May 17, 2012 - plot(d$wavelength, d$extinction, type = "l", lty = 1, xlab = "wavelength", ylab = "cross-section") lines(
Reproducible graphics with R and ggplot2 Baptiste Auguié Victoria University of Wellington
May 17, 2012
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: A loyal guide in )
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d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4] d[, 2:4]
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength d$wavelength
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Histograms
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d = read.table("../data/rods.txt", header = TRUE) par(mfrow = c(2, 1), # split mar = c(4, 4, 1.5, 0.5), mgp = c(2, 1, 0)) ## top histogram with(d, hist(Major, xlim = c(10, 70))) ## bottom histogram with(d, hist(Minor, prob=TRUE, xlim = c(10, 70))) ## add density estimate with(d, lines(density(Minor), col = "red"))
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Histogram of Major
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A natural and coherent language to describe graphics Point-and-click
A grammar of graphics
Yeah but, no but, yeah but, no but, :::::::::::::::::::::::::::: yeah but… I swear *** ***** **** :::::::::::::::::::::::: … but yeah. ::::::::::
. Use data d [x, y, z, t, . . . ] 2. Plot lines of variable y vs x 1
. Colour lines following variable z 4. Split into multiple panels 3
according to variable t .5 Add a layer with new data 6. …
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ggplot2:
A Grammar of Graphics Mapping: data ↔ aesthetic Layers Scales Coordinates + (Stats, …) library(ggplot2) ggplot(data = ..., mapping = ...) + layer(geom = "point", stat = "identity") + layer(geom = "point", data = ...) + facet_grid(... ˜ ...) + coord_polar( ) + scale_colour(...) + opts(...)
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## Reshape to long format m = melt(d, meas=c("extinction", "scattering", "absorption")) head(m, 3)
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extinction scattering absorption
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wavelength variable value 0.400 extinction 0.06616 0.401 extinction 0.06620 0.402 extinction 0.06623
ggplot(m, aes(x = wavelength, y = value, colour = variable)) + geom_path() + opts(legend.position = c(0.8, 0.8), legend.direction = "vertical")
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Data exploration
head(Orange, 10) Tree 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 2 9 2 10 2
age circumference 118 30 484 58 664 87 1004 115 1231 120 1372 142 1582 145 118 33 484 69 664 111
p = ggplot(Orange, aes(x = age, y = circumference, colour = Tree)) + geom_point(aes(shape = Tree )) + layer(geom = "line", stat = "smooth", method = "lm") + opts(legend.position = "top", legend.direction = "horizontal")
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p
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Small multiples revisited 0
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Coordinate transformations
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ggplot(data = d) + geom_path(aes(x = x, y = y)) + scale_y_log10() + coord_polar() + annotate(...) + ...
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DRY principle p + theme_article(10,
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Take home message Reproducible research, reproducible graphics
. Saving time: repeat the analysis instantly with a new data set 2. Readable by others: you can share scripts 1
. Safer: anyone can follow the analysis by reading lines of code 4. Visual aspect: better aesthetic choices (think LATEX vs word processors) 3
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Reproduce this!
. library(knitr); knit2pdf("presentation.rnw") .
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Getting started –– resources
. Get started, download R and RStudio (IDE) 2. ?ggplot : R’s help system 1
. Documentation pages http://had.co.nz/ggplot2/, wiki 4. An introduction to R 3
. R and ggplot2 mailing lists; Stack Overflow 6. Books: R graphics (Murrell), ggplot2 (Wickham) 5
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Automation ## open a pdf file pdf(file = "all_plots.pdf", width = 10, height = 6) par(mar = c(4, 4, 1.5, 0.5), mgp = c(2, 1, 0)) for (file in lf){ # loop over all data files d = read.table(paste0("../data/spectra/", file), header = TRUE) matplot(d$wavelength, d[ ,2:4], type = "l", lty = 1:3, ylim = c(0, 1.1*max(d[ , -1])), yaxs = "i", xlab = expression(wavelength/mu*m), ylab = expression(sigma/mu*m^2)) legend("topright", expression(sigma[ext], sigma[sca], sigma[abs]), lty = 1:3, bg = "grey95", bty = "o", box.col = NA, inset = 0.05) ## extract parameters from filename as plot title title(gsub("_|\\.txt", " ", file))
} ## close pdf file dev.off() .
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model = function(p, x) p[3] / pi * p[2] / ((x - p[1])^2 + p[2]^2) + p[4] objective = function(p, d=NULL, x=d$wavenumber, y=d$intensity, ...){ predicted 450)) .
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