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Curs

Communicating with Data in the Tidyverse

De bazăNivel de competențe

Actualizat 03.2025

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

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RData Visualization

4 h

15 videoclipuri

53 Exerciții

4,350 XP

33,903

Certificat de realizare

Îndrăgit de cursanți din mii de companii

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Descrierea cursului

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.

Cerințe prealabile

Introduction to the Tidyverse

1

Custom ggplot2 Themes

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 data

Join the two data sets together

Change variable types

Filtering and plotting the data

Filter the data for plotting

Some summary statistics

A basic scatter plot

Add labels to the plot

Custom ggplot2 themes

Apply a default theme

Change the appearance of titles

Alter background color and add margins

Începe capitolul

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.

Visualizing aspects of data with facets

Prepare the data set for the faceted plot

Add facets to the plot

Define your own theme function

Apply the new theme function to the plot

A custom plot to emphasize change

A basic dot plot

Add arrows to the lines in the plot

Add some labels to each country

Polishing the dot plot

The fct_reorder function

Reordering elements in the plot

Correct ugly label positions

Finalizing the plot for different audiences and devices

Change the viewport so labels don't overlap with plot border

Optimizing the plot for mobile devices

Începe capitolul

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.

What is RMarkdown?

When is a document reproducible?

Introduction to the RMarkdown exercise interface

Formatting with Markdown

Give your document a structure

Change formatting of text snippets

R code in RMarkdown documents

Specify packages in the first code chunk

R code chunk options

Inline code statements

Images in RMarkdown files

Adjusting figure options, optimizing them for mobile devices.

Add auxiliary images

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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.

Advanced YAML settings

Change the overall appearance of your report

Add a table of contents

More YAML hacks

Custom stylesheets

CSS selectors

Change style attributes of text elements

Reference the style sheet

Beautiful tables

Beautify a table with kable

Repetition: CSS

Începe capitolul

Communicating with Data in the Tidyverse

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