2588 search results for "map"

The “splitstackshape” package for R

August 15, 2013
By

A while ago, a friend of ours presented me with a data problem. Her questionnaire had some questions where the respondent could provide multiple responses. You know, the “Check as many as apply” type of questions. One way that this data is commonly stored is to put a comma separated value into a single cell

Read more »

Golf Scramble Simulation in R

August 14, 2013
By
Golf Scramble Simulation in R

Golf Scramble Simulation Golf Scramble SimulationThis is a simulation of a standard best-ball golf scramble. Conventional wisdom has it that the best golfer (A) should hit last, the idea being that one of the lesser golfers may have a decent shot already so the...

Read more »

The Brand as Affordance: Item Response Modeling of Brand Perceptions

August 13, 2013
By
The Brand as Affordance: Item Response Modeling of Brand Perceptions

It is just too easy to think of a brand as a web of associations.  What comes to mind when I say "Subway Sandwich"?  Did you remember a commercial or the "eat fresh" tagline?  Without much effort, one can generate a long list of associat...

Read more »

Classi-Compare of Raster Satellite Images – Before and After

August 13, 2013
By

For my research on the effect of power outages on fertility , we study a period of extensive power rationing that lasted for almost a whole year and affected most of Latin America, but in particular, it affected Colombia. The key difficult was to determine which areas were exposed to the power-outage and the extent to

Read more »

Short tales of two NCAA basketball conferences (Big 12 and West Coast) using graphs

August 12, 2013
By
Short tales of two NCAA basketball conferences (Big 12 and West Coast) using graphs

Having been at the University of Kansas (Kansas Jayhawks) as a student and now working at Gonzaga University (Gonzaga Bulldogs), discussions about college basketball are inescapable. This post uses R, ggmap, ggplot2 and the shiny server to graphically ...

Read more »

Variable importance in neural networks

August 12, 2013
By
Variable importance in neural networks

If you’re a regular reader of my blog you’ll know that I’ve spent some time dabbling with neural networks. As I explained here, I’ve used neural networks in my own research to develop inference into causation. Neural networks fall under two general categories that describe their intended use. Supervised neural networks (e.g., multilayer feed-forward networks)

Read more »

In case you missed it: July 2013 Roundup

August 9, 2013
By

In case you missed them, here are some articles from July of particular interest to R users: A new 90-second, creative commons video helps R enthusiasts share the history, community and applications of R. Analyst group Butler Analytics reviews 10 predictive analytics platforms, and says that "real analysts use R". An excellent example of Simpsons Paradox: US median wages...

Read more »

R on Your iPhone (Not the Way You Think)

August 8, 2013
By
R on Your iPhone (Not the Way You Think)

If you really love R, you should put it on your iPhone.  Apple gives the measurements for its products here. Let's use a little grid magic with ggplot2 to make a chart for the back of your iphone similar to this. require(grid)require(ggplot2)# thanks for the Apple measurements# https://developer.apple.com/resources/cases/x11( height = as.numeric(convertX(unit(58.55, "mm"), "in")),...

Read more »

Data Science MD July Recap: Python and R Meetup

August 8, 2013
By
Data Science MD July Recap: Python and R Meetup

For July’s meetup, Data Science MD was honored to have Jonathan Street of NIH and Brian Godsey of RedOwl Analytics come discuss using Python and R for data analysis. Jonathan started off by describing the growing ecosystem of Python data … Continue reading → The post Data Science MD July Recap: Python and R Meetup appeared first on...

Read more »

Understanding the *apply() functions and then others in R by asking questions

August 7, 2013
By
Understanding the *apply() functions and then others in R by asking questions

The *apply() functions are powerful but designed poorly. The R documents are written poorly. They combined make R very hard to learn. All the time, it seems there are some explanations missing. (Another bad example of documentation is Python docs. I cannot retrieve information from the long long pages with multi-level bullets. Why don’t they

Read more »