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 start by subseting the data. The following code takes the first 50000 rows within the dat dataset, selects four columns, creates three new columns pertaining … Continue reading Data.Table by Example – Part 3
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 a count of rows by the date. Let us now add a few columns to our dataset on reported crimes in the city of Chicago. … Continue reading Data.Table by Example – Part 2
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 past year, I have come to realize that this was a mistake. Data tables are incredible and provide R users with a syntatically concise and … Continue reading Data.Table by Example – Part 1
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 a competitive fitness sport, Crossfit is a high-intensity fitness program incorporating elements from several sports and exercise protocols such as high-intensity interval training, Olympic weightlifting, … Continue reading Examining the Tweeting Patterns of Prominent Crossfit Gyms
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 has spent the majority of their career on time series problems, this was somewhat surprising because R already has a great suite of tools for … Continue reading Packages for Getting Started with Time Series Analysis in R
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