Data Science

Cupcakes vs Muffins: Round 2

Have you ever pondered whether a muffin is really a breakfast food and not just an excuse to eat cake first thing in the morning? Well, you’ve come to the right blog post! In a previous post, I explained how I created a dataset of the ingredients of 269 cupcake and muffin recipes. In this installment, I’m going to use that dataset to demonstrate some of the important properties of multivariate statistics, specifically the difference between principal component analysis (PCA) and partial least squares regression (PLS).

RStudio::conf reflections

This was my first time attending RStudio::conf, and I went primarily to explore my career options in data science. I mainly stuck to teaching and modeling related talks since that’s how I already use R. Here are my major takeaways from the conference. Shiny is the new hotness Shiny apps are interactive web apps that run on R code, and there was a big focus on Shiny development at the conference this year.

Retrieve chemical retention indices from NIST with {webchem}!

My PhD has involved learning a lot more than I expected about analytical chemistry, and as I’ve been learning, I’ve been trying my best to make my life easier by writing R functions to help me out. Some of those functions have found a loving home in the webchem package, part of rOpenSci. Papers that use gas chromatography to separate and measure chemicals often include a table of the compounds they found along with experimental retention indices and literature retention indices.

Python is Weird (an unabashedly biased intro to Python for R users)

Last semester I took a class that used Python. It was my first time really seriously using any programing language other than R. The students were about half engineers and half biologists. The vast majority of the biologists knew R to varying degrees, but had no experience with Python, and the engineers seemed to generally have some experience with Python, or at least with languages more similar to it than R.

Cupcake Update

I know you’re all waiting on the edge of your seats for an update on the cupcakes vs. muffins data science project, but unfortunately I don’t have any answers to that age-old question* yet. As silly as it may sound, I’m actually considering using this data set for a paper about using PLS (partial least squares regression) for ecological data. So for now, I’m holding off on blogging about any results of analyses in case I end up wanting to use them for the publication.

Cupcakes vs. Muffins

One thing I’ve learned from my PhD at Tufts is that I really enjoy working data wrangling, visualization, and statistics in R. I enjoy it so much, that lately I’ve been strongly considering a career in data science after graduation. As a way to showcase my data science skills, I’ve been working on a side project to use webscraping and multivariate statistics to answer the age old question: Are cupcakes really that different from muffins?

Importing data from a LI-COR photosynthesis meter into R

The LI-6400XT is a portable device used to measure photosynthesis in plant leaves. As you take measurements by pressing a button on the device, they are recorded into memory. In order to keep track of which measurments go with which plants (or experimental treatments), there is an “add remark” option where you can enter sample information before taking measurements. When the data are exported, you get a series of .