For completeness, I attach one of the examples I reproduced. The link to the package is here, and in this link you will find a nice tutorial to use it. ![]() I've found the package ( ggpubr) that seems to work very well for this problem and it considers several possibilities to display the data. Though the data is not provided at this point, the underlying principles should be clear. Scale_x_continuous(name = "Population density (lg10)",ĭens1 <- ggplot(df, aes(x = Density, fill = Group)) +ĭens2 <- ggplot(df, aes(x = Face_sum, fill = Group)) + Scale_y_continuous(name = "Number of fixated faces", Stat_smooth(method = "lm", fullrange = TRUE) + Geom_point(shape = 1, color = "black", size = 3) + Geom_point(aes(color = Group), size = 3) + Plot1 <- ggplot(df, aes(x = Density, y = Face_sum, color = Group)) + With some small modifications one would get e.g. Since histograms heavily depend on the chosen binwidth, one might argue to prefer density plots. All the other arguments should be self-explanatory. The function plot_spacer() adds an empty plot to the top right corner. The only thing left to do, is to add those plots with a simple + and specify the layout with the function plot_layout(). Geom_histogram(color = "black", fill = "white") + If you want to create a scatterplot with marginal histograms, first you'd have to create those three plots seperately. Lucky me, Thomas Lin Pedersen just developed a package called patchwork, which gets the job done in a pretty elegant manner. I tried those options, but wasn't satisfied by the results or the messy code one would need to use to get there. Scale_y_continuous(limit=c(min(y), max(y)), Scale_x_continuous(limit=c(min(x), max(x)), # set the locations of the x-axis labels as Tukey's five numbers Perhaps it would be best to omit grid lines or force them to be in regular locations, and just let the labels show the five number summary. It's a bit tricky because the unevenly spaced grid-lines suggest that the axes have a non-linear scale (in this example they are linear). These five numbers are thus a numerical representation of a boxplot. ![]() The labels are located at the values of Tukey's five number summary (minimum, lower-hinge, median, upper-hinge, maximum), giving a quick impression of the spread of each variable. In my example the axis labels and grid lines also indicate the distribution of the data. Just a very minor variation on BondedDust's answer, in the general spirit of marginal indicators of distribution.Įdward Tufte has called this use of rug plots a 'dot-dash plot', and has an example in VDQI of using the axis lines to indicate the range of each variable. But to make long story short: Is there a way of creating these graphs? None <- qplot(x,y, data=xy) + geom_blank()Īnd arranging them with the function posted here. Scatter <- qplot(x,y, data=xy) + scale_x_continuous(limits=c(min(x),max(x))) + scale_y_continuous(limits=c(min(y),max(y))) Yhist <- yhist + scale_x_continuous(limits=c(min(x),max(x))) + opts( = theme_blank(), =theme_blank(), axis.ticks = theme_blank(), aspect.ratio = 16/5, = theme_blank(), =theme_blank() ) Yhist <- qplot(y, geom="histogram") + coord_flip() + opts(background.fill = "white", lor ="black") Xhist <- qplot(x, geom="histogram") + scale_x_continuous(limits=c(min(x),max(x))) + opts( = theme_blank(), =theme_blank(), axis.ticks = theme_blank(), aspect.ratio = 5/16, = theme_blank(), =theme_blank(), lour="white") I started an attempt by creating the single graphs but don't know how to arrange them properly. Is there a way of creating scatterplots with marginal histograms just like in the sample below in ggplot2? In Matlab it is the scatterhist() function and there exist equivalents for R as well.
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