Comparing Point-and-Click Front Ends for R
For an updated version of this post, see:http://r4stats.com/articles/software-reviews/r-gui-comparison/. Now that I’ve completed seven detailed reviews of Graphical User Interfaces (GUIs) for R, let’s compare them. It’s easy enough to count their features and plot them, so let’s start there. I’m basing the counts on the number of menu... Read more
Some Details on Running xgboost
While reading Dr. Nina Zumel’s excellent note on bias in common ensemble methods, I ran the examples to see the effects she described (and I think it is very important that she is establishing the issue, prior to discussing mitigation). [Related Article: When Less is More: A Brief Story About Feature... Read more
Hierarchical Bayesian Models in R
Hierarchical approaches to statistical modeling are integral to a data scientist’s skill set because hierarchical data is incredibly common. In this article, we’ll go through the advantages of employing hierarchical Bayesian models and go through an exercise building one in R. If you’re unfamiliar with Bayesian... Read more
Factors in R
The factor is a foundational data type in R. Factors are generally used to represent categorical variables, which may be intrinsically unordered (nominal) or ordered (ordinal). While the underlying data is often character, factors can be built on numerics as well. Factor variables are stored as integers pointing to unique... Read more
Deep Learning in R with Keras
The primary professional hat I wear is as a data science consultant working with machine learning in a variety of problem domains. Due to my academic past in computer science and applied statistics, my development environment of choice today is typically R. Lately however, Python is... Read more
Jupyter Notebook: Python or R—Or Both?
I was analytically betwixt and between a few weeks ago. Most of my Jupyter Notebook work is done in either Python or R. Indeed, I like to self-demonstrate the power of each platform by recoding R work in Python and vice-versa. I must have a dozen... Read more
Validating Type I and II Errors in A/B Tests in R
In the below work, we will intentionally leave out statistics theory and attempt to develop an intuitive sense of what type I(false-positive) and type II(false-negative) errors represent when comparing metrics in A/B tests. One of the problems plaguing the analysis of A/B tests today is known... Read more
Introduction to R Shiny
Alyssa is a speaker for ODSC East 2019 this April 30 to May 3! Attend her talk “Data Visualization with R Shiny.” What is R Shiny? Shiny is an R package that enables you to build interactive web apps using both the statistical power of R... Read more
Activities and Insights for R: Monthly Summary of Selected Trends – December 2018
In December, activities across the R ecosystem reduced from levels observed in November. This was notable in StackOverflow, meetup events, and in the downloads of R packages. The December holidays likely caused this general reduction in activities. However, the first two weeks in December saw great... Read more
Monthly Summary of Selected Trends, Activities, and Insights for R – November 2018
In November, activities continued to increase beyond the numbers recorded since July across the R ecosystem. This was most notable in events and in the downloads of R packages. Total package downloads from a single CRAN mirror and in one single year hit half-billion this November... Read more