"Ternary" is an R package to allow the creation of ternary plots in the standard R graphics environment. I hope that it proves simple to use.
For simple use cases, generate Ternary plots using the point-and-click Shiny app:
install.packages("Ternary") Ternary::TernaryApp()
For greater control over your plots, use the full R implementation.
Install the package with:
install.packages("Ternary")
Or if you want the latest development version of the package:
```{R github-package, eval = FALSE} if (!require("devtools")) install.packages("devtools") install_github("ms609/Ternary", args = "--recursive")
Once the package is installed, load it into the current R session with ```{R library-ternary} library("Ternary")
There are two stages to creating a ternary plot: first, rendering the plot, styled as you like it and pointing in any of the four compass directions; secondly, adding data.
At its simplest, all you need to do is type ```{R create-blank-plot, fig.asp = 1} TernaryPlot()
The following charts show which corners are which, under different orientations: ```{R create-simple-plot, fig.width = 7, fig.height = 7} par(mfrow = c(2, 2), mar = rep(0.5, 4)) for (dir in c("up", "right", "down", "le")) { TernaryPlot(point = dir, atip = "A", btip = "B", ctip = "C", alab = "Aness", blab = "Bness", clab = "Cness") TernaryText(list(A = c(10, 1, 1), B = c(1, 10, 1), C = c(1, 1, 10)), labels = c("P1", "P2", "P3"), col = cbPalette8[4], font = 2) }
```{R two-stylised-plots, fig.asp = 1/2}
par(mfrow = c(1, 2), mar = rep(0.3, 4))
TernaryPlot(alab = "Redder \u2192", blab = "\u2190 Greener", clab = "Bluer \u2192", lab.col = c("red", "darkgreen", "blue"), main = "Colours", # Title point = "right", lab.cex = 0.8, grid.minor.lines = 0, grid.lty = "solid", col = rgb(0.9, 0.9, 0.9), grid.col = "white", axis.col = rgb(0.6, 0.6, 0.6), ticks.col = rgb(0.6, 0.6, 0.6), axis.rotate = FALSE, padding = 0.08)
cols <- TernaryPointValues(rgb) ColourTernary(cols, spectrum = NULL)
data_points <- list( R = c(255, 0, 0), O = c(240, 180, 52), Y = c(210, 222, 102), G = c(111, 222, 16), B = c(25, 160, 243), I = c(92, 12, 243), V = c(225, 24, 208) ) AddToTernary(graphics::points, data_points, pch = 21, cex = 2.8, bg = vapply(data_points, function (x) rgb(x[1], x[2], x[3], 128, maxColorValue = 255), character(1)) ) AddToTernary(text, data_points, names(data_points), cex = 0.8, font = 2) legend("bottomright", legend = c("Red", "Orange", "Yellow", "Green"), cex = 0.8, bty = "n", pch = 21, pt.cex = 1.8, pt.bg = c(rgb(255, 0, 0, 128, NULL, 255), rgb(240, 180, 52, 128, NULL, 255), rgb(210, 222, 102, 128, NULL, 255), rgb(111, 222, 16, 128, NULL, 255)), )
TernaryPlot("Steam", "Ice", "Water", grid.lines = 5, grid.lty = "dotted", grid.minor.lines = 1, grid.minor.lty = "dotted", point = "West")
title("Water phases", cex.main = 0.8)
HorizontalGrid()
middle_triangle <- matrix(c( 30, 40, 30, 30, 30, 40, 55, 20, 25 ), ncol = 3, byrow = TRUE)
TernaryPolygon(middle_triangle, col = "#aaddfa", border = "grey")
TernaryLines(list(c(0, 100, 0), middle_triangle[1, ]), col = "grey") TernaryLines(list(c(0, 0, 100), middle_triangle[2, ]), col = "grey") TernaryLines(list(c(100, 0, 0), middle_triangle[3, ]), col = "grey")
TernaryArrows(c(20, 20, 60), c(30, 30, 40), length = 0.2, col = "darkblue")
### Styling points More sophisticated plots can be created, for example styling each point according to additional properties of the data, in a manner analogous to the standard plotting functions: ```r # Configure plotting area par(mar = rep(0.3, 4)) # Example data object dat <- data.frame(sio2 = c(2, 4, 10, 20), fe2o3 = c(5, 6, 7, 8), al2o3 = c(12, 11, 10, 9), grain_size = c(20, 16, 12, 8), reflectance = c(80, 63, 51, 20)) # Define a colour spectrum spectrumBins <- 255 # Number of bins to use mySpectrum <- viridisLite::viridis(spectrumBins) # Cut our reflectance data into categories binnedReflectance <- cut(dat$reflectance, spectrumBins) # Assign each data point a colour from the spectrum pointCol <- mySpectrum[binnedReflectance] # Define a size range maxSize <- 2.4 # Size of largest point, in plotting units sizes <- dat$grain_size pointSize <- sizes * maxSize / max(sizes) # Initialize the plot TernaryPlot(atip = expression(SiO[2]), btip = expression(paste(Fe[2], O[3], " (wt%)")), ctip = expression(paste(Al[2], O[3])) ) # Plot the points TernaryPoints(dat[, c("sio2", "fe2o3", "al2o3")], cex = pointSize, # Point size col = pointCol, # Point colour pch = 16 # Plotting symbol (16 = filled circle) ) # Legend for colour scale PlotTools::SpectrumLegend( "topleft", cex = 0.8, # Font size palette = mySpectrum, legend = paste( seq(from = max(dat$reflectance), to = min(dat$reflectance), length.out = 5), "%" ), bty = "n", # No framing box xpd = NA, # Don't clip at margins # title.font = 2, # Bold. Supported from R 3.6 onwards title = "Reflectance" ) # Legend for point size PlotTools::SizeLegend( "topright", width = c(0, maxSize), lend = "round", # Round end of scale bar legend = paste( signif(seq(max(sizes), 0, length.out = 5), digits = 3), "\u03bcm" # µm ), title = "Grain size", # title.font = 2, # Bold. Supported from R 3.6 onwards bty = "n", # Do not frame with box cex = 0.8 )
See the "Points" tab of the shiny app for a point-and-click implementation.
It is also possible to use cartesian coordinates to plot onto the graph.
By default, the plotting area is a 1×1 square.
par(mar = rep(0, 4)) # Reduce margins TernaryPlot(point = "right", clockwise = FALSE) cat("X range in this orientation:", TernaryXRange()) cat("Y range in this orientation:", TernaryYRange()) arrows(x0 = 0.5, y0 = 0.4, x1 = sqrt(3) / 2, y1 = 0.4, length = 0.1, col = cbPalette8[2]) text(x = mean(c(0.5, sqrt(3) / 2)), y = 0.4, "Increasing X", pos = 3, col = cbPalette8[2]) text(x = 0.5, y = 0, "(0.5, 0)", col = cbPalette8[3]) text(x = 0.8, y = -0.5, "(0.8, -0.5)", col = cbPalette8[3])
Note the anticlockwise axis labelling on this plot, obtained using
clockwise = FALSE
.
A plot can be coloured and contoured according to the output of a mathematical expression:
par(mar = rep(0.2, 4)) TernaryPlot(alab = "a", blab = "b", clab = "c") FunctionToContour <- function(a, b, c) { a - c + (4 * a * b) + (27 * a * b * c) } # Add contour lines values <- TernaryContour(FunctionToContour, resolution = 36L, filled = TRUE) zRange <- range(values$z, na.rm = TRUE) # Continuous legend for colour scale PlotTools::SpectrumLegend( "topleft", legend = round(seq(zRange[1], zRange[2], length.out = 4), 3), palette = viridisLite::viridis(256L, alpha = 0.6), bty = "n", # No framing box inset = 0.02, xpd = NA # Do not clip at edge of figure )
or according to the density of points across the plot:
par(mar = rep(0.2, 4)) TernaryPlot(axis.labels = seq(0, 10, by = 1)) nPoints <- 4000L coordinates <- cbind(abs(rnorm(nPoints, 2, 3)), abs(rnorm(nPoints, 1, 1.5)), abs(rnorm(nPoints, 1, 0.5))) # Colour plot background ColourTernary(TernaryDensity(coordinates, resolution = 10L)) # Add points TernaryPoints(coordinates, col = "red", pch = ".") # Contour by point density TernaryDensityContour(coordinates, resolution = 30L)
The following image demonstrates the behaviour of the density estimates when points fall on boundaries of the triangular grid cells; text denotes the number of points within the cell, with cells straddling n cells contributing 1/n of a point to each cell straddled.
coordinates <- list(middle = c(1, 1, 1), top = c(3, 0, 0), belowTop = c(2, 1, 1), leftSideSolid = c(9, 2, 9), leftSideSolid2 = c(9.5, 2, 8.5), right3way = c(1, 2, 0), rightEdge = c(2.5, 0.5, 0), leftBorder = c(1, 1, 4), topBorder = c(2, 1, 3), rightBorder = c(1, 2, 3) ) par(mfrow = c(2, 2), mar = rep(0.2, 4)) TernaryPlot(grid.lines = 3, axis.labels = 1:3, point = "up") values <- TernaryDensity(coordinates, resolution = 3L) ColourTernary(values) TernaryPoints(coordinates, col = "red") text(values[1, ], values[2, ], paste(values[3, ], "/ 6"), cex = 0.8) TernaryPlot(grid.lines = 3, axis.labels = 1:3, point = "right") values <- TernaryDensity(coordinates, resolution = 3L) ColourTernary(values) TernaryPoints(coordinates, col = "red") text(values[1, ], values[2, ], paste(values[3, ], "/ 6"), cex = 0.8) TernaryPlot(grid.lines = 3, axis.labels = 1:3, point = "down") values <- TernaryDensity(coordinates, resolution = 3L) ColourTernary(values) TernaryPoints(coordinates, col = "red") text(values[1, ], values[2, ], paste(values[3, ], "/ 6"), cex = 0.8) TernaryPlot(grid.lines = 3, axis.labels = 1:3, point = "left") values <- TernaryDensity(coordinates, resolution = 3L) ColourTernary(values) TernaryPoints(coordinates, col = "red") text(values[1, ], values[2, ], paste(values[3, ], "/ 6"), cex = 0.8) TernaryDensityContour(t(vapply(coordinates, I, double(3L))), resolution = 24L, tolerance = -0.02, col = "orange")
Perhaps the action on a plot is constrained to a small region of ternary space. It's possible to "zoom in" -- i.e. magnify and crop the ternary plot to the region of interest.
To do this you can specify the x and y limits of the region of interest.
TernaryCoords
might be useful in establishing the cartesian coordinates of a
particular point in ternary space.
Ensure that dx = dy if you want an isometric plot.
# Define points corresponding to corners of a region to plot my_corners <- list(c(22, 66, 12), c(22, 72, 6), c(15, 80, 5), c(12, 76, 12)) # Print Cartesian coordinates of points vapply(my_corners, TernaryCoords, direction = 1, FUN.VALUE = c(x = 0, y = 0))
The padding
parameter is added as a margin to each side of the region specified
using xlim
and ylim
:
# Remove plot margins par(mar = rep(0, 4)) # Create clipped plotting area TernaryPlot(xlim = c(0.28, 0.38), ylim = c(0.1, 0.2), padding = 0.04) # Annotate grid lines at user-specified points: TernaryText(list(c(8, 72, 20), c(8, 82, 10)), c(20, 10), srt = -60, cex = 0.9, col = "darkgrey") TernaryText(list(c(10, 69, 21), c(20, 64, 16)), c(10, 20), srt = 0, cex = 0.9, col = "darkgrey") # Plot desired polygon TernaryPolygon(my_corners, col = "#2cbe4e88") # Show xlim, ylim and padding, using Cartesian coordinates lines(c(0.28, 0.28, 0.38, 0.38, 0.28), c(0.1, 0.2, 0.2, 0.1, 0.1)) text(0.28, 0.15, "xlim[1]", pos = 2, srt = 90) text(0.38, 0.15, "xlim[2]", pos = 4, srt = 90) text(0.33, 0.1, "ylim[1]", pos = 1) text(0.33, 0.2, "ylim[2]", pos = 3) text(0.38, 0.1, "<padding>", pos = 4, cex = 0.75) text(0.38, 0.1, "<padding> ", pos = 2, cex = 0.75, srt = 90)
An alternative approach is to plot a triangular sub-region of the larger ternary space, allowing axis annotations to be read more easily.
TernaryPlot(region = my_corners) # Fit plotted region to data TernaryPolygon(my_corners, col = "#2cbe4e88")
A region can be defined manually; the smallest triangle that covers the region will be employed.
region <- list( c(amin = 10, bmin = 70, cmin = 0), c(amax = 30, bmax = 70, cmax = 10) ) TernaryPlot(region = region) # Data will be plotted even if it falls outside the plotted axes TernaryPolygon(my_corners, col = "#2cbe4e88")
For further examples of usage, see the accompanying vignettes:
I hope the package proves useful. If there"s anything it can"t do that you wish it could, please let me know by opening a Github issue.
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