I woke up this morning to an email saying my first R package, holodeck, was on it’s way to CRAN! It’s a humble package, providing a framework for quickly slapping together test data with different degrees of correlation between variables and differentiation among levels of a categorical variable.
Example use of holodeck library(holodeck) library(dplyr) df <- #make a categorical variable with 10 observations and 3 groups sim_cat(n_obs = 10, n_groups = 3, name = "Treatment") %>% #add 3 variables that covary sim_covar(n_vars = 3, var = 1, cov = 0.
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).
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.
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?