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I have a question which seems to be very elemental but I have found lot of disagreement about it. I have a situation where we took several measurements of the same individuals. I was suggested to make a plot like this:

enter image description here

Nevertheless, I have found some critics to this approach, since some reviewers mention it is not possible to "easily" see some values just like the mean/median and the variation of the data. In this paper recently published (http://journals.plos.org/plosbiology/article?id=10.1371%2Fjournal.pbio.1002128#pbio.1002128.s007), it is suggested that both, the scatter and lines plot could be used, but I would like to hear other opinions. If somebody knows what is the "best" or most accepted way to plot this data and/or knows an alternative way, I would be really thanked!

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    $\begingroup$ One reason this is hard to read is specific to this dataset. Some series start at 2 and there are other exceptions to being measured at all 4 points. No doubt there are good reasons for this, but it's a complication. That said, there is nothing to stop you adding lines for mean and median, but there is a real question about which mean and which median given the differences in times. $\endgroup$ – Nick Cox May 4 '15 at 9:13
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    $\begingroup$ Are these your data? Either way, posting your data if possible would let people show you their solutions to your problem. $\endgroup$ – Nick Cox May 4 '15 at 9:14
  • $\begingroup$ @NickCox great point. In fact maybe plain min/max/mean in a table might be enough. But again that's why having a clear goal of the visualization is the most important part of making a good visualization $\endgroup$ – shadowtalker May 4 '15 at 12:30
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I agree with the reviewer that it's very hard to read.

I personally love using heatmaps (aka pseudocolor plots aka checkerboard plots) for this kind of thing:

heatmap

And small multiples can be very nice as well:

small multiples

Andrew Gelman has blogged about these kinds of displays before, too. There's a time and place for plots like yours. He calls them "spaghetti plots", and as Nick Cox mentioned in the comments they actually work better when the series starts in one place and fans out, or when the lines don't overlap much, more like raw dried spaghetti than cooked. I tend to like the heat map (which a commenter on that post calls a "lasagna plot") better, because it scales almost arbitrarily. Prof Gelman is also the one who turned me onto small multiples.

Note however that heatmaps tend to work better when they aren't constrained to greyscale. For instance the one I made would greatly benefit from a red/blue diverging color scheme with white at zero

We make graphs to facilitate comparisons. Whenever you make a plot, you should ask yourself which comparisons it facilitates, and which comparisons it obfuscates.


The R code for these:

x <- replicate(8, arima.sim(list(ar = 0.1, ma = 2.5), 4))

## the ugly way ----
library(compactr)  # a very nice convenience package for ploting
eplot(xlim = c(1, 4), ylim = c(-9, 10),
      xat = 1:4, xticklab = paste("Prey", 1:4),
      ylab = "Time", main = "Ugly")
invisible(apply(x, 2, function(xj) {
  points(xj, pch = 16)
  lines(xj)
}))

## checkerboard ----

checkercols <- colorRamp(c("black", "white"))((1:20)/20) / 255
# checkercols <- colorRamp(c("red", "white", "blue"))((1:20)/20) / 255  # for more useful colors
checkercols <- apply(checkercols, 1, function (x) rgb(x[1], x[2], x[3]))

op <- par()
layout(matrix(1:2), heights = c(3, 1))
par(mar = c(1, 1, 2, 1), oma = c(2.5, 2.5, 3, 1))
image(x, col = checkercols, xaxt = "n", yaxt = "n")
axis(1, at = (0:3) / 3, labels = paste("Prey", 1:4))
axis(2, at = (0:7) / 7, labels = 1:8)
mtext("Individual", 2, line = 2)
title(main = "I like these", outer = TRUE)

colorbar <- matrix(1:20, 20)
image(colorbar, col = checkercols, xaxt = "n", yaxt = "n")
axis(1, at = (0:19) / 19, labels = round(quantile(x, seq(1/20,1,1/20)), 2))
par(op)

## small multiples ----

op <- par
par(mfrow = c(2, 4),
    mar = rep(0.75, 4),
    oma = c(2.5, 2.5, 3, 1))
invisible(apply(x, 2, function (xj) {
  eplot(xlim = c(1, 4), ylim = c(-9, 10), xat = 1:4)
  points(xj, pch = 16)
  lines(xj)
}))
title(main = "These can be good, too", outer = TRUE, cex.main = 1.5)
mtext("Time", 2, line = 1, outer = TRUE)
mtext("Prey", 1, line = 1, outer = TRUE)
par(op)
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  • $\begingroup$ It's my understanding that spaghetti plots mean several series superimposed on the same plot, all tangled up like ... spaghetti. Small multiples is the name of one solution to that, not another name for the problem that is spaghetti. $\endgroup$ – Nick Cox May 4 '15 at 9:08
  • $\begingroup$ @NickCox the OP's plot looks like that to me: multiple series, tangled $\endgroup$ – shadowtalker May 4 '15 at 12:28
  • $\begingroup$ Indeed, but your answer seems to equate spaghetti plots and small multiples. That's not the way either phrase is used. $\endgroup$ – Nick Cox May 4 '15 at 12:29

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