Notes of a Dabbler

Wandering through the beautiful world of math, computation and visualization

Using Pyomo from R through the magic of Reticulate

Pyomo is a python based open-source package for modeling optimization problems. It makes it easy to represent optimization problems and can send it to different solvers (both open-source and commercial) to solve the problem and return the results in python. The advantage of pyomo compared to commercial software such as GAMS and AMPL is the ability to code using standard python syntax (with some modifications for pyomo constructs). Another open source package for modeling optimization problems is JuMP in Julia language.

Proofs without Words using gganimate

I recently watched the 2 part workshop (part 1, part 2) on ggplot2 and extensions given by Thomas Lin Pedersen. First of, it was really nice of Thomas to give the close to 4 hour workshop for the benefit of the community. I personally learnt a lot from it. I wanted to try out gganimate extension that was covered during the workshop. There are several resources on the web that show animations/illustrations of proofs of mathematical identities and theorems without words (or close to it).

Keeping up with Tidyverse Functions using Tidy Tuesday Screencasts

David Robinson has done several screencasts where he analyzes a Tidy Tuesday dataset live. I have listened to a few of them and found them very interesting and instructive. As I don’t use R on a daily basis, I have not kept up with what the latest is in Tidyverse. So when I listened to his screencasts, I learnt functions that I was not aware of. Since I sometimes forget which function I learnt, I wanted to extract all the functions used in the screencasts so that it is easier for me to refer to the ones that I am not aware of but should learn.

Fastai Collaborative Filtering with R and Reticulate

Jeremy Howard and Rachel Thomas are founders of fast.ai whose aim is to make deep learning accessible to all. They offer a course called Practical Deep Learning for Coders (Part 1). The last session, taught by Jeremy, was in Fall 2017 and the videos were released early January 2018. Their approach is top down by showing different applications first as black boxes followed by progressive peeling of the black box to teach the details of how things work.

Exploring Instacart Dataset with PCA

Recently, Instacart released a dataset of ~3 million orders made by ~200,000 users at different days of week and times of day. There is also an ongoing Kaggle competition to predict which products a user will buy again. My goal here is more modest where I just wanted to explore the dataset to find patterns of purchasing behaviour by hour of day, day of week and number of days prior to current order.

My First Blogdown Post

Since I have been hearing a lot about virtues of Blogdown in various forums, I thought I will also give it a shot. I am planning to do my future blog posts in blogdown. I used to have my older site in Wordpress. But I don’t have plans at least in the near term to migrate the older content. (function() { var d = document, s = d.