Exploratory Data Analysis in R

Introduction

In data science and analytics, we rarely deal with single values or static equations. Instead, we work with rich datasets containing multiple columns, missing values, and complex relationships. To extract meaningful insights, identify anomalies, and make data-driven decisions, we use Exploratory Data Analysis (EDA).

EDA is an iterative cycle where we:

  1. Generate questions about our data.
  2. Search for answers by visualizing, transforming, and modeling the data.
  3. Use what we learn to refine our questions and generate new ones.

This eBook serves as a hands-on guide to mastering EDA using the R programming language. We will focus extensively on the Tidyverse, an ecosystem of packages designed specifically for data science.

The R-EDA Toolset

Throughout this course, you will learn to use R's core data analytics libraries:

  • ggplot2: The grammar of graphics. You will learn to construct professional, layered visualizations (scatter plots, bar charts, box plots, and line charts) to visually inspect data relationships.
  • dplyr: The grammar of data transformation. You will master data manipulation verbs to select, filter, mutate, sort, group, and aggregate tables.
  • tidyr: Tools for reshaping data. You will learn to pivot datasets between wide and long formats to ensure your tables are "tidy" (each column is a variable, each row is an observation).
  • Hypothesis Testing: You will learn to perform core statistical tests (two-sample t-tests, ANOVA, Chi-squared tests of independence, and correlation tests) to verify if patterns are statistically significant.
  • Statistical Models (lm, glm): You will fit linear, multiple, and logistic regression models to understand and predict trends in data.
  • Databases & SQL: You will query databases using SQL directly inside R.
  • Shiny Apps: You will build interactive, reactive web dashboards to present your analysis results dynamically to stakeholders.

Setup & R Environment

To execute the code examples and complete the exercises, we recommend the following environments:

  1. RStudio Desktop: The standard integrated development environment (IDE) for R. You can download it from Posit's website.
  2. Posit Cloud: A cloud-based version of RStudio that runs directly in your browser.
  3. Google Colab: You can configure Colab notebooks to run R kernels by using the link: https://colab.research.google.com/#create=true&language=r.

Google Colab comes with many tidyverse packages pre-installed. If a library is missing, install it using the install.packages() command inside your code cells:

install.packages("tidyverse")

Let's begin our journey into R-EDA!