Visualizing Data in the Tidyverse

Johns Hopkins University via Coursera

Go to Course: https://www.coursera.org/learn/tidyverse-visualize-data

Introduction

**Course Review: Visualizing Data in the Tidyverse on Coursera** In the realm of data science, the importance of data visualization cannot be overstated. The ability to effectively communicate findings through visual means can significantly enhance understanding and decision-making. For those looking to refine their skills in this area, the Coursera course **Visualizing Data in the Tidyverse** stands out as an excellent choice. This course harnesses the power of R and its tidyverse collection of packages to create aesthetically pleasing and informative visualizations. ### Course Overview **Visualizing Data in the Tidyverse** offers a comprehensive introduction to data visualization techniques, with a focus on the popular ggplot2 package. The course is designed for anyone who wishes to gain a clearer understanding of datasets through visual representation, whether you are a data analyst, statistician, or simply an enthusiast. The course emphasizes a practical approach: it not only teaches the theory behind data visualization but also provides hands-on opportunities to apply learned concepts using rich datasets. Throughout the curriculum, learners will explore foundational visualization types, the process of generating effective plots, and the essential grammar of graphics that underpins ggplot2. ### Syllabus Breakdown 1. **Plot Types**: The course begins by introducing various types of plots, enhancing learners' knowledge of visualization tools available at their disposal. 2. **Making Good Plots**: This section delves into the characteristics that define effective visualizations. It covers best practices and general tips, equipping learners with the skills necessary to convey accurate information visually. 3. **Plot Generation Process**: Essential questions prior to creating plots are discussed, allowing students to approach visualization thoughtfully rather than mechanically. 4. **ggplot2 Basics**: The course lays a solid foundation in ggplot2, showing how to create beautiful graphs using this powerful package within the tidyverse. 5. **ggplot2 Customization**: Beyond basic plotting, learners will discover how to customize the aesthetics of their plots to enhance clarity and interpretation, making it possible to transform exploratory plots into explanatory ones. 6. **Tables**: Although the focus is on graphical visualizations, the course emphasizes the role of tables in displaying summary statistics effectively. 7. **ggplot2 Extensions**: The course introduces additional ggplot2 packages to extend its capabilities, including creating animations and combining plots for intricate presentations. 8. **Case Studies**: Real-world data case studies allow students to apply their knowledge, using visualization to uncover insights and patterns inherent in complex datasets. 9. **Project**: The capstone project enables participants to synthesize their learning by visualizing nutrition and sales data from fast food restaurants, instilling both confidence and competence in their skills. ### Learning Experience The course is presented in an engaging and systematic manner, making it accessible for learners at all levels. The combination of lectures, hands-on exercises, and case studies ensures that students not only grasp the core concepts but also gain practical experience. Additionally, the Coursera platform provides a wealth of resources, including a supportive community, quizzes to test knowledge, and the opportunity to earn a certificate upon completion. The collaborative environment encourages interaction, allowing learners to share insights and problem-solve with peers. ### Recommendation I wholeheartedly recommend **Visualizing Data in the Tidyverse** for anyone interested in mastering data visualization in R. Whether you are embarking on a data science career, looking to bolster your analytical skills, or simply interested in transforming data into compelling stories, this course provides the tools and knowledge necessary to succeed. The course's integration of the tidyverse, particularly the ggplot2 package, enables learners to produce publications-worthy visualizations and fosters a deeper understanding of data. By the end of the course, you will not only feel proficient in visualizing data but also confident in your ability to communicate your findings effectively using R. In summary, this course is an invaluable resource that empowers learners to visualize data with clarity and creativity, making it a must-take for aspiring data analysts and scientists alike.

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.