Visualizing Data in the Tidyverse from Coursera

What's inside

Syllabus

About This Course

Data visualization is a critical part of any data science project. Once data have been imported and wrangled into place, visualizing your data can help you get a handle on what’s going on in the dataset. Similarly, once you’ve completed your analysis and are ready to present your findings, data visualizations are a highly effective way to communicate your results to others.

Plot Types

There are many types of plots that are helpful. We’ll discuss a few basic ones below and will include links to a few galleries where you can get a sense of the many different types of plots out there.

Making Good Plots

The goal of data visualization in data analysis is to improve understanding of the data. As mentioned in the last lesson, this could mean improving our own understanding of the data or using visualization to improve someone else’s understanding of the data. We discussed some general characteristics and basic types of plots in the last lesson, but here we will step through a number of general tips for making good plots. When generating exploratory or explanatory plots, you’ll want to ensure information being displayed is being done so accurately and in a away that best reflects the reality within the dataset. Here, we provide a number of tips to keep in mind when generating plots.

Plot Generation Process

Having discussed some general guidelines, there are a number of questions you should ask yourself before making a plot. There are three main questions you should ask any time you create a visual display of your data. We will discuss these three questions below.

ggplot2 Basics

R was initially developed for statisticians, who often are interested in generating plots or figures to visualize their data. As such, a few basic plotting features were built in when R was first developed. These are all still available; however, over time, a new approach to graphing in R was developed. This new approach implemented what is known as the grammar of graphics, which allows you to develop elegant graphs flexibly in R. Making plots with this set of rules requires the R package ggplot2. This package is a core package in the tidyverse, so as along as the tidyverse has been loaded in, you’re ready to get started.

ggplot2: Customization

So far, we have walked through the steps of generating a number of different graphs (using different geoms) in ggplot2. We discussed the basics of mapping variables to your graph to customize its appearance or aesthetic (using size, shape, and color within aes()). Here, we’ll build on what we’ve previously learned to really get down to how to customize your plots so that they’re as clear as possible for communicating your results to others. The skills learned in this lesson will help take you from generating exploratory plots that help you better understand your data to explanatory plots – plots that help you communicate your results to others. We’ll cover how to customize the colors, labels, legends, and text used on your graph. Since we’re already familiar with it, we’ll continue to use the diamonds dataset that we’ve been using to learn about ggplot2.

Tables

While we have focused on figures here so far, tables can be incredibly informative at a glance too. If you are looking to display summary numbers, a table can also visually display information.

ggplot2: Extensions

Beyond the many capabilities of ggplot2, there are a few additional packages that build on top of ggplot2’s capabilities. We’ll introduce a few packages here so that you can (1) directly annotate points on plots (ggrepel and directlabels); (2) combine multiple plots (cowplot + patchwork); and (3) generate animated plots (gganimate). These are referred to as ggplot2 extensions There are dozens of additional ggplot2 extensions available if you’d like to explore other plotting options beyond what is covered here!

Case Studies

At this point, we’ve done a lot of work with our case studies. We’ve introduced the case studies, read them into R, and have wrangled the data into a usable format. Now, we get to peek at the data using visualizations to better understand each dataset’s observations and variables! When working through the steps of the case studies, you can use either RStudio on your own computer or Coursera lab spaces provided for each case study.

Project: Visualizing Data in the Tidyverse

In this project, you will practice exploring data and creating data visualizations with the tidyverse using nutrition and sales data from fast food restaurants in 2018.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Designed for those with beginner to intermediate knowledge of R programming

Provides guidance for creating stunning data visualizations

Teaches the industry-standard ggplot2 R package

Covers basic to advanced concepts, including plot customization and extensions

Instructors are recognized for their expertise in data visualization

Requires familiarity with the R programming language

Reviews summary

Highly rated tidyverse course

Learners say this highly rated Tidyverse course includes well explained concepts and engaging assignments. Students praise the nice examples and find the course to be well done.

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 Visualizing Data in the Tidyverse with these activities:

Review Basic R Programming Concepts

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A solid foundation in R programming is crucial for success in this course. This activity provides an opportunity to refresh your knowledge of essential R concepts, ensuring that you are well-prepared for the upcoming lessons.

Browse courses on R Programming

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Review online tutorials or documentation on basic R programming
Practice writing simple R scripts to manipulate data
Seek help from classmates or online forums if needed

Organize and Review Course Materials

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Staying organized and reviewing materials regularly is essential for effective learning. This activity encourages you to compile and review the course materials to enhance your understanding and retention.

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Create a dedicated folder or notebook for the course
Organize the materials by topic or week
Review the materials regularly, taking notes and highlighting important concepts

Connect with Experienced Data Visualization Practitioners

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Seeking guidance from experienced professionals can accelerate your learning and provide valuable insights. This activity encourages you to reach out to data visualization experts who can offer support and advice.

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Attend industry events or online meetups related to data visualization
Join online communities and forums dedicated to data visualization
Reach out to individuals whose work you admire and request guidance

Four other activities

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Learn about ggplot2 Fundamentals on Coursera

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This tutorial will help you grasp the basics of ggplot2, a powerful data visualization library in R, which will be essential for creating informative and visually appealing data visualizations throughout the course.

Browse courses on Ggplot2

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Enroll in the Coursera course 'Data Visualization with ggplot2'
Watch the video lectures and complete the hands-on exercises
Practice creating different types of plots using ggplot2

Build a Data Visualization Resource Kit

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Creating a compilation of resources will provide you with a valuable reference tool throughout the course and beyond. This activity encourages you to gather and organize a collection of articles, tutorials, and tools related to data visualization.

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Identify and collect resources that cover various aspects of data visualization
Organize the resources into categories or topics
Create a document or online repository to store and share the resource kit

Practice Creating Data Visualizations with ggplot2

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Regular practice is crucial for mastering data visualization techniques. This activity provides ample opportunities to apply your ggplot2 knowledge and refine your skills.

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Find a dataset that interests you and import it into R
Use ggplot2 to create various types of plots, such as scatterplots, histograms, and bar charts
Experiment with different aesthetics and customizations to enhance the visual appeal of your plots
Share your visualizations with others and seek feedback

Attend a Data Visualization Workshop

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Workshops provide an immersive and interactive learning experience. Participating in a data visualization workshop will allow you to learn from experts, network with peers, and enhance your skills in a hands-on environment.

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Research and identify data visualization workshops that align with your interests
Register and attend the workshop
Actively participate in the exercises and discussions
Follow up with the workshop organizers or attendees to continue learning

Career center

Learners who complete Visualizing Data in the Tidyverse will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst is a professional who uses data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. Data Analysts use their findings to make recommendations to businesses on how to improve their operations. This course can help you become a Data Analyst by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

See salaries and explore the career path for Data Analyst

Data Scientist

A Data Scientist is a professional who uses data to build predictive models. They use these models to help businesses make better decisions. Data Scientists use a variety of statistical and machine learning techniques to build their models. This course can help you become a Data Scientist by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Statistician

A Statistician is a professional who uses data to make inferences about the world. They use statistical methods to analyze data and draw conclusions about the population from which the data was collected. Statisticians work in a variety of fields, including healthcare, finance, and education. This course can help you become a Statistician by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Business Intelligence Analyst

A Business Intelligence Analyst is a professional who uses data to help businesses make better decisions. They analyze data to identify trends and patterns, and they use this information to make recommendations to businesses on how to improve their operations. This course can help you become a Business Intelligence Analyst by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

See salaries and explore the career path for Business Intelligence Analyst

Data Engineer

A Data Engineer is a professional who designs and builds data systems. They work with data scientists and other professionals to ensure that data is stored, processed, and analyzed efficiently. Data Engineers use a variety of programming languages and tools to build their systems. This course can help you become a Data Engineer by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

See salaries and explore the career path for Data Engineer

Financial Analyst

A Financial Analyst is a professional who analyzes financial data to make investment recommendations. They use a variety of financial metrics and models to evaluate the performance of companies and make recommendations on whether to buy, sell, or hold stocks. This course can help you become a Financial Analyst by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Operations Research Analyst

An Operations Research Analyst is a professional who uses mathematical models to solve business problems. They use a variety of techniques, including optimization, simulation, and regression analysis. Operations Research Analysts work in a variety of industries, including healthcare, finance, and manufacturing. This course can help you become an Operations Research Analyst by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Product Manager

A Product Manager is a professional who is responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. Product Managers use a variety of data analysis techniques to track the progress of their products and make decisions about how to improve them. This course can help you become a Product Manager by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Quantitative Analyst

A Quantitative Analyst is a professional who uses mathematical and statistical models to analyze financial data. They use their findings to make investment recommendations and develop trading strategies. Quantitative Analysts work in a variety of financial institutions, including hedge funds, investment banks, and asset management companies. This course can help you become a Quantitative Analyst by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Market Researcher

A Market Researcher is a professional who conducts research to understand the needs and wants of consumers. They use a variety of research methods, including surveys, focus groups, and interviews. Market Researchers use their findings to help businesses develop new products and services. This course can help you become a Market Researcher by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Web Analyst

A Web Analyst is a professional who analyzes data to improve the performance of websites. They use a variety of data analysis techniques to track the traffic to a website and identify areas for improvement. Web Analysts work in a variety of industries, including e-commerce, marketing, and advertising. This course can help you become a Web Analyst by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Data Architect

A Data Architect is a professional who designs and manages data systems. They work with data engineers and other professionals to ensure that data is stored, processed, and analyzed efficiently. Data Architects use a variety of data analysis techniques to track the performance of their systems and make decisions about how to improve them. This course can help you become a Data Architect by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Database Administrator

A Database Administrator is a professional who manages and maintains databases. They work with a variety of database technologies to ensure that data is stored, processed, and analyzed efficiently. Database Administrators use a variety of data analysis techniques to track the performance of their databases and make decisions about how to improve them. This course can help you become a Database Administrator by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Data Journalist

A Data Journalist is a professional who uses data to tell stories. They use a variety of data analysis techniques to find insights in data and then use those insights to write articles, blog posts, and other forms of content. Data Journalists work in a variety of media outlets, including newspapers, magazines, and online news organizations. This course can help you become a Data Journalist by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

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Software Engineer

A Software Engineer is a professional who designs, develops, and maintains software systems. They work with a variety of programming languages and tools to build software that meets the needs of users. Software Engineers use a variety of data analysis techniques to track the performance of their software and make decisions about how to improve it. This course can help you become a Software Engineer by teaching you the skills you need to collect, clean, and analyze data. You will also learn how to use data visualization tools to communicate your findings to others.

See salaries and explore the career path for Software Engineer