README.md
In friendly/HistData: Data Sets from the History of Statistics and Data Visualization
HistData 
Data Sets from the History of Statistics and Data Visualization
Dev. Version: 0.9-2
The HistData package provides a collection of small data sets that are
interesting and important in the history of statistics and data
visualization. The goal of the package is to make these available, both
for instructional use (as examples, problem sets or projects) and for
historical research (extending or criticizing a previous analysis). Some
of these present interesting challenges, or opportunities to “show off”,
with graphics or analysis in R.
Many of the data sets have examples which reproduce an historical graph
or analysis. These are meant mainly as starters for more extensive
re-analysis or graphical elaboration. If you are interested in any of
these problems or data sets, I’ve purposely left lots of room to do
better!
They are part of a program of research called statistical
historiography (Friendly, 2007; Friendly & Denis, 2001; Friendly et-al,
2016) meaning the use of statistical methods to study problems and
questions in the history of statistics and graphics. A main aspect of
this is the increased understanding of historical problems in science
and data analysis trough the process of trying to reproduce a graph or
analysis using modern methods. I call this “Re-visioning”, meaning to
see again, hopefully in a new light.
They are also used in our book, A History of Data Visualization &
Graphic
Communication
(Friendly & Wainer, 2021). See also the companion website for this
book.
If you are looking more widely for datasets to use for examples,
teaching or research, check out Vincent Arel-Bundock’s
Rdatasets package,
with over 2200 datasets from various R packages, with this list of
Available
datasets.
Data science
There is another R aspect that should be noted here: A great deal of
“data sciency” work was involved in constructing this package, alas (for
teaching) not captured in the resulting CRAN-friendly package.
- In some cases, data had to be extracted from historical documents,
using a variety of techniques (web scraping, OCR of PDS files followed
by conversion to a data set), each problem with its own toolbox, in R
or outside. In many cases, transcription errors had to be corrected
with code or manually;
- digitization of data from an image;
- conversion of text-based data sets to a CSV file and then to an
.RData file with proper column names. Ever seen a Unix .shar
(shell archive) file? Well, I have.
- cleaning variable names, e.g., using
janitor::clean_names(), or,
in some cases, manually editing an excel file.
- Applying type-conversion, e.g.,
chr to factor or ordered;
constructing appropriate contrasts for factors to facilitate
re-analysis.
- tidying data.frames: long \<–> wide, abbreviations of character
string labels, …
- documentation: The thankless task? No – considerable effort was
made to give detailed descriptions, notes on methods, executable
examples, references to original sources and analyses, …
Installation
Get the released version from CRAN or
R-universe
install.packages("HistData")
install.packages('HistData', repos = 'https://friendly.r-universe.dev')
The development version can be installed to your R library directly from
github via:
remotes::install_github("friendly/HistData")
Data sets
Here are the data sets in the package, with links to their
documentation. Some topics are represented by two or more data sets.
# link dataset to pkgdown doc
refurl <- "http://friendly.github.io/HistData/reference/"
dsets <- vcdExtra::datasets("HistData") |>
dplyr::select(Item, Title) |>
dplyr::mutate(Item = glue::glue("[{Item}]({refurl}{Item}.html)"))
#knitr::kable(dsets)
library(tinytable)
# tt(dsets) |>
# format_tt(j = 1, markdown = TRUE) |>
# style_tt(j = 1, bootstrap_css = "width: 30%;") |>
# style_tt(j = 2, bootstrap_css = "width: 70%;")
tt(dsets, width = c(.2, .8)) |>
format_tt(j = 1, markdown = TRUE)
| Item | Title |
|------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------|
| Arbuthnot | Arbuthnot’s data on male and female birth ratios |
| Armada | La Felicisima Armada |
| Bowley | Bowley’s data on values of British and Irish trade, 1855-1899 |
| Breslau | Halley’s Breslau Life Table |
| Cavendish | Cavendish’s Determinations of the Density of the Earth |
| ChestSizes | Chest measurements of Scottish Militiamen |
| ChestStigler | Chest measurements of Scottish Militiamen |
| Cholera | William Farr’s Data on Cholera in London, 1849 |
| CholeraDeaths1849 | Daily Deaths from Cholera and Diarrhaea in England, 1849 |
| CushnyPeebles | Cushny-Peebles Data: Soporific Effects of Scopolamine Derivatives |
| CushnyPeeblesN | Cushny-Peebles Data: Soporific Effects of Scopolamine Derivatives |
| Dactyl | Edgeworth’s counts of dactyls in Virgil’s Aeneid |
| DrinksWages | Elderton and Pearson’s (1910) data on drinking and wages |
| EdgeworthDeaths | Edgeworth’s Data on Death Rates in British Counties |
| Fingerprints | Waite’s data on Patterns in Fingerprints |
| Galton | Galton’s data on the heights of parents and their children |
| GaltonFamilies | Galton’s data on the heights of parents and their children, by child |
| Guerry | Data from A.-M. Guerry, “Essay on the Moral Statistics of France” |
| HalleyLifeTable | Halley’s Life Table |
| Jevons | W. Stanley Jevons’ data on numerical discrimination |
| Langren.all | van Langren’s Data on Longitude Distance between Toledo and Rome |
| Langren1644 | van Langren’s Data on Longitude Distance between Toledo and Rome |
| Macdonell | Macdonell’s Data on Height and Finger Length of Criminals, used by Gosset (1908) |
| MacdonellDF | Macdonell’s Data on Height and Finger Length of Criminals, used by Gosset (1908) |
| Mayer | Mayer’s Data on the Libration of the Moon. |
| Michelson | Michelson’s Determinations of the Velocity of Light |
| MichelsonSets | Michelson’s Determinations of the Velocity of Light |
| Minard.cities | Data from Minard’s famous graphic map of Napoleon’s march on Moscow |
| Minard.temp | Data from Minard’s famous graphic map of Napoleon’s march on Moscow |
| Minard.troops | Data from Minard’s famous graphic map of Napoleon’s march on Moscow |
| Nightingale | Florence Nightingale’s data on deaths from various causes in the Crimean War |
| OldMaps | Latitudes and Longitudes of 39 Points in 11 Old Maps |
| PearsonLee | Pearson and Lee’s data on the heights of parents and children classified by gender |
| PolioTrials | Polio Field Trials Data |
| Pollen | Pollen Data Challenge |
| Prostitutes | Parent-Duchatelet’s time-series data on the number of prostitutes in Paris |
| Pyx | Trial of the Pyx |
| Quarrels | Statistics of Deadly Quarrels |
| Saturn | Laplace’s Saturn data. |
| Snow.dates | John Snow’s Map and Data on the 1854 London Cholera Outbreak |
| Snow.deaths | John Snow’s Map and Data on the 1854 London Cholera Outbreak |
| Snow.deaths2 | John Snow’s Map and Data on the 1854 London Cholera Outbreak |
| Snow.polygons | John Snow’s Map and Data on the 1854 London Cholera Outbreak |
| Snow.pumps | John Snow’s Map and Data on the 1854 London Cholera Outbreak |
| Snow.streets | John Snow’s Map and Data on the 1854 London Cholera Outbreak |
| Virginis | John F. W. Herschel’s Data on the Orbit of the Twin Stars gamma Virginis |
| Virginis.interp | John F. W. Herschel’s Data on the Orbit of the Twin Stars gamma Virginis |
| Wheat | Playfair’s Data on Wages and the Price of Wheat |
| Wheat.monarchs | Playfair’s Data on Wages and the Price of Wheat |
| Yeast | Student’s (1906) Yeast Cell Counts |
| YeastD.mat | Student’s (1906) Yeast Cell Counts |
| ZeaMays | Darwin’s Heights of Cross- and Self-fertilized Zea May Pairs |
# save_tt("html") |>
# knitr::asis_output()
Contributors
Please note that the HistData project is released with a Contributor
Code of
Conduct. By
contributing to this project, you agree to abide by its terms.
Over the years, many people have contributed new data sets, offered
corrections, suggestions, or documentation examples. They are
appreciatedly listed below:
David Bellhouse, Brian Clair, Stephane Dray, Luiz Droubi, Antoine de
Falguerolles, Monique Graf, James Hanley, Peter Li, Dennis Murphy, Jim
Oeppen, James Riley, Neville Verlander, Hadley Wickham.
References
Friendly, M. (2007). A Brief History of Data Visualization. In Chen, C.,
Hardle, W. & Unwin, A. (eds.)
Handbook of Computational Statistics: Data Visualization,
Springer-Verlag, III, Ch. 1, 1-34.
Preprint
Friendly, M. & Denis, D. (2001). Milestones in the history of thematic
cartography, statistical graphics, and data visualization. Web stite:
http://datavis.ca/milestones/
Friendly, M. & Sigal, M. & Harnanansingh, D. (2016). “The Milestones
Project: A Database for the History of Data Visualization,” In
Kostelnick, C. & Kimball, M. (ed.), Visible Numbers: The History of
Data Visualization, Ashgate Press, Chapter 10.
Preprint
Friendly, M. & Wainer, H. (2021). A History of Data Visualization and
Graphic
Communication.
Harvard University Press. Companion web
site
friendly/HistData documentation built on May 11, 2024, 1:01 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
HistData
Data Sets from the History of Statistics and Data Visualization
Dev. Version: 0.9-2
The HistData package provides a collection of small data sets that are
interesting and important in the history of statistics and data
visualization. The goal of the package is to make these available, both
for instructional use (as examples, problem sets or projects) and for
historical research (extending or criticizing a previous analysis). Some
of these present interesting challenges, or opportunities to “show off”,
with graphics or analysis in R.
Many of the data sets have examples which reproduce an historical graph or analysis. These are meant mainly as starters for more extensive re-analysis or graphical elaboration. If you are interested in any of these problems or data sets, I’ve purposely left lots of room to do better!
They are part of a program of research called statistical historiography (Friendly, 2007; Friendly & Denis, 2001; Friendly et-al, 2016) meaning the use of statistical methods to study problems and questions in the history of statistics and graphics. A main aspect of this is the increased understanding of historical problems in science and data analysis trough the process of trying to reproduce a graph or analysis using modern methods. I call this “Re-visioning”, meaning to see again, hopefully in a new light.
They are also used in our book, A History of Data Visualization & Graphic Communication (Friendly & Wainer, 2021). See also the companion website for this book.
If you are looking more widely for datasets to use for examples, teaching or research, check out Vincent Arel-Bundock’s Rdatasets package, with over 2200 datasets from various R packages, with this list of Available datasets.
Data science
There is another R aspect that should be noted here: A great deal of “data sciency” work was involved in constructing this package, alas (for teaching) not captured in the resulting CRAN-friendly package.
- In some cases, data had to be extracted from historical documents, using a variety of techniques (web scraping, OCR of PDS files followed by conversion to a data set), each problem with its own toolbox, in R or outside. In many cases, transcription errors had to be corrected with code or manually;
- digitization of data from an image;
- conversion of text-based data sets to a CSV file and then to an
.RDatafile with proper column names. Ever seen a Unix.shar(shell archive) file? Well, I have. - cleaning variable names, e.g., using
janitor::clean_names(), or, in some cases, manually editing an excel file. - Applying type-conversion, e.g.,
chrtofactororordered; constructing appropriate contrasts for factors to facilitate re-analysis. - tidying data.frames: long \<–> wide, abbreviations of character string labels, …
- documentation: The thankless task? No – considerable effort was made to give detailed descriptions, notes on methods, executable examples, references to original sources and analyses, …
Installation
Get the released version from CRAN or R-universe
install.packages("HistData")
install.packages('HistData', repos = 'https://friendly.r-universe.dev')
The development version can be installed to your R library directly from github via:
remotes::install_github("friendly/HistData")
Data sets
Here are the data sets in the package, with links to their documentation. Some topics are represented by two or more data sets.
# link dataset to pkgdown doc
refurl <- "http://friendly.github.io/HistData/reference/"
dsets <- vcdExtra::datasets("HistData") |>
dplyr::select(Item, Title) |>
dplyr::mutate(Item = glue::glue("[{Item}]({refurl}{Item}.html)"))
#knitr::kable(dsets)
library(tinytable)
# tt(dsets) |>
# format_tt(j = 1, markdown = TRUE) |>
# style_tt(j = 1, bootstrap_css = "width: 30%;") |>
# style_tt(j = 2, bootstrap_css = "width: 70%;")
tt(dsets, width = c(.2, .8)) |>
format_tt(j = 1, markdown = TRUE)
| Item | Title | |------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------| | Arbuthnot | Arbuthnot’s data on male and female birth ratios | | Armada | La Felicisima Armada | | Bowley | Bowley’s data on values of British and Irish trade, 1855-1899 | | Breslau | Halley’s Breslau Life Table | | Cavendish | Cavendish’s Determinations of the Density of the Earth | | ChestSizes | Chest measurements of Scottish Militiamen | | ChestStigler | Chest measurements of Scottish Militiamen | | Cholera | William Farr’s Data on Cholera in London, 1849 | | CholeraDeaths1849 | Daily Deaths from Cholera and Diarrhaea in England, 1849 | | CushnyPeebles | Cushny-Peebles Data: Soporific Effects of Scopolamine Derivatives | | CushnyPeeblesN | Cushny-Peebles Data: Soporific Effects of Scopolamine Derivatives | | Dactyl | Edgeworth’s counts of dactyls in Virgil’s Aeneid | | DrinksWages | Elderton and Pearson’s (1910) data on drinking and wages | | EdgeworthDeaths | Edgeworth’s Data on Death Rates in British Counties | | Fingerprints | Waite’s data on Patterns in Fingerprints | | Galton | Galton’s data on the heights of parents and their children | | GaltonFamilies | Galton’s data on the heights of parents and their children, by child | | Guerry | Data from A.-M. Guerry, “Essay on the Moral Statistics of France” | | HalleyLifeTable | Halley’s Life Table | | Jevons | W. Stanley Jevons’ data on numerical discrimination | | Langren.all | van Langren’s Data on Longitude Distance between Toledo and Rome | | Langren1644 | van Langren’s Data on Longitude Distance between Toledo and Rome | | Macdonell | Macdonell’s Data on Height and Finger Length of Criminals, used by Gosset (1908) | | MacdonellDF | Macdonell’s Data on Height and Finger Length of Criminals, used by Gosset (1908) | | Mayer | Mayer’s Data on the Libration of the Moon. | | Michelson | Michelson’s Determinations of the Velocity of Light | | MichelsonSets | Michelson’s Determinations of the Velocity of Light | | Minard.cities | Data from Minard’s famous graphic map of Napoleon’s march on Moscow | | Minard.temp | Data from Minard’s famous graphic map of Napoleon’s march on Moscow | | Minard.troops | Data from Minard’s famous graphic map of Napoleon’s march on Moscow | | Nightingale | Florence Nightingale’s data on deaths from various causes in the Crimean War | | OldMaps | Latitudes and Longitudes of 39 Points in 11 Old Maps | | PearsonLee | Pearson and Lee’s data on the heights of parents and children classified by gender | | PolioTrials | Polio Field Trials Data | | Pollen | Pollen Data Challenge | | Prostitutes | Parent-Duchatelet’s time-series data on the number of prostitutes in Paris | | Pyx | Trial of the Pyx | | Quarrels | Statistics of Deadly Quarrels | | Saturn | Laplace’s Saturn data. | | Snow.dates | John Snow’s Map and Data on the 1854 London Cholera Outbreak | | Snow.deaths | John Snow’s Map and Data on the 1854 London Cholera Outbreak | | Snow.deaths2 | John Snow’s Map and Data on the 1854 London Cholera Outbreak | | Snow.polygons | John Snow’s Map and Data on the 1854 London Cholera Outbreak | | Snow.pumps | John Snow’s Map and Data on the 1854 London Cholera Outbreak | | Snow.streets | John Snow’s Map and Data on the 1854 London Cholera Outbreak | | Virginis | John F. W. Herschel’s Data on the Orbit of the Twin Stars gamma Virginis | | Virginis.interp | John F. W. Herschel’s Data on the Orbit of the Twin Stars gamma Virginis | | Wheat | Playfair’s Data on Wages and the Price of Wheat | | Wheat.monarchs | Playfair’s Data on Wages and the Price of Wheat | | Yeast | Student’s (1906) Yeast Cell Counts | | YeastD.mat | Student’s (1906) Yeast Cell Counts | | ZeaMays | Darwin’s Heights of Cross- and Self-fertilized Zea May Pairs |
# save_tt("html") |>
# knitr::asis_output()
Contributors
Please note that the HistData project is released with a Contributor
Code of
Conduct. By
contributing to this project, you agree to abide by its terms.
Over the years, many people have contributed new data sets, offered corrections, suggestions, or documentation examples. They are appreciatedly listed below:
David Bellhouse, Brian Clair, Stephane Dray, Luiz Droubi, Antoine de Falguerolles, Monique Graf, James Hanley, Peter Li, Dennis Murphy, Jim Oeppen, James Riley, Neville Verlander, Hadley Wickham.
References
Friendly, M. (2007). A Brief History of Data Visualization. In Chen, C., Hardle, W. & Unwin, A. (eds.) Handbook of Computational Statistics: Data Visualization, Springer-Verlag, III, Ch. 1, 1-34. Preprint
Friendly, M. & Denis, D. (2001). Milestones in the history of thematic cartography, statistical graphics, and data visualization. Web stite: http://datavis.ca/milestones/
Friendly, M. & Sigal, M. & Harnanansingh, D. (2016). “The Milestones Project: A Database for the History of Data Visualization,” In Kostelnick, C. & Kimball, M. (ed.), Visible Numbers: The History of Data Visualization, Ashgate Press, Chapter 10. Preprint
Friendly, M. & Wainer, H. (2021). A History of Data Visualization and Graphic Communication. Harvard University Press. Companion web site
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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