Data Visualization and Transformation with R

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Data Visualization and Transformation with R

This course is part of Data Science with R Specialization

Instructors: Mine Çetinkaya-Rundel

2,628 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

No prior experience required

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Transform, visualize, summarize, and analyze data in R, with packages from the Tidyverse, using RStudio
Carry out analyses in a reproducible and shareable manner with Quarto
Learn to effectively communicate results through an optional written project version controlled with Git and hosted on GitHub

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

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This course is part of the Data Science with R 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 3 modules in this course

Learn the foundations of data science by exploring, transforming, and visualizing data with R. In this course, you’ll develop core skills in exploratory data analysis and statistical thinking including: using visualizations to uncover patterns, identifying trends, and generating insights.

You’ll gain hands-on experience with Tidyverse packages in R, work in RStudio, and create reproducible reports with Quarto. Along the way, you’ll also learn version control practices with Git and GitHub to document and share your work. By the end of this course, you’ll be able to transform and summarize data, craft clear and informative graphics, and communicate your findings through professional, reproducible workflows - laying the groundwork for all your future data science projects.

Hello World! In the first module, you will learn about what data science is and how data science techniques are used to make meaning from data and inform data-driven decisions. There is also discussion around the importance of reproducibility in science and the techniques used to achieve this. Next, you will learn the technology languages of R, RStudio, Quarto, and GitHub, as well as their role in data science and reproducibility.

What's included

4 videos11 readings1 assignment1 discussion prompt1 plugin

4 videosTotal 44 minutes

Welcome6 minutes
Code along :: My first data visualization - UN Votes10 minutes
Meet the toolkit: R + RStudio13 minutes
Meet the toolkit: Quarto16 minutes

11 readingsTotal 105 minutes

Course Overview10 minutes
Meet Your Instructors10 minutes
IMS :: Chapter 1 - Hello data10 minutes
Discussion Guidelines10 minutes
Report a problem with the course5 minutes
Get Ready to Compute with R and RStudio!10 minutes
Code along :: My first data visualization - UN Votes - Companion10 minutes
Code along :: My first data visualization - UN Votes - Companion (Complete)10 minutes
R4DS :: Chp 2 - Introduction10 minutes
R4DS :: Chp 28.1-28.5 - Quarto10 minutes
IMS :: Chapter 3 - Applications: Data 10 minutes

1 assignmentTotal 20 minutes

Welcome to Data Science Quiz20 minutes

1 discussion promptTotal 10 minutes

Meet the toolkit: Programming exercises (Optional Reflection)10 minutes

1 pluginTotal 15 minutes

Meet the toolkit: Programming exercises15 minutes

In our second module, we'll advance our understanding of R to set the stage for creating data visualizations using tidyverse’s data visualization package: ggplot2. We'll learn all about different data types and the appropriate data visualization techniques that can be used to plot these data. The majority of this module is to help best understand ggplot2 syntax and how it relates to the Grammar of Graphics. By the end of this module, you will have started building up the foundation of your statistical tool-kit needed to create basic data visualizations in R.

What's included

4 videos5 readings1 assignment1 discussion prompt1 plugin

4 videosTotal 45 minutes

Visualizing data10 minutes
Building a plot step-by-step with ggplot28 minutes
Code along :: First look at Palmer Penguins21 minutes
Grammar of graphics7 minutes

5 readingsTotal 50 minutes

R4DS :: Chp 1 - Data visualization - Sections 1.1 - 1.410 minutes
Code along :: First look at Palmer Penguins - Companion10 minutes
Code along :: First look at Palmer Penguins - Companion (Complete)10 minutes
R4DS :: Chp 1 - Data visualization - Sections 1.5 - 1.810 minutes
R4DS :: Chp 2 - Workflow: basics10 minutes

1 assignmentTotal 60 minutes

Data and Visualization Quiz60 minutes

1 discussion promptTotal 10 minutes

Bechdel + data visualization (Optional Reflection)10 minutes

1 pluginTotal 15 minutes

Bechdel + data visualization15 minutes

In this module, we will take a step back and learn about tools for transforming data that might not yet be ready for visualization as well as for summarizing data with tidyverse’s data wrangling package: dplyr. In addition to describing distributions of single variables, you will also learn to explore relationships between two or more variables. Finally, you will continue to hone your data visualization skills with plots for various data types.

What's included

8 videos15 readings1 assignment2 discussion prompts1 plugin

8 videosTotal 146 minutes

Grammar of data transformation7 minutes
Code along :: Flights and pipes34 minutes
Visualizing and summarizing categorical data14 minutes
Code along :: StarWars characters16 minutes
Visualizing and summarizing numerical data13 minutes
Visualizing and summarizing relationships between variables10 minutes
Code along :: Diving deeper with Palmer Penguins35 minutes
Meet the toolkit: Git + GitHub17 minutes

15 readingsTotal 150 minutes

R4DS :: Chp 3 - Data transformation - Sections 3.1 - 3.410 minutes
Code along :: Flights and pipes - Companion10 minutes
Code along :: Flights and pipes - Companion (Complete)10 minutes
IMS :: Chp 4 - Exploring categorical data10 minutes
R4DS :: Chp 3 - Data transformation - Section 3.510 minutes
Code along :: StarWars characters - Companion10 minutes
Code along :: StarWars characters - Companion (Complete)10 minutes
IMS :: Chp 5 - Exploring numerical data10 minutes
Code along :: Diving deeper with Palmer Penguins - Companion10 minutes
Code along :: Diving deeper with Palmer Penguins - Companion (Complete)10 minutes
IMS :: Chp 6 - Applications: Explore10 minutes
R4DS :: Chp 3 - Data transformation - Sections 3.6 and 3.710 minutes
Happy Git with R :: Chapter 1 - Why Git? Why GitHub?10 minutes
Exploring taxi rides in Chicago10 minutes
Share your learning experience10 minutes

1 assignmentTotal 60 minutes

Data Transformation Quiz60 minutes

2 discussion promptsTotal 20 minutes

Mario games + data visualization (Optional Reflection)10 minutes
Exploratory Data Analysis Reflection10 minutes

1 pluginTotal 15 minutes

Mario games + data visualization15 minutes

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Instructors

Mine Çetinkaya-Rundel

Duke University

11 Courses432,780 learners

Offered by

Duke University

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