03-Tidy-Data.Rmd

---
title: "Tidy Data"
output: html_notebook
---

```{r setup}
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
)

x <- tribble(
  ~x1, ~x2,
  "A",   1,
  "B",  NA,
  "C",  NA,
  "D",   3,
  "E",  NA
)

# To avoid a distracting detail during class
names(who) <- stringr::str_replace(names(who), "newrel", "new_rel")
```

## 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 `gather()` 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 `spread()` to reorganize `table2` into four columns: **country**, **year**, **cases**, and **population**.

```{r}

```

## Your Turn 5

Gather the 5th through 60th columns of `who` into a key column: value column pair named **codes** and **n**. Then select just the `county`, `year`, `codes` and `n` variables.

```{r}

```

## Your Turn 6

Separate the `sexage` column into **sex** and **age** columns.

```{r}

```

## Your Turn 7

Reshape the layout of this data. 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.