long_to_wide_and_back.Rmd

---
title: "long to wide or wide to long"
author: "Evangeline Reynolds"
date: "2/16/2019"
output:
  xaringan::moon_reader:
    chakra: libs/remark-latest.min.js
    lib_dir: libs
    css: [default, hygge, ninjutsu]
    nature:
      ratio: 16:10
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---


```{r}
library(gapminder)
library(tidyverse)
library(flipbookr)
knitr::opts_chunk$set(cache = F, comment = "")
```

---
name: longtowideandback
## Exercise: *spread data, gather data*

Sometimes, we need to transform the shape of our data.  The wide form of data is an efficient way to store data - especially time-series data.  However, we often need data in a long for for data analysis, so shape transformations are important.  



---
## Exercise: *long to wide*

---

```{r long_to_wide, include = F}
gapminder %>% 
  select(country, continent, 
         lifeExp, year) %>% 
  spread(key = year, 
         value = lifeExp) ->
gapminder_life_exp_wide 
```


`r chunk_reveal("long_to_wide")`

---
## Exercise: *wide to long*

---


```{r wide_to_long, include = F}
gapminder_life_exp_wide %>% 
  gather(key = "year", 
         value = "lifeExp",
         -country, -continent) %>% 
  mutate(year = as.numeric(year)) ->
  gapminder_life_exp_long 
```


`r chunk_reveal("wide_to_long")`

---