R Dataset / Package HistData / DrinksWages

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How To Create a Stacked Barplot

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

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Correlation Coefficient

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Cumulative Frequency Histogram

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Dotplot

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Hollow Histogram

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Numerical Summaries

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Pie Chart

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Plot

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Ratio

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Regression

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Stem and Leaf Plots

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Visual Summaries

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<iframe src="https://embed.picostat.com/r-dataset-package-histdata-drinkswages.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-70844.csv 2.05 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the DrinksWages data set which pertains to Elderton and Pearson's (1910) data on drinking and wages. The DrinksWages data set is found in the HistData R package. Try to load the DrinksWages data set in R by issuing the following command at the console data("DrinksWages"). This may load the data into a variable called DrinksWages. If R says the DrinksWages data set is not found, you can try installing the package by issuing this command install.packages("HistData") and then attempt to reload the data with library("HistData") followed by data("DrinksWages"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing DrinksWages at the command-line which should display the entire dataset.

If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the DrinksWages R data set. The size of this file is about 2,098 bytes.


Elderton and Pearson's (1910) data on drinking and wages

Description

In 1910, Karl Pearson weighed in on the debate, fostered by the temperance movement, on the evils done by alcohol not only to drinkers, but to their families. The report "A first study of the influence of parental alcholism on the physique and ability of their offspring" was an ambitious attempt to the new methods of statistics to bear on an important question of social policy, to see if the hypothesis that children were damaged by parental alcoholism would stand up to statistical scrutiny.

Working with his assistant, Ethel M. Elderton, Pearson collected voluminous data in Edinburgh and Manchester on many aspects of health, stature, intelligence, etc. of children classified according to the drinking habits of their parents. His conclusions where almost invariably negative: the tendency of parents to drink appeared unrelated to any thing he had measured.

The firestorm that this report set off is well described by Stigler (1999), Chapter 1. The data set DrinksWages is just one of Pearsons many tables, that he published in a letter to The Times, August 10, 1910.

Usage

data(DrinksWages)

Format

A data frame with 70 observations on the following 6 variables, giving the number of non-drinkers (sober) and drinkers (drinks) in various occupational categories (trade).

class

wage class: a factor with levels A B C

trade

a factor with levels baker barman billposter ... wellsinker wireworker

sober

the number of non-drinkers, a numeric vector

drinks

the number of drinkers, a numeric vector

wage

weekly wage (in shillings), a numeric vector

n

total number, a numeric vector

Details

The data give Karl Pearson's tabulation of the father's trades from an Edinburgh sample, classified by whether they dring or are sober, and giving average weekly wage.

The wages are averages of the individuals' nominal wages. Class A is those with wages under 2.5s.; B: those with wages 2.5s. to 30s.; C: wages over 30s.

Source

Pearson, K. (1910). The Times, August 10, 1910.

Stigler, S. M. (1999). Statistics on the Table: The History of Statistical Concepts and Methods. Harvard University Press, Table 1.1

References

M. E. Elderton & K. Pearson (1910). A first study of the influence of parental alcholism on the physique and ability of their offspring, Eugenics Laboratory Memoirs, 10.

Examples

data(DrinksWages)
plot(DrinksWages) # plot proportion sober vs. wage | class
with(DrinksWages, plot(wage, sober/n, col=c("blue","red","green")[class]))# fit logistic regression model of sober on wage
mod.sober <- glm(cbind(sober, n) ~ wage, family=binomial, data=DrinksWages)
summary(mod.sober)
op <- par(mfrow=c(2,2))
plot(mod.sober)
par(op)# TODO: plot fitted model

--

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