Getting Started with Data Visualization in R

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Getting Started with Data Visualization in R

This course is part of Data Visualization & Dashboarding with R Specialization

Instructor: Collin Paschall

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3 modules

Gain insight into a topic and learn the fundamentals.

311 reviews

Beginner level

No prior experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

3 modules

Gain insight into a topic and learn the fundamentals.

311 reviews

Beginner level

No prior experience required

Flexible schedule

1 week at 10 hours a week

Learn at your own pace

94%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the Data Visualization & Dashboarding with R Specialization

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

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There are 3 modules in this course

Data visualization is a critical skill for anyone that routinely using quantitative data in his or her work - which is to say that data visualization is a tool that almost every worker needs today. One of the critical tools for data visualization today is the R statistical programming language. Especially in conjunction with the tidyverse software packages, R has become an extremely powerful and flexible platform for making figures, tables, and reproducible reports. However, R can be intimidating for first time users, and there are so many resources online that it can be difficult to sort through without guidance.

To fill that need, this course is intended for learners who have little or no experience with R but who are looking for an introduction to this tool. By the end of this course, students will be able to import data into R, manipulate that data using tools from the popular tidyverse package, and make simple reports using R Markdown. The course is designed for students with good basic computing skills, but limited if any experience with programming.

In this module, we will get set up with R to process data for visualizations. You should begin by watching the introductory videos in each lesson. Then, carefully review the readings and reference materials provided. Once you have done that, I recommend watching the videos again to check your understanding. You will take a few quizzes as you progress through the material to make sure you are keeping up.

What's included

8 videos7 readings4 assignments1 peer review

8 videosTotal 40 minutes

Introduction to the Course3 minutes
Introduction to R and Software Installation6 minutes
Basic R, Part 14 minutes
Basic R Part 26 minutes
Functions in R3 minutes
Dataframes8 minutes
Basics of Importing Data into R6 minutes
Base R Visualizations5 minutes

7 readingsTotal 107 minutes

How to Watch the Videos2 minutes
The RStudio Cheat Sheet20 minutes
Base R Cheat Sheet20 minutes
R for Data Science, Chapter 420 minutes
A Note on File Paths10 minutes
CCES Data5 minutes
Cookbook for R: Basic Plots30 minutes

4 assignmentsTotal 35 minutes

Install R and Setup Quiz10 minutes
Base R and Functions Quiz10 minutes
Dataframes and Importing Data in R10 minutes
Base R Visualization Quiz5 minutes

1 peer reviewTotal 30 minutes

Base R Peer Review Practice30 minutes

In this module, we will use functions from the tidyverse to manipulate data. You should begin by watching the introductory videos in each lesson. Then, carefully review the readings and reference materials provided. Once you have done that, I recommend watching the videos again to check your understanding. You will take a few quizzes as you progress through the material to make sure you are keeping up.

What's included

5 videos7 readings2 assignments1 peer review

5 videosTotal 27 minutes

Introduction to the tidyverse6 minutes
Data import and structure in the tidyverse5 minutes
Filtering, selecting, recoding, renaming, and piping6 minutes
Recoding, Renaming, and Calculating Columns6 minutes
Grouping and summarizing data4 minutes

7 readingsTotal 160 minutes

R for Data Science, Introduction and Part II: Wrangle40 minutes
Data Import Cheat Sheet10 minutes
tibble, readr, and tidyr Documentation30 minutes
R for Data Science, Chapter 530 minutes
Data Wrangling Cheat Sheet10 minutes
Getting Started with dplyr20 minutes
Learning to Read R Documentation20 minutes

2 assignmentsTotal 30 minutes

Tidyverse Introduction Quiz15 minutes
Manipulating Variables and Creating Summaries Quiz15 minutes

1 peer reviewTotal 30 minutes

tidyverse Practice Peer Review30 minutes

In this module, we learn to make reproducible reports using R Markdown. You should begin by watching the introductory videos in each lesson. Then, carefully review the readings and reference materials provided. Once you have done that, I recommend watching the videos again to check your understanding. You will take a few quizzes as you progress through the material to make sure you are keeping up. Then, at the end of the module, you will submit an assignment for peer review that covers all of the material in this course.

What's included

3 videos8 readings3 assignments1 peer review

3 videosTotal 15 minutes

Creating reports with R Markdown6 minutes
R Markdown syntax and tables5 minutes
qplots and closing thoughts4 minutes

8 readingsTotal 90 minutes

Note on Installing LaTex10 minutes
Note on Previewing Figures in R Markdown10 minutes
R for Data Science, Chapter 2710 minutes
R Markdown Cheat Sheet10 minutes
R Markdown Reference Guide10 minutes
R Markdown: The Definitive Guide10 minutes
qplot() Documentation20 minutes
A Note About Peer Review Assignments10 minutes

3 assignmentsTotal 20 minutes

R Markdown Intro Quiz10 minutes
R Markdown Syntax Quiz5 minutes
Incorporating Tables and Figures Quiz5 minutes

1 peer reviewTotal 120 minutes

Your R Markdown Report120 minutes

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Instructor

Instructor ratings

(130 ratings)

Collin Paschall

Johns Hopkins University

5 Courses45,548 learners

Offered by

Johns Hopkins University

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Reviewed on May 31, 2021

This course was very well-organized and informative. It was a very good introduction to the material.

Reviewed on Jul 9, 2023

The class was interesting and the assignments were well designed.

Reviewed on Mar 20, 2021

Good exercises and great format having peer-reviewed submissions.Might have done visualizaiton in base R, first, but otherwise great intro.

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