Reporting with R Markdown
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
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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.
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Getting Started with R Markdown
FreeIn 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 Markdown50 xpCreating your first R Markdown file100 xpAdding code chunks to your file100 xpAdding and formatting text50 xpFormatting text100 xpAdding sections to your report100 xpIncluding links and images50 xpThe YAML header50 xpEditing the YAML header100 xpFormatting the date100 xp - 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 data50 xpFiltering for a specific country100 xpFiltering for a specific year100 xpReferencing code results in the report100 xpAdding plots50 xpVisualizing the Investment Annual Summary data100 xpVisualizing all projects for one country100 xpVisualizing all projects for one country and year100 xpPlot options50 xpSetting chunk options globally100 xpSetting chunk options locally100 xpAdding figure captions100 xp - 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 report50 xpCreating a bulleted list100 xpCreating a numbered list100 xpAdding a table100 xpCode chunk options50 xpComparing code chunk options100 xpCollapsing blocks in the knit report100 xpModifying the report using include and echo100 xpWarnings, messages, and errors50 xpExcluding messages100 xpExcluding warnings100 xp - 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 contents50 xpAdding the table of contents100 xpSpecifying headers and number sectioning100 xpAdding table of contents options100 xpCreating a report with a parameter50 xpAdding a parameter to the report100 xpCreating a new report using a parameter100 xpMultiple parameters50 xpAdding multiple parameters to the report100 xpCreating a new report using multiple parameters100 xpCustomizing the report50 xpCustomizing the report style100 xpCustomizing the header and table of contents100 xpCustomizing the title, author, and date100 xpReferencing the CSS file100 xpCongratulations!50 xp
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prerequisites
Introduction to the Tidyverse
Amy PetersonHead of Analytics and Data Science Curriculum at DataCamp
Amy leads the Analytics and Data Science Curriculum team, which focuses on creating courses, practice pools, and projects. She was previously a Content Developer and a Curriculum Manager at DataCamp, working with instructors to develop data science content spanning various topics in Python, R, SQL, and Spreadsheets. Amy enjoys combining her interests in teaching and data to make data skills more accessible.
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