Analyses as Packages TL;DR Instead of writing an analysis as a single or set of R scripts, use a package and include the analysis as a vignette of the package. Read below for the why, the how is in the next post. Analyses and Reports As data science or statistical researchers, we tend to do a lot of analyses, whether for our...
a:class <- b
Usually in strongly typed languages, like C++, the type of a variable comes before the variable, e.g.: int x = 23 ; So x is declared of...
Analyzing package dependencies and download logs from Rstudio, and a start towards building an R recommendation engine
In this post, I will focus on the analysis of available R packages and the behavior of its users. In essence, this involves looking at the data in two...
Shifting Centre of Gravity of US Fossil Fuel Production
My research Fracking Growth investigates the localised impacts of the current oil and gas production boom in the US. The boom is triggered by extraction of oil and gas deposits,...
Statistics, and the Goldilocks Principle
By the end of May, in Toronto, we had that great talk at the SSC by Jeff Rosenthal, on monte carlo techniques, and Jeff mention the name of “the...
Guns are Cool – States
Last week I looked at time effects of the shootingtracker database. This week I will look at the states. Some (smaller) states never made it on the database. Other...
User Request – Shepards Classification of Sediments
I received a request overnight on how to render the Shepard’s classification diagram, which is an alternative to the USDA’s textural soil classification. This is quite simple to produce...
A Few Notes on UseR! 2014
It has been a month since the UseR! 2014 conference, and I'm probably the last one who writes about it. UseR! is my favorite conference because it is technical...
library() vs require() in R
While I was sitting in a conference room at UseR! 2014, I started counting the number of times that require() was used in the presentations, and would rant about...
A package about nothing
nothing is a package about nothing. The idea is that when you do require(nothing) you express that you don't need anything, and therefore nothing assumes you...
Learning from alternative R engines at DSC 2014
I was honoured to be invited earlier this month to the Directions of Statistical Computing meeting in Brixen, Italy. DSC is one of two meetings run by the R...
Things to try after useR! – Part 1: Deep Learning with H2O
Annual R User Conference 2014The useR! 2014 conference was a mind-blowing experience. Hundreds of R enthusiasts and the beautiful UCLA campus, I...
Combining pages of JSON data with jsonlite and plyr
The jsonlite package is a JSON parser/generator for R which is optimized for pipelines and web APIs. It is used by...
Announcing Shiny Server Pro 1.2
RStudio is very pleased to announce the general availability of Shiny Server Pro 1.2. Download a free 45 day evaluation of Shiny Server Pro 1.2 Shiny Server Pro 1.2...
Interactive visualization of non-linear logistic regression decision boundaries with Shiny
(skip to the shiny app) Model building is very often an iterative process that involves multiple steps of choosing an algorithm and hyperparameters, evaluating that model / cross validation,...
An ISO Standard for R (just kidding)
IST/5, the British Standards’ committee responsible for programming languages in the UK, has a new(ish) committee secretary and like all people in a new role wants to see a...
Agent Based Models and RNetLogo
by Joseph Rickert If I had to pick just one application to be the “killer app” for the digital computer I would probably choose Agent Based Modeling (ABM). Imagine...
Six of One (Plot), Half-Dozen of the Other
This is a guest post by Randy Zwitch (@randyzwitch), a digital analytics and predictive modeling consultant in the Greater Philadelphia area. Randy blogs regularly about Data Science and related technologies...
What shall we talk about at ESA?
ESA is just around the corner, and many of us are gearing up and trying to figure out a schedule to cover all the talks...
Left-handed catchers
Benny Distefano – 1985 Donruss #166 (source: baseball-almanac.com) We are approaching the twenty-fifth anniversary of the last time a left-handed throwing catcher appeared behind the...
Coherent population forecasting using R
This is an example of how to use the demography package in R for stochastic population forecasting with coherent components. It is based on the papers by Hyndman and...
magrittr: Simplifying R code with pipes
R is a functional language, which means that your code often contains a lot of ( parentheses ). And complex code often means nesting those parentheses together, which make...
Mideast Graph 3: Slate Middle East Friendship
Slate recently published a great info-graphic about Middle-East Relationships. It shows the relationships (Friend, Enemy, or Complicated) of 13 countries / organizations and the relationships between each pair. One...
Making random draws from an arbitrarily defined pdf
I recently found myself in need of a function to sample randomly from an arbitrarily defined probability density function. An excellent post by Quantitations shows how to accomplish this...
Flood fill a region of an active device in R
The following is a function to "flood fill" a region on the active plotting device. Once called, the user will be asked to click on the desired target region....
Uncovering the Preferences Shaping Consumer Data: Matrix Factorization
How do you limit your search when looking for a hotel? Those trying to save money begin with price. Members of hotel reward programs focus on their brand. At other...
New data packages
I’ve released four new data packages to CRAN: babynames, fueleconomy, nasaweather and nycflights13. The goal of these packages is to provide some interesting, and relatively large, datasets to demonstrate...
Plotting the characteristic roots for ARIMA models
When modelling data with ARIMA models, it is sometimes useful to plot the inverse characteristic roots. The following functions will compute and plot the inverse roots for any fitted...
Advanced R Profiling with pbdPAPI
R has some extremely useful utilities for profiling, such as system.time(), Rprof(), the often overlooked tracemem(), and the rbenchmark package. But if you want more than just simple timings...
Plotly: collaborative, publication-quality graphing.
