JLaw's R Blog
JLaw's R Blog
Home
Posts
Visualizations
About
RSS
Github
Light
Dark
Automatic
Posts
When Will NYC's Subway Ridership Recover?
While writing my posts about COVID’s effect on NYC Subway ridership the New York Times published an article called The Pandemic Wasn’t Supposed to Hurt New York Transit This Much.
Aug 29, 2022
6 min read
R
,
Forecasting
Exploring Types of Subway Fares with Hierarchical Forecasting
In my prior post I used forecasting to look at the effect of COVID on the expected amount of New York City subway swipes. In this post I will drill a level deeper to run forecasts for various types of subway fares to see if any particularly type has recovered better or worse than any others.
Aug 24, 2022
10 min read
R
,
Forecasting
How much has COVID cost the NYC Subway system in "lost fares"?
With things in NYC beginning to return to normal after two years of COVID I found myself thinking about how much money was lost in Subway fares in the 2+ years where people were working from home.
Jul 13, 2022
9 min read
R
,
Forecasting
ML for the Lazy: Can AutoML Beat My Model?
In this fourth (and hopefully final) entry in my “Icing the Kicker” series of posts, I’m going to jump back to the first post where I used tidymodels to predict whether or not a kick attempt would be iced.
May 3, 2022
11 min read
R
,
ML
Ain't Nothin But A G-Computation (and TMLE) Thang: Exploring Two More Causal Inference Methods
In my last post I looked at the causal effect of icing the kicker using weighting. Those results found that icing the kicker had a non-significant effect on the success of the field goal attempt with a point estimate of -2.
Mar 15, 2022
9 min read
R
,
CausalInference
Does Icing the Kicker Really Work? A Causal Inference Exercise
In my prior post I looked at when coaches were most likly to ice a kicker where ‘icing a kicker’ means for a defense to call a timeout right before the offense is about to kick a field goal.
Feb 14, 2022
20 min read
R
,
CausalInference
Predicting When Kickers Get Iced with {tidymodels}
I’m constantly on the lookout for things I can use for future posts for this blog. My goal is usually two-fold. First, what is a tool or technique I want to try/learn and second is there an interesting data set that I can use with those tools.
Jan 24, 2022
20 min read
R
,
ML
Examining College Football Conference Realignment with {ggraph}
In my previous post I looked at College Football Non-Conference games to create a network map overlaid on top of the United States using the {ggraph} package. In this post I’ll be extending that to examine Conference Realignment, which is when colleges change from one conference to the next.
Dec 29, 2021
11 min read
R
,
DataViz
Exploring College Football Non-Conference Rivalries with {ggraph}
We’re in the middle of College Football’s bowl post-season and I’d been wanting to do a more in-depth post on networks using {tidygraph} and {ggraph} for a while. So now seemed like as good a time as any to explore some College Football data.
Dec 27, 2021
10 min read
R
,
DataViz
What's the Most American of American Films? An Analysis with {gt} and {gtExtras}
I love movies. I enjoy watching them, I enjoy reading about the industry (sometimes), and as a bit of a data-nerd (exhibit a: my blog), I enjoy learning about the outliers in the industry.
Oct 20, 2021
17 min read
R
,
DataViz
»
Cite
×