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Collin Paschall
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/sqzmud/getting
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What's inside
Syllabus
Getting Started with Data Management and Visualization with R
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.
Read more
Syllabus
Getting Started with Data Management and Visualization with R
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.
Read more
Using the Tidyverse packages
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.
Using R Markdown to Make Reports
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores data visualization, which is standard in research, academia, and industry
Taught by experienced data science instructors
Provides a practical introduction to data manipulation using R and tidyverse
Teaches learners how to create reproducible reports using R Markdown
Requires learners to have basic computing skills but limited programming experience
Does not cover advanced topics in data visualization
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Reviews summary
Best r data visualization course
Learners say the Getting Started with Data Visualization in R course is largely positive. It provides a strong foundation for beginners, covering the basics of R and data visualization in a logical and well-structured manner. The instructor is clear, engaging, and provides detailed explanations. Assignments are helpful for practicing new skills, and the course includes valuable resources for self-study. Overall, learners highly recommend this course for those looking to learn R and data visualization.
Includes exercises
"The class was interesting and the assignments were well designed."
Provides a cheat sheet
"Cheat sheet very helpful"
Offers peer review on assignments
"Good exercises and great format having peer-reviewed submissions."
Assignments are helpful for practicing new skills
"The assignments/quizzes/projects set forth are good enough to give you enough a good understanding of the language, its syntax and so on."
"Through the courses in this specialization, you will use real data sets (most related to politics)."
Includes valuable resources for self-study
"The diversity of resources it provides - free accessible online text books, relevant websites and so on."
"The instructor understands that a beginner might get overwhelmed by the vast functionality that the language offers and addresses this time and again throughout the course, in effect mollifying any fears or anxieties."
Provides a strong foundation for beginners
"It is quite compact but nevertheless pretty lucid, and well prepared."
"This class is exactly what I was looking for. I have been trying to teach myself R, and I have made huge progress using this class."
Suitable for beginners with no coding experience
"Definitely worth the time and effort - also suitable for absolute beginners with no coding experience."
"This course effectively enhance your knowledge and skill in data processing using R.This course help me a lot."
"I enjoyed this course. First, some information about my previous knowledge: I took a previous course of R, and it was extremely challenging because the course covered too much, leaving little time for clear explanations."
Instructor is clear, engaging, and provides detailed explanations
"Colin is clear, straightforward, and detailed in his explanations, the assignment relates to the content of the videos and helps practice new skills."
"The instructor is clear and the assignments/quizzes/projects set forth are good enough to give you enough a good understanding of the language, its syntax and so on."
Course may take longer than expected to complete
"Great course and material. I feel though that it takes much more time than announced to work yourself through the readings."
Students desire an auto grader
"Very good course but please use auto graded for passing the course smoothly, peer review sometimes really slow"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Getting Started with Data Visualization in R with these
activities:
Configure RStudio and Install Tidyverse
Show steps
Prepares students to use the software and tools they will need to succeed through this course.
Browse courses on
RStudio
Show steps
-
Install RStudio
-
Install Tidyverse
-
Create a new RStudio project
-
Load the Tidyverse library
Organize Course Materials
Show steps
Helps students stay organized and prepared for the course.
Show steps
-
Create a folder for the course
-
Download and save course materials
-
Create a schedule for completing the course
Review Statistics and Data Analysis
Show steps
Refreshes the student's knowledge of statistics and data analysis, which will be essential for success in this course.
Browse courses on
Statistics
Show steps
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review R Markdown
Show steps
Ensures that students have the necessary knowledge of R Markdown to succeed in this course.
Browse courses on
R Markdown
Show steps
Manipulating Data with Tidyverse
Show steps
Reinforces the student's understanding of data manipulation using the Tidyverse package.
Browse courses on
Data Manipulation
Show steps
-
Practice filtering data
-
Practice arranging data
-
Practice grouping data
-
Practice summarizing data
-
Practice joining data
Study Group for R and Data Visualization
Show steps
Provides an opportunity for students to collaborate and learn from each other, improving their understanding of the course material.
Show steps
-
Form a study group
-
Meet regularly
-
Discuss course material
-
Work on projects together
Write a Blog Post on Data Visualization
Show steps
Encourages students to synthesize and communicate their learning through writing.
Show steps
-
Choose a topic
-
Research the topic
-
Write a blog post
-
Publish the blog post
Create a Data Visualization with R and RStudio
Show steps
Provides an opportunity for students to apply the skills they have learned to create a meaningful data visualization.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset
-
Create a visualization
-
Interpret the results
-
Write a report
Volunteer with a Data-Driven Organization
Show steps
Provides students with an opportunity to apply their skills in a real-world setting, enhancing their learning and employability.
Show steps
-
Find a volunteer opportunity
-
Apply for the position
-
Complete the volunteer training
-
Volunteer on a regular basis
-
Reflect on your experience
Configure RStudio and Install Tidyverse
Show steps
Prepares students to use the software and tools they will need to succeed through this course.
Browse courses on
RStudio
Show steps
Show steps
Show steps
- Install RStudio
- Install Tidyverse
- Create a new RStudio project
- Load the Tidyverse library
Organize Course Materials
Show steps
Helps students stay organized and prepared for the course.
Show steps
Show steps
Show steps
- Create a folder for the course
- Download and save course materials
- Create a schedule for completing the course
Review Statistics and Data Analysis
Show steps
Refreshes the student's knowledge of statistics and data analysis, which will be essential for success in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
Six other activities
Expand to see all activities and additional details
Show all nine activities
Review R Markdown
Show steps
Ensures that students have the necessary knowledge of R Markdown to succeed in this course.
Browse courses on
R Markdown
Show steps
Show steps
Show steps
Manipulating Data with Tidyverse
Show steps
Reinforces the student's understanding of data manipulation using the Tidyverse package.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Practice filtering data
- Practice arranging data
- Practice grouping data
- Practice summarizing data
- Practice joining data
Study Group for R and Data Visualization
Show steps
Provides an opportunity for students to collaborate and learn from each other, improving their understanding of the course material.
Show steps
Show steps
Show steps
- Form a study group
- Meet regularly
- Discuss course material
- Work on projects together
Write a Blog Post on Data Visualization
Show steps
Encourages students to synthesize and communicate their learning through writing.
Show steps
Show steps
Show steps
- Choose a topic
- Research the topic
- Write a blog post
- Publish the blog post
Create a Data Visualization with R and RStudio
Show steps
Provides an opportunity for students to apply the skills they have learned to create a meaningful data visualization.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset
- Create a visualization
- Interpret the results
- Write a report
Volunteer with a Data-Driven Organization
Show steps
Provides students with an opportunity to apply their skills in a real-world setting, enhancing their learning and employability.
Show steps
Show steps
Show steps
- Find a volunteer opportunity
- Apply for the position
- Complete the volunteer training
- Volunteer on a regular basis
- Reflect on your experience
Career center
Learners who complete Getting Started with Data Visualization in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, interprets, and presents data to help organizations make informed decisions. This course can help you build a foundation in data visualization, which is a critical skill for Data Analysts. This course covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. This course can help you build a foundation in data visualization, which is a critical skill for Data Scientists. This course covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data to help organizations make informed decisions. This course can help you build a foundation in data visualization, which is a critical skill for Statisticians. This course covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Business Analyst
Business Analyst
A Business Analyst identifies and solves business problems by analyzing data and developing solutions. This course can help you build a foundation in data visualization, which is a critical skill for Business Analysts. This course covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Financial Analyst
Financial Analyst
A Financial Analyst provides financial advice and guidance to individuals and organizations. This course can help you build a foundation in data visualization, which is a critical skill for Financial Analysts. This course covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Market Researcher
Market Researcher
A Market Researcher gathers and analyzes data to understand consumer behavior and market trends. This course can help you build a foundation in data visualization, which is a critical skill for Market Researchers. This course covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Actuary
Actuary
An Actuary uses mathematical and statistical methods to assess risk and uncertainty. This course may be useful for Actuaries, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and patterns of health and disease in populations. This course may be useful for Epidemiologists, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be useful for Software Engineers, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines and infrastructure. This course may be useful for Data Engineers, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course may be useful for Database Administrators, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Web Developer
Web Developer
A Web Developer designs and develops websites and web applications. This course may be useful for Web Developers, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Graphic designer
Graphic designer
A Graphic Designer creates visual concepts, using computer software or by hand, to communicate ideas that inspire, inform, and captivate consumers. This course may be useful for Graphic Designers, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
UX Designer
UX Designer
A UX Designer designs and evaluates the user experience of websites, apps, and other digital products. This course may be useful for UX Designers, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
Product Manager
Product Manager
A Product Manager is responsible for the overall success of a product, from its inception to its launch and beyond. This course may be useful for Product Managers, as it covers the basics of importing data into R, manipulating data using tools from the popular tidyverse package, and making simple reports using R Markdown.
For more career information including salaries, visit:
OpenCourser.com/course/sqzmud/getting
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Getting Started with Data Visualization in R.
Save
This classic text and a standard reference for data visualization best practices. It valuable resource to have on hand as a supplement to this course.
Is an in-depth reference and guide to ggplot2, the R package used for graphics in this course.
Provides the theoretical foundation for ggplot2, the graphics package used in this course. It useful resource for those who want more background on the 'grammar of graphics' that underpins much of modern data visualization.
Save
Provides a good overview of R and is written by the creators of the tidyverse, the software package used in this course for data cleaning and manipulation.
Save
Provides detailed instruction and reference information about R Markdown, the system used in this course for making reports.
Save
This cookbook provides a variety of recipes for creating different types of graphs and visualizations in R.
Focuses primarily on general principles of data visualization, rather than any specific software. It provides good background knowledge and guidelines for how to think about data visualization.
While this book does not focus specifically on R, it does provide useful background information in data visualization, especially the early chapters. The examples are done in Python, but the information is readily transferable to R.
Save
Provides a comprehensive guide to the RStudio development environment, which will be used in this course.
Save
Good general reference volume for more in-depth information about specific R functions and methods. It useful tool to turn to if you encounter something in your work in this course that is not covered by the material.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sqzmud/getting
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Level
Introductory
Via
Coursera
Institution
Johns Hopkins University
Instructor
Collin Paschall
Language
English
Transcripts
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This course belongs to a
collection
called
Data Visualization & Dashboarding with R. It's comprised of five courses:
- Getting Started with Data Visualization in R (viewing)
- Data Visualization Capstone
- Advanced Data Visualization with R
- Publishing Visualizations in R with Shiny and flexdashboard
- Data Visualization in R with ggplot2
Good to know
Explores data visualization, which is standard in research, academia, and industry
Taught by experienced data science instructors
Provides a practical introduction to data manipulation using R and tidyverse
Teaches learners how to create reproducible reports using R Markdown
Requires learners to have basic computing skills but limited programming experience
Does not cover advanced topics in data visualization
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