Resources

For many topics in this class, we are going to be only scratching the surface. Below you can find some links that can help you learn more.

Readings

Textbook readings

Articles

  • Roth et al. (2025). Ten principles for reliable, efficient, and adaptable coding in psychology and cognitive neuroscience. Nature Communications Psychology. link

Coding for all

Useful functions

  • Basic statistics: mean(), median(), min(), max(), sum() calculate basic descriptive statistics on a vector.
  • Data dimension checks: nrow(), ncol() return the number of rows and columns in a data frame length() returns the number of items in an object.
  • Data manipulation: subset() allows filtering data based on conditions, sort() sorts data in ascending order.
  • Data exploration: table() counts frequencies of unique values in a vector, head() gives you a preview of the first six parts of an object, and str() tells you more about the structure of an object.
  • Sampling: sample() generates random samples from a vector.
  • Data analysis with dplyr: filter(), select(), mutate(), group_by() are powerful functions for data manipulation within the dplyr package - covered in the Data Wrangling module
  • Visualization with ggplot2: ggplot() is the primary function to create customized plots using the ggplot2 package - covered in the Data Visualization module
  • More useful functions

R cheatsheets

  • Official cheatsheets from Posit

Working with files

Practice problems

Version control with git

Statistical analysis with R

Data visualization

Natural language processing with R

Advanced programming with R

Resources for learning Python