# Trends in daily R package downloads

**Peter Solymos - R related posts**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

This post was prompted by this blog about using the **cranlogs** package by Gabor Csardi. But my own interest

as long time package developer dates back to this post by Ben Bolker. I like to see that

my packages

are being used. So I thought why stop at counting downloads and plotting

the past. Why not predict into the future?

The R language has seen steady growth over the past decade in terms of its

use relative to other programming languages

and the number of available extension packages,

both reflecting increasing community engagement.

It is thus interesting to have a look at how individual

packages perform over time based on

CRAN download statistics.

As the R user community expands, the disparity between

more and less popular packages is expected to increase.

A handful of packages

are on top of the CRAN download chart harvesting most of the attention

of novice R users.

However, popularity and trending based on downloads

is not necessarily the best measure of overall impact, more like a

measure that is easiest to quantify.

At least I like to think that 100 downloads with 10 users is way better than 100 downloads with a single user. These two scenarios are hard to tell apart though.

So for now, let’s assume that download statistics reflect real impact.

So I wanted to look at temporal patterns in the past 3 years’ download

statistics from RStudio CRAN mirror

for individual R packages. I quantified past trend based

on fitting a log-linear `glm`

model to counts vs. dates.

Then I used the **forecast** package by Rob Hyndman to make short-term forecast

based on an additive non-seasonal exponential smoothing model. Here is the `plot_pkg_trend`

function:

library(cranlogs) library(forecast) plot_pkg_trend <- function(pkg) { op <- par(mar = c(3, 3, 1, 1) + 0.1, las = 1) on.exit(par(op)) ## grab the data x <- cran_downloads(pkg, from = "2013-08-21", to = "2016-08-19") x$date <- as.Date(x$date) x$std_days <- 1:nrow(x) / 365 ## past trend m <- glm(count ~ std_days, x, family = poisson) tr_past <- round(100 * (exp(coef(m)[2L]) - 1), 2) ## future trend s <- ts(x$count) z <- ets(s, model = "AAN") f <- forecast(z, h=365) f$date <- seq(as.Date("2016-08-20"), as.Date("2017-08-19"), 1) tr_future <- round(100 * (f$mean[length(f$mean)] / f$mean[1L] - 1), 2) ## plot plot(count ~ date, x, type = "l", col = "darkgrey", ylab = "", xlab = "", ylim = c(0, quantile(x$count, 0.999)), xlim = c(x$date[1L], as.Date("2017-08-19"))) lines(lowess(x$date, x$count), col = 2, lwd = 2) polygon(c(f$date, rev(f$date)), c(f$upper[,2L], rev(f$lower[,2L])), border = NA, col = "lightblue") lines(f$date, f$mean, col = 4, lwd = 2) legend("topleft", title = paste("Package:", pkg), bty = "n", col = c(2, 4), lwd = 2, cex = 1, legend = c(paste0("past: ", tr_past, "%"), paste0("future: ", tr_future, "%"))) ## return the data invisible(x) }

Next, we list the top 10 R packages from the last month:

cran_top_downloads("last-month") ## rank package count from to ## 1 1 Rcpp 221981 2016-07-23 2016-08-21 ## 2 2 digest 181333 2016-07-23 2016-08-21 ## 3 3 ggplot2 170577 2016-07-23 2016-08-21 ## 4 4 plyr 163590 2016-07-23 2016-08-21 ## 5 5 stringi 154918 2016-07-23 2016-08-21 ## 6 6 stringr 153917 2016-07-23 2016-08-21 ## 7 7 jsonlite 152040 2016-07-23 2016-08-21 ## 8 8 magrittr 139596 2016-07-23 2016-08-21 ## 9 9 curl 134026 2016-07-23 2016-08-21 ## 10 10 scales 130887 2016-07-23 2016-08-21

The top package was **Rcpp** by Dirk Eddelbuettel *et al.* and

here is the daily trend plot with percent annual trend estimates:

plot_pkg_trend("Rcpp")

The increase is clear with increasing day-to-day variation

suggesting that the log-linear model might be appropriate.

Steady increase is predicted (the blue line starts at the

last observed data point, whereas the

red line is a locally weighted `lowess`

smoothing curve on

past data).

The light blue 95% prediction

intervals nicely follow the bulk of the zig-zagging in the

observed trend.

Now for my packages, here are the ones I use most often:

op <- par(mfrow = c(2, 2)) plot_pkg_trend("pbapply") plot_pkg_trend("ResourceSelection") plot_pkg_trend("dclone") plot_pkg_trend("mefa4") par(op)

Two of them increasing nicely, the other two are showing

some leveling-off. And finally,

results for some packages that I am contributing to:

op <- par(mfrow = c(2, 2)) x <- plot_pkg_trend("vegan") x <- plot_pkg_trend("epiR") x <- plot_pkg_trend("adegenet") x <- plot_pkg_trend("plotrix") par(op)

My approach as outlined here works for packages that have been with us

since 2013. However, there are packages that went extinct (orphaned,

excluded from CRAN, etc.) or appeared after 2013.

These need to be handled with care when comparing across

many packages or changing the time horizon.

If you want to take the idea presented here a step further,

you can compare observed and predicted increase in downloads for

many packages, or compare recent download stats and trends.

Let me know what you think in the comments below!

**leave a comment**for the author, please follow the link and comment on their blog:

**Peter Solymos - R related posts**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.