Hands-On Introduction to R

This introductory R programming course provides hands-on experience using R, a programming language for statistical computing, machine learning, and graphics. R is widely used in diverse disciplines to estimate, predict, and display results. Students will learn how to use R to clean, analyse, and graph data in this course.

Introduction to R Delivery Methods

• In-Person

• Online

Introduction to R Course Benefits

• Perform computations in R

• Load data sets from various sources into R

• Transform data sets in preparation for analysis

• Create tidy data using the Tidyverse packages

• Visualize data with ggplot2

• Fit models to data

• Continue learning and face new challenges with after-course one-on-one instructor coaching

Important course information

Prerequisites

• Experience with another procedural or object-oriented programming language, such as C, C++, Java, VB .NET, or SQL
• Familiarity with concepts, such as variables, loops, and branches with some experience using a text editor to edit program code

Exam Information

Optional Learning Tree exam available at the end of class

Chapter 1: Introduction to R

• Introduction to S, S-PLUS, and R
• Design of R
• Advantages of R
• Limitations of R
• The R GUI
• The R GUI

Hands-On Exercise 1.1

• The RStudio Interface
• The RStudio Interface
• RStudio Demo
• Setting Up a Custom CRAN Mirror
• Changing RStudio Options
• Naming Conventions, R Commands and Variables
• Basic Data Types
• Creating and Removing Variables
• Numbers and Character Types
• Functions and Packages
• Common Mathematical Functions
• Common Statistical Functions
• Common Probability Functions
• The tidyverse Family of Packages
• Installing tidyverse
• Character Processing Functions in the stringr Package
• Complex Character Manipulation Functions
• Complex Character Manipulation Functions II
• Complex Character Manipulation Functions III
• Miscellaneous Functions
• The Pipe Operator
• Pipe Operator Example
• Performing Calculations
• Executing Code in R Script File
• Executing Code in R Script File

Hands-On Exercise 1.1

• Introducing the Tidyverse
• Data Input
• Reading From a File
• Reading and Displaying a File
• Structure of the Data
• Reading and Writing to Excel File
• Reading From a Database Using the RODBC Package
• Reading From a Database Using the dbplyr Package
• Saving Data From R to Disk

Hands-On Exercise 1.2

Chapter 2: Aggregate Data Types and Computation

• Data Structures
• Numeric Vectors
• Vector Arithmetic
• Vector Arithmetic
• Generating Sequences
• Repeating with the rep() function
• Logical Vectors
• Boolean Operations
• Missing Values
• Character Vectors
• The paste() function
• Selecting and Modifying Elements of a Vector
• Selecting and Modifying Elements of a Vector
• Selecting and Modifying Elements of a Vector
• Getting Information about R Objects
• Examining a Vector
• Mixing Types in a Vector
• Factor Types
• Factor Types
• Conceptual Framework for Factors
• Factors for Numerical Data
• The forcats Package
• Using fct_infreq()
• Using fct_lump()
• Lists
• Naming List Elements
• Apply Functions to Lists
• Data Frames
• The Tibble
• Creating a Tibble From Vectors
• Column Names That Are Non-syntactic
• Creating a Tibble Using tribble()
• Tibbles in Action
• Matrices
• Creating Matrices
• Accessing Elements of a Matrix
• Matrix Computations
• Transpose and Matrix Multiplication
• Querying a Data Set
• Variable Exclusion I
• Variable Exclusion II
• Variable Exclusion III
• Querying Columns From a Tibble
• Querying Rows From a Tibble
• Exploratory Data Analysis
• The summarize() Function of dplyr
• Working With summarize()
• Using filter()
• summary() Function

Hands-On Exercise 2.1

• Advanced Summary Options
• Aggregate Examples I
• Aggregate Examples II
• Aggregate Examples III
• Aggregate Examples IV
• Data Preparation: Data Frame Manipulation—bind_rows()
• Data Preparation: Data Frame Manipulation—bind_cols()

Hands-On Exercise 2.2

Chapter 3: Data Transformation

• Cleaning and Transforming the Data
• Centring and Rescaling
• Centring and Rescaling II
• Normalizing
• Missing Values
• Missing Values
• Dropping Rows with Missing Entries
• Imputing Missing Values
• Binning
• Additional Recoding Options
• Multilevel Recoding
• The Function cut() in Action
• General Approach for Multilevel Variable Recoding I
• General Approach for Multilevel Variable Recoding II
• Checking for Duplicates and Formatting Dates
• Reordering a Data Set
• Reordering Examples I
• Reordering Examples II
• Reordering Examples III
• Sorting, Ranking, and Ordering Data
• Joining Datasets
• Inner Joins
• Left Joins
• Right Joins
• Getting a Subset of Data
• Another Example of Subset Function
• Sampling

Hands-On Exercise 3.1

Chapter 4: Visualizing Data

• Base Graphics
• Exploring Data Visualization
• Explore the options in qplot()
• Weather Data Set
• Simple Graph Plotting
• Graph Colouring With Attributes
• Shape and Size to Graph
• Box Plots and Violin Plots
• Histogram
• Density Plots
• Graph Labelling
• Pie Charts
• Co-relationship in Data
• Plotting Correlation of Three Variables
• Correlations for All the Numeric Variables

Hands-On Exercise 4.1

Chapter 5: Fitting Models to Data

• tidymodel
• Introduction to Regression
• When Is Regression Used?
• Sample Use Cases
• Dependent and Independent Variables
• Calculating Regression Equation
• Multiple Linear Regression
• Equation for Multiple Linear Regression
• R’s Built-In Function for Linear Regression
• Additional Linear Modelling functions
• Example: Predicting Prestige
• The Data Set
• Exploring and Preparing the Data
• Creating a Training and a Testing Data Set
• The Model
• Fitting a Linear Model to the Data
• Making Predictions From the Model
• Fitting the Model With Parsnip
• Interpreting the Model
• Interpreting the Model
• Evaluating the Model
• Evaluating the Model
• Evaluating the Model
• Tidying Up the Output

Hands-On Exercise 5.1