These online tutorials will help you to learn essential concepts related to research methods, while introducing you to the software R at the same time. R is a program that will allow you to visualize data and conduct statistical analyses on that data. The best thing about it is that it is completely free and open-source! For these tutorials, you won't need to download any software: everything will take place in your browser. R is similar to a computer programming language and so it can be a bit scary or intimidating for those who aren't familiar to programming. But don't worry, we keep things simple and give you a very gentle introduction to this powerful tool. These tutorials involve running code, editing code, writing your own code, and completing quizzes and other exercises. Once you are a bit more comfortable with R, you should consult the additional resources we've compiled to continue your learning.
TIP: Although the tutorials work best when you progress through them without stopping, you can always re-set the tutorial by clicking "Start Over" at the bottom of the index (top-left of your screen).
Unit 1: Introducing R
Learn the basics of using R, become familiar with the tutorial format
What is R code? ∙ How do I run code and edit code?
How does R store information as objects? ∙ How can I see inside an object?
Learn how to concatenate data (put it all in a row)
Use a function to perform a calculation on an object
[Alternative Link 1| Link 2]
Unit 2: Central Tendency
Learn more about R and descriptive statistics, summarizing your data
Learn about different measures of central tendency ∙ Calculate the mean
Learn the function for calculating the median ∙ Load a package containing
many different functions ∙ Look up the help file for a function ∙ Calculate the mode using an R function ∙ Import a dataset of data from the real-world ∙ Look at a subset of a larger dataset ∙ Calculate measures of central tendency for a real-world dataset
[Alternative Link 1 | Link 2]
Unit 3: Central Tendency II and Variability
Create graphs in R, learn about how scores differ and the influence of outliers
Interpreting different measures of central tendency ∙ How outliers influence the mean and the median ∙ Adding data to dataset by combining objects ∙ Visualize a dataset using a histogram ∙ Learn about variability and the standard deviation ∙ Generate data that conforms to certain parameters
[Alternative Link 1 | Link 2]
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