It's easy to run a basic hypothesis test in R, once you know how. For example, if you've a nice set of data that you know meets the relevant assumptions, then you can run a t test in the following sort of way . Here we'll assume that you're interested in comparing the differences in … Continue reading Using R to run many hypothesis tests (or other functions) on subsets of your data in one go
Tag: R
Analysing your 23andme genetic data in R part 2: exploring the traits associated with your genome
In part one of this mini-series, you heroically obtained and imported your 23andme raw genome data into R. Fun as that was, let's see if we can learn something interesting from it. After all, 23andme does automatically provide several genomic analysis reports, but - for many sensible reasons - it is certainly limited in what … Continue reading Analysing your 23andme genetic data in R part 2: exploring the traits associated with your genome
Analysing your 23andme genetic data in R part 1: importing your genome into R
23andme is one of the ever-increasing number of direct to consumer DNA testing companies. You send in a vial of your spit; and they analyse parts of your genome, returning you a bunch of reports on ancestry, traits and - if you wish - health. Their business is highly regulated, as of course it should … Continue reading Analysing your 23andme genetic data in R part 1: importing your genome into R
R packages for summarising data – part 2
In a recent post, I searched a tiny percentage of the CRAN packages in order to check out the options for R functions that quickly and comprehensively summarise data, in a way conducive to tasks such as data validation and exploratory analytics. Since then, several generous people have been kind enough to contact me with … Continue reading R packages for summarising data – part 2
My favourite R package for: summarising data
Hot on the heels of delving into the world of R frequency table tools, it's now time to expand the scope and think about data summary functions in general. One of the first steps analysts should perform when working with a new dataset is to review its contents and shape. How many records are there? … Continue reading My favourite R package for: summarising data
My favourite R package for: frequency tables
Back for the next part of the "which of the infinite ways of doing a certain task in R do I most like today?" series. This time, what could more more fascinating an aspect of analysis to focus on than: frequency tables? OK, most topics might actually be more fascinating. Especially when my definition of … Continue reading My favourite R package for: frequency tables
My favourite R package for: correlation
R is a wonderful, flexible, if somewhat arcane tool for analytics of all kinds. Part of its power, yet also its ability to bewilder, comes from the fact that there are so many ways of doing the same, or similar, things. Many of these ways are instantly available thanks to many heroes of the R … Continue reading My favourite R package for: correlation
Retrieving Adobe SiteCatalyst data with R
Adobe SiteCatalyst (part of Adobe Analytics) is a nicely comprehensive tool for tracking user interactions upon one's website, app and more. However, in the past I've had a fair amount of trouble de-siloing its potentially immensely useful data into external tools, such that I could connect, link and process it for insights over and above those you can get … Continue reading Retrieving Adobe SiteCatalyst data with R
Clustering categorical data with R
Clustering is one of the most common unsupervised machine learning tasks. In Wikipedia's current words, it is: the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups Most "advanced analytics" … Continue reading Clustering categorical data with R
Kruskal Wallis significance testing with Tableau and R
Whilst Tableau has an increasing number of advanced statistical functions - a case in point being the newish analytics pane from Tableau version 9 - it is not usually the easiest tool to use to calculate any semi-sophisticated function that hasn't yet been included. Various clever people have tried to work some magic aroud this, for instance by … Continue reading Kruskal Wallis significance testing with Tableau and R
Dabbling with Data 