Communicating with Data in the Tidyverse

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
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Clock4 HoursPlay15 VideosCode53 ExercisesGroup17,654 Learners
Database4350 XP

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Course Description

They say that a picture is worth a thousand words. Indeed, successfully promoting your data analysis is not only a matter of accurate and effective graphics, but also of aesthetics and uniqueness. This course teaches you how to leverage the power of ggplot2 themes for producing publication-quality graphics that stick out from the mass of boilerplate plots out there. It shows you how to tweak and get the most out of ggplot2 in order to produce unconventional plots that draw attention on social media. In the end, you will combine that knowledge to produce a slick and custom-styled report with RMarkdown and CSS – all of that within the powerful tidyverse.

  1. 1

    Custom ggplot2 themes

    Free
    In this chapter, you will have a first look at the data you're going to work with throughout this course: the relationship between weekly working hours and monetary compensation in European countries, according to the International Labour Organization (ILO). After that, you'll dive right in and discover a stunning correlation by employing an exploratory visualization. You will then apply a custom look to that graphic – you'll turn an ordinary plot into an aesthetically pleasing and unique data visualization.
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  2. 2

    Creating a custom and unique visualization

    Barcharts, scatter plots, and histograms are probably the most common and effective data visualizations. Yet, sometimes, there are even better ways to visually highlight the finding you want to communicate to your audience. So-called "dot plots" make us better grasp and understand changes in data: development over time, for example. In this chapter, you'll build a custom and unique visualization that emphasizes and explains exactly one aspect of the story you want to tell.
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  3. 3

    Introduction to RMarkdown

    Back in the old days, researchers and data analysts used to generate plots in R and then tediously copy them into their LaTeX or Word documents. Nowadays, whole reports can be produced and reproduced from within R and RStudio, using the RMarkdown language – combining R chunks, formatted prose, tables and plots. In this chapter, you'll take your previous findings, results, and graphics and integrate them into such a report to tell the story that needs to be told.
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  4. 4

    Customizing your RMarkdown report

    Your boss, your client, or your professor usually expects your results to be accurate and presented in a clear and concise structure. However, coming up with a nicely formatted and unique report on top of that is certainly a plus and RMarkdown can be customized to accomplish this. In this last chapter, you'll take your report from the last chapter and brand it with your own custom and unique style.
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In the following tracks
Data Analyst Tidyverse Fundamentals
Collaborators
Yashas RoyChester Ismay
Timo Grossenbacher Headshot

Timo Grossenbacher

Project Lead Automated Journalism at Tamedia
Timo Grossenbacher is a project lead for automated journalism at Swiss publisher Tamedia. Prior to that, he used to be a data journalist working with the Swiss Public Broadcast (SRF), where he used scripting and databases for almost every data-driven story he published. He also teaches data journalism at the University of Zurich and is the creator of rddj.info – resources for doing data journalism with R. Follow him at grssnbchr on Twitter or visit his personal website.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA

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