shinyplyr: Data Transformation and Visualisation - 2020 Shiny Contest Submission

shinyplyr: Data Transformation and Visualisation

Authors: David Barkemeyer
Working with Shiny more than 1 year

Abstract: Transform tabular data and create plots with an interface you are used to from dplyr and ggplot2. A dataset explorer and a dynamic help system are also presented. The application is aimed at different target groups: Data scientists who want to speed up explorative data analysis; teachers who want to present important concepts of the tidyverse more interactively; Shiny enthusiasts who are looking for inspiration.

Full Description:


I am a student assistant and give tutorials for two lectures: one on the introduction to data analysis with the tidyverse and one on statistics for quality engineering. For the presentation, we currently use mainly PowerPoint and RMarkdown and exercise directly in RStudio. To make the presentation more interactive, we plan to replace PowerPoint with Shiny. A few weeks ago, as part of a project at work, I created a filter module that filters a data set based on several conditions. Since then I thought about extending the functionality to other dplyr functions. The result is this app which supports most of the dplyr functions and allows to create plots with ggplot2.

Transformation Table

The heart of the app is the Transformation Table. It allows to apply different operations to a data set. The data set is piped from one operation to the next, i.e. the output of the first operation is the input of the second operation and so on.

Dataset Explorer

The app provides all data.frames/tibbles from the packages datasets, dplyr and ggplot2. Custom datasets can be uploaded in csv or rds format. The data selection is done via the dataset explorer. Datasets can be stored, renamed or removed in an arbitrarily nested folder structure. A datatable is used for visualization.

Help Pages

To make the app easier to use, I have added a help system inspired by the R Help. The individual topics are linked to each other. An overview of all topics can be found in the index. Additionally there is a Getting Started Guide.


I am happy to receive feedback of any kind. I decided to take part in the contest less than two weeks ago and since then I have been working exclusively on the app. I have tried to catch as many bugs as possible, but certainly missed some. If you find a bug or have suggestions for improvements, feel free to file an issue on GitHub.

Category: Education
Keywords: Transformation, Visualisation, Dataset Explorer, Help
Shiny app:
RStudio Cloud:


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