R Programming: Setup and Data Processing

R Programming: Setup and Data Processing

This course is part of R Programming for Data Science Specialization

Instructor: Bill Rosenthal

Access provided by KSR Educational Institutions

4 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Configure the RStudio development environment to manage projects, execute console commands, and write clean scripts following standard coding styles.
Process atomic data types by manipulating character strings, executing numeric operations, and evaluating logical comparison operators.
Manage complex data structures like vectors, matrices, factors, and data frames using advanced subsetting, indexing, and masking techniques.
Learn by doing. Perform guided, step-by-step hands-on activities on your own computer.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

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Build your subject-matter expertise

This course is part of the R Programming for Data Science Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

This course will be useful to anyone who wants to learn the R programming language, particularly to leverage it for data analysis and data science tasks. You will begin by setting up an R development environment and executing simple code. Then, you'll process atomic data types like characters, numbers, and logical. You'll also process data structures like vectors, factors, and data frames.

Before you can start developing data science projects in R, you must set up a system with the necessary tools. Then you can create a new programming project and start writing and testing your code.

What's included

1 reading5 plugins

Now that you have some experience with writing code in R, you can take a deeper dive into the characteristics of the language itself. Like all languages, R categorizes data in different ways. You'll need to know how to process each data type in order to successfully work with your data.

What's included

5 plugins

In the previous lesson, you worked with R's atomic data types. These atomic types are components of larger objects that structure data in more complex, yet practical forms. In this lesson, you'll learn how to process the main data structures that R supports.