DataEditR: The GUI for Interactive Dataframe Editing in R

Written by Matt Dancho on May 18, 2021



This article is part of R-Tips Weekly, a weekly video tutorial that shows you step-by-step how to do common R coding tasks.


Here are the links to get set up. 👇

DataEditR Video Tutorial
For those that prefer Full YouTube Video Tutorials.

Learn how to use DataEditR in our free YouTube video.

Watch our full YouTube Tutorial

GUI for Editing Dataframes in R
Edit dataframes as if they were Excel tables.

DataEditR is a great addition to the R package ecosystem. I see it being immediately useful for beginners coming from Excel where they are used to being able to edit data interactively in an Excel Worksheet.

Find out how easy it is to edit data with the DataEditR GUI (Graphical User Interface).

BONUS:

I have an extra ggplot2 code showing boxplots of the Fuel Economy at the end of the tutorial (a vision of where you can go once you learn R beyond the GUI).

ggplot fuel economy


Beginners Struggle with R
Simple tools like DataEditR can make your transition much easier.

One of my biggest challenges when I moved from Excel to R was the transition from an interactive worksheet where I could edit data using point-click-edit to a data frame that requires code to edit. This was a serious hurdle. I wish I had a tool like DataEditR when I was first starting out.

Fast-forward to 2021, and here we are: DataEditR, the GUI I never had. Today, you’ll learn how to use this Excel-style dataframe editing tool.

DataEditR

Image Source:
DataEditR GitHub

Before we get started, get the R Cheat Sheet

DataEditR is great for making simple edits. But, you’ll still need to learn how to wrangle data with dplyr and visualize data with ggplot2. For those topics, I’ll use the Ultimate R Cheat Sheet to refer to dplyr and ggplot2 code in my workflow.

Quick Example:

Download the Ultimate R Cheat Sheet Then Click the “CS” next to “ggplot2” opens the Data Visualization with GGplot2 Cheat Sheet.

Now you’re ready to quickly reference ggplot2 functions.

Onto the tutorial.

ggplot2 cheat sheet

How DataEditR works

It’s super-simple. Just run this code to:

  1. Load Libraries: Load DataEditR , tidyverse and tidyquant.
  2. Import Data: We’re using the mpg dataset that comes with ggplot2.
  3. Start Data Editing: Use the data_edit() function.

Get the code.

This launches the Data Editor.

The Data Editor

Try Editing Cells

Click on a cell and make any edits.

Editing Cells

Try Selecting Columns

Click the target icon. Then select columns you are interested in.

Selecting Columns

When you’re done, save a CSV

After you’ve made your edits, you can optionally save a CSV File. Alternatively, you can return a data frame in your active R Session.

Save as CSV File

Going Further
with dplyr and ggplot2

DataEditR is great for making simple edits. But, eventually you’re going to need to go further by using code to wrangle data and prepare visualizations. For this, I’ll circle back to dplyr and ggplot2, and my Ultimate R Cheat Sheet.

Fuel Economy by Vehicle Model

Say that you wanted to make a visualization that shows the differences in vehicle models and their fuel economy measured as miles per gallon (MPG). We can do this with dplyr and ggplot2.

Get the code.

Visualization and Insights

The code makes a stunning ggplot2 visualization that highlights the differences in fuel economy by vehicle model and class. We can see:

  • SUV’s clearly have the lowest fuel economy although the Subaru Forester AWD seems to be an outlier.
  • Toyota Corolla is leading the pack with Highway MPG in the mid-30s.

ggplot fuel economy

In Summary

You’ve seen how DataEditR can be used for making simple edits inside of R. You’ve also seen that learning dplyr and ggplot2 can generate insights through visualizations.

What if you want to go further? Read on.

My Struggles with Learning Data Science

At the beginning of the article, I talked briefly about my struggles learning R. It took me a long time. And I made a lot of mistakes as I fumbled through learning R. I specifically had a tough time navigating the ever increasing landscape of tools and packages, trying to pick between R and Python, and getting lost along the way.

If you feel like this, you’re not alone.

In fact, that’s the driving reason that I created Business Science and Business Science University (You can read about my personal journey here).

What I found out is that:

  1. Data Science does not have to be difficult, it just has to be taught smartly

  2. Anyone can learn data science fast provided they are motivated.

How I can help

If you are interested in learning R and the ecosystem of tools at a deeper level, then I have a streamlined program that will get you past your struggles and improve your career in the process.

It’s called the 5-Course R-Track System. It’s an integrated system containing 5 courses that work together on a learning path. Through 5+ projects, you learn everything you need to help your organization: from data science foundations, to advanced machine learning, to web applications and deployment.

The result is that you break through previous struggles, learning from my experience & our community of 2000+ data scientists that are ready to help you succeed.

Ready to take the next step? Then let’s get started.




👇 Top R-Tips Tutorials you might like:

  1. mmtable2: ggplot2 for tables
  2. ggside: Plot linear regression with marginal distributions
  3. DataEditR: Interactive Data Editing in R
  4. openxlsx: How to Automate Excel in R
  5. officer: How to Automate PowerPoint in R
  6. DataExplorer: Fast EDA in R
  7. esquisse: Interactive ggplot2 builder
  8. gghalves: Half-plots with ggplot2
  9. rmarkdown: How to Automate PDF Reporting
  10. patchwork: How to combine multiple ggplots

Want these tips every week? Join R-Tips Weekly.