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, analyze, and graph data in this course. Hands-On Introduction to R 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 Centering and Rescaling Centering 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 Coloring With Attributes Shape and Size to Graph Box Plots and Violin Plots Histogram Density Plots Graph Labeling 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 Modeling 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