R Dataset / Package HistData / MacdonellDF

How To Create a Barplot

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Description

Describes how to create a bar plot based on count data. For an example of count data, see the email50 curated data set which was taken from the Open Intro AHSS textbook (not affiliated). An example of count data in this dataset would be the spam column.

Usage

Select one (1) column to create its barplot and then click 'Submit'. If you do not choose count data, you may get unexpected results.

See Also

Students may also be interested in creating barplots for contingency tables.

For a stacked side-by-side barplot, see the other barplot app.

How To Create a Stacked Barplot

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Usage

Select 1 (one) column from a contingency table like the Gender and Politics or VADeaths curated datasets.

If you do not choose a contingency table, you may get unexpected results. You can import a dataset if you are logged-in.

Details

Shows the student how to create a single stacked bar plot based on a column in a contingency table.

See Also

For a basic barplot (single column) based on count data see the count data barplot app.

For a stacked side-by-side barplot see the other stacked barplot app for categorical data.

How To Create a Pie Chart

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Usage

Select 1 (one) column from a contingency table. If you don't have your own dataset, you can choose the Gender and Politics or VADeaths curated datasets. If a contingency table is not chosen, you may get unexpected results.

A contingency table has columns like a regular dataset, but the first row contains row names that categorize and "split-up" the dataset. An example of a contingency table would be something like this:

LIBERAL CONSERVATIVE
F 762 468
M 484 477

This contingency table is take from the Gender and Politics dataset. You can get a preview by selecting the dataset from the Curated Data dropdown above.

Details

This app shows the student how to create a pie chart from a contingency table by hand using a Quadstat dataset.

A pie chart shows proportions of a sample or population. Each piece of a pie chart corresponds to some subset of the sample or population. In this case, we will use the contingency table rows to subset the sample.

See Also

Students may also want to view the app for creating a pie chart from count data.

How To Compute the Mean

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Usage

Click "Submit" after selecting one column to see how to compute the arithmetic mean (average) of data (vectors).

Description

If all the values of a sample were plotted on a number line, the average would be the point in the middle that would balance the two sides.

The average is greatly influenced by outliers, meaning extreme points can pull the average to the left or right.

If we are referring to the average of population (all observations), the symbol for the average (arithmetic mean) is $\mu$.

If we are referring to the average of a sample (a subset of the population), the symbol for the average (arithmetic mean) is $\bar{x}$.

Computing the average

Suppose we have a sample consisting of $x_1, x_2, x_3,...,x_n$. This means we have $n$ observations. Then,

$$\bar{x}=\frac{x_1, x_2, x_3,...,x_n}{n}.$$

The formula tells us that we need to add all the observations and then divide by the number of observations to compute the mean.

Example 1

Compute the mean of $A = \{1,2,3\}$.

$$\bar{x} = \frac{1+2+3}{3} = 2.$$

How To Create a Plot

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Usage

Select two columns which are to be used in the scatterplot. The first column clicked will be the independent variable (X-axis).

Description

This web application describes how to create a scatterplot of two dataset variables plotted on the xy-axes.

How to Compute the Median

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Median Value

Description

Compute the sample median.

Usage

median(x, na.rm = FALSE, ...)

Arguments

x

an object for which a method has been defined, or a numeric vector containing the values whose median is to be computed.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

...

potentially further arguments for methods; not used in the default method.

Value

The default method returns a length-one object of the same type as x, except when x is logical or integer of even length, when the result will be double.

If there are no values or if na.rm = FALSE and there are NA values the result is NA of the same type as x (or more generally the result of x[FALSE][NA]).

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

Please see the full application for additional options and documentation.

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Dotplot

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Hollow Histogram

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Mean

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Pie Chart

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Plot

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Regression

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Stem and Leaf Plots

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Summary

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Visual Summaries

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Attachment Size
dataset-63790.csv 55.94 KB
Dataset License
GNU General Public License v2.0
Documentation

Macdonell's Data on Height and Finger Length of Criminals, used by Gosset (1908)

Description

In the second issue of Biometrika, W. R. Macdonell (1902) published an extensive paper, On Criminal Anthropometry and the Identification of Criminals in which he included numerous tables of physical characteristics 3000 non-habitual male criminals serving their sentences in England and Wales. His Table III (p. 216) recorded a bivariate frequency distribution of height by finger length. His main purpose was to show that Scotland Yard could have indexed their material more efficiently, and find a given profile more quickly.

W. S. Gosset (aka "Student") used these data in two classic papers in 1908, in which he derived various characteristics of the sampling distributions of the mean, standard deviation and Pearson's r. He said, "Before I had succeeded in solving my problem analytically, I had endeavoured to do so empirically." Among his experiments, he randomly shuffled the 3000 observations from Macdonell's table, and then grouped them into samples of size 4, 8, ..., calculating the sample means, standard deviations and correlations for each sample.

Usage

data(Macdonell)
data(MacdonellDF)

Format

Macdonell: A frequency data frame with 924 observations on the following 3 variables giving the bivariate frequency distribution of height and finger.

height

lower class boundaries of height, in decimal ft.

finger

length of the left middle finger, in mm.

frequency

frequency of this combination of height and finger

MacdonellDF: A data frame with 3000 observations on the following 2 variables.

height

a numeric vector

finger

a numeric vector

Details

Class intervals for height in Macdonell's table were given in 1 in. ranges, from (4' 7" 9/16 - 4' 8" 9/16), to (6' 4" 9/16 - 6' 5" 9/16). The values of height are taken as the lower class boundaries.

For convenience, the data frame MacdonellDF presents the same data, in expanded form, with each combination of height and finger replicated frequency times.

Source

Macdonell, W. R. (1902). On Criminal Anthropometry and the Identification of Criminals. Biometrika, 1(2), 177-227. doi:10.1093/biomet/1.2.177 http://www.jstor.org/stable/2331487

The data used here were obtained from:

Hanley, J. (2008). Macdonell data used by Student. http://www.medicine.mcgill.ca/epidemiology/hanley/Student/

References

Hanley, J. and Julien, M. and Moodie, E. (2008). Student's z, t, and s: What if Gosset had R? The American Statistican, 62(1), 64-69.

Gosett, W. S. [Student] (1908). Probable error of a mean. Biometrika, 6(1), 1-25. http://www.york.ac.uk/depts/maths/histstat/student.pdf

Gosett, W. S. [Student] (1908). Probable error of a correlation coefficient. Biometrika, 6, 302-310.

Examples

data(Macdonell)# display the frequency table
xtabs(frequency ~ finger+round(height,3), data=Macdonell)## Some examples by james.hanley@mcgill.ca    October 16, 2011
## http://www.biostat.mcgill.ca/hanley/
## See:  http://www.biostat.mcgill.ca/hanley/Student/###############################################
##  naive contour plots of height and finger ##
###############################################
 
# make a 22 x 42 table
attach(Macdonell)
ht <- unique(height) 
fi <- unique(finger)
fr <- t(matrix(frequency, nrow=42))
detach(Macdonell)
dev.new(width=10, height=5)  # make plot double wide
op <- par(mfrow=c(1,2),mar=c(0.5,0.5,0.5,0.5),oma=c(2,2,0,0))dx <- 0.5/12
dy <- 0.5/12plot(ht,ht,xlim=c(min(ht)-dx,max(ht)+dx),
           ylim=c(min(fi)-dy,max(fi)+dy), xlab="", ylab="", type="n" )# unpack  3000 heights while looping though the frequencies 
heights <- c()
for(i in 1:22) {
	for (j in 1:42) {
	 f  <-  fr[i,j]
	 if(f>0) heights <- c(heights,rep(ht[i],f))
	 if(f>0) text(ht[i], fi[j], toString(f), cex=0.4, col="grey40" ) 
	}
}
text(4.65,13.5, "Finger length (cm)",adj=c(0,1), col="black") ;
text(5.75,9.5, "Height (feet)", adj=c(0,1), col="black") ;
text(6.1,11, "Observed bin\nfrequencies", adj=c(0.5,1), col="grey40",cex=0.85) ;
# crude countour plot
contour(ht, fi, fr, add=TRUE, drawlabels=FALSE, col="grey60")
# smoother contour plot (Galton smoothed 2-D frequencies this way)
# [Galton had experience with plotting isobars for meteorological data]
# it was the smoothed plot that made him remember his 'conic sections'
# and ask a mathematician to work out for him the iso-density
# contours of a bivariate Gaussian distribution... dx <- 0.5/12; dy <- 0.05  ; # shifts caused by averagingplot(ht,ht,xlim=c(min(ht),max(ht)),ylim=c(min(fi),max(fi)), xlab="", ylab="", type="n"  )
 
sm.fr <- matrix(rep(0,21*41),nrow <- 21)
for(i in 1:21) {
	for (j in 1:41) {
	   smooth.freq  <-  (1/4) * sum( fr[i:(i+1), j:(j+1)] ) 
	   sm.fr[i,j]  <-  smooth.freq
	   if(smooth.freq > 0 )
	   text(ht[i]+dx, fi[j]+dy, sub("^0.", ".",toString(smooth.freq)), cex=0.4, col="grey40" )
	   }
	}
 
contour(ht[1:21]+dx, fi[1:41]+dy, sm.fr, add=TRUE, drawlabels=FALSE, col="grey60")
text(6.05,11, "Smoothed bin\nfrequencies", adj=c(0.5,1), col="grey40", cex=0.85) ;
par(op)
dev.new()    # new default device#######################################
## bivariate kernel density estimate
#######################################if(require(KernSmooth)) {
MDest <- bkde2D(MacdonellDF, bandwidth=c(1/8, 1/8))
contour(x=MDest$x1, y=MDest$x2, z=MDest$fhat,
	xlab="Height (feet)", ylab="Finger length (cm)", col="red", lwd=2)
with(MacdonellDF, points(jitter(height), jitter(finger), cex=0.5))
}#############################################################
## sunflower plot of height and finger with data ellipses  ##
#############################################################with(MacdonellDF, 
	{
	sunflowerplot(height, finger, size=1/12, seg.col="green3",
		xlab="Height (feet)", ylab="Finger length (cm)")
	reg <- lm(finger ~ height)
	abline(reg, lwd=2)
	if(require(car)) {
	dataEllipse(height, finger, plot.points=FALSE, levels=c(.40, .68, .95))
		}
  })
############################################################
## Sampling distributions of sample sd (s) and z=(ybar-mu)/s
############################################################# note that Gosset used a divisor of n (not n-1) to get the sd.
# He also used Sheppard's correction for the 'binning' or grouping.
# with concatenated height measurements...mu <- mean(heights) ; sigma <- sqrt( 3000 * var(heights)/2999 )
c(mu,sigma)# 750 samples of size n=4 (as Gosset did)# see Student's z, t, and s: What if Gosset had R? 
# [Hanley J, Julien M, and Moodie E. The American Statistician, February 2008] # see also the photographs from Student's notebook ('Original small sample data and notes")
# under the link "Gosset' 750 samples of size n=4" 
# on website http://www.biostat.mcgill.ca/hanley/Student/
# and while there, look at the cover of the Notebook containing his yeast-cell counts
# http://www.medicine.mcgill.ca/epidemiology/hanley/Student/750samplesOf4/Covers.JPG
# (Biometrika 1907) and decide for yourself why Gosset, when forced to write under a 
# pen-name, might have taken the name he did!# PS: Can you figure out what the 750 pairs of numbers signify?
# hint: look again at the numbers of rows and columns in Macdonell's (frequency) Table III.
n <- 4
Nsamples <- 750y.bar.values <- s.over.sigma.values <- z.values <- c()
for (samp in 1:Nsamples) {
	y <- sample(heights,n)
	y.bar <- mean(y)
	s  <-  sqrt( (n/(n-1))*var(y) ) 
	z <- (y.bar-mu)/s
	y.bar.values <- c(y.bar.values,y.bar) 
	s.over.sigma.values <- c(s.over.sigma.values,s/sigma)
	z.values <- c(z.values,z)
	}	
op <- par(mfrow=c(2,2),mar=c(2.5,2.5,2.5,2.5),oma=c(2,2,0,0))
# sampling distributions
hist(heights,breaks=seq(4.5,6.5,1/12), main="Histogram of heights (N=3000)")
hist(y.bar.values, main=paste("Histogram of y.bar (n=",n,")",sep=""))hist(s.over.sigma.values,breaks=seq(0,4,0.1),
	main=paste("Histogram of s/sigma (n=",n,")",sep="")); 
z=seq(-5,5,0.25)+0.125
hist(z.values,breaks=z-0.125, main="Histogram of z=(ybar-mu)/s")
# theoretical
lines(z, 750*0.25*sqrt(n-1)*dt(sqrt(n-1)*z,3), col="red", lwd=1)
par(op)#####################################################
## Chisquare probability plot for bivariate normality
#####################################################mu <- colMeans(MacdonellDF)
sigma <- var(MacdonellDF)
Dsq <- mahalanobis(MacdonellDF, mu, sigma)Q <- qchisq(1:3000/3000, 2)
plot(Q, sort(Dsq), xlab="Chisquare (2) quantile", ylab="Squared distance")
abline(a=0, b=1, col="red", lwd=2)
--

Dataset imported from https://www.r-project.org.

Documentation License
GNU General Public License v2.0

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