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Learn R Shiny online with courses and programs

R Shiny is a tool used in the fields of business, data science, research, and statistics. Explore how online courses delivered through edX can help you learn about R Shiny.

What is R Shiny?

R Shiny is RStudio’s free extension package of R, which is an open source programming language and environment for organizing and displaying data.Footnote 1 R Shiny allows its users to build interactive web applications with the R programming language. Shiny apps can be run locally or hosted on a web server, and they’re especially useful for data visualization and interactive data exploration.Footnote 2 Different types of Shiny apps include:Footnote 3

  • Data visualization tools

  • Text boxes, buttons, and plots

  • Dashboards that display real-time data

  • Machine learning models

  • Applications that allow users to input functions and perform calculations

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R Shiny course curriculum 

R Shiny training is available for beginner, intermediate, and advanced learners. The curriculum for an R Shiny course can vary depending on the instructor and course level. However, many courses can cover the following topics:Footnote 4

  • Using R Shiny application features

  • Building user interfaces (UI) with R Shiny

  • Making reactive R Shiny applications

  • Understanding R Shiny data input and output

  • Using R Shiny with databases

  • Creating and deploying R Shiny applications 

  • Testing and debugging R Shiny code

R Shiny is used to create applications with R, so a basic understanding of R programming concepts is a necessary precursor to R Shiny training. To understand R programming basics you will need to build knowledge of R data types, variables, control structures, and functions. Learners can find various R programming course tutorials online, many of which are part of a larger statistics or data science curriculum. These courses take learners through the fundamental programming concepts, analytical skills, and R basics needed to analyze data sets with R. 

If you are interested in developing new professional skills, explore the educational opportunities made available through edX. edX offers boot camps for building specialized skills, as well as full degree programs that enable you to earn a bachelor’s degree or pursue a master's degree in a variety of subjects. 

Benefits of learning R Shiny

R Shiny learning provides hands-on experience in creating interactive web applications, helping learners improve their skills in R programming, data visualization, and web development. A few benefits of learning R Shiny include:

  • Collaboration: Shiny apps can be easily shared and embedded, which makes them an excellent tool for collaboration and communication. Shiny allows you to create and share visually appealing and interactive visualizations and data exploration tools, which can be more engaging and informative than static plots. This makes R Shiny an excellent tool for gaining insights and sharing them with others.

  • Versatility: R Shiny can be used to create a wide range of applications, from simple data visualizations to complex data analysis tools.Footnote 5 This makes it a useful tool to have in your toolbox for a variety of projects. As of 2022, Shiny can also be used with Python frameworks in addition to R.Footnote 6

  • Career opportunities: The ability to create interactive web applications is a sought-after skill in many industries, such as business, finance, healthcare, and research.Footnote 7

Explore jobs that use R Shiny

If you are interested in data analysis and web development, jobs in these fields may require or recommend R and R Shiny experience. Adding R Shiny skills to a resume can help analysts be more efficient in their ability to create interactive web applications. R Shiny dashboard and Shiny apps make the framework a powerful tool for monitoring and studying data in many industries.

Data scientists also use R Shiny to create interactive dashboards and visualizations that allow them to explore, analyze, and communicate findings. R Shiny is particularly useful for data exploration and visualization, which are key tasks in data science.

Every job is different, and each company will have different hiring requirements. Some jobs may require specific educational credentials, such as a bachelor’s in computer science or possibly a master’s in data science for more senior positions. Others may weigh experience and portfolio of work more heavily when making hiring decisions. Before you decide on an educational pathway, research the roles you hope to apply for and align your learning with your professional aspirations. 

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