Exploratory Data Analysis


by Zach Bogart, Joyce Robbins


This resource is a collaborative collection of resources designed to help students succeed in GR5702 Exploratory Data Analysis and Visualization, a course offered at Columbia University. While the course lectures and textbook focus on theoretical issues, this resource, in contrast, provides coding tips and examples to assist students as they create their own analyses and visualizations. It is our hope that students will contribute to edav.info and it will grow with the course. Read more →


An Incomplete Solutions Guide to the NIST/SEMATECH e-Handbook of Statistical Methods

by Ray Hoobler


Analysis of case studies and exercies with a focus on using the tidyverse and ggplot2. This handbook was created using the bookdown package in RStudio. The output format for this example is bookdown::gitbook. […] Exploratory Data Analysis (EDA) is a philosophy on how to work with data, and for many applications, the workflow is better suited for most working scientist and engineers. As a scientist, we are trained to formulate a hypothesis and design a series of experiments that will allow us to test the hypothesis effectively. Unfortunately, most data doesn’t from carefully controlled … Read more →


APS 135: Introduction to Exploratory Data Analysis with R

by Dylan Z. Childs


Course book for Introduction to Exploratory Data Analysis with R (APS 135) in the Department of Animal and Plant Sciences, University of Sheffield. […] This is the online course book for the Introduction to Exploratory Data Analysis with R component of (APS 135) module. You can view this book in any modern desktop browser, as well as on your phone or tablet device. The site is self-contained—it contains all the material you are expected to learn this year. Bethan Hindle is running the course this year. Please email her if you spot any problems. You will be introduced to the R ecosystem. R … Read more →


Exploratory Data Analysis with R

by Roger D. Peng

Exploratory Data Analysis with R

This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you have. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing informative data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data. Read more →