R packaging industry close-up: How fast are we growing?
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I worked a bit over the weekend preparing my talk to be delivered at the seminar organized by IBPAD this week at University of Brasilia, addressing the interfaces of Big Data and Society. I was invited to present the R package SciencesPo for an eclectic crowd. Eclectic in terms of background as well as familiarity with R, so, I thought it would be a fair introduction to call the audience’s attention to the R ecosystem, particularly, the growing number of specialized packages made available through CRAN.
I gathered some log data from package downloads to produce the following figure. The main plot shows the number of published packages since 2005 (that are still available). Notice that the y-axis is in log scale. The small multiples inside also shows the count of packages published on CRAN, but only for packages submitted after 2013-01-01. It’s an arbitrary date that makes my job of estimating a growth rate of package submission a lot easier. The red line represents the modeled growth rate estimated for the period with an approximation of 5.6%/month.
Needed packages
library(ggplot2)
library(grid)
library(rvest)
library(dplyr)
library(zoo)
library(SciencesPo)Data manipulation
url <- "https://cran.r-project.org/web/packages/available_packages_by_date.html"
page <- read_html(url)
page %>%
html_node("table") %>%
html_table() %>%
mutate(count = rev(1:nrow(.))) %>%
mutate(Date = as.Date(Date)) %>%
mutate(Month = format(Date, format = "%Y-%m")) %>%
group_by(Month) %>%
summarise(published = min(count)) %>%
mutate(Date = as.Date(as.yearmon(Month))) -> pkgsThe main plot
gg <- ggplot(pkgs, aes(x = Date, y = published))
gg <- gg + geom_line(size = 1.5)
gg <- gg + scale_y_log10( breaks = c(0, 10, 100, 1000, 10000),
labels = c("1", "10", "100", "1.000", "10.000"))
gg <- gg + labs(x = "", y = "# Packages (log)", title = "Packages published on CRAN ever since")
gg <- gg + theme_538(base_size = 14, base_family = "Tahoma")
gg <- gg + theme(panel.grid.major.x = element_blank())
gg <- gg + geom_hline(yintercept = 0,
size = 1, colour = "#535353")
ggpkgs %>%
filter(Date > as.Date("2012-12-31")) %>%
mutate(publishedGrowth = c(tail(.$published,-1), NA) / published) %>%
mutate(counter = 1:nrow(.)) -> new_pkgsThe small multiples plot
gg2 <- ggplot(new_pkgs, aes(x = Date, y = published))
gg2 <- gg2 + geom_line(size = 1)
gg2 <- gg2 + geom_line(data = new_pkgs, aes(y = (min(published) * 1.056 ^ counter)),
color = 'red',size = .7, linetype = 1)
gg2 <- gg2 + annotate("segment", x = as.Date("2014-08-01"), xend = as.Date("2014-11-01"), y = 500, yend = 500, colour = "red", size = 1)
gg2 <- gg2 + annotate("text", x = as.Date("2015-10-01"), y = 500, label = "5.6% growth estimation", size = 3.5)
gg2 <- gg2 + scale_y_continuous()
gg2 <- gg2 + labs(y = "# Packages", x = "", title = "Packages published on CRAN since 2013")
gg2 <- gg2 + theme_538(legend = "top", base_size = 11, base_family = "Tahoma", colors = c("gray97", "#D2D2D2", "#2b2b2b", "#2b2b2b"))
gg2 <- gg2 + theme(panel.grid.major.x = element_blank())
gg2 <- gg2 + geom_hline(yintercept = 0, size = .6, colour = "#535353")
gg2
gg
print(gg2, vp=viewport(.775, .31, .43, .43))R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
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