A Beginner’s Guide to Data Analysis Using JavaScript


In this article, we will explore the fundamentals of data manipulation and analysis using JavaScript. Whether you’re working in the browser or in node.js, these techniques will help you prepare and visualize your data effectively.

We will cover various tasks and demonstrate how to implement them using core JavaScript API, as well as popular libraries like d3.js and lodash. This guide assumes that you already have a basic understanding of JavaScript.


Let’s dive into the different tasks you’ll learn in this guide:

  • Getting Started
  • Reading in Data
  • Combining Data
  • Summarizing Data
  • Iterating and Reducing
  • Nesting and Grouping Data
  • Working with Strings
  • Regular Expressions
  • Working with Time
  • Checking Data Assumptions
  • Using Node


Recent updates and additions to the guide:

  • 01/11/2019: We now have an amazing Observable version of this guide by the talented @dakoop. Try it out to interactively experiment with JavaScript!

  • 01/10/2019: The guide has been updated to use D3v5, thanks to the valuable contribution from Erin Brown. Most of the changes affect the “Reading in Data” section. We truly appreciate the help!

  • 03/20/2017: The guide has been updated to use D3v4. Special thanks to Kanit Ham Wong and the UW Interactive Data Lab for their support and motivation throughout this process. And kudos to Adam Pearce for the majority of the conversion work!

If you need access to the old D3v3 guide, it’s been archived here.


Each document in this repository is executed when loaded into a browser. You can verify this by opening the Developer Tools Console. If everything is set up correctly, you should see the following output:

console.log("This is the index!");

You can find the full source code on our GitHub repository.

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Why Choose JavaScript for Data Analysis?

You might be wondering why you should consider using JavaScript for data cleaning and processing. While there are other languages specifically designed for data wrangling (such as R with the Tidyverse and Python with pandas), JavaScript can be a viable option in certain scenarios.

Sometimes, you may already be familiar with JavaScript and want to gradually explore data analysis. Additionally, if you’re already working in a JavaScript environment, switching to another language may not be feasible.

In these situations, JavaScript can serve as a practical choice for your data analysis needs. This guide provides a stepping stone for you to try out data analysis with JavaScript, whether as a hobby or for professional reasons.

So, explore the different tasks and decide if JavaScript data analysis is something you want to pursue.


This guide is the collective effort of many individuals. It all started with a tweet from the amazing Lynn Cherney, who highlighted the lack of JavaScript data guidance at the time.

We owe a huge thanks to Bocoup for making this guide possible. Their support and dedication allowed us to create this valuable resource. Special thanks to Jory Burson and Boaz Sender for fostering a culture that encourages the creation of such guides.

The primary contributors to this guide were Irene Ros, Yannick Assogba, and Jim Vallandingham during their time at Bocoup. We are also grateful to the numerous contributors who have helped improve this guide over time.

As the saying goes, “It takes a village.” To all the individuals who have contributed to this project, we extend our heartfelt gratitude.

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