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Course

Reporting with R Markdown

IntermediateSkill Level

4.7+

Updated 03/2022

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

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RReporting4 hr14 videos49 Exercises4,150 XP45,263Statement of Accomplishment

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Course Description

R Markdown is an easy to use formatting language you can use to reveal insights from data and author your findings as a PDF, HTML file, or Shiny app. In this course, you'll learn how to create and modify each element of a Markdown file, including the code, text, and metadata. You'll analyze data with dplyr, create visualizations with ggplot2, and author your analyses and plots as reports. You’ll gain hands-on experience of building reports as you work with real-world data from the International Finance Corporation (IFC)—learning how to efficiently organize reports using code chunk options, create lists and tables, and include a table of contents. By the end of the course, you'll have the skills you need to add your brand’s fonts and colors using parameters and Cascading Style Sheets (CSS), to make your reports stand out.

Prerequisites

Introduction to the Tidyverse

1

Getting Started with R Markdown

In this chapter, you'll learn about the three components of a Markdown file: the code, the text, and the metadata. You'll also learn to add and modify each of these elements to your own reports, as you create your first Markdown files.

Introduction to R Markdown

Creating your first R Markdown file

Adding code chunks to your file

Adding and formatting text

Formatting text

Adding sections to your report

Including links and images

The YAML header

Editing the YAML header

Formatting the date

2

Adding Analyses and Visualizations

In this chapter, you’ll use dplyr to begin to analyze the World Bank IFC datasets and include the analyses in your report. You’ll then create visualizations of the data using ggplot2 and learn to modify how the plots display in your knit report.

Analyzing the data

Filtering for a specific country

Filtering for a specific year

Referencing code results in the report

Adding plots

Visualizing the Investment Annual Summary data

Visualizing all projects for one country

Visualizing all projects for one country and year

Plot options

Setting chunk options globally

Setting chunk options locally

Adding figure captions

3

Improving the Report

Now that you've learned how to add, label, and modify code chunks, you'll learn about code chunk options. You can use these to determine whether the code and results appear in the knit report. You'll also discover how to create lists and tables to include in your report.

Organizing the report

Creating a bulleted list

Creating a numbered list

Adding a table

Code chunk options

Comparing code chunk options

Collapsing blocks in the knit report

Modifying the report using include and echo

Warnings, messages, and errors

Excluding messages

Excluding warnings

4

Customizing the Report

In this final chapter, you'll learn how to customize your report by adding a table of contents and adding a CSS file to the YAML header, to personalize reports with your brand’s fonts and colors. You'll also learn how to efficiently create new reports from your data using parameters, which will save you time from manually updating existing reports to create new ones.

Adding a table of contents

Adding the table of contents

Specifying headers and number sectioning

Adding table of contents options

Creating a report with a parameter

Adding a parameter to the report

Creating a new report using a parameter

Multiple parameters

Adding multiple parameters to the report

Creating a new report using multiple parameters

Customizing the report

Customizing the report style

Customizing the header and table of contents

Customizing the title, author, and date

Referencing the CSS file

Congratulations!

Reporting with R Markdown

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FAQs

What output formats can I create with R Markdown in this course?

You will learn to produce reports as PDF and HTML files. The course also mentions Shiny apps as a possible output format for R Markdown documents.

What dataset is used throughout the course?

You will work with real-world data from the International Finance Corporation to build and customize reports using dplyr for analysis and ggplot2 for visualizations.

Does the course cover how to brand reports with custom styles?

Yes. The final chapter teaches you to add custom fonts and colors using CSS files in the YAML header and efficiently create new reports from templates using parameters.

What are code chunk options and why do they matter?

Code chunk options let you control whether code and results appear in your final report. Chapter 3 covers these options along with creating lists and tables.

What prior R knowledge is needed?

You need to have completed Introduction to the Tidyverse. This gives you the dplyr and ggplot2 skills used throughout the course for data analysis and visualization.

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