long to wide or wide to long

https://images.unsplash.com/photo-1590605103416-230704277b05?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1500&q=80

class: left, top, inverse
background-image: url(https://images.unsplash.com/photo-1590605103416-230704277b05?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=1500&q=80)
background-size: cover

# .large[Reshaping]
##  Long <--> Wide 
### Walk-throughs <br>with {flipbookr}<br>and {xaringan}

#### .right[Gina Reynolds<br>Photo Credit: Courtney Cook]

---

```r
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*

---

count: false

.panel1-long_to_wide-auto[

```r
*library(gapminder)
```
]
 
.panel2-long_to_wide-auto[

]

---
count: false

.panel1-long_to_wide-auto[

```r
library(gapminder)
*library(tidyverse)
```
]
 
.panel2-long_to_wide-auto[

]

---
count: false

.panel1-long_to_wide-auto[

```r
library(gapminder)
library(tidyverse)
*gapminder
```
]
 
.panel2-long_to_wide-auto[

```
# A tibble: 1,704 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# … with 1,694 more rows
```
]

---
count: false

.panel1-long_to_wide-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
* select(country, continent,
*        lifeExp, year)
```
]
 
.panel2-long_to_wide-auto[

```
# A tibble: 1,704 x 4
   country     continent lifeExp  year
   <fct>       <fct>       <dbl> <int>
 1 Afghanistan Asia         28.8  1952
 2 Afghanistan Asia         30.3  1957
 3 Afghanistan Asia         32.0  1962
 4 Afghanistan Asia         34.0  1967
 5 Afghanistan Asia         36.1  1972
 6 Afghanistan Asia         38.4  1977
 7 Afghanistan Asia         39.9  1982
 8 Afghanistan Asia         40.8  1987
 9 Afghanistan Asia         41.7  1992
10 Afghanistan Asia         41.8  1997
# … with 1,694 more rows
```
]

---
count: false

.panel1-long_to_wide-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  select(country, continent,
         lifeExp, year) %>%
* pivot_wider(names_from = year,
*             values_from = lifeExp)
```
]
 
.panel2-long_to_wide-auto[

```
# A tibble: 142 x 14
   country continent `1952` `1957` `1962` `1967` `1972` `1977` `1982` `1987`
   <fct>   <fct>      <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
 1 Afghan… Asia        28.8   30.3   32.0   34.0   36.1   38.4   39.9   40.8
 2 Albania Europe      55.2   59.3   64.8   66.2   67.7   68.9   70.4   72  
 3 Algeria Africa      43.1   45.7   48.3   51.4   54.5   58.0   61.4   65.8
 4 Angola  Africa      30.0   32.0   34     36.0   37.9   39.5   39.9   39.9
 5 Argent… Americas    62.5   64.4   65.1   65.6   67.1   68.5   69.9   70.8
 6 Austra… Oceania     69.1   70.3   70.9   71.1   71.9   73.5   74.7   76.3
 7 Austria Europe      66.8   67.5   69.5   70.1   70.6   72.2   73.2   74.9
 8 Bahrain Asia        50.9   53.8   56.9   59.9   63.3   65.6   69.1   70.8
 9 Bangla… Asia        37.5   39.3   41.2   43.5   45.3   46.9   50.0   52.8
10 Belgium Europe      68     69.2   70.2   70.9   71.4   72.8   73.9   75.4
# … with 132 more rows, and 4 more variables: `1992` <dbl>, `1997` <dbl>,
#   `2002` <dbl>, `2007` <dbl>
```
]

---
count: false

.panel1-long_to_wide-auto[

```r
library(gapminder)
library(tidyverse)
gapminder %>%
  select(country, continent,
         lifeExp, year) %>%
  pivot_wider(names_from = year,
              values_from = lifeExp) ->
*gapminder_life_exp_wide
```
]
 
.panel2-long_to_wide-auto[

]

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

---

count: false

.panel1-wide_to_long-auto[

```r
*gapminder_life_exp_wide
```
]
 
.panel2-wide_to_long-auto[

---
count: false

.panel1-wide_to_long-auto[

```r
gapminder_life_exp_wide %>%
* pivot_longer(`1952`:`2007`,  # backticks needed because numeric
# column names look like number values without them
*              names_to = "year",
*              values_to = "lifeExp")
```
]
 
.panel2-wide_to_long-auto[

```
# A tibble: 1,704 x 4
   country     continent year  lifeExp
   <fct>       <fct>     <chr>   <dbl>
 1 Afghanistan Asia      1952     28.8
 2 Afghanistan Asia      1957     30.3
 3 Afghanistan Asia      1962     32.0
 4 Afghanistan Asia      1967     34.0
 5 Afghanistan Asia      1972     36.1
 6 Afghanistan Asia      1977     38.4
 7 Afghanistan Asia      1982     39.9
 8 Afghanistan Asia      1987     40.8
 9 Afghanistan Asia      1992     41.7
10 Afghanistan Asia      1997     41.8
# … with 1,694 more rows
```
]

---
count: false

.panel1-wide_to_long-auto[

```r
gapminder_life_exp_wide %>%
  pivot_longer(`1952`:`2007`,  # backticks needed because numeric
# column names look like number values without them
               names_to = "year",
               values_to = "lifeExp") %>%
* mutate(year = as.numeric(year))
```
]
 
.panel2-wide_to_long-auto[

```
# A tibble: 1,704 x 4
   country     continent  year lifeExp
   <fct>       <fct>     <dbl>   <dbl>
 1 Afghanistan Asia       1952    28.8
 2 Afghanistan Asia       1957    30.3
 3 Afghanistan Asia       1962    32.0
 4 Afghanistan Asia       1967    34.0
 5 Afghanistan Asia       1972    36.1
 6 Afghanistan Asia       1977    38.4
 7 Afghanistan Asia       1982    39.9
 8 Afghanistan Asia       1987    40.8
 9 Afghanistan Asia       1992    41.7
10 Afghanistan Asia       1997    41.8
# … with 1,694 more rows
```
]

---
count: false

.panel1-wide_to_long-auto[

```r
gapminder_life_exp_wide %>%
  pivot_longer(`1952`:`2007`,  # backticks needed because numeric
# column names look like number values without them
               names_to = "year",
               values_to = "lifeExp") %>%
  mutate(year = as.numeric(year)) ->
* gapminder_life_exp_long
```
]
 
.panel2-wide_to_long-auto[

]