# Exercises for 2.1: Descriptive statistics

## Preliminaries

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

## Exercise 2.1.1: Central tendency

• compute the mean age of the participants
• compute the median age of the participants
• compute the modal age of the participants (note what happens when there are multiple modal frequencies)

## Exercise 2.1.2: Spread

• compute the standard deviation age of the participants
• compute the age range of the participants
• compute the interquartile range on the age of the participants
• compute the 10th, 25th, 75th and 90th percentiles for age

## Exercise 2.1.3: Tabulation part 1

• use table() to construct a tabulation of the distractor varible
• now use table() to cross-tabulate distractor by peak.hour

## Exercise 2.1.4: Tabulation part 2

• use xtabs() to cross-tabulate distractor by peak.hour

## Exercise 2.1.5: Getting descriptives en masse

• use describe to get descriptives for all variables in driving (this will produce some warnings!)
• use summary to get descriptives for all variables in driving

## Exercise 2.1.6: Descriptives by group

• use describeBy to get descriptives separately depending on whether the tests were conducted in peak hour or not (this will produce some warnings!)
• use aggregate to calculate the mean number of errors in the first test (i.e. errors_time1) broken down by peak.hour and distractor

### Exercise 2.1.7: Correlations

• calculate the correlation of the number of errors at time 1 with the number of errors at time 2 using cor()
• calculate the Spearman correlation for the same variables, again using cor()
• use correlate() to compute all pairwise correlations.