Last week on one of my favourite podcasts, ESPN’s Football Today, Matt Williamson & Kevin Weidl discussed the standout prospects from the NFL Combine. A lot of the conversation was around how the 40 yard dash times have improved year … Continue reading →
In last week’s post I used R heatmaps to visualise the performance of NFL Quarterbacks in 2012. This was done in a 2 step process, Clustering QB performance based on the 12 performance metrics using hierarchical clustering Plotting the performance … Continue reading →
With only 24 hours remaining in the 2012 NFL season, this is a good time to review how the league's QBs performed during the regular season using performance data from KFFL and the heat mapping capabilities of R. #scale data … Continue reading →
Are your for loops too slow in R ? Are loops that should take seconds actually taking hours ? As I found out recently, how you structure your code can make a huge difference in execution times. Fortunately making a … Continue reading →
One of the great things about following blogs on R is seeing what others are doing & being able to replicate and try out things on my own data sets. For example, some great links on rapidly creating heat maps … Continue reading →
The tolower() function returns an error where it can't map to the Unicode character set of the input data – a common occurrence when analysing social media data with emoticons. Emoticons are those symbols such as 😊🐶🎅 that are commonly … Continue reading →
Joining or merging two data sets is one of the most common tasks in preparing and analysing data. In fact a Google search returns 253 million results. However most examples assume that the columns that you want to merge by have … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
