This book will teach how to import, transform, and visualize data and communicate results using R.
Introduction – R for Data Science (2e) This section will cover the installation of R and RStudio, as well as installation of the extension packages that will be used in this course.
1 Data visualization – R for Data Science (2e) This section explains how to create data visualizations and how different visualizations may apply to different variable types.
2 Workflow: basics – R for Data Science (2e) This section will show the basics of how R code works for loading objects and calling functions.
6 Workflow: Scripts & Projects This section provides greater depth into the use of R scripts and design of project folders.
7 Data import – R for Data Science (2e) This section will explain how to import structured data from various file types, such as comma-delimited files.
From these sections, you will learn to compute descriptive statistics, which represent a summarization of obtained data variables.
Measures of Central Tendency (pp. 114 – 122)
Measures of Variability (pp. 115 – 131)
Skew and Kurtosis (pp. 131 – 133)
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