Introduction I found that I was constantly referring to the IPython documentation to look up the code for the autoreload magic command and wished there was an easy place for me to keep and access these sort of “oft-copied” texts. I realized...

16 January 2022 A couple of weeks ago I created my very first twitter bot which was immensely satisfying. It was also quite a frustrating process and this post should save you a lot of frustration :). You can view the code in the github repo. I can’t give enough … The post ...

“Destroy your money, you can earn more. Destroy your data, your existence is erased.” — Kurt Seapoint” In this post I am going to analyse my departmental Whatsapp chat group “Statistics Class of 2019”. Thanks to a special package called rwhatsapp...

Cover photo by Hannah Busing on Unsplash Go to R-bloggers for R news and tutorials contributed by hundreds of R bloggers. Introduction This is the fifth of a series of 6 articles about time series forecasting with panel data and ensemble stacking with ...

Cover photo by christian buehner on Unsplash Go to R-bloggers for R news and tutorials contributed by hundreds of R bloggers. Introduction This is the fourth of a series of 6 articles about time series forecasting with panel data and ensemble stackin...

How do consumers react to an increase in gasoline taxes? How much will they drive less? Will they buy more fuel efficient cars? Do tax increases have a stronger impact than gasoline price increases from other sources, like higher oil prices? In their ...

Animated Slides: Keynote Static Slides: PDF Session Recording: YouTube I also demonstrate Owlstown’s tool — which I found to be most beginner friendly. It starts at 27:00 in the above YouTube video. Abstract Websites used to b...

Most of the time when I answer questions on Stack Overflow, I end up learning a thing or two about R myself. Answering questions gets me warmed up on unrelated topics. These questions are one of their kind. This blog post documents all my answers so th...

A Shiny application illustrating multivariate forecasting with ahead::ridge2f (in R) and ahead.Ridge2Regressor (in Python)

This is part 4 of a series on “Handling Categorical Data in R” where we are learning to read, store, summarize, reshape & visualize categorical data. Below are the links to the other articles of this series: Part 1 - Introduction to Factor Par...

It’s hard to argue with the fact that user experience can make or break digital products, Shiny apps included. A custom login page is one of those early UX touchpoints with the potential to infuse users with brand awareness from the get-go. But achieving that ultimate first impression when ...

This is part 3 of a series on “Handling Categorical Data in R where we are learning to read, store, summarize, visualize & manipulate categorical data..” In part 1 and part 2 of this series, we understood how R stores categorical data using f...