Tidy Data

There are three interrelated rules which make a dataset tidy:

• Each variable must have its own column.
• Each observation must have its own row.
• Each value must have its own cell.

That interrelationship leads to an even simpler set of practical instructions:

• Put each dataset in a tibble.
• Put each variable in a column.

Lesson

Download the script that generates the tables for the lesson here

1. Tidy Data

Exercise

1. Are the bike counts data tidy data?
2. If not, why not? And how can we tidy it?
3. Convert the total bike counts data to a wide format, with each row representing a day, and a column representing the total bike counts for each of the three bridges;
4. Convert the above data frame in wide format back to long format.
5. [Challenge] After tidying the bike counts, using functions in the tidyr package, create tables summarizing the average bike counts by bridge and day of week in two different formats:

Sample code: tidy_counts.R

Resources

1. Dataframe Manipulation with tidyr
2. Data Wrangling Cheat sheet
3. Introduction to tidyr