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Dr. Hain's various/sundry has helpful information about things that Dr. Hain has discovered by trial and error.

Composite scores in posturography, by decade.

This page discusses plotting posturography scores by decade.

The simple but useless approach is as follows:

Scatterplot of CDP comp vs age

A scatterplot of posturography scores vs. age, gives you a pretty random looking set of dots. In order to make sense of this (to some extent anyway), one needs to compute means and SD by age.

Step 1: Load data

This is the R code that takes a table in csv format and converts it into a dataframe.

## this reads a dump from the query program into a dataframe called cdp.
cdp=read.csv("cdp_gainTC.csv", header=TRUE, skip=11, sep=",",stringsAsFactors=FALSE)

# Reformat
cdp$DOS_1 = as.Date(cdp$DOS_1, format="%m/%d/%Y")
cdp$DOS_2 = as.Date(cdp$DOS_2, format="%m/%d/%Y")
cdp$Birth = as.Date(cdp$Birth, format="%m/%d/%Y")
cdp$FirstDt = as.Date(cdp$FirstDt, format="%m/%d/%Y")
cdp$Age = as.numeric(format(cdp$DOS_1, "%Y")) - as.numeric(format(cdp$Birth, "%Y"))

cdp<-rename(cdp,"Composite" = "COMPOSITE_1") # Using dplyr to rename a dataframe name, new_name, old_name.
cdp$Composite[cdp$Age < 10]<-NA # Crazy ages, remove Com
cdp$Composite[cdp$Age > 90]<-NA # Too old
cdp$Age[cdp$Age < 10]<-NA # Crazy ages, remove Comp
cdp$Age[cdp$Age > 90]<-NA # Too old
cdp$Composite[cdp$Composite >100]<-NA
cdp$Composite[cdp$Composite <0]<-NA
cdp$decade<-as.integer(cdp$decade) # This causes the plot function to separate out the data by decade.

total <- sprintf("Posturography vs age: n=%d, Chicago Dizziness and Hearing", nrow(cdp))
means <- aggregate(Composite~decade, cdp, mean)


Step 2: Create a boxplot of CDP vs decade.

CDP by decade

This graph was produced using the following code:

plot(cdp$decade, cdp$Composite, col="coral", main=total, # main="Posturography vs Age", xlab="Decade", ylab="Composite score")


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