After doing these courses, I feel confident creating professional visualizations and dashboards
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Course Description
Learn R Programming
R programming language is a useful tool for data scientists, analysts, and statisticians, especially those working in academic settings. R's ability to handle complex analyses such as machine learning, financial modeling, and more makes it a valuable asset for a wide range of data-related tasks.This introduction to R course covers the basics of this open source language, including vectors, factors, lists, and data frames. You’ll gain useful coding skills and be ready to start your own data analysis in R.
Gain an Introduction to R
You’ll get started with basic operations, like using the console as a calculator and understanding basic data types in R. Once you’ve had a chance to practice, you’ll move on to creating vectors and try out your new R skills on a data set based on betting in Las Vegas.Next, you’ll learn how to work with matrices in R, learning how to create them, and perform calculations with them. You’ll also examine how R uses factors to store categorical data. Finally, you’ll explore how to work with R data frames and lists.
Master the R Basics for Data Analysis
By the time you’ve completed our Introduction to R course, you’ll be able to use R for your own data analysis. These sought-after skills can help you progress in your career and set you up for further learning. This course is part of several tracks, including Data Analyst with R, Data Scientist with R, and R Programming, all of which can help you develop your knowledge.Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Identify core R data types and recognize how variables store and manipulate values.
- Define and differentiate vector operations by creating, naming, subsetting, and comparing one-dimensional data.
- Recognize matrix structures by constructing, naming, and operating on two-dimensional data, including arithmetic and summaries.
- Differentiate nominal and ordinal factors and identify appropriate use of factor creation, level setting, and ordered comparisons.
- Evaluate outputs from vectors, matrices, and factors to assess totals, patterns, and logical conditions in R workflows.
Prerequisites
There are no prerequisites for this course
Curriculum
Course outline
1
Intro to basics
Take your first steps with R. In this chapter, you will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let's get started.
2
Vectors
We take you on a trip to Vegas, where you will learn how to analyze your gambling results using vectors in R. After completing this chapter, you will be able to create vectors in R, name them, select elements from them, and compare different vectors.
3
Matrices
In this chapter, you will learn how to work with matrices in R. By the end of the chapter, you will be able to create matrices and understand how to do basic computations with them. You will analyze the box office numbers of the Star Wars movies and learn how to use matrices in R. May the force be with you!
4
Factors
Data often falls into a limited number of categories. For example, human hair color can be categorized as black, brown, blond, red, grey, or white—and perhaps a few more options for people who color their hair. In R, categorical data is stored in factors. Factors are very important in data analysis, so start learning how to create, subset, and compare them now.
5
Data frames
Most datasets you will be working with will be stored as data frames. By the end of this chapter, you will be able to create a data frame, select interesting parts of a data frame, and order a data frame according to certain variables.
6
Lists
As opposed to vectors, lists can hold components of different types, just as your to-do lists can contain different categories of tasks. This chapter will teach you how to create, name, and subset these lists.
R
Introduction to R
Course
Complete
