Those most productive R developers

This post was kindly contributed by SAS ANALYSIS - go there to comment and to read the full post.

The number of R packages on CRAN is 3,483 on 2011-12-12. The growth of R package in the past years can be fitted by a quadratic regression perfectly.

I am always interested in who are maintaining those packages. Then I wrote an R script to extract the package head information from CRAN’s website and stored them in a SQLite database. Most R developers are maintaining 1-3 R packages. Some of them are really productive. By the correspondence addresses (Email), the top 50 R developers are listed below:

developer package
1 Kurt Hornik 2 Martin Maechler 3 Hadley Wickham 4 Rmetrics Core Team 5 Achim Zeileis 6 Henrik Bengtsson 7 Paul Gilbert 8 Brian Ripley 9 Roger D. Peng 10 Torsten Hothorn 11 Karline Soetaert 12 Philippe Grosjean 13 Robin K. S. Hankin 14 Charles J. Geyer 15 Matthias Kohl 16 Charlotte Maia 17 Mikis Stasinopoulos 18 Simon Urbanek (1) 19 Thomas Lumley 20 Arne Henningsen 21 Gregory R. Warnes 22 Jonathan M. Lees 23 Michael Hahsler 24 Peter Ruckdeschel 25 A.I. McLeod 26 Brian Lee Yung Rowe 27 Dirk Eddelbuettel 28 John Fox 29 Kaspar Rufibach 30 Korbinian Strimmer 31 Michael Friendly 32 Peter Solymos 33 Roger Bivand 34 Simon Urbanek (2) 35 Christopher Brown 36 David Meyer 37 ORPHANED 38 Revolution Analytics 39 Rob J Hyndman 40 Romain Francois 41 Ulrike Groemping 42 Christophe Genolini 43 Frank Schaarschmidt 44 G. Grothendieck 45 Hana Sevcikova 46 Jeffrey A. Ryan 47 Kjetil Halvorsen 48 Pei Wang 49 Trevor Hastie 50 Yihui Xie 23
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### A script of R to extract R package information and 
### build a SQLite databse by hchao8@gmail.com 
library(ggplot2)
library(XML)
library(RSQLite)

# Create and connect a SQLite database
conn <- dbConnect("SQLite", dbname = "c:/Rpackage.db")

# Extract names of R packages available from web
allPackageURL <-
  "http://cran.r-project.org/web/packages/available_packages_by_name.html"
allPackage <- na.omit(melt(readHTMLTable(allPackageURL))[, c("V1")])

# Extract individual package information from web and store data in SQLite 
for (i in 1:length(allPackage)){
  packageName <- allPackage[i]
  packageURL <- paste("http://cran.r-project.org/web/packages/",packageName,
                      "/index.html", sep="")
  y <- melt(readHTMLTable(packageURL))
  y$L1 <- packageName
  if(dbExistsTable(conn, "Rpackage")) {
     dbWriteTable(conn, "Rpackage", y, append = TRUE)
  } else {
     dbWriteTable(conn, "Rpackage", y)
  }
} 
# Pull out maintainer information from SQLite database
all <- fetch(dbSendQuery(conn, "
          select v2 as author, count(v2) as package
          from rpackage
          where v1 = 'Maintainer:'
          group by v2
          order by package desc
          ;"))

# Disconnect SQLite database
dbDisconnect(conn)

# Draw a histogram
qplot(package, data = all, binwidth = 1, ylab = "Frequency",
      xlab = "R packages maintained by individual developer")
ggsave("c:/Rlist.png")

# Find 50 most productive developers
head(all, 50)

This post was kindly contributed by SAS ANALYSIS - go there to comment and to read the full post.

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