Data Visualization

Tidy Data Structure

Overview

Teaching: 5 min
Exercises: 5 min
Questions

  • What is tidy data structure?

  • How do I use tidyr to restructure messy data?

Objectives

  • To understand the importance of tidy data structure

  • To tidy our data in preparation for the final figure

Tidy data structure rules:

  1. Order doesn’t matter
  2. No duplicate rows
  3. Every cell contains one value
  4. One column per type of information
  5. No redundant information

Restructure tables with messy data.

Table Restructure

Important tidyr functions:

Gapminder data time series is stored cross-tabulated.

tidyr Fill-in Challenge

Fill in the missing pieces of the following code to generate a tidy data table.

pop_by_continent <- ________ %>%
  gather(year, population, ________) %>%
  separate(________, c("pop", "year"), sep = "_") %>%
  select(continent, country, ________, ________)

Output Solution

> str(pop_by_continent)
'data.frame':	1704 obs. of  4 variables:
 $ continent : Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ country   : Factor w/ 142 levels "Afghanistan",..: 3 4 11 14 17 18 20 22 23 27 ...
 $ year      : chr  "1952" "1952" "1952" "1952" ...
 $ population: num  9279525 4232095 1738315 442308 4469979 ...

> head(pop_by_continent)
  continent      country year population
1    Africa      Algeria 1952    9279525
2    Africa       Angola 1952    4232095
3    Africa        Benin 1952    1738315
4    Africa     Botswana 1952     442308
5    Africa Burkina Faso 1952    4469979
6    Africa      Burundi 1952    2445618

Code Solution

pop_by_continent <- data %>%
 gather(year, population, pop_1952:pop_2007) %>%
 separate(year, c("pop", "year"), sep = "_") %>%
 select(continent, country, year, population)

dplyr Review Fill-in Challenge

Fill in the missing pieces of the following code to filter data for 2007.

pop_2007 <- filter(________)

Output Solution

> str(pop_2007)
'data.frame':	142 obs. of  4 variables:
 $ continent : Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ country   : Factor w/ 142 levels "Afghanistan",..: 3 4 11 14 17 18 20 22 23 27 ...
 $ year      : chr  "2007" "2007" "2007" "2007" ...
 $ population: num  33333216 12420476 8078314 1639131 14326203 ...

> head(pop_2007)
  continent      country year population
1    Africa      Algeria 2007   33333216
2    Africa       Angola 2007   12420476
3    Africa        Benin 2007    8078314
4    Africa     Botswana 2007    1639131
5    Africa Burkina Faso 2007   14326203
6    Africa      Burundi 2007    8390505

Code Solution

pop_2007 <- filter(pop_by_continent, year==2007)

Key Points