R Visualization: Introduction to Data Visualization

Introduction to Data Visualization

• Plots of data easily communicate information that is difficult to extract from tables of raw values.
• Data visualization is a key component of exploratory data analysis (EDA), in which the properties of data are explored through visualization and summarization techniques.
• Data visualization can help discover biases, systematic errors, mistakes and other unexpected problems in data before those data are incorporated into potentially flawed analysis.
• Data visualization and EDA in R uses the ggplot2 package.

Introduction to Distributions

• The most basic statistical summary of a list of objects is its distribution.
• We will learn ways to visualize and analyze distributions in the upcoming videos.
• In some cases, data can be summarized by a two-number summary: the average and standard deviation. We will learn to use data visualization to determine when that is appropriate.

Data Types

• Categorical data are variables that are defined by a small number of groups.
  • Ordinal categorical data have an inherent order to the categories (mild/medium/hot, for example).
  • Non-ordinal categorical data have no order to the categories.
• Numerical data take a variety of numeric values.
  • Continuous variables can take any value.
  • Discrete variables are limited to sets of specific values.