668342
PORTUGUESE 0.722513089005236
PROTO_KAREN 0.92
PROTO_MAYAN 0.934131736526946
PROTO_PHILIPPINE 0.952054794520548
PROTO_QUECHUA 0.897142857142857
PUNJABI_MAJHI 0.82
ROMA 0.889570552147239
ROMANIAN 0.645833333333333
RUSSIAN 0.87719298245614
SANGO 0.859756097560976
SENA 0.889473684210526
SENUFO_CEBAARA 0.85427135678392
SERBOCROATIAN 0.796875
SHAN 0.900763358778626
SHONA 0.888297872340426
SINHALA 0.872340425531915
SIWA_BERBER 0.915492957746479
SLOVAK 0.792553191489362
SLOVENIAN 0.774358974358974
SOMALI 0.896373056994819
SOTHO_SOUTHERN 0.903448275862069
SPANISH 0.658031088082902
SRANAN_TONGO 0.770114942528736
STANDARD_ARABIC 0.940217391304348
STANDARD_GERMAN 0.744791666666667
SUNDANESE 0.907216494845361
SUSU 0.931578947368421
SUZHOU_WU 0.954314720812183
SWAHILI 0.880829015544041
SWAZI 0.887323943661972
SWEDISH 0.841530054644809
TAJIK 0.83419689119171
TAMASHEQ 0.935643564356436
TAMIL 0.859223300970874
TELUGU 0.890995260663507
TETUN_DILI 0.894736842105263
THAI 0.9
THEMNE 0.902597402597403
TIGRE 0.914691943127962
TIGRIGNA 0.907834101382488
TIGRINYA 0.912037037037037
TIKAR_AKUEN 0.944444444444444
TOK_PISIN 0.773869346733668
TOKYO_JAPANESE 0.913875598086124
TSHANGLA 0.935643564356436
TSONGA 0.887700534759358
TSWANA 0.890547263681592
TUMBUKA_ZAMBIE 0.906976744186046
TURKISH 0.916666666666667
TURKMEN 0.916666666666667
TWI_ASANTE 0.917582417582418
TWI_FANTE 0.937984496124031
UKRAINIAN 0.757731958762887
UMBUNDU 0.875
URDU 0.845744680851064
UZBEK 0.865591397849462
VALPEI 0.861702127659574
VIETNAMESE 0.903954802259887
W_OROMO 0.948979591836735
WE_SOUTHERN 0.911764705882353
WELSH 0.835106382978723
WEST_GREENLANDIC 0.911504424778761
WESTERN_ARMENIAN 0.91005291005291
WOLOF 0.868613138686131
XHOSA 0.919075144508671
YAKOUBA 0.953488372093023
YANGON_BURMESE 0.892857142857143
YAO_1 0.896174863387978
YORUBA 0.898936170212766
ZANDE 0.89261744966443
ZARMA 0.840425531914894
ZIMBABWE_NDEBELE 0.890995260663507
ZULU 0.891402714932127
ESPERANTO 0
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 -
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MMR A myanmar 1 54.6 1 25 - 0 1 0
NAM F namibia 0 2.3 0 21 - 0 0 0
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NCL A newkaledonia 0 0.268 2 46 - 1 6 -
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NIC M nicaragua 1 6.1 19 295 0.16 1 8 -
NER F niger 0 20.7 3 25 - 1 1 -
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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
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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
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SEN F senegal 0 14.8 4 129 - 6 12 -
SRB E serbien 5 7.1 28 234 0.72 5 15 -
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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
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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
We can make this file beautiful and searchable if this error is corrected: It looks like row 3 should actually have 3 columns, instead of 62. in line 2.
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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
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
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%
We can make this file beautiful and searchable if this error is corrected: It looks like row 3 should actually have 3 columns, instead of 62. in line 2.
"Data Source","World Development Indicators",
"Last Updated Date","2017-02-01",
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