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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.
Custom ggplot2 themesFree
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.Introduction to the data50 xpJoin the two data sets together100 xpChange variable types100 xpFiltering and plotting the data50 xpFilter the data for plotting100 xpSome summary statistics100 xpA basic scatter plot100 xpAdd labels to the plot100 xpCustom ggplot2 themes50 xpApply a default theme100 xpChange the appearance of titles100 xpAlter background color and add margins100 xp
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.Visualizing aspects of data with facets50 xpPrepare the data set for the faceted plot100 xpAdd facets to the plot100 xpDefine your own theme function100 xpApply the new theme function to the plot100 xpA custom plot to emphasize change50 xpA basic dot plot100 xpAdd arrows to the lines in the plot100 xpAdd some labels to each country100 xpPolishing the dot plot50 xpThe fct_reorder function50 xpReordering elements in the plot100 xpCorrect ugly label positions100 xpFinalizing the plot for different audiences and devices50 xpChange the viewport so labels don't overlap with plot border100 xpOptimizing the plot for mobile devices100 xp
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.What is RMarkdown?50 xpWhen is a document reproducible?50 xpIntroduction to the RMarkdown exercise interface100 xpFormatting with Markdown50 xpGive your document a structure100 xpChange formatting of text snippets100 xpR code in RMarkdown documents50 xpSpecify packages in the first code chunk100 xpR code chunk options100 xpInline code statements100 xpImages in RMarkdown files50 xpAdjusting figure options, optimizing them for mobile devices.100 xpAdd auxiliary images100 xp
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.Advanced YAML settings50 xpChange the overall appearance of your report100 xpAdd a table of contents100 xpMore YAML hacks100 xpCustom stylesheets50 xpCSS selectors50 xpChange style attributes of text elements100 xpReference the style sheet100 xpBeautiful tables50 xpBeautify a table with kable100 xpRepetition: CSS50 xpSummary50 xp
In the following tracksTidyverse Fundamentals
PrerequisitesIntroduction to the Tidyverse
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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