Exercises for 2.5: Data manipulation

Preliminaries

  • load the driving.Rdata file.
  • type head( driving ) to look at the first few observations
  • load the following packages: lsr
load( "~/Work/Research/Rbook/workshop_dsto/datasets/driving.Rdata")
head( driving )
library(lsr)

Exercise 2.5.1: Reshaping from wide to long

  • use wideToLong() to make a long-form version of the driving data frame. Save the results to driving.2
  • if you didn’t already give the within-subjects factor a meaningful name, repeat the command but this time use the within argument to specifiy a good name for it

Exercise 2.5.2: Reshaping from long to wide

  • use the longToWide() function to make a wide-form version of the driving.2 data frame that you created in 2.5.1. Save the results to driving.3

Exercise 2.5.3: Cutting a continuous variable into categories

  • use cut() to cut driving$age into 3 bins of approximately equal size (i.e. similar age ranges). Save the result to age.group.1.
  • use table() to look at how many people fall in the different age groups, and look at the category names to see how wide each of the age groups are.
  • use quantileCut() to cut driving$age into 3 bins of approximately equal frequency (i.e., similar number of people in each group). Save the result to age.group.2.
  • use table() to look at how many people fall in the different age groups, and look at the category names to see how wide each of the age groups are.

Exercise 2.5.4: Permuting factor levels

  • print out driving$distractor and take note of the ordering of factor levels
  • use bars() to plot means and confidence intervals for RT at time 1 for each distractor
  • use permuteLevels() to reorder the factor levels for distractor.
  • print out driving$distractor to check that you have successfully reordered the groups, and now use bars() to redraw the plot.