Data Analytics with R Training (RPR102)

R is a very popular, open-source environment for statistical computing, data analytics and graphics. This Data Analytics with R Training class introduces the R programming language to students. It covers language fundamentals, commonly used packages, plotting and data visualization and exploratory analysis with real world data.

1. Introduction to R
   1. Downloading and Install R and RStudio
   2. Introduction to RStudio
   3. The R Environment
   4. Writing and Executing R Scripts
   5. Variables and Assignment
   6. R’s Working Directory
   7. Importing CSV Files
   8. Introduction to DataFrames
   9. Make Your First Plots!
   10. Exercise: Importing and Plotting Data
2. Why “R for Data Science”?
   1. A brief history of R
   2. The Atomic Data Types
   3. Introduction to R Data Structures (Vectors and DataFrames) include named vectors here
   4. Indexing With Base R
   5. Introduction to Vectorized Calculations
   6. Stats 101: Getting Statistical Summaries
   7. Exercise: Indexing and Summarizing DataFrames
3. Welcome to the Tidyverse
   1. What are R Packages?
   2. Introduction to the Tidyverse
   3. R is Functional
   4. The Primary Data Verbs: dplyr
   5. The Pipe Operator
   6. Coding Style
   7. Exercise: Manipulating DataFrames with dplyr
4. Plotting with ggplot2
   1. Understanding the “Grammar of Graphics”
   2. Building Graphics by Pieces
   3. Understanding Geometries
   4. Linking Chart Elements to Variable Values
   5. Controlling Legends and Axes
   6. Exporting Graphics
   7. Exercise: Plotting with ggplot2
5. Intermediate Data Management with dplyr
   1. Introduction to Tibbles and More on Importing Data
   2. Renaming Columns
   3. Adding New Columns (If-Else and Case-When)
   4. Binning data (Continuous to Categorical)
   5. Exercise: Building Data Pipes
6. More Packages from the Tidyverse
   1. Dates and Times and the lubridate Package
   2. Factors with the forcats Package
   3. Exercise: Manipulating Dates and Times, and Building Factors
7. Merging and Reshaping Data
   1. Merging DataFrames
   2. Concatenating DataFrames
   3. Reshaping DataFrames (Melt and Cast)
   4. Random Sampling From DataFrames
   5. Exercise: Merging and Reshaping Data
8. Summarizing Data with a Group-By Analysis
   1. Adding Group-By to a Data Pipeline
   2. A Pivot Table is Like a Group-By
   3. A Cross-Tabulation is Like a Pivot Table
   4. Exercise: Implementing EDA of Categorical by Continuous Variables
9. Exploratory Data Analysis
   1. How to Perform EDA
   2. Univariate EDA
   3. Multivariate EDA
   4. Exercise: Exploratory Data Analysis
10. User-Defined Functions and Control Flow
    1. Functional Programming with dplyr
    2. Control Flow in R: Looping and Apply Functions
    3. EDA: Stanford Admissions Example
    4. Exercise: Creating User-Defined Functions
11. Reproducible Reports with RMarkdown
    1. RMarkdown overview
    2. Elements of an Rmarkdown Notebook
    3. Parameterized Reports
    4. Exercise: Generating an Rmarkdown Report

Each student receives a comprehensive set of materials, including course notes and all class examples.

Experience in the following is required for this R Programming class: