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Reporting with R Markdown

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

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4 Hours14 Videos49 Exercises18,120 Learners
4150 XP

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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.

  1. 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.

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    Introduction to R Markdown
    50 xp
    Creating your first R Markdown file
    100 xp
    Adding code chunks to your file
    100 xp
    Adding and formatting text
    50 xp
    Formatting text
    100 xp
    Adding sections to your report
    100 xp
    Including links and images
    50 xp
    The YAML header
    50 xp
    Editing the YAML header
    100 xp
    Formatting the date
    100 xp
  2. 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.

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  3. 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.

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  4. 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.

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In the following tracks

Data Analyst Data Scientist


Maggie Matsui
Amy Peterson Headshot

Amy Peterson

Head of Core Curriculum at DataCamp

Amy leads the Core Curriculum team at DataCamp, focusing on Python, R, and SQL content. She holds a Master of Public Health in Biostatistics and Epidemiology from Johns Hopkins.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA