tidy-data.Rmd

---
title: '"Tidy" data'
---

```{r, include=FALSE}
library(tidyverse)
library(tufte)
```

## Tidying data {- #tidyingdata}

'Tidying' data means converting it into the format that is most useful for data
analyses, and so we have already covered many of the key techniques: selecting
and filtering data, reshaping and summarising.

However the ideas behind 'tidying' draw together other related concepts which
link together the way we enter, store and process data: for example the idea of
'[relational data]()' and techniques to join together related datasets.

#### A philosophy of tidy data {-}

The chapter on tidying in 'R for data science' is well worth reading for it's
thoughtful explanation of why we want tidy data, and the core techniques to
clean up untidy data: <http://r4ds.had.co.nz/tidy-data.html>

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