Finally! Tracking CRAN packages downloads
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[Update June 12: Data.tables functions have been improved (thanks to a comment by Matthew Dowle); for a similar approach see also Tal Galili’s post]
The guys from RStudio now provide CRAN download logs (see also this blog post). Great work!
I always asked myself, how many people actually download my packages. Now I finally can get an answer (… with some anxiety to get frustrated 
Here are the complete, self-contained R scripts to analyze these log data:
Step 1: Download all log files in a subfolder (this steps takes a couple of minutes)
## ======================================================================
## Step 1: Download all log files
## ======================================================================
# Here's an easy way to get all the URLs in R
start <- as.Date('2012-10-01')
today <- as.Date('2013-06-10')
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')
# only download the files you don't have:
missing_days <- setdiff(as.character(all_days), tools::file_path_sans_ext(dir("CRANlogs"), TRUE))
dir.create("CRANlogs")
for (i in 1:length(missing_days)) {
print(paste0(i, "/", length(missing_days)))
download.file(urls[i], paste0('CRANlogs/', missing_days[i], '.csv.gz'))
} |
Step 2: Combine all daily files into one big data table (this steps also takes a couple of minutes…)
## ======================================================================
## Step 2: Load single data files into one big data.table
## ======================================================================
file_list <- list.files("CRANlogs", full.names=TRUE)
logs <- list()
for (file in file_list) {
print(paste("Reading", file, "..."))
logs[[file]] <- read.table(file, header = TRUE, sep = ",", quote = "\"",
dec = ".", fill = TRUE, comment.char = "", as.is=TRUE)
}
# rbind together all files
library(data.table)
dat <- rbindlist(logs)
# add some keys and define variable types
dat[, date:=as.Date(date)]
dat[, package:=factor(package)]
dat[, country:=factor(country)]
dat[, weekday:=weekdays(date)]
dat[, week:=strftime(as.POSIXlt(date),format="%Y-%W")]
setkey(dat, package, date, week, country)
save(dat, file="CRANlogs/CRANlogs.RData")
# for later analyses: load the saved data.table
# load("CRANlogs/CRANlogs.RData") |
Step 3: Analyze it!
## ======================================================================
## Step 3: Analyze it!
## ======================================================================
library(ggplot2)
library(plyr)
str(dat)
# Overall downloads of packages
d1 <- dat[, length(week), by=package]
d1 <- d1[order(V1), ]
d1[package=="TripleR", ]
d1[package=="psych", ]
# plot 1: Compare downloads of selected packages on a weekly basis
agg1 <- dat[J(c("TripleR", "RSA")), length(unique(ip_id)), by=c("week", "package")]
ggplot(agg1, aes(x=week, y=V1, color=package, group=package)) + geom_line() + ylab("Downloads") + theme_bw() + theme(axis.text.x = element_text(angle=90, size=8, vjust=0.5))
agg1 <- dat[J(c("psych", "TripleR", "RSA")), length(unique(ip_id)), by=c("week", "package")]
ggplot(agg1, aes(x=week, y=V1, color=package, group=package)) + geom_line() + ylab("Downloads") + theme_bw() + theme(axis.text.x = element_text(angle=90, size=8, vjust=0.5)) |
Here are my two packages, TripleR and RSA. Actually, ~30 downloads per week (from this single mirror) is much more than I’ve expected!
To put things in perspective: package psych included in the plot:
Some psychological sidenotes on social comparisons:
- Downward comparisons enhance well-being, extreme upward comparisons are detrimental. Hence, do never include
ggplot2into your graphic! - Upward comparisons instigate your achievement motive, and give you drive to get better. Hence, select some packages, which are slightly above your own.
- Of course, things are a bit more complicated than that …
All source code on this post is licensed under the FreeBSD license.
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