Month: January 2019

Extracting Public Transactions from Venmo API with R

Public by default, your Venmo transactions are surprisingly accessible to anyone with an internet connection. Although Venmo has removed functionality to query historical transactions, it’s public API still provides a real-time snapshot view of transactions processed through the system, including usernames and payment subjects (though not the amount sent or received). Try it for yourself here.

With that said, it was straight-forward to collect a bit of data from this API using R. The bulk of the script was needed to parse the JSON file returned by the API to extract interesting information. In this post, I’ll highlight sample data from the API in an effort to expose the kind of information being openly shared. If you use Venmo, follow these instructions to change your transactions to private by default.

Sample Data

Using the API, I collected data from 1,250 payments. From each of these transactions, I was able to view the following information:

  • Payment Id
  • Payment Date, Time
  • Payment Message
  • Sender & Receiver Name, Username
  • Sender & Receiver Profile Photo
  • Sender & Receiver Venmo Account Creation Date

For example, on January 1, 2019 Scott Perkinson sent Patrick Miller an undisclosed payment for “Caroline Bachelorette Party & Wine tasting”. That same day, Kerry McCarthy paid Anna McCarthy for “Barbie dream house furniture.” You can’t make this stuff up.

Bottom line, privacy is important, and you should take any available steps to limit how your information is shared. If you use Venmo, start by making your payments private by default. You can find the datasets I compiled here and the R code to access the API here.

Visualizing Baby Name Popularity Trends with R

From the earliest days of our marriage, my wife and I talked about baby names. Your name is a core part of your identity, so choosing the right name for your child feels like a weighty affair. Now, with a baby on the way, the topic surfaces in conversation more than ever.

Like always, I turned to data to assist with the decision process. Using a dataset provided by the Social Security Administration, I created functions with R to visualize and compare the popularity of names over time.

There are two functions: the comparison of two names over time and the comparison of a name against a birth year over time. Below is sample implementation:

I first compared the popularity of the spelling of my name, Erik, to the more common spelling, Eric.

Erik has never been as popular as Eric, although both are currently down from their 1970 – 1990 peak.

Next, I compared the popularity of my name in the context of my birth year. It looks like my parents named me around the start of the decline in popularity.

How does my name compare with my wife’s?

What can I say? She’s always been more popular. What about the names of my six sisters? (including sisters-in-law).

Finally, let’s take a look at the most popular boy and girl names of 2017, Liam and Emma.

This was a fun and simple way to interact with publicly available baby name data. You can find the dataset here and the code to create the functions here.

css.php