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simpleR - Using R for Introductory Statistics

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These notes are an introduction to using the statistical software package R for an introductory statistics course. They are meant to accompany an introductory statistics book such as Kitchens “Exploring Statistics”. The goals are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course.

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  1. Book
  2. R

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Chapter 3 – The R Programming Language

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Chapter from "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (Second Edition)" by John K. Kruschke

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  1. R

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R Base Graphics: An Idiot's Guide

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Last year, I presented an informal course on the basics of R Graphics University of Turku. In this blog post, I am providing some of the slides and the full code from that practical, which shows how to build different plot types using the basic (i.e. pre-installed) graphics in R

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  1. data visualization
  2. R
  3. R base graphics

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How to format plots for publication using ggplot2

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Creating publication quality graphs in R

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by Tim Appelhans

This tutorial was developed as part of a one-day workshop held within the Ecosystem Informatics PhD-programme at the Philipps University of Marburg. The contents of this turtorial are published under the creatice commons license 'Attribution-ShareAlike CC BY-SA'.

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Visual ANOVA

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Created by Thomas Malloy (requires Flash)

  1. ANOVA
  2. visualization

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Visualizing a One-Way ANOVA using D3.js

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A while ago I was playing around with the JavaScript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I wanted to make the visualization interactive, and I did integrate some interactive elements. For instance, if you hover over a data point it will show the residual, and its value will be highlighted in the combined computation. The circle diagram show the partitioning of the sums of squares, and if you hover a part it will show from where the variation is coming. I tried to make the plots look like plots from the R-package ggplot2.

Created by Kristoffer Magnusson

  1. ANOVA
  2. visualization

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Understanding Statistical Power and Significance Testing: An Interactive Visualization

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This visualization is meant as an aid for students when they are learning about statistical hypothesis testing. The visualization is based on a one-sample Z-test. You can vary the sample size, power, signifance level and effect size using the sliders to see how the sampling distributions change.

Created by Kristoffer Magnusson

  1. animation
  2. hypothesis testing
  3. statistical power
  4. visualization

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Statistical Power and Significance Testing (R Shiny App)

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Note:  To run this visualization, you need a QUBES account.  Click here to register (it's free!)

  1. animation
  2. hypothesis testing
  3. statistical power
  4. visualization

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Central Limit Theorem

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From the authors of the textbook:

We have put together a few interactive animations that will illustrate some important statistics concepts (with funding from the University of British Columbia). These are all released into the public domain, so anyone can use them as they wish. Click on the “tutorial” button to be walked through the concepts, or just explore them on your own.
  1. animation
  2. central limit theorem
  3. visualization

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Confidence limits for the mean

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From the authors of the textbook:

We have put together a few interactive animations that will illustrate some important statistics concepts (with funding from the University of British Columbia). These are all released into the public domain, so anyone can use them as they wish. Click on the “tutorial” button to be walked through the concepts, or just explore them on your own.
  1. animation
  2. confidence interval
  3. Tutorial
  4. visualization

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Sampling means from a normal distribution

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From the authors of the textbook:

We have put together a few interactive animations that will illustrate some important statistics concepts (with funding from the University of British Columbia). These are all released into the public domain, so anyone can use them as they wish. Click on the “tutorial” button to be walked through the concepts, or just explore them on your own.
  1. animation
  2. Normal Distribution
  3. sampling distributions
  4. Tutorial
  5. visualization

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Chapter 18: Multiple explanatory variables

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Chapter 17: Regression

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Chapter 16: Correlation between numerical variables

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Chapter 15: The analysis of variance

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Visualizing a One-Way Anova

Understanding ANOVA Visually

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Chapter 13: Handling violations of assumptions

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Chapter 12: Comparing two means

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Chapter 11: Inference for a normal population

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Chapter 10: The normal distribution

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Chapter 9: Contingency analysis - associations between categorical variables

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Chapter 8: Fitting probability models to frequency data

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Chapter 7: Analyzing proportions

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Chapter 6: Hypothesis testing

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Visualizing statistical power

Visualizing statistical power (alternate)

  1. hypothesis testing

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Chapter 4: Estimating with uncertainty

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Sampling from a normally distributed population

Confidence intervals for the mean

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