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In the previous installment we sucked some data from the National Health and Nutrition Examination Survey into R and did some preliminary work: selecting only the fields of interest, renaming columns and removing missing data. Now we are going to play with some categorical data. There is already one categorical field in the data representing gender. However, the labels are not ideal: > head(DS0012) id gender age mass height BMI 1 41475 2 62 138. Read more »
I have just started preparing a series of talks aimed at introducing the use of R to a rather broad audience consisting of physicists, chemists, statisticians, biologists and computer scientists (plus a few other disciplines thrown in for good measure). I want to use a single consistent set of data throughout the talks. Finding something that would resonate with such a disparate set of people was quite a challenge. After playing around with a couple of options, I settled on using data for age, height and mass. Read more »
I was recently browsing through the variety of of MetaTrader indicators for support and resistance levels. None of them ticked all of my boxes. Either they were not aesthetically pleasing (making a mess of my pristine charts) or they failed to produce what I consider to be reasonable levels. So, embracing my pioneering spirit, I set out to fashion my own indicator, one which will ultimately tick all of my boxes! Read more »
A year or so ago I went to a talk which included the diagram below. It shows the locations of the Earth’s fleet of geosynchronous satellites. According to the speaker, the information in this diagram was already quite dated: the satellites and their locations had changed. I decided to update the diagram using the locations of satellites from the list of geosynchronous satellites published on Wikipedia. Probably not the most definitive source of data on this subject, but it was a good starting point. Read more »