R and Stats Explorers

Below is a collection of the interactive tools I’ve built for my stats students (which I call “Explorers”). The goal is to make coding and stats more accessible and help students more clearly see the connection between what they want the code to do (e.g., “make the points in my ggplot different colors for different groups” or “take my dataset, grab only the first three columns, and filter out the rows in which some criterion is met”) and how that’s reflected in the code.

In most cases, the Explorers consist of three panels: The left one has the controllers, the middle one displays the data or plot, and the right one displays the code (often with comments to help the user understand the function). Short descriptions will pop up if you hover over portions of the code, and detailed descriptions can be found below the code chunks. Be sure to read the welcome message below the code on the right for instructions about how to use each tool!

Note that these are meant to complement teaching rather than replace it! They’re meant to be warm and accessible, but rather concise. Be sure to seek additional resources for more in-depth explorations of each topic.

Good luck, have fun, and remember: WE ARE ALL MATH PEOPLE.

ggplot Visualizer
Build layered plots by tweaking aesthetics and geoms
dplyr Explorer
Filter, select, mutate, and arrange data using dplyr verbs
Pivot Explorer
Reshape data between wide and long formats with tidyr
Object Assignment Explorer
Why does it sometimes seem like R is ignoring you when you're trying to modify a column or dataframe?
Indexing Explorer
Learn how R indexes vectors and data frames, and practice using logical operators and Booleans to filter rows
File Path Explorer
Practice navigating file paths to build intuition about working directories and reading data into R
Reliability & Validity Explorer
Explore reliability (including the Spearman-Brown formula) and its relationship with validity
Regression Explorer
Understand and visualize sums of squares, variance, standard deviation, mean square error, root mean square error, and R² by adjusting the regression line to try to get the best fit
Distribution (Area Under the Curve) Explorer
Adjust the parameters of probability distributions to visualize area under the curve and learn about the p*() family of functions
Sampling Distribution Explorer
Simulate drawing samples from a population to build intuition about sampling distributions, the Central Limit Theorem, and the t-distribution
Length:
2 minute read, 401 words
See Also: