Facts About R Packages (1)

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R Packages growth Curve

Why R is so popular? There are a lot of reasons, such as: easy to learn and convenient to use, active community, open source, etc. Another important reason is the numerous contributed packages. Up to yesterday, there are 4033 R packages on CRAN. How is the growth curve of R packages in the pasted decade? How many packages were contributed to CRAN every month?

The following figure shows the growth curve of R package:

File c:/tianhd.me/source/gvis/RpkgCurve1.html could not be found

R is getting more and more popular which can be seen from the number of packages contributed every month:

File c:/tianhd.me/source/gvis/RpkgCurve2.html could not be found

The first contributed R package is called leaps: regression subset selection. Uploaded by Thomas Lumley.

Here is the R code for above result. The code generated more information behind the above, which will be used in the next blogs.

Download package information from CRAN
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# Load packages needed;
library(XML)
library(googleVis)

set CRAN depository;

CRAN.mirr <- “http://cran.r-project.org/” CRAN.home <- “web/packages/available_packages_by_name.html”

read in packages name and description;

pkg <- readHTMLTable(paste(CRAN.mirr, CRAN.home, sep = “”), skip = 1,)[[1]] names(pkg) <- c(“Name”, “Description”) pkg <- pkg[!is.na(pkg$Name),] pkg[,1] <- as.character(pkg[,1]) pkg[,2] <- as.character(pkg[,2])

Define a function to convert date format “11-Jun-2011” to “2011-06-11”;

as.posix <- function(x) { day <- substr(x, 1, 2) mth <- substr(x, 4, 6) yr <- substr(x, 8, 11) Mth <- c(“Jan”, “Feb”, “Mar”, “Apr”, “May”, “Jun”, “Jul”, “Aug”, “Sep”, “Oct”, “Nov”, “Dec”) mth <- unlist(sapply(mth, FUN = function(x) { m <- which(Mth == x) if (nchar(m) == 1) m <- paste(“0”, m, sep = “”) return(m)})) paste(yr, mth, day, sep = “-“) }

Create a list to contain detail information of each package;

# This process will take about 15 minutes; PKG <- list() pb <- txtProgressBar(min = 0, max = nrow(pkg), style = 3) for (i in 1:nrow(pkg)) { pkg.nam <- pkg$Name[i] pkg.url <- paste(CRAN.mirr, “web/packages/”, pkg.nam, “/index.html”, sep = “”) pkg.des <- readHTMLTable(pkg.url) names(pkg.des) <- c(“Description”, “Downloads”, “Dependency”)[1:length(pkg.des)] if (“Old sources:” %in% pkg.des$Downloads$V1) { hist.url <- paste(CRAN.mirr, “src/contrib/Archive/”, pkg.nam, sep = “”) hist.dat <- readHTMLTable(hist.url, skip = 2)[[1]][, 2:3] names(hist.dat) <- c(“Name”, “Date”) hist.dat <- hist.dat[!is.na(hist.dat$Name),] hist.dat$Date <- as.posix(hist.dat$Date) pkg.des[[“History”]] <- hist.dat } for (l in 1:length(pkg.des)) { pkg.des[[l]][,1] <- as.character(pkg.des[[l]][,1]) pkg.des[[l]][,2] <- as.character(pkg.des[[l]][,2]) } PKG[[pkg.nam]] <- pkg.des setTxtProgressBar(pb, i) } close(pb)

Extract the date of the first version of each package;

pkg.trend <- data.frame(pkg.name = names(PKG)) for (i in 1:nrow(pkg.trend)) { pkg <- pkg.trend$pkg.name[i] pkg.des <- PKG[[pkg]] if (“History” %in% names(pkg.des)) { pkg.trend$Date.1[i] <- as.character(min(pkg.des$History$Date)) }else { pkg.trend$Date.1[i] <- pkg.des$Description$V2[which(pkg.des$Description$V1 == “Published:”)] } }

aggregates the package number for each month;

pkg.trend$Date.2 <- paste(substr(pkg.trend$Date.1, 1, 7), “01”, sep = “-“) pkg.trend$Date.2 <- as.POSIXct(pkg.trend$Date.2, format = “%Y-%m-%d”) pkg.dat <- with(pkg.trend, aggregate(list(Num = Date.2), list(Date = Date.2), length)) pkg.dat$Num1 <- cumsum(pkg.dat$Num)

Display growth curve using GoogleVis;

Line1 <- gvisLineChart(pkg.dat, xvar=”Date”, yvar=”Num1”) Line2 <- gvisLineChart(pkg.dat, xvar=”Date”, yvar=”Num”) plot(Line1) plot(Line2)

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