R Programming for Data Science

After completing this course, students will use R to perform common data science tasks:

• Set up an R development environment and execute simple code.
• Perform operations on atomic data types in R, including characters, numbers, and logicals.
• Perform operations on data structures in R, including vectors, lists, and data frames.
• Write conditional statements and loops.
• Structure code for reuse with functions and packages.
• Manage data by loading and saving datasets, manipulating data frames, and more.
• Analyze data through exploratory analysis, statistical analysis, and more.
• Create and format data visualizations using base R and ggplot2.
• Create simple statistical models from data.

This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language.
 

To ensure your success in this course, you should be comfortable with basic computer programming concepts, including but not limited to: syntax, data types, conditional statements, loops, and functions. You can obtain this level of skills and knowledge by taking the Introduction to Programming with Python® course. You should also have at least a high-level understanding of fundamental data science concepts, including but not limited to: data engineering, data analysis, data storage, data visualization, and statistics. You can obtain this level of knowledge by taking the CertNexus DSBIZ™ (Exam DSZ-110): Data Science for Business Professionals course.
 

Setting Up R and Executing Simple Code

• Set Up the R Development Environment
• Write R Statements

Processing Atomic Data Types

• Process Characters
• Process Numbers
• Process Logicals

Processing Data Structures

• Process Vectors
• Process Factors
• Process Data Frames
• Subset Data Structures

Writing Conditional Statements and Loops

• Write Conditional Statements
• Write Loops

Structuring Code for Reuse

• Define and Call Functions
• Apply Loop Functions
• Manage R Packages

Managing Data in R

• Load Data
• Save Data
• Manipulate Data Frames Using Base R
• Manipulate Data Frames Using dplyr
• Handle Dates and Times

Analyzing Data in R

• Examine Data
• Explore the Underlying Distribution of Data
• Identify Missing Values

Visualizing Data in R

• Plot Data Using Base R Functions
• Plot Data Using ggplot2
• Format Plots in ggplot2
• Create Combination Plots

Modeling Data in R

• Create Statistical Models in R
• Create Machine Learning Models in R
• Appendix A: Handling Issues in Code
• Appendix B: R Resources