# R Programming for Data Science

In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business.

- Course Objectives
- Target Student
- Prerequisites
- Outline

In this course, you will use R to perform common data science tasks. You will:

• 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 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.

**Lesson 1: Setting Up R and Executing Simple Code**

• Set Up the R Development Environment

• Write R Statements

**Lesson 2: Processing Atomic Data Types**

• Process Characters

• Process Numbers

• Process Logicals

**Lesson 3: Processing Data Structures**

• Process Vectors

• Process Factors

• Process Data Frames

• Subset Data Structures

**Lesson 4: Writing Conditional Statements and Loops**

• Write Conditional Statements

• Write Loops

Lesson 5: Structuring Code for Reuse

• Define and Call Functions

• Apply Loop Functions

• Manage R Packages

**Lesson 6: Managing Data in R**

• Load Data

• Save Data

• Manipulate Data Frames Using Base R

• Manipulate Data Frames Using dplyr

• Handle Dates and Times

**Lesson 7: Analyzing Data in R**

• Examine Data

• Explore the Underlying Distribution of Data

• Identify Missing Values

**Lesson 8: Visualizing Data in R**

• Plot Data Using Base R Functions

• Plot Data Using ggplot2

• Format Plots in ggplot2

• Create Combination Plots

**Lesson 9: Modeling Data in R**

• Create Statistical Models in R

• Create Machine Learning Models in R