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This resource was created at the University of Maryland (UMD) for instructors who want to teach, students who want to learn, and researchers who want to use R for statistical discovery and analysis. While this is a book, it is largely based on hands-on examples with video walk-throughs to take students through accessing R and RStudio, the basics of R and progressing to analyses step by step. The goal is to build confidence with programming early on and demonstrate best coding practices from the start.
While there are a number of excellent free R textbooks (we especially love: https://benwhalley.github.io/just-enough-r/ and https://learningstatisticswithr.com/) none we have found offered the level of support students in our classes wanted. We sincerely hope this resource benefits not only students taking our PSYC300 course but a much wider population of R learners and instructors.
After completing this course, you will be able to:
Explain the benefits of learning R
Conduct descriptive and inferential statistics in R
Visualize data in R
Interpret output from R and draw appropriate conclusions
Apply knowledge of statistics and R programming to new datasets and research problems
This is an open education resource (OER) that we continually update to create the best learning materials. If you have any feedback, questions or other resources, we welcome your thoughts to collaboratively build better material and a more open science! To leave feedback you may fill out this form or contact us at ttomlin1@umd.edu or achicoli@umd.edu.
A free and awesome programming language widely used in psychology research and data analysis
Career marketability, ease of use, open and free format, we can go on...
Open science improves transparency, reproducibility and fosters collaboration
At UMD and beyond!
Is the language of love...of data
What to do when you and R have an disagreement
This will help us organize small datasets and build additional career skills
Making sure you have data appropriately organized is the first step
Take a look at the example dataset we will be using throughout the course and this book
Always get to know your data with descriptive statistics before making any conclusions with inferential statistics
Not causation! But still very useful!
Is all about predicting those (linear) relationships (with one predictor)!
Is all about predicting those (linear) relationships (with multiple predictors)!
Is all about predicting those (linear) relationships (with multiple predictors)!
A MEdiator explains the MEchanism of the relationship between two other variables.
Testing differences in means of two independent groups or mean differences in correlated groups.
It's all about those means: Are there statistically significant differences between the means of three or more independent groups?
Are there significant differences between multiple measures of the same variable taken on correlated subjects either under different conditions or over multiple time periods.
Does the mean of a continuous variable change according to the levels of two categorical variables?
It's about control with Analysis of Covariance: A mix of ANOVA and regression used when there is at least one categorical predictor and one continuous outcome variable, but there is also a continuous predictor that needs to be controlled.
How to make clear and beautiful plots to communicate your science to others
Condensed summaries of how to complete common tasks in R
This work is licensed under a Creative Commons Copyright Attribution (CC BY SA-4.0) license, except where noted otherwise.
This work is licensed under a Creative Commons Copyright Attribution (CC BY SA-4.0) license, except where noted otherwise.
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