• Capri Granville

Free Book: Statistics, Dataviz, and Data Cleaning with R

I stumbled upon this book by chance, when searching for material about time series (probably the most interesting chapter in this collection.)

The various chapters are accessible from the top tabs, on this web page. It is mostly about R, but it has a few interesting chapters on statistical science too. Below is a summary.

Time series decomposition (in chapter 23)

This website was created with 6 major sections: Programming, Plotting, Regression, ANOVA, Advanced topics,and R-Apps.The tutorials build on each other, but can also be utilized independently from one another, and refer back to other chapters that cover related topics in greater depth.

  1. R-programming: includes 9 chapters which covers the basics of how install R, review of the important basic functions, and some advanced concepts such data manipulation and transformations to prepare your data for analysis.

  2. Plotting: included 2 chapters on how to make pretty plots for the most common uses in psychology.

  3. Regression: included 8 chapters spanning how to conduct different types of regressions (linear, multiple, moderation/mediation,moderated mediation, logistic, Poisson, and multilevel and Mixed). Chapters focus on how to be able to run models and check assumptions. Some have short theoretical reviews.

  4. ANOVA: included 2 chapters on how to run between-, within-, and mixed-subjects ANOVAs with simple set of follow-up tests.

  5. Advanced topics: included 4 chapters on selecting correlation types, AIRMA, decision trees and signal detection.

  6. R Apps: includes a chapter which shows how to make a Shiny application, a living online document which is reactive to user input and a chapter which shows how an ANOVA parses variance.

Some the chapters simulate datasets and others have links for you to download csv files. Each chapter might use different packages (i.e., library of functions), please install.packages("name of package") indicated at the start of each chapter for doing the tutorial. For more information on installing packages see https://www.r-bloggers.com/installing-r-packages/.

List of chapters

  • The Basics

  • Indexing

  • Logicals and Loops

  • apply Functions

  • plyr

  • Sampling and Replication

  • Melting & Casting

  • Reshaping Data Using Tidyr

  • GGplot for Scatterplots & Density Plots

  • Boxplots and Bar Graphs

  • Regression: Basics, Assumptions, & Diagnostics

  • Plotting Regression Interactions

  • Mediation and Moderation

  • Moderated Mediation

  • Multilevel Modeling

  • Mixed Effects Modeling

  • Testing the Assumptions of Multilevel Models

  • Logistic and Poisson Regression

  • Between-Subjects ANOVA in R

  • ANOVA (afex): Within Subjects and Mixed Designs

  • Correlation Types and When to Use Them

  • Using ARIMA for Time Series Analysis

  • Decision Trees

  • Shiny Apps in RStudio

The authors of the tutorials were all graduate students in the department of psychology at the University of Illinois at Chicago.

Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed in https://www.datasciencecentral.com/profiles/blogs/great-reference

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