Alex and I had a great time in Albacete this summer, where the annual useR! conference took place. Of course we were really interested in the exciting news on R development, new packages and other related topics that we hoped to hear about there, and we also wanted to present what we have created with our R packages and rapporter.net beside socializing and to meet some guys from SO and GH in real life at last, but I also really expected to meet some other Hungarian guys at the conference.

It turned out that I was the only attendant from Hungary – except for Szilárd Pafka, who has been living in the USA for a long time, so he does not really count by the strict standard 🙂 Since then, I know that there are a lot more R users living in Hungary, but I’ve just had the chance to verify my feeling that the number of attendees from East-Europe was rather low – as the official list of attendants has been recently published at the homepage of the conference:

> library(XML)
> d <- readHTMLTable('http://www.edii.uclm.es/~useR-2013/asistentes.html', which = 1, stringsAsFactors = FALSE)

That looks like:

> library(pander)
> pander(table(d[, 2]), split.table = Inf)

Converted to HMTL:

Australia	Austria	Bangladesh	Belgium	Canada	Czech Republic	Denmark	Estonia	Finland	France	Germany	Hungary	Iran	Ireland	Israel	Italy	Japan	Korea	Latvia	Mexico	New Zealand	Norway	Poland	Portugal	Russia	Serbia	Singapore	Slovenia	South Africa	South Korea	Spain	Sweden	Switzerland	Taiwan	The Netherlands	Turkey	United Kingdom	United States	USA
6	14	1	13	3	2	8	1	3	11	24	1	1	4	1	12	4	4	1	1	2	6	11	5	3	1	1	3	1	2	66	2	6	1	11	3	31	6	52

Well, it really seems that I was the only guy from Hungary, but at least Polish users were a lot more active from this region. Anyway, this list could use some cleaning and finishing touches with the help of e.g. the countrycode package:

> names(d) <- gsub(' ', '', names(d))
> library(countrycode)
> d$COUNTRY[which(is.na(countrycode(d$COUNTRY, 'country.name', 'iso2c')))]
[1] "England" ""        "Letonia" "Madrid" 

It seems that there are some unidentified countries and even a missing one, let's fix that (with some desktop research):

> d$COUNTRY[which(d$COUNTRY == 'England')] <- 'United Kingdom'
> d$COUNTRY[which(d$COUNTRY == 'Letonia')] <- 'Latvia'
> d$COUNTRY[which(d$COUNTRY == 'Madrid')]  <- 'Spain'
> d$COUNTRY[which(d$NAME == 'Yurii Aulchenko')]  <- 'The Netherlands'

Much better! And I really hope that my guess was right about Yurii.

As I really liked the "Where is the R Activity?" post and found it extremely inspiring, I was also thinking about reproducing that kind of plot based on this data set. After fetching and loading the world map referenced in the article and aggregated our cleaned data, I have also created a new country ID variable in the aggregated dataset so that we could easily merge that to the shape file:

> ## aggregate
> d$flag <- 1
> counts <- aggregate(d$flag, by = list(d$COUNTRY), sum)
> names(counts) <- c("country.name", "count")
> ## std name
> library(countrycode)
> counts$COUNTRY <- countrycode(counts$country.name, 'country.name', 'iso2c')

Merging, magic and plotting was done just like in the original article:
Just cannot wait to render a similar cartogram next year!