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

Getting Started with R Markdown

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

Adding Analyses and Visualizations

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

Options

include

echo

eval

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

Improving 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!

Customizing the Report

Investment Services Projects

Investment Annual Summary

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.

Introduction to the Tidyverse

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

Reporting with R Markdown

Code runs but does not appear in report, results do not appear

Code runs but does not appear in report, results appear

Code appears in report but does not run, results do not appear

Comparing code chunk options

Reporting with R Markdown

Hands-on interactive exercise