Writing Functions in R: Example One
A. Background In previous posts, I covered a number of useful functions and packages for writing reusable code. I wanted to extend on that information by providing a working example...continue reading.
A. Background In previous posts, I covered a number of useful functions and packages for writing reusable code. I wanted to extend on that information by providing a working example...continue reading.
A. Background The growth of womens powerlifting has been a interesting thing to witness over the past decade. There have been massive increases in strength and world records have been...continue reading.
A. Background The Open Powerlifting initiative attempts to create an accurate and open archive of all powerlifting meet data throughout the world. As someone who recently started competing again after...continue reading.
A. Introduction The growth of Crossfit has been one of the biggest developments in the fitness industry over the past decade. Promoted as both a physical exercise philosophy and also as...continue reading.
Both R and Python possess libraries for using SQL statements to interact with data frames. While both languages have native facilities for manipulating data, the sqldf and pandasql provide a simple and...continue reading.
In an older post, I discussed a number of functions that are useful for programming in R. I wanted to expand on that topic by covering other functions, packages, and...continue reading.
I’ve been on a note taking binge recently. This post covers a variety of topics related to programming in R. The contents were gathered from many sources and structured in...continue reading.
A. INTRODUCTION When building statistical models, the goal is to define a compact and parsimonious mathematical representation of some data generating process. Many of these techniques require that one make...continue reading.
A. Motivation During the recent RStudio Conference, an attendee asked the panel about the lack of support provided by the tidyverse in relation to time series data. As someone who...continue reading.
For this final post, I will cover some advanced topics and discuss how to use data tables within user generated functions. Once again, let’s use the Chicago crime data. Let’s...continue reading.
In part one, I provided an initial walk through of some nice features that are available within the data.table package. In particular, we saw how to filter data and get...continue reading.
For many years, I actively avoided the data.table package and preferred to utilize the tools available in either base R or dplyr for data aggregation and exploration. However, over the...continue reading.
The following post was republished from two previous posts that were on an older blog of mine that is no longer available. These are from several years ago, and related to...continue reading.
As data analysts, we’re frequently presented with comma-separated value files and tasked with reporting insights. While it’s tempting to import that data directly into R or Python in order to...continue reading.