write.table(jd, "data/jd-factorized.txt")

write.table(jd, "data/jd-factorized.txt")

jdFact <- read.table("data/jd-factorized.txt")

Both packages are already installed, so they can be loaded with `library`

:

library(tidyr)

For example, if we want to convert the object task columns to a tidyr format:

jdTidy <- tidyr::gather( jdFact[c(1, 10:ncol(jdFact))] , key = "Complexity" , value = "ObjectAve" , ObjectsSimpleAve , ObjectComplexAve )

Here are some things to try out:

- Use the data and formula syntax to do a regression with
`lm`

- Use
`t.test`

to do a one- or two-sample t-test - Use
`aov`

to do an ANOVA - Use the
`density`

or`lines`

functions to spice up your graphs

You should be able to figure these functions out with the information we've covered!

# Enter your code here!