- 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)
```

- 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

- 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`

- 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.

- 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.