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07-tidyr.Rmd
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07-tidyr.Rmd
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---
title: "07-tidyr"
author: "Silvia P. Canelón"
date: "9/19/2020"
output: html_document
---
class: penguin-tour
```{r, echo=FALSE, out.width=1200}
knitr::include_graphics("images/pptx/07-tidyr.png")
```
.footnote[<span>Photo by <a href="https://unsplash.com/@eadesstudio?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">James Eades</a> on <a href="https://unsplash.com/collections/12240655/palmerpenguins/d5aed8c855e26061e5e651d3f180b76d?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a></span>
]
---
background-image: url(images/hex/tidyr.png)
background-position: 1050px 50px
background-size: 80px
# tidyr: info
.panelset[
.panel[.panel-name[Overview]
[From R for Data Science](https://r4ds.had.co.nz/tidy-data.html):
> There are three interrelated rules which make a dataset tidy:
> - Each variable must have its own column.
> - Each observation must have its own row.
> - Each value must have its own cell.
]
.panel[.panel-name[Cheatsheet]
`r icon::fa("file-pdf")` PDF: https://github.com/rstudio/cheatsheets/raw/master/data-import.pdf
]
.panel[.panel-name[Reading]
.left-column[
```{r echo=FALSE}
knitr::include_graphics("images/r4ds-cover.png")
```
]
.right-column[
### R for Data Science: [Ch 12 Tidy data](https://r4ds.had.co.nz/tidy-data.html)
### Package documentation: https://tidyr.tidyverse.org/
]
]
]
---
background-image: url(images/hex/tidyr.png)
background-position: 1050px 50px
background-size: 80px
# tidyr: exercise
.panelset[
.panel[.panel-name[Un-tidying]
### Both penguin datasets are already tidy!
We can pretend that `penguins` wasn't tidy and that it looked instead like `untidy_penguins` below, where **body_mass_g** was recorded separately for *male*, *female*, and *NA* **sex** penguins.
```{r}
untidy_penguins <-
penguins %>% pivot_wider(names_from = sex, values_from = body_mass_g)
untidy_penguins
```
]
.panel[.panel-name[Re-tidying]
### Now let's make it tidy again!
We'll use the help of `pivot_longer()`
```{r}
untidy_penguins %>%
pivot_longer(cols = male:`NA`, #<<
names_to = "sex", #<<
values_to = "body_mass_g") #<<
```
]
]