Which R packages do scientists use?

pkgs = c(
    "renv",
    "ggplot2",
    "anytime",
    "DT",
    "here",
    "parallel",
    "patchwork",
    "data.table"
)
sapply(pkgs, require, character.only = TRUE)

>       renv    ggplot2    anytime         DT       here   parallel  patchwork 
>       TRUE       TRUE       TRUE       TRUE       TRUE       TRUE       TRUE 
> data.table 
>       TRUE

theme_set(theme_minimal())
dat = readRDS("usage.rds")
n_projects = 4539
n_scripts = 19162
# n_projects = length(Sys.glob("~/Dropbox/research/dataverse/archive/*"))
# n_scripts = length(Sys.glob("~/Dropbox/research/dataverse/archive/**/*R"))

package ‘renv’ was built under R version 4.5.2
package ‘ggplot2’ was built under R version 4.5.2
package ‘data.table’ was built under R version 4.5.2

Loading required package: renv


Attaching package: ‘renv’


The following objects are masked from ‘package:stats’:

    embed, update


The following objects are masked from ‘package:utils’:

    history, upgrade


The following objects are masked from ‘package:base’:

    autoload, load, remove, use


Loading required package: ggplot2

Loading required package: anytime

Loading required package: DT

Loading required package: here

here() starts at /Users/vincent/repos/arelbundockcom

Loading required package: parallel

Loading required package: patchwork

Loading required package: data.table

I downloaded r n_scripts R scripts from r n_projects projects hosted by the The Dataverse Project. This notebook reports usage statistics for R packages in this large sample of real-life scientific applications.

To download data from Dataverse, I adapted a script from Trisovic et al. (2022) and wrote original Python code. Then, I used the renv::dependencies() function from the renv package for R (Ushey, 2022) to extract the names of R packages used in each script.¹

WARNING: This was a very quick job and I did very little quality control on the data. Please take all this with a grain of salt.

Trisovic, A., Lau, M.K., Pasquier, T. et al. A large-scale study on research code quality and execution. Sci Data 9, 60 (2022). https://doi.org/10.1038/s41597-022-01143-6

Ushey K (2022). renv: Project Environments. R package version 0.16.0, https://rstudio.github.io/renv/.

Number of projects and packages over time

dat = dat[date > anytime("2013-12-31 UTC")]
dat[, month := anytime(format(date, "%Y-%m-15"))]

>        dataset_id   Package                date
>                               
>     1:      46760       car 2015-07-29 11:32:37
>     2:      46890      epiR 2016-03-11 17:54:53
>     3:      46890       irr 2016-03-11 17:54:53
>     4:      46890    austin 2016-03-11 17:54:53
>     5:      46935  MCMCpack 2016-03-11 17:33:12
>    ---                                         
> 41818:    6789403   sjstats 2022-12-06 10:41:05
> 41819:    6789403     srvyr 2022-12-06 10:41:05
> 41820:    6789403 tidyverse 2022-12-06 10:41:05
> 41821:    6789403   weights 2022-12-06 10:41:05
> 41822:    6789403   writexl 2022-12-06 10:41:05
>                                                                                                                                                                                                                                                                                       dataset_citation
>                                                                                                                                                                                                                                                                                                 
>     1:    Simons, Joseph; Mallinson, Daniel J., 2015, ""Replication data for: Party Control and Perverse Effects in Majority-Minority Districting: Replication Challenges When Using DW-NOMINATE"", https://doi.org/10.7910/DVN/28763, Harvard Dataverse, V1, UNF:6:COakdf2t21U/4QgnYTB5cQ== [fileUNF]
>     2: Dolezal, Martin; Ennser-Jedenastik, Laurenz; Müller, Wolfgang C.; Winkler, Anna Katharina, 2016, ""Replication data for: Analyzing Manifestos in their Electoral Context: A New Approach Applied to Austria, 2002â\u0080\u00932008"", https://doi.org/10.7910/DVN/27864, Harvard Dataverse, V1
>     3: Dolezal, Martin; Ennser-Jedenastik, Laurenz; Müller, Wolfgang C.; Winkler, Anna Katharina, 2016, ""Replication data for: Analyzing Manifestos in their Electoral Context: A New Approach Applied to Austria, 2002â\u0080\u00932008"", https://doi.org/10.7910/DVN/27864, Harvard Dataverse, V1
>     4: Dolezal, Martin; Ennser-Jedenastik, Laurenz; Müller, Wolfgang C.; Winkler, Anna Katharina, 2016, ""Replication data for: Analyzing Manifestos in their Electoral Context: A New Approach Applied to Austria, 2002â\u0080\u00932008"", https://doi.org/10.7910/DVN/27864, Harvard Dataverse, V1
>     5:                                           Schwarz, Daniel; Traber, Denise; Benoit, Kenneth, 2016, ""Replication data for: Estimating Intra-Party Preferences: Comparing Speeches to Votes"", https://doi.org/10.7910/DVN/27702, Harvard Dataverse, V1, UNF:6:lzjSYjrMMhScH7QDurVAAw== [fileUNF]
>    ---                                                                                                                                                                                                                                                                                                
> 41818:                     Stefkovics, Adam, 2022, ""Global warming vs. climate change frames. Revisiting framing effects based on new experimental evidence collected in 30 European countries"", https://doi.org/10.7910/DVN/OYWFB9, Harvard Dataverse, V1, UNF:6:q7NQHWV34EwjSer8wMmT+g== [fileUNF]
> 41819:                     Stefkovics, Adam, 2022, ""Global warming vs. climate change frames. Revisiting framing effects based on new experimental evidence collected in 30 European countries"", https://doi.org/10.7910/DVN/OYWFB9, Harvard Dataverse, V1, UNF:6:q7NQHWV34EwjSer8wMmT+g== [fileUNF]
> 41820:                     Stefkovics, Adam, 2022, ""Global warming vs. climate change frames. Revisiting framing effects based on new experimental evidence collected in 30 European countries"", https://doi.org/10.7910/DVN/OYWFB9, Harvard Dataverse, V1, UNF:6:q7NQHWV34EwjSer8wMmT+g== [fileUNF]
> 41821:                     Stefkovics, Adam, 2022, ""Global warming vs. climate change frames. Revisiting framing effects based on new experimental evidence collected in 30 European countries"", https://doi.org/10.7910/DVN/OYWFB9, Harvard Dataverse, V1, UNF:6:q7NQHWV34EwjSer8wMmT+g== [fileUNF]
> 41822:                     Stefkovics, Adam, 2022, ""Global warming vs. climate change frames. Revisiting framing effects based on new experimental evidence collected in 30 European countries"", https://doi.org/10.7910/DVN/OYWFB9, Harvard Dataverse, V1, UNF:6:q7NQHWV34EwjSer8wMmT+g== [fileUNF]
>             month
>            
>     1: 2015-07-15
>     2: 2016-03-15
>     3: 2016-03-15
>     4: 2016-03-15
>     5: 2016-03-15
>    ---           
> 41818: 2022-12-15
> 41819: 2022-12-15
> 41820: 2022-12-15
> 41821: 2022-12-15
> 41822: 2022-12-15

projects = dat[, .(N = length(unique(dataset_id))), by = "month"]
packages = dat[, .N, by = "month"]

p1 = ggplot(projects, aes(month, N)) + 
    geom_line() +
    labs(x = "", y = "", title = "Projects")
p2 = ggplot(packages, aes(month, N)) + 
    geom_line() +
    labs(x = "", y = "", title = "Packages")
p1 + p2

'tzone' attributes are inconsistent

Usage statistics for R packages loaded at least twice

# count only one use per project
dat_count = dat[, .(date = min(date)), by = c("dataset_id", "Package")]

dat_count = dat_count[, .(`Number of times loaded` = .N), by = "Package"]
dat_count = dat_count[order(-`Number of times loaded`)]
dat_count = dat_count[`Number of times loaded` > 1]

DT::datatable(dat_count, options = list(pageLength = 50), rownames = FALSE, width = 300)

1. renv was not able to parse every script. ↩

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