R trends in 2015 (based on cranlogs)

What are the current tRends? The image is CC from coco + kelly.
What are the current tRends? The image is CC from coco + kelly.

It is always fun to look back and reflect on the past year. Inspired by Christoph Safferling’s post on top packages from published in 2015, I decided to have my own go at the top R trends of 2015. Contrary to Safferling’s post I’ll try to also (1) look at packages from previous years that hit the big league, (2) what top R coders we have in the community, and then (2) round-up with my own 2015-R-experience.

Everything in this post is based on the CRANberries reports. To harvest the information I’ve borrowed shamelessly from Safferling’s post with some modifications. He used the number of downloads as proxy for package release date, while I decided to use the release date, if that wasn’t available I scraped it off the CRAN servers. The script now also retrieves package author(s) and description (see code below for details).

library(rvest)
library(dplyr)
# devtools::install_github("hadley/multidplyr")
library(multidplyr)
library(magrittr)
library(lubridate)

getCranberriesElmnt <- function(txt, elmnt_name){
  desc <- grep(sprintf("^%s:", elmnt_name), txt)
  if (length(desc) == 1){
    txt <- txt[desc:length(txt)]
    end <- grep("^[A-Za-z/@]{2,}:", txt[-1])
    if (length(end) == 0)
      end <- length(txt)
    else
      end <- end[1]
    
    desc <-
      txt[1:end] %>% 
      gsub(sprintf("^%s: (.+)", elmnt_name),
           "\\1", .) %>% 
      paste(collapse = " ") %>% 
      gsub("[ ]{2,}", " ", .) %>% 
      gsub(" , ", ", ", .)
  }else if (length(desc) == 0){
    desc <- paste("No", tolower(elmnt_name))
  }else{
    stop("Could not find ", elmnt_name, " in text: \n",
         paste(txt, collapse = "\n"))
  }
  return(desc)
}

convertCharset <- function(txt){
  if (grepl("Windows", Sys.info()["sysname"]))
    txt <- iconv(txt, from = "UTF-8", to = "cp1252")
  return(txt)
}

getAuthor <- function(txt, package){
  author <- getCranberriesElmnt(txt, "Author")
  if (grepl("No author|See AUTHORS file", author)){
    author <- getCranberriesElmnt(txt, "Maintainer")
  }
  
  if (grepl("(No m|M)aintainer|(No a|A)uthor|^See AUTHORS file", author) || 
      is.null(author) ||
      nchar(author)  <= 2){
    cran_txt <- read_html(sprintf("http://cran.r-project.org/web/packages/%s/index.html",
                                  package))
    author <- cran_txt %>% 
      html_nodes("tr") %>% 
      html_text %>% 
      convertCharset %>% 
      gsub("(^[ \t\n]+|[ \t\n]+$)", "", .) %>% 
      .[grep("^Author", .)] %>% 
      gsub(".*\n", "", .)
    
    # If not found then the package has probably been
    # removed from the repository
    if (length(author) == 1)
      author <- author
    else
      author <- "No author"
  }
  
  # Remove stuff such as:
  # [cre, auth]
  # (worked on the...)
  # 
  # "John Doe"
  author %<>% 
    gsub("^Author: (.+)", 
         "\\1", .) %>% 
    gsub("[ ]*\\[[^]]{3,}\\][ ]*", " ", .) %>% 
    gsub("\\([^)]+\\)", " ", .) %>% 
    gsub("([ ]*<[^>]+>)", " ", .) %>% 
    gsub("[ ]*\\[[^]]{3,}\\][ ]*", " ", .) %>% 
    gsub("[ ]{2,}", " ", .) %>% 
    gsub("(^[ '\"]+|[ '\"]+$)", "", .) %>% 
    gsub(" , ", ", ", .)
  return(author)
}

getDate <- function(txt, package){
  date <- 
    grep("^Date/Publication", txt)
  if (length(date) == 1){
    date <- txt[date] %>% 
      gsub("Date/Publication: ([0-9]{4,4}-[0-9]{2,2}-[0-9]{2,2}).*",
           "\\1", .)
  }else{
    cran_txt <- read_html(sprintf("http://cran.r-project.org/web/packages/%s/index.html",
                                  package))
    date <- 
      cran_txt %>% 
      html_nodes("tr") %>% 
      html_text %>% 
      convertCharset %>% 
      gsub("(^[ \t\n]+|[ \t\n]+$)", "", .) %>% 
      .[grep("^Published", .)] %>% 
      gsub(".*\n", "", .)
    
    
    # The main page doesn't contain the original date if 
    # new packages have been submitted, we therefore need
    # to check first entry in the archives
    if(cran_txt %>% 
       html_nodes("tr") %>% 
       html_text %>% 
       gsub("(^[ \t\n]+|[ \t\n]+$)", "", .) %>% 
       grepl("^Old.{1,4}sources", .) %>% 
       any){
      archive_txt <- read_html(sprintf("http://cran.r-project.org/src/contrib/Archive/%s/",
                                       package))
      pkg_date <- 
        archive_txt %>% 
        html_nodes("tr") %>% 
        lapply(function(x) {
          nodes <- html_nodes(x, "td")
          if (length(nodes) == 5){
            return(nodes[3] %>% 
                     html_text %>% 
                     as.Date(format = "%d-%b-%Y"))
          }
        }) %>% 
        .[sapply(., length) > 0] %>% 
        .[!sapply(., is.na)] %>% 
        head(1)
      
      if (length(pkg_date) == 1)
        date <- pkg_date[[1]]
    }
  }
  date <- tryCatch({
    as.Date(date)
  }, error = function(e){
    "Date missing"
  })
  return(date)
}

getNewPkgStats <- function(published_in){
  # The parallel is only for making cranlogs requests
  # we can therefore have more cores than actual cores
  # as this isn't processor intensive while there is
  # considerable wait for each http-request
  cl <- create_cluster(parallel::detectCores() * 4)
  parallel::clusterEvalQ(cl, {
    library(cranlogs)
  })
  set_default_cluster(cl)
  on.exit(stop_cluster())
  
  berries <- read_html(paste0("http://dirk.eddelbuettel.com/cranberries/", published_in, "/"))
  pkgs <- 
    # Select the divs of the package class
    html_nodes(berries, ".package") %>% 
    # Extract the text
    html_text %>% 
    # Split the lines
    strsplit("[\n]+") %>% 
    # Now clean the lines
    lapply(.,
           function(pkg_txt) {
             pkg_txt[sapply(pkg_txt, function(x) { nchar(gsub("^[ \t]+", "", x)) > 0}, 
                            USE.NAMES = FALSE)] %>% 
               gsub("^[ \t]+", "", .) 
           })
  
  # Now we select the new packages
  new_packages <- 
    pkgs %>% 
    # The first line is key as it contains the text "New package"
    sapply(., function(x) x[1], USE.NAMES = FALSE) %>% 
    grep("^New package", .) %>% 
    pkgs[.] %>% 
    # Now we extract the package name and the date that it was published
    # and merge everything into one table
    lapply(function(txt){
      txt <- convertCharset(txt)
      ret <- data.frame(
        name = gsub("^New package ([^ ]+) with initial .*", 
                     "\\1", txt[1]),
        stringsAsFactors = FALSE
      )
      
      ret$desc <- getCranberriesElmnt(txt, "Description")
      ret$author <- getAuthor(txt, ret$name)
      ret$date <- getDate(txt, ret$name)
      
      return(ret)
    }) %>% 
    rbind_all %>% 
    # Get the download data in parallel
    partition(name) %>% 
    do({
      down <- cran_downloads(.$name[1], 
                             from = max(as.Date("2015-01-01"), .$date[1]), 
                             to = "2015-12-31")$count 
      cbind(.[1,],
            data.frame(sum = sum(down), 
                       avg = mean(down))
      )
    }) %>% 
    collect %>% 
    ungroup %>% 
    arrange(desc(avg))
  
  return(new_packages)
}

pkg_list <- 
  lapply(2010:2015,
         getNewPkgStats)

pkgs <- 
  rbind_all(pkg_list) %>% 
  mutate(time = as.numeric(as.Date("2016-01-01") - date),
         year = format(date, "%Y"))

Downloads and time on CRAN

The longer a package has been on CRAN the more downloaded it gets. We can illustrate this using simple linear regression, slightly surprising is that this behaves mostly linear:

pkgs %<>% 
  mutate(time_yrs = time/365.25)
fit <- lm(avg ~ time_yrs, data = pkgs)

# Test for non-linearity
library(splines)
anova(fit,
      update(fit, .~.-time_yrs+ns(time_yrs, 2)))
Analysis of Variance Table

Model 1: avg ~ time
Model 2: avg ~ ns(time, 2)
  Res.Df       RSS Df Sum of Sq      F Pr(>F)
1   7348 189661922                           
2   7347 189656567  1    5355.1 0.2075 0.6488

Where the number of average downloads increases with about 5 downloads per year. It can easily be argued that the average number of downloads isn't that interesting since the data is skewed, we can therefore also look at the upper quantiles using quantile regression:

library(quantreg)
library(htmlTable)
lapply(c(.5, .75, .95, .99),
       function(tau){
         rq_fit <- rq(avg ~ time_yrs, data = pkgs, tau = tau)
         rq_sum <- summary(rq_fit)
         c(Estimate = txtRound(rq_sum$coefficients[2, 1], 1), 
           `95 % CI` = txtRound(rq_sum$coefficients[2, 1] + 
                                        c(1,-1) * rq_sum$coefficients[2, 2], 1) %>% 
             paste(collapse = " to "))
       }) %>% 
  do.call(rbind, .) %>% 
  htmlTable(rnames = c("Median",
                       "Upper quartile",
                       "Top 5%",
                       "Top 1%"))
Estimate 95 % CI
Median 0.6 0.6 to 0.6
Upper quartile 1.2 1.2 to 1.1
Top 5% 9.7 11.9 to 7.6
Top 1% 182.5 228.2 to 136.9

The above table conveys a slightly more interesting picture. Most packages don't get that much attention while the top 1% truly reach the masses.

Top downloaded packages

In order to investigate what packages R users have been using during 2015 I've looked at all new packages since the turn of the decade. Since each year of CRAN-presence increases the download rates, I've split the table by the package release dates. The results are available for browsing below (yes - it is the new brand interactive htmlTable that allows you to collapse cells - note it may not work if you are reading this on R-bloggers and the link is lost under certain circumstances).

  Downloads  
Name Author   Total Average/day   Description
Top 10 packages published in 2015
xml2 Hadley Wickham, Jeroen Ooms, RStudio, R Foundation   348,222 1635   Work with XML files ...
rversions Gabor Csardi   386,996 1524   Query the main R SVN...
git2r Stefan Widgren   411,709 1303   Interface to the lib...
praise Gabor Csardi, Sindre Sorhus   96,187 673   Build friendly R pac...
readxl David Hoerl   99,386 379   Import excel files i...
readr Hadley Wickham, Romain Francois, R Core Team, RStudio   90,022 337   Read flat/tabular te...
DiagrammeR Richard Iannone   84,259 236   Create diagrams and ...
visNetwork Almende B.V. (vis.js library in htmlwidgets/lib,   41,185 233   Provides an R interf...
plotly Carson Sievert, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec, Pedro Despouy   9,745 217   Easily translate ggp...
DT Yihui Xie, Joe Cheng, jQuery contributors, SpryMedia Limited, Brian Reavis, Leon Gersen, Bartek Szopka, RStudio Inc   24,806 120   Data objects in R ca...
Top 10 packages published in 2014
stringi Marek Gagolewski and Bartek Tartanus ; IBM and other contributors ; Unicode, Inc.   1,316,900 3608   stringi allows for v...
magrittr Stefan Milton Bache and Hadley Wickham   1,245,662 3413   Provides a mechanism...
mime Yihui Xie   1,038,591 2845   This package guesses...
R6 Winston Chang   920,147 2521   The R6 package allow...
dplyr Hadley Wickham, Romain Francois   778,311 2132   A fast, consistent t...
manipulate JJ Allaire, RStudio   626,191 1716   Interactive plotting...
htmltools RStudio, Inc.   619,171 1696   Tools for HTML gener...
curl Jeroen Ooms   599,704 1643   The curl() function ...
lazyeval Hadley Wickham, RStudio   572,546 1569   A disciplined approa...
rstudioapi RStudio   515,665 1413   This package provide...
Top 10 packages published in 2013
jsonlite Jeroen Ooms, Duncan Temple Lang   906,421 2483   This package is a fo...
BH John W. Emerson, Michael J. Kane, Dirk Eddelbuettel, JJ Allaire, and Romain Francois   691,280 1894   Boost provides free ...
highr Yihui Xie and Yixuan Qiu   641,052 1756   This package provide...
assertthat Hadley Wickham   527,961 1446   assertthat is an ext...
httpuv RStudio, Inc.   310,699 851   httpuv provides low-...
NLP Kurt Hornik   270,682 742   Basic classes and me...
TH.data Torsten Hothorn   242,060 663   Contains data sets u...
NMF Renaud Gaujoux, Cathal Seoighe   228,807 627   This package provide...
stringdist Mark van der Loo   123,138 337   Implements the Hammi...
SnowballC Milan Bouchet-Valat   104,411 286   An R interface to th...
Top 10 packages published in 2012
gtable Hadley Wickham   1,091,440 2990   Tools to make it eas...
knitr Yihui Xie   792,876 2172   This package provide...
httr Hadley Wickham   785,568 2152   Provides useful tool...
markdown JJ Allaire, Jeffrey Horner, Vicent Marti, and Natacha Porte   636,888 1745   Markdown is a plain-...
Matrix Douglas Bates and Martin Maechler   470,468 1289   Classes and methods ...
shiny RStudio, Inc.   427,995 1173   Shiny makes it incre...
lattice Deepayan Sarkar   414,716 1136   Lattice is a powerfu...
pkgmaker Renaud Gaujoux   225,796 619   This package provide...
rngtools Renaud Gaujoux   225,125 617   This package contain...
base64enc Simon Urbanek   223,120 611   This package provide...
Top 10 packages published in 2011
scales Hadley Wickham   1,305,000 3575   Scales map data to a...
devtools Hadley Wickham   738,724 2024   Collection of packag...
RcppEigen Douglas Bates, Romain Francois and Dirk Eddelbuettel   634,224 1738   R and Eigen integrat...
fpp Rob J Hyndman   583,505 1599   All data sets requir...
nloptr Jelmer Ypma   583,230 1598   nloptr is an R inter...
pbkrtest Ulrich Halekoh Søren Højsgaard   536,409 1470   Test in linear mixed...
roxygen2 Hadley Wickham, Peter Danenberg, Manuel Eugster   478,765 1312   A Doxygen-like in-so...
whisker Edwin de Jonge   413,068 1132   logicless templating...
doParallel Revolution Analytics   299,717 821   Provides a parallel ...
abind Tony Plate and Richard Heiberger   255,151 699   Combine multi-dimens...
Top 10 packages published in 2010
reshape2 Hadley Wickham   1,395,099 3822   Reshape lets you fle...
labeling Justin Talbot   1,104,986 3027   Provides a range of ...
evaluate Hadley Wickham   862,082 2362   Parsing and evaluati...
formatR Yihui Xie   640,386 1754   This package provide...
minqa Katharine M. Mullen, John C. Nash, Ravi Varadhan   600,527 1645   Derivative-free opti...
gridExtra Baptiste Auguie   581,140 1592   misc. functions
memoise Hadley Wickham   552,383 1513   Cache the results of...
RJSONIO Duncan Temple Lang   414,373 1135   This is a package th...
RcppArmadillo Romain Francois and Dirk Eddelbuettel   410,368 1124   R and Armadillo inte...
xlsx Adrian A. Dragulescu   401,991 1101   Provide R functions ...


Just as Safferling et. al. noted there is a dominance of technical packages. This is little surprising since the majority of work is with data munging. Among these technical packages there are quite a few that are used for developing other packages, e.g. roxygen2, pkgmaker, devtools, and more.

R-star authors

Just for fun I decided to look at who has the most downloads. By splitting multi-authors into several and also splitting their downloads we can find that in 2015 the top R-coders where:

top_coders <- list(
  "2015" = 
    pkgs %>% 
    filter(format(date, "%Y") == 2015) %>% 
    partition(author) %>% 
    do({
      authors <- strsplit(.$author, "[ ]*([,;]| and )[ ]*")[[1]]
      authors <- authors[!grepl("^[ ]*(Inc|PhD|Dr|Lab).*[ ]*$", authors)]
      if (length(authors) >= 1){
        # If multiple authors the statistic is split among
        # them but with an added 20% for the extra collaboration
        # effort that a multi-author envorionment calls for
        .$sum <- round(.$sum/length(authors)*1.2)
        .$avg <- .$avg/length(authors)*1.2
        ret <- .
        ret$author <- authors[1]
        for (m in authors[-1]){
          tmp <- .
          tmp$author <- m
          ret <- rbind(ret, tmp)
        }
        return(ret)
      }else{
        return(.)
      }
    }) %>% 
    collect() %>% 
    group_by(author) %>% 
    summarise(download_ave = round(sum(avg)),
              no_packages = n(),
              packages = paste(name, collapse = ", ")) %>% 
    select(author, download_ave, no_packages, packages) %>% 
    collect() %>% 
    arrange(desc(download_ave)) %>% 
    head(10),
  "all" =
    pkgs %>% 
    partition(author) %>% 
    do({
      if (grepl("Jeroen Ooms", .$author))
        browser()
      authors <- strsplit(.$author, "[ ]*([,;]| and )[ ]*")[[1]]
      authors <- authors[!grepl("^[ ]*(Inc|PhD|Dr|Lab).*[ ]*$", authors)]
      if (length(authors) >= 1){
        # If multiple authors the statistic is split among
        # them but with an added 20% for the extra collaboration
        # effort that a multi-author envorionment calls for
        .$sum <- round(.$sum/length(authors)*1.2)
        .$avg <- .$avg/length(authors)*1.2
        ret <- .
        ret$author <- authors[1]
        for (m in authors[-1]){
          tmp <- .
          tmp$author <- m
          ret <- rbind(ret, tmp)
        }
        return(ret)
      }else{
        return(.)
      }
    }) %>% 
    collect() %>% 
    group_by(author) %>% 
    summarise(download_ave = round(sum(avg)),
              no_packages = n(),
              packages = paste(name, collapse = ", ")) %>% 
    select(author, download_ave, no_packages, packages) %>% 
    collect() %>% 
    arrange(desc(download_ave)) %>% 
    head(30))

interactiveTable(
  do.call(rbind, top_coders) %>% 
    mutate(download_ave = txtInt(download_ave)),
  align = "lrr",
  header = c("Coder", "Total ave. downloads per day", "No. of packages", "Packages"),
  tspanner = c("Top coders 2015",
               "Top coders 2010-2015"),
  n.tspanner = sapply(top_coders, nrow),
  minimized.columns = 4, 
  rnames = FALSE, 
  col.rgroup = c("white", "#F0F0FF"))
Coder Total ave. downloads No. of packages Packages
Top coders 2015
Gabor Csardi 2,312 11 sankey, franc, rvers...
Stefan Widgren 1,563 1 git2r
RStudio 781 16 shinydashboard, with...
Hadley Wickham 695 12 withr, cellranger, c...
Jeroen Ooms 541 10 rjade, js, sodium, w...
Richard Cotton 501 22 assertive.base, asse...
R Foundation 490 1 xml2
David Hoerl 455 1 readxl
Sindre Sorhus 409 2 praise, clisymbols
Richard Iannone 294 2 DiagrammeR, stationa...
Top coders 2010-2015
Hadley Wickham 32,115 55 swirl, lazyeval, ggp...
Yihui Xie 9,739 18 DT, Rd2roxygen, high...
RStudio 9,123 25 shinydashboard, lazy...
Jeroen Ooms 4,221 25 JJcorr, gdtools, bro...
Justin Talbot 3,633 1 labeling
Winston Chang 3,531 17 shinydashboard, font...
Gabor Csardi 3,437 26 praise, clisymbols, ...
Romain Francois 2,934 20 int64, LSD, RcppExam...
Duncan Temple Lang 2,854 6 RMendeley, jsonlite,...
Adrian A. Dragulescu 2,456 2 xlsx, xlsxjars
JJ Allaire 2,453 7 manipulate, htmlwidg...
Simon Urbanek 2,369 15 png, fastmatch, jpeg...
Dirk Eddelbuettel 2,094 33 Rblpapi, RcppSMC, RA...
Stefan Milton Bache 2,069 3 import, blatr, magri...
Douglas Bates 1,966 5 PKPDmodels, RcppEige...
Renaud Gaujoux 1,962 6 NMF, doRNG, pkgmaker...
Jelmer Ypma 1,933 2 nloptr, SparseGrid
Rob J Hyndman 1,933 3 hts, fpp, demography
Baptiste Auguie 1,924 2 gridExtra, dielectri...
Ulrich Halekoh Søren Højsgaard 1,764 1 pbkrtest
Martin Maechler 1,682 11 DescTools, stabledis...
Mirai Solutions GmbH 1,603 3 XLConnect, XLConnect...
Stefan Widgren 1,563 1 git2r
Edwin de Jonge 1,513 10 tabplot, tabplotGTK,...
Kurt Hornik 1,476 12 movMF, ROI, qrmtools...
Deepayan Sarkar 1,369 4 qtbase, qtpaint, lat...
Tyler Rinker 1,203 9 cowsay, wakefield, q...
Yixuan Qiu 1,131 12 gdtools, svglite, hi...
Revolution Analytics 1,011 4 doParallel, doSMP, r...
Torsten Hothorn 948 7 MVA, HSAUR3, TH.data...

It is worth mentioning that two of the top coders are companies, RStudio and Revolution Analytics. While I like the fact that R is free and open-source, I doubt that the community would have grown as quickly as it has without these companies. It is also symptomatic of 2015 that companies are taking R into account, it will be interesting what the R Consortium will bring to the community. I think the r-hub is increadibly interesting and will hopefully make my life as an R-package developer easier.

My own 2015-R-experience

My own personal R experience has been dominated by magrittr and dplyr, as seen in above code. As most I find that magrittr makes things a little easier to read and unless I have som really large dataset the overhead is small. It does have some downsides related to debugging but these are negligeable.

When I originally tried dplyr out I came from the plyr environment and was disappointed by the lack of parallelization, I found the concepts a little odd when thinking the plyr way. I had been using sqldf a lot in my data munging and merging, when I found the left_join, inner_joint, and the brilliant anti_join I was completely sold. Combined with RStudio I find the dplyr-workflow both intuitive and more productive than my previous.

When looking at those packages (including more than just the top 10 here) I did find some additional gems that I intend to look into when I have the time:

  • DiagrammeR An interesting new way of producing diagrams. I've used it for gantt charts but it allows for much more.
  • checkmate A neat package for checking function arguments.
  • covr An excellent package for testing how much of a package's code is tested.
  • rex A package for making regular easier.
  • openxlsx I wish I didn't have to but I still get a lot of things in Excel-format - perhaps this package solves the Excel-import inferno...
  • R6 The successor to reference classes - after working with the Gmisc::Transition-class I appreciate the need for a better system.

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4 Responses to R trends in 2015 (based on cranlogs)

  1. Pingback: Distilled News | Data Analytics & R

  2. jangorecki says:

    Looking at the CRAN downloads totals can be highly misleading due to dependencies which are automatically downloaded for install “desired package”. As you have the precise date and time of events you can detect which of packages has been installed automatically as dependency and which were the “desired packages”. While total downloads is still a measure, it is usually less valuable measure to detect the trends, which are driven by “desired package” downloads.

    • Gabor says:

      I agree completely, although detecting desired installs is challenging at best. E.g. how do you detect that I want both httr and devtools in
      install.packages(c(“httr”, “devtools”)) ?

      • Max Gordon says:

        Odd, I was certain that I had already replied to your questions.

        I completely agree with the limitations of the current metric. It took me a little longer than anticipated to compile and adding fancier adjustments was not within the time frame. If I’ll revisit the subject I’ll consider adding some more fancy statistics.

        One thought that I’ve had is to add the dependencies to each package. One can then look at how popular the dependencies are and reduce the downloads/day from that regression estimate. This could be a partial reduction as the packages can very well be useful on their own. A problem with this is that dependencies change over time, making this even trickier. I’m also not sure that CRAN would agree with me scraping their entire site…

        The large proportion of packages used in package development indicates that the dependency issue is huge. I would though argue that a package like checkmate will make other packages more useful and therefor should get merits in some way. This is probably also something that is symptomatic of the package explosion and arguably part of the R trend.

        Another thing to remember is that RStudio is currently dominating the IDE market. While being a RStudio fan the metrics get a little impacted by the IDE dominance. Their packages get most likely a boost as they are part of the RStudio-concept, and perhaps less because of their excellence (although they do produce very high quality packages in my mind). Still, them appearing in the lists indicates that they continue to dominate the IDE-market and are arguably also part of the current trend.

        One thing that is completely lacking from the current analysis is GitHub – arguably one of the biggest open software trends in recent years. I guess we won’t reach the truth anytime soon and this is partly why I added the (based on cranlogs) in the title.

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