Big in Japan

June 13, 2013
By

(This article was first published on Gianluca Baio's blog, and kindly contributed to R-bloggers)

Inspired by this post on R-bloggers, I decided to check how BCEA was doing. Unfortunately, it does not feature in the top 100 most downloaded R packages. However, I think it's doing well $-$ considering the book (which is the main medium of advertising of the package) has been out for only a few months (since October last year) and it's kind of a specialised software, which basically you only need if you do health economic evaluations...

I've used some simple R code to download the log files containing all hits to http://cran.rstudio.com/ since October of 2012. Once the files (in .csv format, compressed in .gz files, one per each day) are downloaded, I have R extract the original file and then create a table, only selecting the records for BCEA.

The resulting dataset contains the date(s) and time(s) in which the library has been downloaded from CRAN, some information about the R version and architecture of the person who has downloaded the package, as well as their country.

Overall, BCEA has been officially downloaded 862 times (I suppose I should have a big celebration as soon as I hit 1000); most of the times, the download was from a user in the US (185). Surprisingly, BCEA is big in Japan (135 downloads). I did not see this coming, I have to say, but 日本ありがとう$-$ that's "thank you Japan", for those of you who can't speak Japanese (or can't use Google Translate).

Here's the (quickly prepared and hence not particularly elegant, nor necessarily super-efficient) code to download and format the data:
start <- as.Date('2012-10-01')
today <- as.Date('2013-06-12')
all_days <- seq(start, today, by = 'day')
year <- as.POSIXlt(all_days)$year + 1900
urls <- paste0('http://cran-logs.rstudio.com/', year, '/', all_days, '.csv.gz')
file <- basename(urls)
download.file(urls[1], file[1])
data <- read.table(gzfile(file[1]),sep=",",header=TRUE)
data <- data[data$package=="BCEA",]
for (i in 2:length(urls)) {
download.file(urls[i], file[i])
tmp <- read.table(gzfile(file[i]),sep=",",header=TRUE)
tmp <- tmp[tmp$package=="BCEA",]
data <- rbind(data,tmp)
}
data <- na.omit(data)

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