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drosera: Leaf And Petiole Size For Three African Sundew Species

Roman M. Link

Description

drosera is a data package that provides a botanical alternative for Anderson and Fisher’s famous iris dataset which is free from its troublesome past (see e.g. this blog post on Armchair Ecology and this great Twitter thread by Maryclare Griffin). It is based on a set of plants that I am growing on my windowsill which I measured with a caliper in a matter of a couple of hours to have an example dataset for the corrmorant package and is entirely devoid of scientific rigor. However, I believe it is a useful example dataset that will hopefully enable some people to replace iris in their code and package documentation, and maybe even spark an interest in carnivorous plants in a couple of R users.

The dataset contains 150 observations of leaf and petiole size for five varieties of three African sundew species: the typical and red forms of the Cape sundew, Drosera capensis, (left), the typical form of Drosera madagascarienis (center) and the typical and anthocyanin-reduced forms of Drosera venusta (right, photos taken by myself). <img src="man/figures/droseras.png" width="700" />

It is published under a Creative Commons Zero license, so it is free to use in any kind of commercial or non-commercial setting. In particular, if you need a replacement dataset for iris for the examples in your R package, feel free to add drosera to the package without any additional precautions.

However, it would be kind if you add a link to the package’s Github page in case you decide to use it (and maybe drop me a line so I can link your project in the repository), but I won’t be mad if you don’t.

Drosera is a wide-spread genus of carnivorous plants (around 200 species) in the family Droseraceae that catch their (mostly insect) prey with a sticky trap mechanism based on a mucilaginous secretion from stalked glands covering their leaf surface. The glistering drops of mucilage on their tentacle-like glands have earned them the common name sundews.

<img src="man/figures/madagascariensis1.jpg" align="center" width="400" />

Usage

The drosera data package can be installed from Github using remotes::install_github():

# install remotes package if necessary
install.packages("remotes")
# install corrmorant from the github repository
remotes::install_github("r-link/drosera")

Afterwards, the package can be loaded regularly via library() to access the data:

library(drosera)
data(package = "drosera")

Afterwards, the drosera data can be used as a regular loaded dataset:

head(drosera)

##    species variety petiole_length petiole_width blade_length blade_width
## 1 capensis   rubra          54.00          1.95        38.30        3.95
## 2 capensis   rubra          45.90          1.15        27.60        2.95
## 3 capensis   rubra          21.20          0.85        21.85        2.65
## 4 capensis   rubra          56.40          1.65        38.20        3.30
## 5 capensis   rubra          28.60          1.00        15.80        2.80
## 6 capensis   rubra          32.85          1.50        24.65        4.40

Examples for visualizations with corrmorant

The dataset shows strong correlations between the different variables, and pronounced inter- and intraspecific differences. Here’s a basic illustration based on the corrmorant package.

# load packages
library(tidyverse)
library(corrmorant)

# create plot
ggcorrm(drosera,                                 # dataset
        aes(color = species, fill = species),    # settings of non-standard aesthetics: color and fill by species
        labels = paste(str_to_sentence(gsub("_", " ", names(drosera)[3:6])), "(mm)")) + # labels for variable names
  lotri(geom_point(alpha = 0.4)) +               # points in lower triangle 
  utri_corrtext() +                              # indicator of (Pearson) correlation in upper triangle
  dia_density(color = "black", size = .3, alpha = .5) + # density plots on the plot diagonal
  dia_names() +                        # variable names on the plot diagonal
  scale_color_viridis_d(option = "C", begin = .1, end = .9,  # color scale settings
                        aesthetics = c("fill", "color"))

## The following column names were replaced:
## petiole_length   ->  Petiole length (mm)
## petiole_width    ->  Petiole width (mm)
## blade_length ->  Blade length (mm)
## blade_width  ->  Blade width (mm)

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The patterns become even clearer when assessed on a log scale:

ggcorrm(
  drosera,
  aes(color = species, fill = species),   
  labels = paste(str_to_sentence(gsub("_", " ", names(drosera)[3:6])), "(mm)")) + 
  lotri(geom_point(alpha = 0.4)) +              
  utri_corrtext() +                              
  dia_density(color = "black", size = .3, alpha = .5) + 
  dia_names() +                        
  scale_color_viridis_d(begin = .15, end = .85, aesthetics = c("fill", "color")) +
  scale_x_log10() + scale_y_log10()

## The following column names were replaced:
## petiole_length   ->  Petiole length (mm)
## petiole_width    ->  Petiole width (mm)
## blade_length ->  Blade length (mm)
## blade_width  ->  Blade width (mm)

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Notably, for the two species where different varieties were measured, there are also subtle but notable differences between the distinct forms:

# Function for plots
pfun <- function(data){
  ggcorrm( data,
    aes(color = variety, fill = variety), 
    bg_dia   = "grey20",
    bg_lotri = "grey40",
    bg_utri  = "grey40",
    labels = paste(str_to_sentence(gsub("_", " ", names(drosera)[3:6]))),
  ) + 
    lotri(geom_smooth(alpha = .3, method = "lm", size = .35)) +
    lotri(geom_point(alpha = .65)) +              
    utri_corrtext(ncol = 1, squeeze = .3) +        
    dia_density(alpha = .5) +
    dia_names(col = "#DDDDDD") +  
    theme(panel.border    = element_rect(fill = NA, color = "#DDDDDD", size = .8),
          legend.background = element_blank(),
          plot.background = element_rect(fill = "#DDDDDD", , color = "#DDDDDD"),
          plot.title = element_text(face = "italic", hjust = 0.5, size = 15))
}

# Create plot for Drosera capensis 
capensis <- pfun(filter(drosera, species == "capensis")) +     
    scale_color_manual(values = c("#BB4456", "#25BB04"), aesthetics = c("color", "fill"),
                       name = "Variety") +
    ggtitle("Drosera capensis") 

# Create plot for Drosera venusta
venusta <- pfun(filter(drosera, species == "venusta")) +     
  scale_color_manual(values = c("#88FF96", "#25BB04"), aesthetics = c("color", "fill"),
                     name = "Variety") +
  ggtitle("Drosera venusta") 

# Combine both plots using cowplot::plotgrid()
cowplot::plot_grid(capensis, venusta, nrow = 1) +
  theme(plot.background = element_rect(fill = "#DDDDDD", color = NA))

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You can clearly see that for Drosera capensis, the plants of the red form have notably more slender leaves and petioles than the typical form. For Drosera venusta, the differences in proportions were less pronounced, but the plants of the anthocyanin-reduced form on average are smaller than the plants of the typical plants.