Tibbles are beautiful constructs that allow for a new column type, list columns, to exist, capturing bundles of (untidy) data into the structure of a data frame. They are especially great for when you want to do cool things like apply a function to multiple datasets (kept in the aforementioned list column), and generate a new column of (say, tidier) data. One application might be if you want to do a quick t-test for various datasets, possibly even varying the contrasts at each iteration.
Robert Amezquita
Computational Immunologist. Working at the intersection of data science, immunology, and genomics, with some cooking, travel, and dogs in the mix.
In the process of working on a new R package, one of the TODO’s on my list was testing it on a new version of R. However, upgrading R is a somewhat dreaded process, as this involves (re)installing all your old packages. While solutions like packrat deal with R package dependencies, this doesn’t seem to work for R upgrades. Another solution involves simply copying the R package library into the new R version’s package library, but this introduces the issue of potential breakage.
This is the second part of a series of posts working with an NFL quarterback data, following up after doing some initial cleanup. Here, I’ll focus on how I like to format data for optimal tidyness - the tidy (also known as long) format. A Small Example Typically, when we get a dataset, we’ll see it as a series of columns (variables) with values across many rows (each an observation). This format - the wide format - is certainly amenable for human parsing, and also implies a relationship between a single observation across multiple variables.