Note: This is a cross-post of an article I published in August on our company blog. Thanks so much to Lisa for editing my writing.
Tools like Datawrapper try to make it easy to visualize a dataset. Charts, tables, and even interactive maps can be created in no time — if you have a fitting dataset!
But what if your data comes in a format or size that you can’t just throw into Datawrapper? So, as a challenge for this week, I wanted to look at a massive dataset and found one for over 95 million taxi rides in New York City in 2023.
So before I go into how I analyzed the data, here’s the first map I created.
The data for the map is published by the NYC Taxi & Limousine Commission (TLC) and comes as Parquet files, each of which stores taxi rides for one month. They publish separate files for “yellow” and “green” taxis, but for this blog post, I picked the biggest dataset which is about the “for-hire vehicles” aka. Uber and Lyft.
# What is DuckDB, and why use it?
Usually, I would write a script in some data analysis framework, like R or Python Pandas. But I wanted to try a more traditional approach to analyzing the data. Back in university, I learned that there are programs for managing and analyzing large datasets, and they are called databases . Yet, I’ve rarely encountered or used databases in over ten years of doing data journalism.
One thing that’s great about databases is that you can write your analysis queries in a language called SQL (Structured Query Language) that almost reads like English sentences, like this one, for example:
SELECT name FROM employees WHERE birthday = TODAY()
The reason that working with databases such as MySQL or PostgreSQL can be cumbersome is that there’s a lot of setup work to install a database server on your computer, and often it’s not trivial to understand where the data is actually stored. Also loading and exporting data from various formats is not trivial. And that’s where DuckDB comes in!
It’s a relatively new database designed for exactly this task: analyzing datasets that are too big to handle in Excel but still small enough to fit in your computer’s memory. You can use DuckDB on the command line, but also inside many tools and frameworks (like DBeaver or R – it even runs directly in web browsers).
So let’s move on to importing the taxi ride dataset into DuckDB.
# Reading and analyzing the data
The first step is downloading the Parquet files to your computer and loading them into a DuckDB database. Before doing so, let’s take a look what variables are included in the tables.
The TLC publishes a data dictionary that explains all the available columns. There is a lot more interesting information in the data (exact time of the trips, tips paid to the driver, passenger counts), but for this analysis, we’ll just look at these columns:
|hvfhs_license_num||The TLC license number of the HVFHS base or business|
|PULocationID||TLC Taxi Zone in which the trip began|
|DOLocationID||TLC Taxi Zone in which the trip ended|
|trip_miles||Total miles for passenger trip|
|base_passenger_fare||Base passenger fare before tolls, tips, taxes, and fees|
I used the following query to read the files. Note that DuckDB automatically recognizes the file extension
.parquet and knows to use the read_parquet function. The wildcard
* in the query told DuckDB to conveniently read all the matching Parquet files in the current directory into one table.
CREATE TABLE rides AS SELECT
This took about 5-6 seconds to run, and now I have a table
rides with over 95 million rows .
Now we want to group the taxi rides by pickup zone (the city region where the trip started) and calculate the fare per mile by dividing the fare by the trip distance. Note that the query excludes rides with an unknown drop-off zone (which I assume are rides ending outside New York City).
With my data already loaded into memory, the above query took about 100 milliseconds! Before exporting the data to CSV, I wanted to exclude the numbers for pickup zones where the sample is too small (less than 100 rides). One way SQL lets you do this is to nest queries inside other queries. In the inner query, we’re counting the rides for each pickup zone and storing the result as
num_rides, and in the outer query, we’re setting the
NULL for all zones where we have fewer than 100 rides:
One more thing I needed to do was to merge the resulting table with the names of the pickup zones. The TLC publishes a separate table with the names of the taxi zones, and thanks to DuckDB, we can simply address it by throwing in the URL of the CSV file! The final query I used for the map above is this:
Et voila, that’s the data we can throw into Datawrapper, along with a custom basemap for the Taxi zones. To create the basemap, I downloaded the Shapefile from the TLC website and converted it to TopoJSON in Mapshaper (here’s a handy tutorial if you’ve never done this before).
The map confirms that Lyft is generally the cheaper option, except for some remote areas where Uber is cheaper. Of course, that also means that Lyft pays its drivers less – but if you care about that, you probably shouldn’t use Uber or Lyft and just order a regular taxi.