Introduction to R Programming and Tidyverse

Introduction to R Programming and Tidyverse

This course is part of Expressway to Data Science: R Programming and Tidyverse Specialization

Instructor: Jane Wall

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4 modules

Gain insight into a topic and learn the fundamentals.

4.2

(46 reviews)

Beginner level

Recommended experience

Flexible schedule

Approx. 23 hours

Learn at your own pace

Earn a Certificate

With paid plans

4 modules

Gain insight into a topic and learn the fundamentals.

4.2

(46 reviews)

Beginner level

Recommended experience

Flexible schedule

Approx. 23 hours

Learn at your own pace

Earn a Certificate

With paid plans

What you'll learn

By successfully completing this course, you will be able to write functions in R.
You will be able to analyze and visualize a data set.
You will be able to use RMarkdown to share documents in reports with the global R community.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

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This course is part of the Expressway to Data Science: R Programming and Tidyverse Specialization

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

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There are 4 modules in this course

This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:

• you want to do data analysis but don’t know programming • you know programming but aren’t familiar with R • you know some R programming but want to learn the tidyverse verbs You will learn to do data visualization and analysis in a reproducible manner and use functions that allow your code to be easily read and understood. You will use RMarkdown to create nice documents and reports that execute your code freshly every time it’s run and that capture your thoughts about the data along the way. This course has been designed for learners from non-STEM backgrounds to help prepare them for more advanced data science courses by providing an introduction to programming and to the R language. I am excited for you to join me on the journey! The course logo was created using images of stickers from the RStudio shop. Please visit https://swag.rstudio.com/s/shop.

In the first module of this course, you will install and configure R and RStudio. You will review the fundamentals of R and reproducibility, install R packages required for the course, and input basic commands using the RStudio console. Finally, you will create an RMarkdown document - the deliverable for this module.

What's included

5 videos6 readings2 assignments1 peer review1 ungraded lab6 plugins

5 videosTotal 24 minutes

Course Introduction3 minutesPreview module
Configuring RStudio3 minutes
Installing R Packages5 minutes
RMarkdown Overview2 minutes
Creating an RMarkdown Document8 minutes

6 readingsTotal 60 minutes

Welcome and Course Organization10 minutes
R Resources10 minutes
Installing R and RStudio10 minutes
Fundamentals of R Summary10 minutes
Reproducible Research - Why and How10 minutes
Helpful Tips and Resources on RMarkdown10 minutes

2 assignmentsTotal 30 minutes

R Fundamentals15 minutes
RMarkdown15 minutes

1 peer reviewTotal 60 minutes

RMarkdown60 minutes

1 ungraded labTotal 60 minutes

RMarkdown Lab60 minutes

6 pluginsTotal 171 minutes

R for Data Science - Introduction1 minute
RStudio - What and Why15 minutes
Hands-On Programming with R - Chapter 260 minutes
R for Data Science - Chapter 430 minutes
Reproducible Research20 minutes
R for Data Science - RMarkdown45 minutes

In this module, we will explore functions in R. You will review the syntax of functions and best practices of function creation. You will also practice writing functions with default arguments and argument validation.

What's included

10 videos7 readings2 assignments1 peer review4 ungraded labs

10 videosTotal 50 minutes

Our First Function10 minutesPreview module
Naming a Function2 minutes
Function Inputs3 minutes
A Change in Function Requirements4 minutes
Conditional Execution7 minutes
Multiple Conditions5 minutes
For Loops1 minute
Checking Inputs4 minutes
Function Output3 minutes
Introduction to Pipes6 minutes

7 readingsTotal 37 minutes

Resources2 minutes
Writing a Function10 minutes
Statement Conditions5 minutes
The Switch Statement5 minutes
Using the Stop Function5 minutes
Return Specific Values5 minutes
Using Pipes5 minutes

2 assignmentsTotal 30 minutes

Functions15 minutes
Input Checking and Outputs15 minutes

1 peer reviewTotal 60 minutes

Functions60 minutes

4 ungraded labsTotal 240 minutes

Practice Problems: Set 160 minutes
Practice Problems: Set 260 minutes
Practice Problems: Set 360 minutes
Functions60 minutes

In this module, you will be introduced to ggplot2 - an R package for data visualization. You will explore the different grammatical elements and aesthetic mappings (layers) that are essential to visualize data in ggplot2.

What's included

7 videos2 readings1 assignment1 peer review1 ungraded lab

7 videosTotal 31 minutes

Introduction to ggplot25 minutesPreview module
Aesthetics3 minutes
Geometric Objects4 minutes
Statistical Transformations5 minutes
Position Adjustments5 minutes
Facets3 minutes
Coordinate Systems4 minutes

2 readingsTotal 20 minutes

Resources10 minutes
Using ggplot210 minutes

1 assignmentTotal 30 minutes

Layers30 minutes

1 peer reviewTotal 60 minutes

Data Visualization60 minutes

1 ungraded labTotal 60 minutes

Data Visualization60 minutes

In the final module of this course, you will be introduced to data analysis using dplyr. You will learn and practice with the many dplyr verbs including select, filter, arrange, mutate, group_by, and summarize.

What's included

14 videos3 readings2 assignments1 peer review3 ungraded labs

14 videosTotal 78 minutes

Introduction to dplyr5 minutesPreview module
Chaining Functions4 minutes
Selecting Variables (Columns)6 minutes
Conditionally Selecting Rows5 minutes
Selecting Rows by Location7 minutes
Arrange Rows by Value4 minutes
Renaming Data3 minutes
Distinct, Mutate, and Transmute4 minutes
Rename, Relocate, and Summarize4 minutes
Summary Functions8 minutes
Counting Observations5 minutes
Grouping Variables: Part 15 minutes
Grouping Variables: Part 25 minutes
Grouping Variables: Part 35 minutes

3 readingsTotal 22 minutes

Main dplyr Functions10 minutes
R and NA2 minutes
Grouping datasets10 minutes

2 assignmentsTotal 35 minutes

dplyr Verbs5 minutes
Select, Filter, and Arrange30 minutes

1 peer reviewTotal 60 minutes

Data Analysis60 minutes

3 ungraded labsTotal 180 minutes

Practice Problems: Set 460 minutes
Practice Problems: Set 560 minutes
Data Analysis60 minutes

Instructor

Instructor ratings

4.1 (13 ratings)

Jane Wall

University of Colorado Boulder

6 Courses33,057 learners

Offered by

University of Colorado Boulder

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Learner reviews

4.2

46 reviews

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Reviewed on Jun 19, 2023

The course was a solid introduction to R Markdown using the R language. It covers using the Tidyverse libraries for basic analysis and visualizations.

Reviewed on Feb 2, 2024

Helpful content! However, the sequence of practice questions and lecture contents are not fully aligned.

Reviewed on Nov 2, 2022

Very good course for first time R learners. Challenging but doable with some determination and attention to detail.

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