## Passing data between sub-projects

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

## Tidyr & dplyr

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
)

## Workshop

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!