\name{Depends}
\alias{Depends}
\docType{data}
\title{
Dependencies of R Packages
}
\description{
This one-way table gives the type-token distribution of the number of
dependencies declared in 4983 packages listed on CRAN on January 17, 2014.
}
\usage{data(Depends)}
\format{
  The format is:
 'table' int [1:15(1d)] 986 1347 993 685 375 298 155 65 32 19 ...
 - attr(*, "dimnames")=List of 1
  ..$ Depends: chr [1:15] "0" "1" "2" "3" ...
}
%\details{
%%%  ~~ If necessary, more details than the __description__ above ~~
%}
\source{
Using code from
\url{https://blog.revolutionanalytics.com/2013/12/a-look-at-the-distribution-of-r-package-dependencies.html}

}
%\references{
%%%  ~~ possibly secondary sources and usages ~~
%}
\examples{
data(Depends)
plot(Depends, xlab="Number of Dependencies", ylab="Number of R Packages", lwd=8)

\dontrun{
# The code below, from Joseph Rickert, downloads and tabulates the data
p <- as.data.frame(available.packages(),stringsAsFactors=FALSE)
names(p)

pkgs <- data.frame(p[,c(1,4)])                  # Pick out Package names and Depends
row.names(pkgs) <- NULL                         # Get rid of row names
pkgs <- pkgs[complete.cases(pkgs[,2]),]         # Remove NAs

pkgs$Depends2 <-strsplit(pkgs$Depends,",")      # split list of Depends
pkgs$numDepends <- as.numeric(lapply(pkgs$Depends2,length)) # Count number of dependencies in list
zeros <- c(rep(0,dim(p)[1] - dim(pkgs)[1]))     # Account for packages with no dependencies
Deps <- as.vector(c(zeros,pkgs$numDepends))     # Set up to tablate
Depends <- table(Deps)

}
}
\keyword{datasets}
