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View mountains2.txt
"V1","V2","V3","V4"
"Everest",8849,688.582,"Indian Ocean"
"Kanchenjunga",8586,670,"Indian Ocean"
"Chomolhari",7090,614,"Indian Ocean"
"Gangkhar Puensum",7570,605,"Indian Ocean"
"Chimborazo",6263.47,175,"Pacific"
"Mishahuanga",4118,91,"Pacific"
"Huascaran",6746,97.7,"Pacific"
"Coropuna",6405,112,"Pacific"
"El Misti",5822,103,"Pacific"
View esperantujodirectory2.txt
name ujo pop uea lernu www pas edu nat
AFG afghanistan 0 27.7 1 48 - 0 0 0
ALB albanien 0 2.8 14 64 0.6 1 5 33
DZA algeriet 1 39.2 6 503 0.15 1 8 0
AND andorra - 0.078 0 167 - 0 8 0
AGO angola 1 21.5 2 42 0.19 0 0 -
ARG argentino 27 41.5 29 1965 0.60 16 61 140
ARM armenien 0 3.0 12 92 - 0 1 27
AUS australio 23 23.1 40 2568 0.83 16 52 130
AUT austria 4 8.6 44 780 0.81 8 23 79
View calculate_LDN_and_MDS.Rmd
---
title: "Linguistic differences"
output: html_notebook
---
Read in all the data. In the working directory, I have put several files from the AJSP database. Any subset of files will do.
```{r}
lf=list.files(pattern=".csv")
@svendvn
svendvn / Linguistic Diversity.txt
Last active Sep 30, 2017
Explaining the density of Esperanto speakers with language and politics
View 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%
View babynames.R
a=read.table('usa_big.txt', header=F)
remove_and_make_numeric=function(s){
return(as.numeric(substr(x = s, start = 1, stop = nchar(s)-1)))
}
summarize=function(x){
y=x/100
within=sum(y)
res=0
for(i in 1:length(y)){
View amikumu_plot.R
ad=read.csv('parsed_data.txt', header=T)
ad=as.data.frame(apply(ad, c(1,2), function(x) ifelse(is.na(x),0,x)), stringsAsFactors = F)
ad[,3:ncol(ad)]=apply(ad[,3:ncol(ad)], c(1,2), as.numeric)
View(ad)
colnames(ad)[1] <- 'Rank'
barplot(height=ad$Speakers[1:10], names.arg=ad$Language[1:10])
ad$Learners=apply(ad[,c("Advanced","Intermediate","Beginner")],1,sum)
ad$Learners=sapply(ad$Learners, function(x) max(x,1))
normed_d=ad[,c("Advanced","Intermediate","Beginner")]/ad$Learners
apply(normed_d,1,sum)
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