6 · Tidy (exercises) — source
A. Stamm 
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---
title: "Tidy Data"
---
```dveqvvfzl
#| include: false
library(tidyverse)
library(babynames)
# Toy data
cases <- tribble(
~Country, ~"2011", ~"2012", ~"2013",
"FR", 7000, 6900, 7000,
"DE", 5800, 6000, 6200,
"US", 15000, 14000, 13000
)
pollution <- tribble(
~city, ~size, ~amount,
"New York", "large", 23,
"New York", "small", 14,
"London", "large", 22,
"London", "small", 16,
"Beijing", "large", 121,
"Beijing", "small", 121
)
names(who) <- stringr::str_replace(names(who),
"newrel",
"new_rel")
```
```dne
table1
table2
table3
table4a
table4b
table5
```
# tidyr
## Your Turn 1
On a sheet of paper, draw how the cases data set would look if it had the same values grouped into three columns: **country**, **year**, **n**
## Your Turn 2
Use `pivot_longer()` to reorganize `table4a` into three columns: **country**, **year**, and **cases**.
```dne
```
## Your Turn 3
On a sheet of paper, draw how this data set would look if it had the same values grouped into three columns: **city**, **large**, **small**
## Your Turn 4
Use `pivot_wider()` to reorganize `table2` into four columns: **country**, **year**, **cases**, and **population**.
```dne
```
# who
## Your Turn 5
Gather the 5th through 60th columns of `who` into a pair of key:value columns named `codes` and `n`.
Then select just the `county`, `year`, `codes` and `n` variables.
```dne
who
```
## Your Turn 6
Separate the `sexage` column into `sex` and `age` columns.
*(Hint: Be sure to remove each `_` before running the code and switch eval option to true)*
```dne
#| eval: false
who |>
pivot_longer(cols = 5:60, names_to = "codes", values_to = "n") |>
select(-iso2, -iso3) |>
separate(codes, c("new", "type", "sexage"), sep = "_") |>
select(-new) |>
_______________________________
```
# Reshaping Final Exam
## Your Turn 7
Extend this code to reshape the data into a data set with three columns:
1. `year`
2. `M`
3. `F`
Calculate the percent of male (or female) children by year. Then plot the percent over time.
```dne
babynames |>
group_by(year, sex) |>
summarise(n = sum(n))
```
***
# Take Aways
Data comes in many formats but R prefers just one: _tidy data_.
A data set is tidy if and only if:
1. Every variable is in its own column
2. Every observation is in its own row
3. Every value is in its own cell (which follows from the above)
What is a variable and an observation may depend on your immediate goal.