Using csvkit to Summarize Data: A Quick Example

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 perform data munging and exploratory data analysis, there are also a number of utilities to examine, fix, slice, transform, and summarize data through the command Continue reading Using csvkit to Summarize Data: A Quick Example

Examining Website Pathing Data Using Markov Chains

A markov model can be used to examine a stochastic process describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Let’s define a stochastic process  that takes on a finite number of possible values which are nonnegative integers. Each state, , represents it’s value Continue reading Examining Website Pathing Data Using Markov Chains

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 Continue reading Introduction to the RMS Package

Extract Google Trends Data with Python

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

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 Continue reading Statistical Reading Rainbow

Weekly R-Tips: Visualizing Predictions

Lets say that we estimated a linear regression model on time series data with lagged predictors. The goal is to estimate sales as a function of inventory, search volume, and media spend from two months ago. After using the lm function to perform linear regression, we predict sales using values from two month ago. If Continue reading Weekly R-Tips: Visualizing Predictions

Weekly R-Tips: Importing Packages and User Inputs

Number 1: Importing Multiple Packages Anyone who has used R for some time has written code that required the use of multiple packages. In most cases, this will be done by using the library or require function to bring in the appropriate extensions. That’s nice and gets the desired result, but can’t we just import Continue reading Weekly R-Tips: Importing Packages and User Inputs