Blog Archives

Weekly R-Tips: Importing Packages and User Inputs

December 11, 2015
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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

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Automate the Boring Stuff: GGPlot2

November 26, 2015
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Automate the Boring Stuff: GGPlot2

The majority of my interaction with the ggplot2 package involves the interactive execution of code to visualize data within the context of exploratory data analysis. This is often a manual process and quite laborious. I recently sought to improve these tasks by creating a series of user defined functions that contained my most commonly used

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Applied Statistical Theory: Quantile Regression

November 13, 2015
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Applied Statistical Theory: Quantile Regression

This is part two of the ‘applied statistical theory’ series that will cover the bare essentials of various statistical techniques. As analysts, we need to know enough about what we’re doing to be dangerous and explain approaches to others. It’s not enough to say “I used X because the misclassification rate was low.” Standard linear

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Applied Statistical Theory: Belief Networks

October 21, 2015
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Applied Statistical Theory: Belief Networks

Applied statistical theory is a new series that will cover the basic methodology and framework behind various statistical procedures. As analysts, we need to know enough about what we’re doing to be dangerous and explain approaches to others. It’s not enough to say “I used X because the misclassification rate was low.” At the same

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Basic Forecasting

October 17, 2015
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Basic Forecasting

Forecasting refers to the process of using statistical procedures to predict future values of a time series based on historical trends. For businesses, being able gauge expected outcomes for a given time period is essential for managing marketing, planning, and finances. For example, an advertising agency may want to utilizes sales forecasts to identify which

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A Few Days of Python: Using R in Python

September 28, 2015
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A Few Days of Python: Using R in Python

Using R Functions in Python

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Working With SEM Keywords in R

September 20, 2015
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Working With SEM Keywords in R

The following post is taken from two previous posts from an older blog of mine that is no longer available. These are from several years ago, and related to two critical questions that I encountered. One, how can I automatically generate hundreds of thousands of keywords for a search engine marketing campaign. Two, how can I

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Logistic Regression in R – Part Two

September 2, 2015
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Logistic Regression in R – Part Two

My previous post covered the basics of logistic regression. We must now examine the model to understand how well it fits the data and generalizes to other observations. The evaluation process involves the assessment of three distinct areas – goodness of fit, tests of individual predictors, and validation of predicted values – in order to

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Logistic Regression in R – Part One

September 1, 2015
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Logistic Regression in R – Part One

Please note that an earlier version of this post had to be retracted because it contained some content which was generated at work. I have since chosen to rewrite the document in a series of posts. Please recognize that this may take some time. Apologies for any inconvenience.   Logistic regression is used to analyze the

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Examining Email Addresses in R

August 22, 2015
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Examining Email Addresses in R

I don’t normally work with personal identifiable information such as emails. However, the recent data dump from Ashley Madison got me thinking about how I’d examine a data set composed of email addresses. What are the characteristics of an email that I’d look to extract? How would I perform that task in R? Here’s some

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