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## Package party: Conditional Inference Trees

I am going to be using the party package for one of my projects, so I spent some time today familiarising myself with it. The details of the package are described in Hothorn, T., Hornik, K., & Zeileis, A. (1999). “party: A Laboratory for Recursive Partytioning” which is available from CRAN.
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## Plotting categorical variables

In the previous installment we generated a few plots using numerical data straight out of the National Health and Nutrition Examination Survey. This time we are going to incorporate some of the categorical variables into the plots. Although going from raw numerical data to categorical data bins (like we did for age and BMI) does give you less precision, it can make drawing conclusions from plots a lot easier.
We will start off with a simple plot of two numerical variables: age against BMI.
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## Plotting numerical variables

In the previous installment we generated some simple descriptive statistics for the National Health and Nutrition Examination Survey data. Now we are going to move on to an area in which R really excels: making plots and visualisations. There are a variety of systems for plotting in R, but we will start off with base graphics.
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## Descriptive Statistics

## Categorical Variables

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.
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## Loading Data

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.
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## Support & Resistance Indicator

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!
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## Locations of Geosynchronous Satellites

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.
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