9 minute read

I’m excited to announce that my first R package, noaaoceans, is now available on CRAN. The current version focuses on accessing data from the CO-OPS API. The package also facilitates the collection of basic metadata for each of the stations that collect the data available in the API. Installation The package can be installed from CRAN or GitHub.

Install from CRAN install.packages('noaaoceans') # Install from GitHub remotes::install_github('warlicks/noaaoceans') Introduction There are two functions included in the package and they are often used together to provide the information of interest.

Sean Warlick

3 minute read

I have a confession to make. When I first started using R, I hated RStudio. I want to emphasize the past tense of the previous sentence, given that it is a rather adversarial statement. However, over the past year I have really come to appreciate the power of RStudio and have since rebuilt my workflow around it. The restructuring of my work flow around RStudio has been driven by three things.

A Comparision of tidytext and tm

Part 1: Data Structures

Sean Warlick

6 minute read

The default package for many working with text and Natural Language Processing in R has been tm. This past fall, a new package tidytext entered the ring and offers new ways to work with text in R. Having done all of my text analysis using the tm package, I thought it was time to take a look at tidytext and compare the two libraries. We’ll start the comparison by looking at the underlying data structures of the two packages.

Sean Warlick

5 minute read

Introduction {#introduction} In my recent post Learning Python, I promised an article about using python to gather data from web APIs. I was in the midst of building several functions to gather and clean real estate data from Trulia, when I received an email announcing that they were shutting down the API. While it did bring that project to an end, it did inspire me to resume a project to analyze college swimming meet results that had been long abandoned.

Sean Warlick

4 minute read

Clean up days in my apartment building netted me two computers that are perfect for running some basic experiments. The nicer of the two - a Dell Latitude C640- has a 2.40 GHz processor, 1 Gb of RAM and a 60 GB hard drive. It’s basically an over-sized Raspbery Pi. Despite the relatively low power the machine is perfect for learning how to set up an R Studio Server.