Blog Archives

Examining the Tweeting Patterns of Prominent Crossfit Gyms

December 19, 2018
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Examining the Tweeting Patterns of Prominent Crossfit Gyms

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...

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Semiparametric Regression in R

March 4, 2018
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Semiparametric Regression in R

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 assumptions about the data or how the analysis is specified. For example, Auto Regressive Integrated Moving Average (ARIMA) models require that the time series is … Continue reading Semiparametric...

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Packages for Getting Started with Time Series Analysis in R

February 18, 2018
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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...

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Data.Table by Example – Part 3

September 30, 2017
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Data.Table by Example – Part 3

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

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Data.Table by Example – Part 2

September 26, 2017
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Data.Table by Example – Part 2

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.

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Data.Table by Example – Part 1

September 26, 2017
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Data.Table by Example – Part 1

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

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R Programming Notes – Part 2

July 17, 2017
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R Programming Notes – Part 2

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 tools that are useful. Over the past year, I have been working as an R programmer and these are some of the new learnings that

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Statistical Reading Rainbow

October 16, 2016
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Statistical Reading Rainbow

For those of us who received statistical training outside of statistics departments, it often emphasized procedures over principles. This entailed that we learned about various statistical techniques and how to perform analysis in a particular statistical software, but glossed over the mechanisms and mathematical statistics underlying these practices. While that training methodology (hereby referred to

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Introduction to the RMS Package

July 4, 2016
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Introduction to the RMS Package

The rms package offers a variety of tools to build and evaluate regression models in R. Originally named ‘Design’, the package accompanies the book “Regression Modeling Strategies” by Frank Harrell, which is essential reading for anyone who works in the ‘data science’ space. Over the past year or so, I have transitioned my personal modeling

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Batch Forecasting in R

February 29, 2016
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Batch Forecasting in R

Given a data frame with multiple columns which contain time series data, let’s say that we are interested in executing an automatic forecasting algorithm on a number of columns. Furthermore, we want to train the model on a particular number of observations and assess how well they forecast future values. Based upon those testing procedures,

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