Uncovering Developer Trends with JavaScript Pivot Tables and Charts

As a full-stack developer always seeking to learn and grow, I love digging into data to uncover interesting insights. One of my favorite datasets to explore each year is Stack Overflow‘s annual Developer Survey, which collects responses from over 100,000 developers around the world on topics ranging from their favorite technologies to job preferences and work habits.

While poring through this extensive dataset can be daunting, powerful tools like JavaScript pivot table and charting libraries make it much easier to slice and dice the data in meaningful ways. In this article, I‘ll walk through how I used these tools to analyze the latest Survey data and share some of the most fascinating trends I discovered.

Why Pivot Tables?

For the uninitiated, a pivot table is an interactive way of summarizing and rearranging data in a spreadsheet or database table to identify useful information. Pivot tables allow you to group, filter, sort and aggregate data by various dimensions and measures.

For instance, if you had a table of sales data, you could use a pivot table to quickly calculate total revenue by region, average sale price by product category, top selling products by month, and so on—all without having to write complex formulas or queries. Pivot tables are an invaluable tool for anyone who regularly works with data.

The Power of JavaScript Libraries

While pivot tables have historically been associated with spreadsheet programs like Microsoft Excel, JavaScript developers now have access to a variety of powerful open source libraries for creating interactive pivot tables and charts right in the browser. For this analysis, I chose to use:

• Flexmonster Pivot Table – A customizable JavaScript pivot table library with a simple API for integrating with web apps and charting libraries.

• Chart.js – A popular open source library for creating beautiful, animated charts using HTML5 canvas. Includes a variety of common chart types like line, bar, pie, and scatter plots.

By combining these two libraries, I was able to build an interactive dashboard that allows me to quickly explore different dimensions and measures in the Developer Survey data, and visualize the results using Chart.js. Here‘s how I did it:

Preparing the Data

The first step was to download the raw survey data from Stack Overflow and convert it into a format that could be easily loaded into Flexmonster. I chose to export the data as a CSV file, which I then uploaded to a cloud storage bucket for easy access.

Next, I initialized a new instance of the Flexmonster pivot table in my HTML page, specifying the location of the CSV file and the container element:

<div id="pivot-container"></div>

<script>
var pivot = new Flexmonster({
  container: "#pivot-container",
  toolbar: true,
  report: {
    dataSource: {
      filename: "https://my-bucket.s3.amazonaws.com/survey_results_public.csv"
    }
  }
});
</script>

Analyzing Key Trends

With the data loaded into the pivot table, I was ready to start exploring. I decided to focus my analysis on three key categories: demographics, work, and technology.

Demographics

Right away, the country field caught my eye. By dragging it into the row area of the pivot table, I could see a breakdown of survey respondents by country. Unsurprisingly, the United States had the highest representation at over 20%, followed by India, Germany, and the UK.

The age and gender fields also looked interesting, so I decided to plot them on a stacked bar chart. I selected the age and gender fields in the pivot table, chose the "Horizontal Stacked Bar Chart" type in the Flexmonster UI, and clicked "Create Chart". This sent the aggregated data over to a Chart.js bar chart, which I could customize further.

The chart shows that the survey has pretty good representation across age groups, with the majority of respondents being between 25-34 years old. However, there is a notable gender gap, with over 90% of respondents identifying as male. It would be great to see this gap close in future years as the industry becomes more diverse and inclusive.

Work

Next, I was curious to explore the employment and work data. What types of companies do most developers work for? What are the most common job titles? How much are developers earning?

I started by looking at the breakdown of developers by employment status. Adding the "Employment" field to the pivot table revealed that over 90% of respondents are employed full-time, with only small percentages working part-time, freelancing or looking for work.

I then plotted the "Company Size" field on a pie chart, which showed a fairly even split between small (<100 employees), medium and large (>1000 employees) companies. To drill down further, I added "Developer Role" to the row area and selected a few of the most common job titles to create a smaller pie chart.

This revealed that larger companies tend to have more data scientists and DevOps specialists, while smaller companies and startups have more full-stack developers wearing multiple hats.

Speaking of different roles, I was also keen to understand how salaries varied by job title and location. By plotting "Developer Role" on the columns and "Salary" on the rows (aggregated as an average), with "Country" as a report filter, I was able to see the highest and lowest paying roles in each country.

The results were intriguing – data scientists and machine learning specialists topped the charts in countries like the US and UK. But in countries like India and Brazil, DevOps and Site Reliability Engineers were the highest earners on average. Meanwhile, academic researchers consistently earned the least across geographies. This just goes to show how in-demand skills and experience levels can vary significantly by market.

Technology

Finally, I wanted to look at the most popular and loved technologies. I was especially interested to see which programming languages, frameworks and databases developers were most excited about.

To start, I grouped the data by the "LanguageWorkedWith" field which contained a semicolon-delimited list of languages for each respondent. This allowed me to see both the total number and percentage of survey takers using each language. JavaScript reigned supreme, followed by HTML/CSS and SQL. Python just beat out Java for the fourth spot.

I also wanted to gauge developer sentiment by looking at the most loved and wanted languages. For this, I plotted the "LanguageWorkedWith" and "LanguageDesiredNextYear" fields on a scatter chart, with circle sizes representing the number of respondents.

The outliers were the most fascinating to me here. Languages like Rust, Kotlin, and TypeScript had relatively few current users compared to juggernauts like JavaScript and Java. Yet they had a much higher percentage of developers who wanted to learn or use them in the next year. To me this signals that they have passionate communities and are seen as up-and-coming.

I repeated this process for the most loved and wanted web frameworks and databases. While established players like React, Node, and MySQL continued to dominate in usage, newcomers like Svelte, FastAPI, and Firebase stood out as having the highest interest-to-usage ratios.

To cap off the analysis, I was curious if there was any relationship between a developer‘s role and their language preferences. I grouped by "DevType" and "LanguageWorkedWith", which showed, for example, that:

• Data scientists and machine learning engineers overwhelmingly favor Python
• Developers who work on embedded devices or in gaming are more likely to use C++
• Relatively few back-end or full-stack developers use C# compared to JavaScript, Java or Go
  

  Conclusion

Slicing and dicing the 2022 Stack Overflow Developer Survey dataset with Flexmonster and Chart.js was a fun way to uncover new insights about the global developer community. While some of the findings reaffirmed existing assumptions, others challenged or enriched my understanding of the state of the industry.

Some key takeaways were:

• JavaScript and Python continue to dominate, but Rust, Kotlin and TypeScript are rising stars
• Data science and machine learning roles command high salaries, especially in the US/UK
• DevOps and site reliability are more lucrative than average in developing countries
• Full-stack development is the most common job, especially in startups and small companies
• Developer demographics are still heavily skewed towards young males, with room for more diversity

However, this really just scratches the surface of all the possible questions we could answer with this data. I encourage you to check out the raw survey data and conduct your own analyses. What other trends or correlations can you find? Share them with the community!

I hope this article has inspired you to explore data in new ways with JavaScript pivot tables and charting libraries. With the right tools and a curious mindset, you never know what fascinating insights await!