Reinventing the wheel for ordination biplots with ggplot2

I’ll be the first to admit that the topic of plotting ordination results using ggplot2 has been visited many times over. As is my typical fashion, I started creating a package for this purpose without completely searching for existing solutions. Specifically, the ggbiplot and factoextra packages already provide almost complete coverage of plotting results from multivariate and ordination analyses in R. Being the stubborn individual, I couldn’t give up on my own package so I started exploring ways to improve some of the functionality of biplot methods in these existing packages. For example, ggbiplot and factoextra work almost exclusively with results from principal components analysis, whereas numerous other multivariate analyses can be visualized using the biplot approach. I started to write methods to create biplots for some of the more common ordination techniques, in addition to all of the functions I could find in R that conduct PCA. This exercise became very boring very quickly so I stopped adding methods after the first eight or so. That being said, I present this blog as a sinking ship that was doomed from the beginning, but I’m also hopeful that these functions can be built on by others more ambitious than myself.

The process of adding methods to a default biplot function in ggplot was pretty simple and not the least bit interesting. The default ggpord biplot function (see here) is very similar to the default biplot function from the stats base package. Only two inputs are used, the first being a two column matrix of the observation scores for each axis in the biplot and the second being a two column matrix of the variable scores for each axis. Adding S3 methods to the generic function required extracting the relevant elements from each model object and then passing them to the default function. Easy as pie but boring as hell.

I’ll repeat myself again. This package adds nothing new to the functionality already provided by ggbiplot and factoextra. However, I like to think that I contributed at least a little bit by adding more methods to the biplot function. On top of that, I’m also naively hopeful that others will be inspired to fork my package and add methods. Here you can view the raw code for the ggord default function and all methods added to that function. Adding more methods is straightforward, but I personally don’t have any interest in doing this myself. So who wants to help??

Visit the package repo here or install the package as follows.


Available methods and examples for each are shown below. These plots can also be reproduced from the examples in the ggord help file.

##  [1] ggord.acm       ggord.coa      ggord.default 
##  [5] ggord.lda      ggord.mca      ggord.MCA      ggord.metaMDS 
##  [9] ggord.pca      ggord.PCA      ggord.prcomp   ggord.princomp
# principal components analysis with the iris data set
# prcomp
ord <- prcomp(iris[, 1:4])

p <- ggord(ord, iris$Species)

p + scale_colour_manual('Species', values = c('purple', 'orange', 'blue'))

p + theme_classic()

p + theme(legend.position = 'top')

p + scale_x_continuous(limits = c(-2, 2))

# principal components analysis with the iris dataset
# princomp
ord <- princomp(iris[, 1:4])

ggord(ord, iris$Species)

# principal components analysis with the iris dataset

ord <- PCA(iris[, 1:4], graph = FALSE)

ggord(ord, iris$Species)

# principal components analysis with the iris dataset
# dudi.pca

ord <- dudi.pca(iris[, 1:4], scannf = FALSE, nf = 4)

ggord(ord, iris$Species)

# multiple correspondence analysis with the tea dataset
tea <- tea[, c('Tea', 'sugar', 'price', 'age_Q', 'sex')]

ord <- MCA(tea[, -1], graph = FALSE)

ggord(ord, tea$Tea)

# multiple correspondence analysis with the tea dataset
# mca

ord <- mca(tea[, -1])

ggord(ord, tea$Tea)

# multiple correspondence analysis with the tea dataset
# acm
ord <- dudi.acm(tea[, -1], scannf = FALSE)

ggord(ord, tea$Tea)

# nonmetric multidimensional scaling with the iris dataset
# metaMDS
ord <- metaMDS(iris[, 1:4])

ggord(ord, iris$Species)

# linear discriminant analysis
# example from lda in MASS package
ord <- lda(Species ~ ., iris, prior = rep(1, 3)/3)

ggord(ord, iris$Species)

# correspondence analysis
# dudi.coa
ord <- dudi.coa(iris[, 1:4], scannf = FALSE, nf = 4)

ggord(ord, iris$Species)

# correspondence analysis
# ca
ord <- ca(iris[, 1:4])

ggord(ord, iris$Species)



33 thoughts on “Reinventing the wheel for ordination biplots with ggplot2

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  3. Adding other methods like you did is actually very useful. The package can be used as a ‘map’ to all these PCA/MCA/etc. functions lying around.

    Have you taken a look at this package and that package? Perhaps you could also use autoplot function.

    • Thanks, I hope that at least a few people find these added methods useful… and that even some might contribute! I haven’t seen either of the packages you referenced but the autoplot function looks very useful. I’ll keep it in mind for future updates.

  4. Ha that’s really useful! I was just making the same thing when I discovered this. Still a fair few methods that could be included – nipals, principal, factanal,, PLS, pcaridge, prm, pcaRes, bfa, rda, NMDS… The ordination methods in the vegan package could perhaps be most useful still. Did you also see the package UBbipl of the book “Understanding biplots” and the book “Biplots in practice” is also good to check out (can send you the pdf if you wouldn’t have them). Convex hull could be nice alternative for confidence ellipses, or confidence bags, perhaps filled with semi-transp colours as opposed to outlines. Are you planning to send this to CRAN?

    • Hi Tom,

      Thanks for reading and I’m glad you found the package useful. I know the package was missing several methods for other ordination functions in R… and I was hoping that others would fork and contribute to the repo on GitHub. So far, no progress has been made but I’m still hopeful! I don’t have any plans to submit this to CRAN, unless I make significant progress on at least making this inclusive to a majority of the ordination functions in R. Some of the minor suggestions you make would be pretty straightforward to add (convex hull, confidence bags, etc.), so you should put in a request on the issues page if you’d like them included in the develoment repo.


    • Yep, you can do this with the arguments as follows:

      ord <- prcomp(iris[, 1:4])
      ggord(ord, iris$Species, vec_ext = 0, txt = NULL, arrow = 0)

      This removes both the vectors and the labels. I’d have to change the function to manipulate both, which wouldn’t be hard. You can put in an issue request on the GitHub page if you want this feature.

      • Thanks! I have a final question (hopefully!!!) Is it possible to change the vector text labels in the LDA, to make them smaller? I can’t figure out whether I need to use MASS or ggplot2 to do so.

  5. Hi,

    Thanks for this very helpful package. I tried to use ‘ggord’ to plot the results of a CCA for a site x species matrix as a function of a four-level categorical variable (edge). All looks fine except that in addition to the points for sites, there are additional points coming up in the plot (a different color from the four categories). I’m wondering if the species scores are being plotted and if so how might I suppress them?

    I tried to specify addpts=NULL, but that gave me an error:
    Error in ggord.default(obs, vecs = constr, axes, addpts = addpts, …) :
    formal argument “addpts” matched by multiple actual arguments


    • Also, I am unable to convert the site points to black with different characters for groups used in the CCA. Any help will be much appreciated.

      • Hi Meghna,

        You can suppress the species points by using addsize = NA. The sites are hard-coded in the function as points, all you can do with the current package is make the points different colors using the grp_in argument. I would need to modify the package to show what you want. You can submit an issue on the GitHub page if you think this feature would be useful.

        # data and model
        ord <- cca(varespec, varechem)
        # random groups
        grps <- sample(letters[1:4], nrow(varespec), replace = T)
        # plot, no species points, color-coded groups at sites
        ggord(ord, grp_in = grps, addsize = NA)
  6. Hi,

    Thanks for your feedback; it worked to suppress the points. I had previously done this by specifying addsize = -1, but this is better.

    As for black and white, the journal to which I have submitted has steep charges for color images, so I am also trying to make B&W images for the print version.

    I think using color for groups looks much clearer than using different characters. However, allowing the possibility of grouping by ‘pch’ might be a helpful feature to replicate from the ordiplot series.


  7. Hello, thank you for this really useful package! I was wondering if it is possible to use ggord in a scaterplot matrix setup?

    • Hi Pamela,

      I’m not sure I follow completely, do you mean setup multiple plots in a matrix? If so, you can do this with the gridExtra package.

      # principal components analysis with the iris data set
      # prcomp
      ord <- prcomp(iris[, 1:4])
      p1 <- ggord(ord, iris$Species)
      p2 <- ggord(ord, iris$Species)
      p3 <- ggord(ord, iris$Species)
      p4 <- ggord(ord, iris$Species)
      grid.arrange(p1, p2, p3, p4, ncol = 2)
      • Hello thank you for your reply,

        No I was hoping to plot several different LDAs in a scatterplot matrix. Is it possible to see different combinations of LDs with the ggord funciton? Meaning for example, LD2 vs LD3?

      • Okay, like this?

        ord <- lda(manufacturer ~ displ + cty + hwy, mpg, prior = rep(1, 15)/15)
        ggord(ord, mpg$manufacturer, axes = c('2', '3'))
  8. Yes! That is perfect, thank you so much!

    I have one more question…. I saw on comments above about getting rid of the vectors and labels, do you know how to get specific vectors and labels to show up? To eliminate just a couple of them?

    • No, it’s currently setup as an all or nothing approach. You can do it manually pulling the vector data from the ordination object and adding the vectors to ggord using ggplot geoms.

      # example from lda in MASS package
      ord <- lda(Species ~ ., iris, prior = rep(1, 3)/3)
      # get vectors from ordination object
      vecs <- ord %>% 
        .$scaling %>% %>% 
        rownames_to_column('var') %>% 
        filter(var %in% c('Petal.Width', 'Sepal.Width'))
      # get labels for vectors
      vecs_lab <-  ord %>% 
        .$scaling %>% %>% 
        `*`(1.1) %>% 
        rownames_to_column('var') %>% 
        filter(var %in% c('Petal.Width', 'Sepal.Width'))
      # show only petal and sepal width vectors
      ggord(ord, iris$Species, vec_ext = 0, txt = NULL, arrow = 0) + 
          data = vecs,
          aes_string(x = 0, y = 0, xend = 'LD1', yend = 'LD2'),
          arrow = grid::arrow(length = grid::unit(0.4, "cm"))
        ) + 
        geom_text(data = vecs_lab, aes_string(x = 'LD1', y = 'LD2', label = 'var'),
                  size = 4
      • Thank you. Thank you so much! How do I add credit where credit is due?? I am new to using github and you made my LDA go from so-so to a visually beautiful plot!

      • Hello again, I was wondering about one more thing…. how can I get only one group legend to show up for the combined gridarrange plot? Meaning I have 3 plotted LDAs (like the PCA example you posted above) but I only want one legend for all 3 to show up. I looked for suppressing legends in ggord but couldn’t find anything.

      • That’s more a ggplot thing. You can always suppress a legend by adding the appropriate theme() element to the plot:

        ord <- lda(manufacturer ~ displ + cty + hwy, mpg, prior = rep(1, 15)/15)
        ggord(ord, mpg$manufacturer, axes = c('2', '3')) + 
             theme(legend.position = 'none')

        The combined plot legend is more tricky and requires lots of hacks. You’ll have to create a ‘fake’ plot that has all of the elements you want to include in the legend, then extract the legend from the plot, and combine all with grid.arrange. This is a royal pain so maybe just consider separate legends? Either way, this code might get you started.

        # get legend from an existing ggplot object
        g_legend <- function(a.gplot){
          tmp <- ggplot_gtable(ggplot_build(a.gplot))
          leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
          legend <- tmp$grobs[[leg]]
        # prcomp
        grp1 <- iris[iris$Species %in% c('setosa', 'versicolor'), ]
        grp2 <- iris[iris$Species %in% c('setosa', 'virginica'), ]
        ord1 <- prcomp(grp1[, 1:4])
        ord2 <- prcomp(grp2[, 1:4])
        ordall <- prcomp(iris[, 1:4])
        # plots
        p1 <- ggord(ord1, grp1$Species, cols = c('red', 'green')) + theme(legend.position = 'none')
        p2 <- ggord(ord2, grp2$Species, cols = c('red', 'blue')) + theme(legend.position = 'none')
        # get legend from a plot with all three
        pleg <- ggord(ordall, iris$Species, cols = c('red', 'green', 'blue'))
        pleg <- g_legend(pleg)
        # combin plots with legend
          arrangeGrob(p1, p2, ncol = 1), 
          pleg, widths = c(1, 0.2)

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