# Langren: van Langren's Data on Longitude Distance between Toledo and... In HistData: Data Sets from the History of Statistics and Data Visualization

## Description

Michael Florent van Langren (1598-1675) was a Dutch mathematician and astronomer, who served as a royal mathematician to King Phillip IV of Spain, and who worked on one of the most significant problems of his time— the accurate determination of longitude, particularly for navigation at sea.

In order to convince the Spanish court of the seriousness of the problem (often resulting in great losses through ship wrecks), he prepared a 1-dimensional line graph, showing all the available estimates of the distance in longitude between Toledo and Rome, which showed large errors, for even this modest distance. This 1D line graph, from Langren (1644), is believed to be the first known graph of statistical data (Friendly etal., 2010). It provides a compelling example of the notions of statistical variability and bias.

The data frame `Langren1644` gives the estimates and other information derived from the previously known 1644 graph. It turns out that van Langren produced other versions of this graph, as early as 1628. The data frame `Langren.all` gives the estimates derived from all known versions of this graph.

## Usage

 ```1 2 3``` ``` data(Langren1644) data(Langren.all) ```

## Format

`Langren1644`: A data frame with 12 observations on the following 9 variables, giving determinations of the distance in longitude between Toledo and Rome, from the 1644 graph.

`Name`

The name of the person giving a determination, a factor with levels `A. Argelius` ... `T. Brahe`

`Longitude`

Estimated value of the longitude distance between Toledo and Rome

`Year`

Year associated with this determination

`Longname`

A longer version of the `Name`, where appropriate; a factor with levels `Andrea Argoli` `Christoph Clavius` `Tycho Brahe`

`City`

The principal city where this person worked; a factor with levels `Alexandria` `Amsterdam` `Bamberg` `Bologna` `Frankfurt` `Hven` `Leuven` `Middelburg` `Nuremberg` `Padua` `Paris` `Rome`

`Country`

The country where this person worked; a factor with levels `Belgium` `Denmark` `Egypt` `Flanders` `France` `Germany` `Italy` `Italy `

`Latitude`

Latitude of this `City`; a numeric vector

`Source`

Likely source for this determination of Longitude; a factor with levels `Astron` `Map`

`Gap`

A numeric vector indicating whether the `Longitude` value is below or above the median

`Langren.all`: A data frame with 61 observations on the following 4 variables, giving determinations of Longitude between Toledo and Rome from all known versions of van Langren's graph.

`Author`

Author of the graph, a factor with levels `Langren` `Lelewel`

`Year`

Year of publication

`Name`

The name of the person giving a determination, a factor with levels `Algunos1` `Algunos2` `Apianus` ... `Schonerus`

`Longitude`

Estimated value of the longitude distance between Toledo and Rome

## Details

In all the graphs, Toledo is implicitly at the origin and Rome is located relatively at the value of `Longitude` To judge correspondence with an actual map, the positions in (lat, long) are

``` toledo <- c(39.86, -4.03); rome <- c(41.89, 12.5) ```

## Source

The longitude values were digitized from images of the various graphs, which may be found on the Supplementary materials page for Friendly etal. (2009).

## References

Friendly, M., Valero-Mora, P. and Ulargui, J. I. (2010). The First (Known) Statistical Graph: Michael Florent van Langren and the "Secret" of Longitude. The American Statistician, 64 (2), 185-191. Supplementary materials: http://datavis.ca/gallery/langren/.

Langren, M. F. van. (1644). La Verdadera Longitud por Mar y Tierra. Antwerp: (n.p.), 1644. English translation available at http://www.math.yorku.ca/SCS/Gallery/langren/verdadera.pdf.

Lelewel, J. (1851). G?ographie du Moyen ?ge. Paris: Pilliet, 1851.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141``` ```data(Langren1644) #################################################### # reproductions of Langren's graph overlaid on a map #################################################### if (require(jpeg, quietly=TRUE)) { gimage <- readJPEG(system.file("images", "google-toledo-rome3.jpg", package="HistData")) # NB: dimensions from readJPEG are y, x, colors gdim <- dim(gimage)[1:2] ylim <- c(1,gdim[1]) xlim <- c(1,gdim[2]) op <- par(bty="n", xaxt="n", yaxt="n", mar=c(2, 1, 1, 1) + 0.1) # NB: necessary to scale the plot to the pixel coordinates, and use asp=1 plot(xlim, ylim, xlim=xlim, ylim=ylim, type="n", ann=FALSE, asp=1 ) rasterImage(gimage, 1, 1, gdim[2], gdim[1]) # pixel coordinates of Toledo and Rome in the image, measured from the bottom left corner toledo.map <- c(131, 59) rome.map <- c(506, 119) # confirm locations of Toledo and Rome points(rbind(toledo.map, rome.map), cex=2) text(131, 95, "Toledo", cex=1.5) text(506, 104, "Roma", cex=1.5) # set a scale for translation of lat,long to pixel x,y scale <- data.frame(x=c(131, 856), y=c(52,52)) rownames(scale)=c(0,30) # translate from degrees longitude to pixels xlate <- function(x) { 131+x*726/30 } # draw an axis lines(scale) ticks <- xlate(seq(0,30,5)) segments(ticks, 52, ticks, 45) text(ticks, 40, seq(0,30,5)) text(xlate(8), 17, "Grados de la Longitud", cex=1.7) # label the observations with the names points(x=xlate(Langren1644\$Longitude), y=rep(57, nrow(Langren1644)), pch=25, col="blue", bg="blue") text(x=xlate(Langren1644\$Longitude), y=rep(57, nrow(Langren1644)), labels=Langren1644\$Name, srt=90, adj=c(-.1, .5), cex=0.8) par(op) } ### Original implementation using ReadImages, now deprecated & shortly to be removed ## Not run: if (require(ReadImages)) { gimage <- read.jpeg(system.file("images", "google-toledo-rome3.jpg", package="HistData")) plot(gimage) # pixel coordinates of Toledo and Rome in the image, measured from the bottom left corner toledo.map <- c(130, 59) rome.map <- c(505, 119) # confirm locations of Toledo and Rome points(rbind(toledo.map, rome.map), cex=2) # set a scale for translation of lat,long to pixel x,y scale <- data.frame(x=c(130, 856), y=c(52,52)) rownames(scale)=c(0,30) lines(scale) xlate <- function(x) { 130+x*726/30 } points(x=xlate(Langren1644\$Longitude), y=rep(57, nrow(Langren1644)), pch=25, col="blue") text(x=xlate(Langren1644\$Longitude), y=rep(57, nrow(Langren1644)), labels=Langren1644\$Name, srt=90, adj=c(0, 0.5), cex=0.8) } ## End(Not run) ### First attempt using ggplot2; temporarily abandonned. ## Not run: require(maps) require(ggplot2) require(reshape) require(plyr) require(scales) # set latitude to that of Toledo Langren1644\$Latitude <- 39.68 # x/long y/lat bbox <- c( 38.186, -9.184, 43.692, 28.674 ) bbox <- matrix(bbox, 2, 2, byrow=TRUE) borders <- as.data.frame(map("world", plot = FALSE, xlim = expand_range(bbox[,2], 0.2), ylim = expand_range(bbox[,1], 0.2))[c("x", "y")]) data(world.cities) # get actual locations of Toledo & Rome cities <- subset(world.cities, name %in% c("Rome", "Toledo") & country.etc %in% c("Spain", "Italy")) colnames(cities)[4:5]<-c("Latitude", "Longitude") mplot <- ggplot(Langren1644, aes(Longitude, Latitude) ) + geom_path(aes(x, y), borders, colour = "grey60") + geom_point(y = 40) + geom_text(aes(label = Name), y = 40.1, angle = 90, hjust = 0, size = 3) mplot <- mplot + geom_segment(aes(x=-4.03, y=40, xend=30, yend=40)) mplot <- mplot + geom_point(data = cities, colour = "red", size = 2) + geom_text(data=cities, aes(label=name), color="red", size=3, vjust=-0.5) + coord_cartesian(xlim=bbox[,2], ylim=bbox[,1]) # make the plot have approximately aspect ratio = 1 windows(width=10, height=2) mplot ## End(Not run) ########################################### # show variation in estimates across graphs ########################################### library(lattice) graph <- paste(Langren.all\$Author, Langren.all\$Year) dotplot(Name ~ Longitude, data=Langren.all) dotplot( as.factor(Year) ~ Longitude, data=Langren.all, groups=Name, type="o") dotplot(Name ~ Longitude|graph, data=Langren.all, groups=graph) # why the gap? gap.mod <- glm(Gap ~ Year + Source + Latitude, family=binomial, data=Langren1644) anova(gap.mod, test="Chisq") ```

HistData documentation built on April 5, 2018, 1:03 a.m.