Moreover, there are some R package developers that prefer to forgo bundling data within a package.

Note, that some of these packages are associated with textbooks and others are complementary packages that tie into a package containing only statistical methodology. Some examples of data specific package are:įaraway, HistData, HSAUR, Ecdat, learnEDA, ElemStatLearn, abc.data, wpp2012, astrodatR, ocedata, TH.data, USAboundaries, mapdata, hglm.data, MM2Sdata, synbreedData, usdanutrients, babynames, fueleconomy, nasaweather, nycflights13 Where a large amount of data is required (even after compression), consideration should be given to a separate data-only package which can be updated only rarely (since older versions of packages are archived in perpetuity).Īs a result, there is a considerable amount of data packages on CRAN. That is to say, authors create a stand-alone data package, which is then uploaded alongside the original package to CRAN. Therefore, datasets referenced within these packages are either very small or are hosted in an external package. The Advent of the Data PackageĪs was shown in the previous entry, many packages listed on CRAN are below the threshold of 5 MB. The next entry deals with constructing an external repository when the size of the package is considerably over the 5 MB limit imposed by CRAN. To do so, we reflect upon different methods used to construct a data package on CRAN.

Within this post, the aim is to address the best way to create a data package that is able to be distributed via CRAN. In the previous entry, there was a discussion regarding CRAN’s R package policy, specifically on the size of R data packages.

Build a package in r