NA - Not Available/Not applicable is R’s way of denoting empty or missing values. When doing comparisons - such as equal to, greater than, etc. - extra care and thought needs to go into how missing values (NAs) are handled. More explanations about this can be found in the Chapter 2: R basics of our book that is freely available at the HealthyR website This post lists a couple of different ways of keeping or discarding rows based on how important the variables with missing values are to you.

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TLDR: there are two new and very intuitive R functions for reshaping data: see Examples of pivot_longer() and pivot_wider() below. At the time of writing, these new functions are extremely fresh and only exist in the development version on GitHub (see Installation), we should probably wait for the tidyverse team to officially release them (in CRAN) before putting them into day-to-day use. Exciting! Introduction The juxtapose of data collection vs data analysis: data that was very easy to collect, is probably very hard to analyse, and vice versa.

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