Category: Work

LLMs are good coders, useless writers

Post author By Sam
Post date 2023-12-22
4 Comments on LLMs are good coders, useless writers

My writer friends say Large Language Models (LLMs) like ChatGPT and Bard are overhyped and useless. Software developer friends say they’re a valuable tool, so much so that some pay out-of-pocket for ChatGPT Plus. They’re both correct: the writing they spew is pointless at best, pernicious at worst. … and coding with them has become an exciting part of my job as a data analyst.

Here I share a few concrete examples where they’ve shined for me at work and ruminate on why they’re good at coding but of limited use in writing. Compared to the general public, computer programmers are much more convinced of the potential of so-called Generative AI models. Perhaps these examples will help explain that difference.

Example 1: Finding a typo in my code

I was getting a generic error message from running this command, something whose Google results were not helpful. My prompt to Bard:

What’s the matter with this Python dict definition?

details = {
‘electrification_lvl’: decoded_vin.get(‘ElectrificationLevel’, None),
‘engine_kw’ = decoded_vin.get(‘EngineKW’, None),
‘fuel_type_primary’ = decoded_vin.get(‘FuelTypePrimary’, None),
‘fuel_type_secondary’ = decoded_vin.get(‘FuelTypeSecondary’, None),
‘battery_kwh’ = decoded_vin.get(‘BatteryKWh’, None)}

Bard told me I had a “significant issue”:

“You used an assignment operator (=) instead of a colon (:) to define the key-value pairs. This will cause a syntax error because assignment statements are not allowed within dictionary definitions.”

Yep! So trivial, but I wasn’t seeing it. It also suggested a styling change and, conveniently, gave me back the fixed code so that I could copy-paste it instead of correcting my typos. Here the LLM was able to work with my unique situation when StackOverflow and web searches were not helping. I like that the LLM can audit my code.

Example 2: Writing a SQL query

Today I started writing a query to check an assumption about my data. I could see that in translating my thoughts directly to code, I was getting long-winded, already on my third CTE (common table expression). There had to be a simpler way. I described my problem to Bard and it delivered.

My prompt:

I have a table in SQL Server called EV_VINs_Presence. It contains columns “vin”, “snapshot_date”, and “locale”. Every record that has a locale value of “Ann Arbor” should have a corresponding record with the same “vin” and “snapshot_date” but with the locale value of “State”.

Write a SQL Server query to return any records that do not have such a corresponding record.

Bard replied:

Tags 2, Apache Airflow, LLMs, Programming, Superset

#rstats Data analysis ruminations Software Work

Same Developer, New Stack

Post author By Sam
Post date 2023-08-31
No Comments on Same Developer, New Stack

I’ve been fortunate to work with and on open-source software this year. That has been the case for most of a decade: I began using R in 2014. I hit a few milestones this summer that got me thinking about my OSS journey.

I became a committer on the Apache Superset project. I’ve written previously about deploying Superset at work as the City of Ann Arbor’s data visualization platform. The codebase (Python and JavaScript) was totally new to me but I’ve been active in the community and helped update documentation.

Those contributions were sufficient to get me voted in as a committer on the project. It’s a nice recognition and vote of confidence but more importantly gives me tools to have a greater impact. And I’m taking baby steps toward learning Superset’s backend. Yesterday I made my first contribution to the codebase, fixing a small bug just in time for the next major release.

Superset has great momentum and a pleasant and involved (and growing!) community. It’s a great piece of software to use daily and I look forward to being a part of the project for the foreseeable future.

I used pyjanitor for the first time today. I had known of pyjanitor‘s existence for years but only from afar. It started off as a Python port of my janitor R package, then grew to encompass other functionality. My janitor is written for beginners, and that came full circle today as I, a true Python beginner, used pyjanitor to wrangle some data. That was satisfying, though I’m such a Python rookie that I struggled to import the dang package.

Tags foss, rstats, self-congratulatory, Superset

Local reporting ruminations Work

Coworking spaces aren’t profitable

Post author By Sam
Post date 2023-06-14
1 Comment on Coworking spaces aren’t profitable

I gave a tour of Workantile this week to a prospective new member who shared her experience working out of The Wing’s DC branch. We got to talking about how WeWork and The Wing were valued in the billions and hundreds of millions of dollars, respectively, before crashing to nothing. Those valuations were clearly absurd, but as a coworking insider, I’ll go a step farther and say there’s not much money in operating a coworking space.

That doesn’t mean coworking spaces aren’t valuable. Workantile has grown friendships, mentorships, careers, side projects, community services and made its members significantly happier. We kick around ideas, eat together, share recommendations and hand-me-downs. A long-time member swears that Workantile saved her marriage. But those benefits accrue to members and their networks and can’t easily be monetized by the space.

And it doesn’t mean people shouldn’t create coworking spaces. On the contrary, now’s a perfect time. Office rents are down, the boom of newly-remote workers are getting lonely, and concern about COVID transmission is receding. But don’t launch a coworking space – or invest in someone else’s – thinking you’ll get rich. The numbers don’t work.

Tags Ann Arbor, coworking, workantile

Data analysis Local reporting Software Work

Making the Switch to Apache Superset

Post author By Sam
Post date 2023-06-02
4 Comments on Making the Switch to Apache Superset

This is the story of how the City of Ann Arbor adopted Apache Superset as its business intelligence (BI) platform. Superset has been a superior product for both creators and consumers of our data dashboards and saves us 94% in costs compared to our prior solution.

Background

As the City of Ann Arbor’s data analyst, I spend a lot of time building charts and dashboards in our business intelligence / data visualization platform. When I started the job in 2021, we were halfway through a contract and I used that existing software as I completed my initial data reporting projects.

After using it for a year, I was feeling its pain points. Building dashboards was a cumbersome and finicky process and my customers wanted more flexible and aesthetically-pleasing results. I began searching for something better.

Being a government entity makes software procurement tricky – we can’t just shop and buy. Our prior BI platform was obtained via a long Request for Proposals (RFP) process. This time I wanted to try out products to make sure they would perform as expected. Will it work with our data warehouse? Can we embed charts in our public-facing webpages?

The desire to try before buying led me to consider open-source options as well as products that we already had access to through existing contracts (i.e., Microsoft Power BI).

Tags foss, foss does it better, self-congratulatory, Superset

#rstats Data analysis ruminations Work

Reflections on five years of the janitor R package

Post author By Sam
Post date 2021-03-13
1 Comment on Reflections on five years of the janitor R package

One thing led to another. In early 2016, I was participating in discussions on the #rstats Twitter hashtag, a community for users of the R programming language. There, Andrew Martin and I met and realized we were both R users working in K-12 education. That chance interaction led to me attending a meeting of education data users that April in NYC.

Going through security at LaGuardia for my return flight, I chatted with Chris Haid about data science and R. Chris affirmed that I’d earned the right to call myself a “data scientist.” He also suggested that writing an R package wasn’t anything especially difficult.

My plane home that night was hours late. Fired up and with unexpected free time on my hands, I took a few little helper functions I’d written for data cleaning in R and made my initial commits in assembling them into my first software package, janitor, following Hilary Parker’s how-to guide.

That October, the janitor package was accepted to CRAN, the official public repository of R packages. I celebrated and set a goal of someday attaining 10,000 downloads.

Yesterday janitor logged its one millionth download, wildly exceeding my expectations. I thought I’d take this occasion to crunch some usage numbers and write some reflections. This post is sort of a baby book for the project, almost five years in.

By The Numbers

This chart shows daily downloads since the package’s first CRAN release. The upper line (red) is weekdays, the lower line (green) is weekends. Each vertical line represents a new version published on CRAN.

From the very beginning I was excited to have users, but this chart makes that exciting early usage seem miniscule. janitor’s most substantive updates were published in March 2018, April 2019, and April 2020, with it feeling more done each time, but most user adoption has occurred more recently than that. I guess I didn’t have to worry so much about breaking changes.

Another way to look at the growth is year-over-year downloads:

Year	Downloads	Ratio vs. Prior Year
2016-17	13,284	–
2017-18	47,304	3.56x
2018-19	161,411	3.41x
2019-20	397,390	2.46x
2020-21 (~5 months)	383,5956	–

Download counts are from the RStudio mirror, which does not represent all R user activity. That said, it’s the only available count and the standard measure of usage.

Tags foss, janitor, package development, self-congratulatory

#rstats Making Work

That feeling when your first user opens an issue

Post author By Sam
Post date 2019-11-18
No Comments on That feeling when your first user opens an issue

You know how new businesses frame the first dollar they earn?

I wrote an R package that interfaces with the SurveyMonkey API. I worked hard on it, on and off the clock, and it has a few subtle features of which I’m quite proud. It’s paying off, as my colleagues at TNTP have been using it to fetch and analyze their survey results.

The company and I open-sourced the project, deciding that if we have already invested the work, others might as well benefit. And maybe some indirect benefits will accrue to the company as a result. I made the package repository public, advertised it in a few places, then waited. Like a new store opening its doors and waiting for that first customer.

They showed up on Friday! With the project’s first GitHub star and a bug report that was good enough for me to quickly patch the problem. Others may have already been quietly using the package, but this was the first confirmed proof of use. It’s a great feeling as an open-source developer wondering, “I built it: will they come?”

Consider this blog post to be me framing that dollar.

Tags foss, software development, surveymonkey

DIY Work

Measuring CO2 accumulation during phone meetings

Post author By Sam
Post date 2019-08-22
2 Comments on Measuring CO2 accumulation during phone meetings

I am part of the remote co-working community Workantile, in downtown Ann Arbor. We have small private rooms for taking conference calls and I often find them stuffy and notice I’m tired by the end of a meeting. I’d read that excessive CO2 build-up in meetings can impair cognitive function. Was that the case, or was I just bored from the meetings?

I borrowed an Indoor Air Quality Meter from my amazing local library (by Sper Scientific, normally $400, for me, $0) and went to find out.

Work

Fully-remote jobs don’t need in-person interviews

Post author By Sam
Post date 2018-10-04
No Comments on Fully-remote jobs don’t need in-person interviews

I hired a data analyst last year who started working for me in December. He lives in Colorado, I live in Michigan. After 10 months of working together, week in and week out, I finally “met” him at our annual company conference last month (September). Does that seem funny? I was surprised by how tall he was, but otherwise, no, it’s business as usual around here.

I’ve now embraced the idea of hiring someone and working with them without first meeting them in person. If you’ll be working with them remotely, and your team and organization have the right culture and systems in place for that, why would you insist on in-person interviews?

In a truly remote-first organization, there’s little cause to fly someone out for an in-person interview. And there are many reasons not to. When you weigh the costs and benefits, it’s not worth it. You’re a remote organization – embrace it!