INTRODUCTION
In general data analysis includes four parts: Data collection, Data manipulation, Data visualization and Data Conclusion or Analysis. The tidyr package is one of the most useful packages for the second category of data manipulation as tidy data is the number one factor for a succesfull analysis.
Tidy data means that every column stands for a variable and every row represents for an observation.
The tidyr package offers useful functions, which we are going to see, that help us organize raw data.
Before proceeding, please follow our short tutorial.
Look at the examples given and try to understand the logic behind them. Then try to solve the exercises below using R and without looking at the answers. Then check the solutions.
to check your answers.
Exercise 1
Gather “day1points” and “day2points” into a new column “day” and their values to a new column named “points”. HINT: Use gather().
Exercise 2
Reverse the position of day and points to understand the significance of their initial position.
Exercise 3
Reverse what you did in Exercise 1 by giving to the dataset its initial form. HINT: Use spread().
Exercise 4
Reverse the position of “day” and “points” in the answer of Exercise 3 to understand why the code is not working.
Exercise 5
Create two columns one for the “team” and the other for the “state” from the column “team”. Set the sep to 3. HINT: Use separate().
Exercise 6
Change the sep argument from 3 to 2 and find the mistake.
Exercise 7
Unite the two columns you created in Exercise 6 to one as its intial form. HINT: Use unite().
Exercise 8
Use the right commands to tidy up your dataset by creating 5 columns: “player”, “Team”, “State”, “day” and “points”.
Exercise 9
Plot your dataset by creating a scatterplot with day in x-axis and points in y-axis. HINT: Use ggplot().
Exercise 10
Separate the plot of Exercise 9 according to “Team”. HINT: Use facet_wrap().
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