---
title: "Tidy Data"
---

```{r setup}
#| 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")
```

```{r}
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**.

```{r}

```

## 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**.

```{r}

```

# 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.

```{r}
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)*

```{r}
#| 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.

```{r}
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.