1. Dplyr Tidyr & R Markdown
Snow Day, Spring 2017

2. Installing packages install.packages(“package name”)
So for diplyr… install.packages(“dplyr”)
library(dplyr)

3. Concepts of Tidy Data
Data is often messy! We need a precise way to talk about “Tidy” data Goal: Represent one fact in one place If one fact in multiple places, chance to record different values!

4. Messy? Tidy?
TIDY!
Information remains the same, but values, variables, and observations are more clear

5. Common problems with messy data
• Column headers are values, not variable names.
• Multiple variables are stored in one column.
• Variables are stored in both rows and columns.
• Multiple types of observational units are stored in the same table.
• A single observational unit is stored in multiple tables

6. An Example What are the variables here? Religion, Income, Frequency
Problem!
Column headers are values, not variable names.

7. “Melting” Data

8. The “Molten” set

9. Another example

10. Its “Tidy” version

11. For other examples
…and also a great read. See the link on the GH 811 site