View source: R/RStudio_CRAN_data.R
lineplot_package_downloads | R Documentation |
This function gets a vector of package names, and returns a line plot of number of downloads for these packages per week.
lineplot_package_downloads( pkg_names, dataset, by_time = c("date", "week"), ... )
pkg_names |
a character vector of packages we are interested in checking. |
dataset |
a dataset output from running read_RStudio_CRAN_data, after going through format_RStudio_CRAN_data. |
by_time |
by what time frame should packages be plotted? defaults to "date", but can also be "week" |
... |
not in use. |
RStudio maintains its own CRAN mirror, https://cran.rstudio.com/ and offers its log files.
invisible aggregated data that was used for the plot
Felix Schonbrodt, Tal Galili
https://www.nicebread.de/finally-tracking-cran-packages-downloads/
download_RStudio_CRAN_data, read_RStudio_CRAN_data,barplot_package_users_per_day
## Not run: # The first two functions might take a good deal of time to run (depending on the date range) RStudio_CRAN_data_folder <- download_RStudio_CRAN_data(START = '2013-04-02', END = '2013-04-05') # around the time R 3.0.0 was released my_RStudio_CRAN_data <- read_RStudio_CRAN_data(RStudio_CRAN_data_folder) my_RStudio_CRAN_data <- format_RStudio_CRAN_data(my_RStudio_CRAN_data) head(my_RStudio_CRAN_data) lineplot_package_downloads(pkg_names = c("ggplot2", "reshape", "plyr", "installr"), dataset = my_RStudio_CRAN_data) # older plots: # barplots: (more functions can easily be added in the future) barplot_package_users_per_day("installr", my_RStudio_CRAN_data) barplot_package_users_per_day("plyr", my_RStudio_CRAN_data) ## End(Not run)
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