• How to assign elements to objects (<-)
• How to run code
• How to save R-scripts
• How to create vector, matrix, dataframe, and list objects
• How to obtain specific elements, rows, or columns from those objects (objectname[2,4])
• How to use functions

• How to import your data into R
• How install and load packages
• Recoding items and calculating sumscores
• Regression analysis
• ANOVA
• SEM with package lavaan
• multilevel modeling with package lme4

There are several 'layers' in R. For example:

• The global environment
• environments in functions
• Packages
• There is more, but that is beyond the scope of this course :)

• If you create a function, it is positioned in the global environment.

• Everything that happens in a function, stays in a function. Unless you specifically tell the function to share the information with the global environment.

• Packages have their own space.

• this is important, because different packages may have functions with the same name.

• for example, summary() is a function in both package lavaan and package lme4.

• If you load package lavaan, and then package lme4, the summary function will refer to the one in package lme4.

• but you can use both by using: lavaan::summary() and lme4::summary()

A workspace contains all changes you made to environments.

You can save a workspace and continue exactly where you left.

Workspaces are compressed and require relatively little memory when stored.

R by default saves (part of) the code history. (Use up arrow key in the console)

• savehistory(file = "Stuff.Rhistory")
• loadhistrory(file = "Stuff.Rhistory")

• You can save specific objects in an .Rdata file with function save().

• You can load .Rdata files with function load()

• save(object1,object2,object3, file = "savedobjects.Rdata")

• load(file="savedobjects.Rdata")

• You can save an object in an .cvs, .txt or .dat file with function write.table().

• You can load such files with function read.table()

• write.table(object1, file = "savedobject.csv", sep=";", col.names = TRUE, row.names=FALSE)

• read.table(file="savedobject.csv", sep=";", header=TRUE)

• General tutorials

  • Quick-R: http://www.statmethods.net/. Brief R tutorials for people who are used to spss and sas.
  • https://cran.r-project.org/ -> documentation -> contributed: Many elaborate R tutorials.

• When you are stuck

  • Google what you want to do, or the error you got, and so on.
  • Ask on http://stackoverflow.com/questions/tagged/r. Be precise with your question and provide a working example if relevant.

• Some things we did not do in this course, that may become important:

  • if statements in R: http://www.dummies.com/how-to/content/how-to-use-if-statements-in-r.html
  • Loops in R : http://blog.datacamp.com/tutorial-on-loops-in-r/
  • Graphics in R: https://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html

• Rstudio

  • https://www.rstudio.com/

Exercise 1 is a recap of last week: Try to make Exercise 1 without looking at the answers.

There probably is not enough time to do all the exercises. Pick the ones that interest you.

- Exercise 2: Loading spss data, recoding items, making sum score variables
- Exercise 3: More elaborate regression analysis in R
- Exercise 4: ANOVA in R
- Exercise 5: Basic SEM in R with lavaan.
- Exercise 6: Basic multilevel modeling in R with lme4.

After you've tried an exercise you can try something similar with your own data.