While recently looking at submissions for H1B Visas, I noticed that a not insignificant number of companies had submitted proposals to receive visas for persons who would be working as recruiters. While I know very little about the labor maket for recruiters, I wanted to examine that data to see which companies were applying for … Continue reading Is There A Shortage of Recruiters? A Look at H1B Proposals
If are looking to hire an analytics professional, please send me a message at [email protected]. I am looking for new opportunities and available to start ASAP. sPowerlifting in the United States has seen explosive growth in recent years, both in popularity and participation. National federations like USA Powerlifting have reported a sharp increase in membership, with … Continue reading The Rise of Powerlifting in the United States: A Data-Driven Look at Trends and Patterns
I’ve been putting together a basic SQL cheat sheet that could be used as a reference guide. Here are a series of common procedures that should be of use for anyone who uses SQL to extract data. No explanations are provided as they should largely be known to the end user.
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 a six year delay from powerlifting, I often mess around with the Open Powerlifting data as it’s of personal interest. Most of the anlysis that … Continue reading Powerlytics: Impact of Age, Gender, and Body Weight on Total Weight Lifted in Powerlifting Meets
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 elegant interface for conducting tasks using an intuitive framework that’s widely used by analysts. R and sqldf sqldf(“SELECT COUNT(*) FROM … Continue reading Turning Data Into Awesome With sqldf and pandasql
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
Anyone who has regularly worked with Google Trends data has had to deal with the slightly tedious task of grabbing keyword level data and reformatting the spreadsheet provided by Google. After looking for a seamless way to pull the data, I came upon the PyTrends library on GitHub, and sought to put together some quick … Continue reading Extract Google Trends Data with Python
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 … Continue reading R Programming Notes – Part 2
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 such a way that it provided the author with a useful reference guide for understanding a number of useful R functions. DO.CALL The do.call function … Continue reading R Programming Notes – Part 1
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 of how to put together a function. In particular, I will walk through the process of generating a function that executes evaluation of a time … Continue reading Writing Functions in R: Example One
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