Nothing
Customised plots"
In mFD: Compute and Illustrate the Multiple Facets of Functional Diversity
About this tutorial
The alpha.fd.multim() function of the mFD package computes plots of
functional indices for up to two assemblages with no distinction of species
shapes. However, it is sometimes interesting to plot more than two assemblages
or to plot species with different shapes according to categories of a given
non-continuous trait. This tutorial illustrates both cases using functions
present in the mFD package.
Plots in the mFD package are based on the ggplot2 philosophy: by adding several layers of graphic contents, you retrieve your
global plot. Plotting more than two convex hulls or shaping species according to
a trait values thus relies on this technic using basic graphical functions
available in mFD. In order to plot an indice for a given pair of functional axes,
you need to use:
-
A first function: background.plot()
create a ggplot object with customized axis ranges, names and background
-
A second function: pool.plot()
plots all species from the study cases with customized shape and colour and the
associated convex-hull with customized colour.
-
A third function: fric.plot()
plots the convex hulls of all the studied assemblages with customized colours and opacities for
each assemblage. It also plots species from each assemblage in a customized colour/shape/
fill. This function can be replaced by any other function to plot other functional
indices (fdiv.plot(),
fdis.plot(),
feve.plot(),
fide.plot(),
fnnd.plot(),
fori.plot() or
fspe.plot()).
The challenging part of this tutorial is here as these functions need informations
such as the species coordinates for each assemblage and the vertices identity which
must be retrieved before. Don't worry, we'll see it step by step thereafter ;)
-
Once these functions have been used as many times as needed to plot all the
combinations of axis pairs, all the plots are combined into a nice design using
the patchwork
package.
Load data
The data used here is the same as the one used in the mFD General workflow tutorial.
The dataset contains 25 types of fruits (i.e. species) distributed in 10 fruits
baskets (i.e. assemblages). Each fruit is characterized by five traits values summarized in the
following table:
| Trait name | Trait measurement | Trait type | Number of classes | Classes code | Unit |
|:------------:|:------------------:|:-------------:|:-------------------:|:----------------------------------:|:------:|
| Size | Maximal diameter | Ordinal | 5 | 0-1 ; 1-3 ; 3-5 ; 5-10 ; 10-20 | cm |
| Plant | Growth form | Categorical | 4 | tree; shrub; vine; forb | NA |
| Climate | Climatic niche | Ordinal | 3 | temperate ; subtropical ; tropical | NA |
| Seed | Seed type | Ordinal | 3 | none ; pip ; pit | NA |
| Sugar | Sugar | Continuous | NA | NA | g/kg |
Let's call the species*assemblages matrix:
# Load data:
data("baskets_fruits_weights", package = "mFD")
# Display the table:
knitr::kable(as.data.frame(baskets_fruits_weights[1:6, 1:6]),
centering = TRUE,
caption = "Species x assemblages matrix based on the **fruits** dataset")
Let's call the traits dataframe:
# Load data:
data("fruits_traits", package = "mFD")
# Remove fuzzy traits in this tutorial:
fruits_traits <- fruits_traits[ , -c(6:8)]
# Display the table:
knitr::kable(head(fruits_traits),
caption = "Species x traits data frame")
Let's call the dataframe which summarise the type of each traits:
# Load data:
data("fruits_traits_cat", package = "mFD")
# Remove fuzzy traits in this tutorial:
fruits_traits_cat <- fruits_traits_cat[-c(6:8), ]
# Thus remove the "fuzzy_name" column:
fruits_traits_cat <- fruits_traits_cat[ , -3]
# Display the table:
knitr::kable(head(fruits_traits_cat),
caption = "Traits types based on **fruits & baskets** dataset")
Plotting more than two assemblages
Basic workflow before plotting
For more information about the basic workflow before plotting, have a look at the
mFD General Worklow
Part 3 to 6. We here assume that these steps are ok for you and just compute
functional distances and functional indices based on the 4D space which is the
best given the data we have.
Compute functional distances between all the species in the data:
USAGE
sp_dist_fruits <- mFD::funct.dist(
sp_tr = fruits_traits,
tr_cat = fruits_traits_cat,
metric = "gower",
scale_euclid = "scale_center",
ordinal_var = "classic",
weight_type = "equal",
stop_if_NA = TRUE)
Compute the quality of the functional spaces and species coordinates in the
chosen functional space:
USAGE
# Quality of functional spaces:
fspaces_quality_fruits <- mFD::quality.fspaces(
sp_dist = sp_dist_fruits,
maxdim_pcoa = 10,
deviation_weighting = "absolute",
fdist_scaling = FALSE,
fdendro = "average")
# retrieve species (fruits) coordinates in the 4D space (see General tutorial):
sp_faxes_coord_fruits <- fspaces_quality_fruits$"details_fspaces"$"sp_pc_coord"
Compute alpha FD indices (here only Functional Richness but if other indices
have to be plotted, then compute them):
USAGE
alpha_fd_indices_fruits <- mFD::alpha.fd.multidim(
sp_faxes_coord = sp_faxes_coord_fruits[ , c("PC1", "PC2", "PC3", "PC4")],
asb_sp_w = baskets_fruits_weights,
ind_vect = c("fric"),
scaling = TRUE,
check_input = TRUE,
details_returned = TRUE)
Now, we have the data to begin the plot!
Plotting functional convex-hulls for more than two assemblages for one pair of axis
In this part, we'll show you how to plot more than two convex-hulls for more than
two assemblages. Then, we'll gather all the information in a loop to do the plots
for all pairs of axis.
a - Background
The first step is to compute the background of the plot using the background.plot()
function. This function needs three main inputs:
-
range_faxes: a vector containing the
minimum and maximum values of axes.
Note: in order to have a fair
representation of species postition in all plots combining different pairs of
axes, they should have the same axes ranges. Next, we will show how to compute
ranges according to the range of valus among all axes.
-
faxes_nm: a vector containing
the axes labels in the figure.
-
color_bg: a R color name or an
hexadecimal code used to fill the plot background.
Let's plot the background of the plot for one combination of axis (PC1 and PC2)!
USAGE Compute the range of functional axes
# Compute the range of functional axes:
range_sp_coord <- range(sp_faxes_coord_fruits)
# Based on the range of species coordinates values, compute a nice range ...
# ... for functional axes:
range_faxes <- range_sp_coord +
c(-1, 1) * (range_sp_coord[2] - range_sp_coord[1]) * 0.05
range_faxes
USAGE Plot background for PC1 and PC2 plot
# get species coordinates along the two studied axes:
sp_faxes_coord_xy <- sp_faxes_coord_fruits[, c("PC1", "PC2")]
# Plot background with grey backrgound:
plot_k <- mFD::background.plot(range_faxes = range_faxes,
faxes_nm = c("PC1", "PC2"),
color_bg = "grey95")
plot_k
b - Add the global convex-hull
The convex-hull shaping the global pool of species is then added to the
background plot. Species can be plotted with a minimal shape and color or even not
displayed (what we will do here) thus when plotting species from wanted assemblages,
there is not too much information on the graph. There are two steps:
first, retrieving the coordinates of species being vertices along the two studied
functional axes and second, add species from the global pool to the background
plot (called plot_k in this tutorial):
Step 1: vertices
Realised using the vertices() function
which identifies species being vertices of the minimal convex-hull enclosing a
community. It needs three main inputs:
-
sp_faxes_coord: the matrix of species
coordinates in the chosen functional space. Here, as we are interested in PC1
and PC2, the sp_faxes_coord matrix only contains coordinates along PC1 and PC2.
-
order_2D: TRUE/FALSE indicating whether
vertices names are reordered so that they define a convex polygon in two
dimensions which is convenient for plotting.
-
check_input: same argument as many of the
mFD functions allowing to have customized error messages and not R basic ones.
USAGE Retrieve vertices coordinates along PC1 and PC2
# Retrieve vertices coordinates along the two studied functional axes:
vert <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_xy,
order_2D = FALSE,
check_input = TRUE)
Step 2: Add global convex-hull of species
Realised using the pool.plot() function
which plot all species from the global pool and the associated convex hull with
customized shape and colors for species and customised colours and opacity for
the convex-hull. Species being vertices can also be plotted with a different
shape or color. It needs
three main inputs:
-
ggplot_bg: the ggplot object created
on the step before ie the plot of the background retrieved through the background.plot()
function.
-
sp_coord_2D: the matrix of species
coordinates but with coordinates only for the ais to plot thus here only
PC1 and PC2. It corresponds to the sp_faxes_coord_xy object created before.
-
vertices_nD: a vector containing
the name of species being vertices along the two studied dimensions.
It correspond to the vert object created before. We will here first show how
to plot vertices with different shape/color. Yet, in order to have a clear plot
with more than two convex-hulls (goal of this tutorial) we will then remove
vertices shape and color by setting null to the vertices_nD argument.
-
arguments to customise species shape
and colours: color_pool, fill_pool, shape_pool and size_pool
arguments can be used. We will here first show how to plot species with
customised shape/color. Yet, in order to have a clear plot
with more than two convex-hulls (goal of this tutorial) we will then not
display species by setting NA to the color_pool argument.
-
arguments to customise vertices shape
and colours: color_vert, fill_vert, shape_vert and size_vert
arguments can be used. We will here first show how
to plot vertices with different shape/color. Yet, in order to have a clear plot
with more than two convex-hulls (goal of this tutorial) we will then remove
vertices shape and color by setting aestiteic arguments of vertices to NA.
-
arguments to customise the convex hull of
the global pool: color_ch, fill_ch and alpha_ch
arguments can be used.
USAGE Add convex-hull, species & vertices from the global pool (plot not used in the workflow of this tutorial because it is too complex to be able to easily read the
final plot with more than two convex-hulls)
plot_sp_vert <- mFD::pool.plot(ggplot_bg = plot_k,
sp_coord2D = sp_faxes_coord_xy,
vertices_nD = vert,
plot_pool = TRUE,
color_pool = "black",
fill_pool = NA,
alpha_ch = 0.8,
color_ch = NA,
fill_ch = "white",
shape_pool = 3,
size_pool = 0.8,
shape_vert = 16,
size_vert = 1,
color_vert = "green",
fill_vert = "green")
plot_sp_vert
USAGE Let's continue the workflow on how to plot more than two convex-hulls! Thus remove species otherwise the final plot will be difficult to read ;)
plot_k <- mFD::pool.plot(ggplot_bg = plot_k,
sp_coord2D = sp_faxes_coord_xy,
vertices_nD = vert,
plot_pool = FALSE,
color_pool = NA,
fill_pool = NA,
alpha_ch = 0.8,
color_ch = "white",
fill_ch = "white",
shape_pool = NA,
size_pool = NA,
shape_vert = NA,
size_vert = NA,
color_vert = NA,
fill_vert = NA)
plot_k
c - Add the convex-hulls and species of the wanted assemblages
Convex-hulls from the wanted assemblages can then be added to the plot_k object
which now contains the background and the convex-hull from the global pool of
species. Here, we will plot three assemblages: basket 1, basket 4 and basket 10
with different colors using the fric.plot() function. You can
play with colors and opacities of each convex-hull so the graph is easy to read
and convex-hulls are easily distinguisable.
Step 1: Get the coordinates of species of the assemblages to plot
Realised using the sp.filter()
and the vertices()
functions to first retrieve the coordinates of species belonging to the
assemblages to plot and then the names of species being vertices.
USAGE Retrieve the coordinates of species
belonging to basket_1, basket_6 and basket_10:
# basket_1:
## filter species from basket_1:
sp_filter_basket1 <- mFD::sp.filter(asb_nm = c("basket_1"),
sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_1):
sp_faxes_coord_basket1 <- sp_filter_basket1$`species coordinates`
# basket_6:
## filter species from basket_6:
sp_filter_basket6 <- mFD::sp.filter(asb_nm = c("basket_6"),
sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_6):
sp_faxes_coord_basket6 <- sp_filter_basket6$`species coordinates`
# basket_10:
## filter species from basket_10:
sp_filter_basket10 <- mFD::sp.filter(asb_nm = c("basket_10"),
sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_10):
sp_faxes_coord_basket10 <- sp_filter_basket10$`species coordinates`
Let's have a look at the coordinates of species from basket_1: note that we are still
working with the coordinates along the two studied functional axis PC1 and PC2
sp_faxes_coord_basket1
USAGE Retrieve the names of species
being vertices of basket_1, basket_6 and basket_10:
# basket_1:
vert_nm_basket1 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket1,
order_2D = TRUE,
check_input = TRUE)
# basket_6:
vert_nm_basket6 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket6,
order_2D = TRUE,
check_input = TRUE)
# basket_10:
vert_nm_basket10 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket10,
order_2D = TRUE,
check_input = TRUE)
Step 2: Adding assemblages convex_hulls and species
Realised using the fric.plot() function.
This function has a lot of argument to customise the plot and play with colours,
shapes and opacity. Its main inputs are:
-
plot_k: the ggplot object created
on the steps before ie the plot of the background retrieved through the background.plot()
function with the global convex hull added through the pool.plot() function.
-
asb_sp_coord_2D: a list containing
the coordinates of species belonging to each assemblage to plot across the
two functional ais studied (here PC1 and PC2). Note: each
element of this list reflects a given assemblage and each element must be
nammed after the assemblage.
-
asb_vertices_nD: a list containing
a list (with names as in asb_sp_coord2D) of vectors with names of species being
vertices in n dimensions. Note: each element of this list reflects a given
assemblage and each element must be nammed after the assemblage.
-
plot_sp: a TRUE/FALSE value
indicating whether species from the studied assemblages must be plotted. If
TRUE, then the arguments shape_sp, size_sp, color_sp and fill_sp help
to customise species shapes/sizes/colours and the arguments shape_vert,
size_vert, color_vert and fill_vert help to customise vertices.
For shape/size/colour arguments, each element of the input list reflects a given
assemblage and each element must be nammed after the
assemblage see the function help for more information
-
color_ch,
fill_ch and
alpha_ch: are lists containing
colors and opacity values to caracterise each convex-hull of the studied
assemblage. Note: each element of these lists reflects a given
assemblage and each element must be nammed after the assemblage and the order in which each assemblage is given must reflect the same order than the asb_sp_coord_2D order. Can be set up
to NA if no colors are to be plotted.
USAGE Add the convex-hulls of the three
studied assemblages: only convex hulls with transparent surroundings, no species plotted
plot_try <- mFD::fric.plot(ggplot_bg = plot_k,
asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1,
"basket_6" = sp_faxes_coord_basket6,
"basket_10" = sp_faxes_coord_basket10),
asb_vertices_nD = list("basket_1" = vert_nm_basket1,
"basket_6" = vert_nm_basket6,
"basket_10" = vert_nm_basket10),
plot_sp = FALSE,
color_ch = NA,
fill_ch = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
alpha_ch = c("basket_1" = 0.4,
"basket_6" = 0.4,
"basket_10" = 0.4),
shape_sp = NA,
size_sp = NA,
color_sp = NA,
fill_sp = NA,
shape_vert = NA,
size_vert = NA,
color_vert = NA,
fill_vert = NA)
plot_try
USAGE Add the convex-hulls of the three
studied assemblages: only convex hulls with coloured surroundings, no species plotted
plot_try <- mFD::fric.plot(ggplot_bg = plot_k,
asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1,
"basket_6" = sp_faxes_coord_basket6,
"basket_10" = sp_faxes_coord_basket10),
asb_vertices_nD = list("basket_1" = vert_nm_basket1,
"basket_6" = vert_nm_basket6,
"basket_10" = vert_nm_basket10),
plot_sp = FALSE,
color_ch = c("basket_1" = "#7a0177",
"basket_6" = "#c51b8a",
"basket_10" = "#fa9fb5"),
fill_ch = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
alpha_ch = c("basket_1" = 0.4,
"basket_6" = 0.4,
"basket_10" = 0.4),
shape_sp = NA,
size_sp = NA,
color_sp = NA,
fill_sp = NA,
shape_vert = NA,
size_vert = NA,
color_vert = NA,
fill_vert = NA)
plot_try
USAGE Add the convex-hulls of the three
studied assemblages: only convex hulls with transparent surroundings, species plotted
but with no differences between non-vertices and vertices species
plot_try <- mFD::fric.plot(ggplot_bg = plot_k,
asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1,
"basket_6" = sp_faxes_coord_basket6,
"basket_10" = sp_faxes_coord_basket10),
asb_vertices_nD = list("basket_1" = vert_nm_basket1,
"basket_6" = vert_nm_basket6,
"basket_10" = vert_nm_basket10),
plot_sp = TRUE,
color_ch = NA,
fill_ch = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
alpha_ch = c("basket_1" = 0.4,
"basket_6" = 0.4,
"basket_10" = 0.4),
shape_sp = c("basket_1" = 21,
"basket_6" = 22,
"basket_10" = 24),
size_sp = c("basket_1" = 2,
"basket_6" = 2,
"basket_10" = 2),
color_sp = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
fill_sp = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
shape_vert = c("basket_1" = 21,
"basket_6" = 22,
"basket_10" = 24),
size_vert = c("basket_1" = 2,
"basket_6" = 2,
"basket_10" = 2),
color_vert = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
fill_vert = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"))
plot_try
USAGE Add the convex-hulls of the three
studied assemblages: only convex hulls with transparent surroundings, species plotted
but with different colour between non-vertices and vertices species
plot_try <- mFD::fric.plot(ggplot_bg = plot_k,
asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1,
"basket_6" = sp_faxes_coord_basket6,
"basket_10" = sp_faxes_coord_basket10),
asb_vertices_nD = list("basket_1" = vert_nm_basket1,
"basket_6" = vert_nm_basket6,
"basket_10" = vert_nm_basket10),
plot_sp = TRUE,
color_ch = NA,
fill_ch = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
alpha_ch = c("basket_1" = 0.4,
"basket_6" = 0.4,
"basket_10" = 0.4),
shape_sp = c("basket_1" = 21,
"basket_6" = 22,
"basket_10" = 24),
size_sp = c("basket_1" = 2,
"basket_6" = 2,
"basket_10" = 2),
color_sp = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
fill_sp = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
shape_vert = c("basket_1" = 21,
"basket_6" = 22,
"basket_10" = 24),
size_vert = c("basket_1" = 2,
"basket_6" = 2,
"basket_10" = 2),
color_vert = c("basket_1" = "#7a0177",
"basket_6" = "#c51b8a",
"basket_10" = "#fa9fb5"),
fill_vert = c("basket_1" = "#7a0177",
"basket_6" = "#c51b8a",
"basket_10" = "#fa9fb5"))
plot_try
Now that we have seen the basic workflow of how to add graph components with
only two functional axis using the mFD functions which relies on the
ggplot2 package, let's compute as many graph as wanted! ;)
Plotting functional convex-hulls for more than two assemblages for several pair of axis
For this part, we will shamelessly use a for loop as follow:
USAGE Structure of the loop to
compute as many FRic plots as wanted (combination of functional axes):
here plots for PC1, PC2, PC3 and PC4
####### Preliminary steps:
## Compute the range of functional axes:
range_sp_coord <- range(sp_faxes_coord_fruits)
## Based on the range of species coordinates values, compute a nice range ...
## ... for functional axes:
range_faxes <- range_sp_coord +
c(-1, 1) * (range_sp_coord[2] - range_sp_coord[1]) * 0.05
####### Create a list that will contains plots for each combination of axis:
plot_FRic <- list()
####### Compute all the combiantion we can get and the number of plots
axes_plot <- utils::combn(c("PC1", "PC2", "PC3", "PC4"), 2)
plot_nb <- ncol(axes_plot)
######## Loop on all pairs of axes:
# for each combinaison of two axis:
for (k in (1:plot_nb)) {
# get names of axes to plot:
xy_k <- axes_plot[1:2, k]
####### Steps previously showed
# a - Background:
# get species coordinates along the two studied axes:
sp_faxes_coord_xy <- sp_faxes_coord_fruits[, xy_k]
# Plot background with grey backrgound:
plot_k <- mFD::background.plot(range_faxes = range_faxes,
faxes_nm = c(xy_k[1], xy_k[2]),
color_bg = "grey95")
# b - Global convex-hull:
# Retrieve vertices coordinates along the two studied functional axes:
vert <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_xy,
order_2D = FALSE,
check_input = TRUE)
plot_k <- mFD::pool.plot(ggplot_bg = plot_k,
sp_coord2D = sp_faxes_coord_xy,
vertices_nD = vert,
plot_pool = FALSE,
color_pool = NA,
fill_pool = NA,
alpha_ch = 0.8,
color_ch = "white",
fill_ch = "white",
shape_pool = NA,
size_pool = NA,
shape_vert = NA,
size_vert = NA,
color_vert = NA,
fill_vert = NA)
# c - Assemblages convex-hulls and species:
# Step 1: Species coordinates:
# basket_1:
## filter species from basket_1:
sp_filter_basket1 <- mFD::sp.filter(asb_nm = c("basket_1"),
sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_1):
sp_faxes_coord_basket1 <- sp_filter_basket1$`species coordinates`
# basket_6:
## filter species from basket_6:
sp_filter_basket6 <- mFD::sp.filter(asb_nm = c("basket_6"),
sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_6):
sp_faxes_coord_basket6 <- sp_filter_basket6$`species coordinates`
# basket_10:
## filter species from basket_10:
sp_filter_basket10 <- mFD::sp.filter(asb_nm = c("basket_10"),
sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_10):
sp_faxes_coord_basket10 <- sp_filter_basket10$`species coordinates`
# Step 1 follow-up Vertices names:
# basket_1:
vert_nm_basket1 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket1,
order_2D = TRUE,
check_input = TRUE)
# basket_6:
vert_nm_basket6 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket6,
order_2D = TRUE,
check_input = TRUE)
# basket_10:
vert_nm_basket10 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket10,
order_2D = TRUE,
check_input = TRUE)
# Step 2: plot convex-hulls and species of studied assemblages:
plot_k <- mFD::fric.plot(ggplot_bg = plot_k,
asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1,
"basket_6" = sp_faxes_coord_basket6,
"basket_10" = sp_faxes_coord_basket10),
asb_vertices_nD = list("basket_1" = vert_nm_basket1,
"basket_6" = vert_nm_basket6,
"basket_10" = vert_nm_basket10),
plot_sp = TRUE,
color_ch = NA,
fill_ch = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
alpha_ch = c("basket_1" = 0.4,
"basket_6" = 0.4,
"basket_10" = 0.4),
shape_sp = c("basket_1" = 21,
"basket_6" = 22,
"basket_10" = 24),
size_sp = c("basket_1" = 2,
"basket_6" = 2,
"basket_10" = 2),
color_sp = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
fill_sp = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
shape_vert = c("basket_1" = 21,
"basket_6" = 22,
"basket_10" = 24),
size_vert = c("basket_1" = 2,
"basket_6" = 2,
"basket_10" = 2),
color_vert = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"),
fill_vert = c("basket_1" = "#1c9099",
"basket_6" = "#67a9cf",
"basket_10" = "#d0d1e6"))
####### Save the plot on the plot list:
plot_FRic[[k]] <- plot_k
}
Let's now have a look at the plot_FRic list which contains the FRic plots for
the three studied assemblages (basket_1, basket_6 and basket_10). It contains as
many element as there are combination of two axis with the four studied axis:
plot_FRic
Gathering plots together using the patchwork package
We are now close to the final graph! Using the patchwork package, we then combine the plots altogether:
USAGE Combine Fric plots into a single
graph using the patchwork package
patchwork_FRic <- (plot_FRic[[1]] + patchwork::plot_spacer() + patchwork::plot_spacer() +
plot_FRic[[2]] + plot_FRic[[4]] + patchwork::plot_spacer() +
plot_FRic[[3]] + plot_FRic[[5]] + plot_FRic[[6]]) +
patchwork::plot_layout(byrow = TRUE, heights = rep(1, 3),
widths = rep(1, 3), ncol = 3, nrow = 3,
guides = "collect")
patchwork_FRic
You can now play with colours, shapes and
add as many assemblages as wanted!
Plotting species differently according to one trait value (coming soon...)
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About this tutorial
The alpha.fd.multim() function of the mFD package computes plots of
functional indices for up to two assemblages with no distinction of species
shapes. However, it is sometimes interesting to plot more than two assemblages
or to plot species with different shapes according to categories of a given
non-continuous trait. This tutorial illustrates both cases using functions
present in the mFD package.
Plots in the mFD package are based on the ggplot2 philosophy: by adding several layers of graphic contents, you retrieve your
global plot. Plotting more than two convex hulls or shaping species according to
a trait values thus relies on this technic using basic graphical functions
available in mFD. In order to plot an indice for a given pair of functional axes,
you need to use:
-
A first function: background.plot() create a ggplot object with customized axis ranges, names and background
-
A second function: pool.plot() plots all species from the study cases with customized shape and colour and the associated convex-hull with customized colour.
-
A third function: fric.plot() plots the convex hulls of all the studied assemblages with customized colours and opacities for each assemblage. It also plots species from each assemblage in a customized colour/shape/ fill. This function can be replaced by any other function to plot other functional indices (fdiv.plot(), fdis.plot(), feve.plot(), fide.plot(), fnnd.plot(), fori.plot() or fspe.plot()). The challenging part of this tutorial is here as these functions need informations such as the species coordinates for each assemblage and the vertices identity which must be retrieved before. Don't worry, we'll see it step by step thereafter ;)
-
Once these functions have been used as many times as needed to plot all the combinations of axis pairs, all the plots are combined into a nice design using the patchwork package.
Load data
The data used here is the same as the one used in the mFD General workflow tutorial.
The dataset contains 25 types of fruits (i.e. species) distributed in 10 fruits baskets (i.e. assemblages). Each fruit is characterized by five traits values summarized in the following table:
| Trait name | Trait measurement | Trait type | Number of classes | Classes code | Unit | |:------------:|:------------------:|:-------------:|:-------------------:|:----------------------------------:|:------:| | Size | Maximal diameter | Ordinal | 5 | 0-1 ; 1-3 ; 3-5 ; 5-10 ; 10-20 | cm | | Plant | Growth form | Categorical | 4 | tree; shrub; vine; forb | NA | | Climate | Climatic niche | Ordinal | 3 | temperate ; subtropical ; tropical | NA | | Seed | Seed type | Ordinal | 3 | none ; pip ; pit | NA | | Sugar | Sugar | Continuous | NA | NA | g/kg |
Let's call the species*assemblages matrix:
# Load data: data("baskets_fruits_weights", package = "mFD") # Display the table: knitr::kable(as.data.frame(baskets_fruits_weights[1:6, 1:6]), centering = TRUE, caption = "Species x assemblages matrix based on the **fruits** dataset")
Let's call the traits dataframe:
# Load data: data("fruits_traits", package = "mFD") # Remove fuzzy traits in this tutorial: fruits_traits <- fruits_traits[ , -c(6:8)] # Display the table: knitr::kable(head(fruits_traits), caption = "Species x traits data frame")
Let's call the dataframe which summarise the type of each traits:
# Load data: data("fruits_traits_cat", package = "mFD") # Remove fuzzy traits in this tutorial: fruits_traits_cat <- fruits_traits_cat[-c(6:8), ] # Thus remove the "fuzzy_name" column: fruits_traits_cat <- fruits_traits_cat[ , -3] # Display the table: knitr::kable(head(fruits_traits_cat), caption = "Traits types based on **fruits & baskets** dataset")
Plotting more than two assemblages
Basic workflow before plotting
For more information about the basic workflow before plotting, have a look at the mFD General Worklow Part 3 to 6. We here assume that these steps are ok for you and just compute functional distances and functional indices based on the 4D space which is the best given the data we have.
Compute functional distances between all the species in the data:
USAGE
sp_dist_fruits <- mFD::funct.dist( sp_tr = fruits_traits, tr_cat = fruits_traits_cat, metric = "gower", scale_euclid = "scale_center", ordinal_var = "classic", weight_type = "equal", stop_if_NA = TRUE)
Compute the quality of the functional spaces and species coordinates in the chosen functional space:
USAGE
# Quality of functional spaces: fspaces_quality_fruits <- mFD::quality.fspaces( sp_dist = sp_dist_fruits, maxdim_pcoa = 10, deviation_weighting = "absolute", fdist_scaling = FALSE, fdendro = "average") # retrieve species (fruits) coordinates in the 4D space (see General tutorial): sp_faxes_coord_fruits <- fspaces_quality_fruits$"details_fspaces"$"sp_pc_coord"
Compute alpha FD indices (here only Functional Richness but if other indices have to be plotted, then compute them):
USAGE
alpha_fd_indices_fruits <- mFD::alpha.fd.multidim( sp_faxes_coord = sp_faxes_coord_fruits[ , c("PC1", "PC2", "PC3", "PC4")], asb_sp_w = baskets_fruits_weights, ind_vect = c("fric"), scaling = TRUE, check_input = TRUE, details_returned = TRUE)
Now, we have the data to begin the plot!
Plotting functional convex-hulls for more than two assemblages for one pair of axis
In this part, we'll show you how to plot more than two convex-hulls for more than two assemblages. Then, we'll gather all the information in a loop to do the plots for all pairs of axis.
a - Background
The first step is to compute the background of the plot using the background.plot() function. This function needs three main inputs:
-
range_faxes: a vector containing the minimum and maximum values of axes. Note: in order to have a fair representation of species postition in all plots combining different pairs of axes, they should have the same axes ranges. Next, we will show how to compute ranges according to the range of valus among all axes.
-
faxes_nm: a vector containing the axes labels in the figure.
-
color_bg: a R color name or an hexadecimal code used to fill the plot background.
Let's plot the background of the plot for one combination of axis (PC1 and PC2)!
USAGE Compute the range of functional axes
# Compute the range of functional axes: range_sp_coord <- range(sp_faxes_coord_fruits) # Based on the range of species coordinates values, compute a nice range ... # ... for functional axes: range_faxes <- range_sp_coord + c(-1, 1) * (range_sp_coord[2] - range_sp_coord[1]) * 0.05 range_faxes
USAGE Plot background for PC1 and PC2 plot
# get species coordinates along the two studied axes: sp_faxes_coord_xy <- sp_faxes_coord_fruits[, c("PC1", "PC2")] # Plot background with grey backrgound: plot_k <- mFD::background.plot(range_faxes = range_faxes, faxes_nm = c("PC1", "PC2"), color_bg = "grey95") plot_k
b - Add the global convex-hull
The convex-hull shaping the global pool of species is then added to the
background plot. Species can be plotted with a minimal shape and color or even not
displayed (what we will do here) thus when plotting species from wanted assemblages,
there is not too much information on the graph. There are two steps:
first, retrieving the coordinates of species being vertices along the two studied
functional axes and second, add species from the global pool to the background
plot (called plot_k in this tutorial):
Step 1: vertices Realised using the vertices() function which identifies species being vertices of the minimal convex-hull enclosing a community. It needs three main inputs:
-
sp_faxes_coord: the matrix of species coordinates in the chosen functional space. Here, as we are interested in PC1 and PC2, the
sp_faxes_coordmatrix only contains coordinates along PC1 and PC2. -
order_2D: TRUE/FALSE indicating whether vertices names are reordered so that they define a convex polygon in two dimensions which is convenient for plotting.
-
check_input: same argument as many of the
mFDfunctions allowing to have customized error messages and not R basic ones.
USAGE Retrieve vertices coordinates along PC1 and PC2
# Retrieve vertices coordinates along the two studied functional axes: vert <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_xy, order_2D = FALSE, check_input = TRUE)
Step 2: Add global convex-hull of species Realised using the pool.plot() function which plot all species from the global pool and the associated convex hull with customized shape and colors for species and customised colours and opacity for the convex-hull. Species being vertices can also be plotted with a different shape or color. It needs three main inputs:
-
ggplot_bg: the ggplot object created on the step before ie the plot of the background retrieved through the background.plot() function.
-
sp_coord_2D: the matrix of species coordinates but with coordinates only for the ais to plot thus here only PC1 and PC2. It corresponds to the
sp_faxes_coord_xyobject created before. -
vertices_nD: a vector containing the name of species being vertices along the two studied dimensions. It correspond to the
vertobject created before. We will here first show how to plot vertices with different shape/color. Yet, in order to have a clear plot with more than two convex-hulls (goal of this tutorial) we will then remove vertices shape and color by settingnullto thevertices_nDargument. -
arguments to customise species shape and colours:
color_pool,fill_pool,shape_poolandsize_poolarguments can be used. We will here first show how to plot species with customised shape/color. Yet, in order to have a clear plot with more than two convex-hulls (goal of this tutorial) we will then not display species by settingNAto thecolor_poolargument. -
arguments to customise vertices shape and colours:
color_vert,fill_vert,shape_vertandsize_vertarguments can be used. We will here first show how to plot vertices with different shape/color. Yet, in order to have a clear plot with more than two convex-hulls (goal of this tutorial) we will then remove vertices shape and color by setting aestiteic arguments of vertices toNA. -
arguments to customise the convex hull of the global pool:
color_ch,fill_chandalpha_charguments can be used.
USAGE Add convex-hull, species & vertices from the global pool (plot not used in the workflow of this tutorial because it is too complex to be able to easily read the final plot with more than two convex-hulls)
plot_sp_vert <- mFD::pool.plot(ggplot_bg = plot_k, sp_coord2D = sp_faxes_coord_xy, vertices_nD = vert, plot_pool = TRUE, color_pool = "black", fill_pool = NA, alpha_ch = 0.8, color_ch = NA, fill_ch = "white", shape_pool = 3, size_pool = 0.8, shape_vert = 16, size_vert = 1, color_vert = "green", fill_vert = "green") plot_sp_vert
USAGE Let's continue the workflow on how to plot more than two convex-hulls! Thus remove species otherwise the final plot will be difficult to read ;)
plot_k <- mFD::pool.plot(ggplot_bg = plot_k, sp_coord2D = sp_faxes_coord_xy, vertices_nD = vert, plot_pool = FALSE, color_pool = NA, fill_pool = NA, alpha_ch = 0.8, color_ch = "white", fill_ch = "white", shape_pool = NA, size_pool = NA, shape_vert = NA, size_vert = NA, color_vert = NA, fill_vert = NA) plot_k
c - Add the convex-hulls and species of the wanted assemblages
Convex-hulls from the wanted assemblages can then be added to the plot_k object
which now contains the background and the convex-hull from the global pool of
species. Here, we will plot three assemblages: basket 1, basket 4 and basket 10
with different colors using the fric.plot() function. You can
play with colors and opacities of each convex-hull so the graph is easy to read
and convex-hulls are easily distinguisable.
Step 1: Get the coordinates of species of the assemblages to plot Realised using the sp.filter() and the vertices() functions to first retrieve the coordinates of species belonging to the assemblages to plot and then the names of species being vertices.
USAGE Retrieve the coordinates of species belonging to basket_1, basket_6 and basket_10:
# basket_1: ## filter species from basket_1: sp_filter_basket1 <- mFD::sp.filter(asb_nm = c("basket_1"), sp_faxes_coord = sp_faxes_coord_xy, asb_sp_w = baskets_fruits_weights) ## get species coordinates (basket_1): sp_faxes_coord_basket1 <- sp_filter_basket1$`species coordinates` # basket_6: ## filter species from basket_6: sp_filter_basket6 <- mFD::sp.filter(asb_nm = c("basket_6"), sp_faxes_coord = sp_faxes_coord_xy, asb_sp_w = baskets_fruits_weights) ## get species coordinates (basket_6): sp_faxes_coord_basket6 <- sp_filter_basket6$`species coordinates` # basket_10: ## filter species from basket_10: sp_filter_basket10 <- mFD::sp.filter(asb_nm = c("basket_10"), sp_faxes_coord = sp_faxes_coord_xy, asb_sp_w = baskets_fruits_weights) ## get species coordinates (basket_10): sp_faxes_coord_basket10 <- sp_filter_basket10$`species coordinates`
Let's have a look at the coordinates of species from basket_1: note that we are still
working with the coordinates along the two studied functional axis PC1 and PC2
sp_faxes_coord_basket1
USAGE Retrieve the names of species being vertices of basket_1, basket_6 and basket_10:
# basket_1: vert_nm_basket1 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket1, order_2D = TRUE, check_input = TRUE) # basket_6: vert_nm_basket6 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket6, order_2D = TRUE, check_input = TRUE) # basket_10: vert_nm_basket10 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket10, order_2D = TRUE, check_input = TRUE)
Step 2: Adding assemblages convex_hulls and species Realised using the fric.plot() function. This function has a lot of argument to customise the plot and play with colours, shapes and opacity. Its main inputs are:
-
plot_k: the ggplot object created on the steps before ie the plot of the background retrieved through the background.plot() function with the global convex hull added through the pool.plot() function.
-
asb_sp_coord_2D: a list containing the coordinates of species belonging to each assemblage to plot across the two functional ais studied (here PC1 and PC2). Note: each element of this list reflects a given assemblage and each element must be nammed after the assemblage.
-
asb_vertices_nD: a list containing a list (with names as in asb_sp_coord2D) of vectors with names of species being vertices in n dimensions. Note: each element of this list reflects a given assemblage and each element must be nammed after the assemblage.
-
plot_sp: a TRUE/FALSE value indicating whether species from the studied assemblages must be plotted. If
TRUE, then the argumentsshape_sp,size_sp,color_spandfill_sphelp to customise species shapes/sizes/colours and the argumentsshape_vert,size_vert,color_vertandfill_verthelp to customise vertices. For shape/size/colour arguments, each element of the input list reflects a given assemblage and each element must be nammed after the assemblage see the function help for more information -
color_ch, fill_ch and alpha_ch: are lists containing colors and opacity values to caracterise each convex-hull of the studied assemblage. Note: each element of these lists reflects a given assemblage and each element must be nammed after the assemblage and the order in which each assemblage is given must reflect the same order than the asb_sp_coord_2D order. Can be set up to
NAif no colors are to be plotted.
USAGE Add the convex-hulls of the three studied assemblages: only convex hulls with transparent surroundings, no species plotted
plot_try <- mFD::fric.plot(ggplot_bg = plot_k, asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1, "basket_6" = sp_faxes_coord_basket6, "basket_10" = sp_faxes_coord_basket10), asb_vertices_nD = list("basket_1" = vert_nm_basket1, "basket_6" = vert_nm_basket6, "basket_10" = vert_nm_basket10), plot_sp = FALSE, color_ch = NA, fill_ch = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), alpha_ch = c("basket_1" = 0.4, "basket_6" = 0.4, "basket_10" = 0.4), shape_sp = NA, size_sp = NA, color_sp = NA, fill_sp = NA, shape_vert = NA, size_vert = NA, color_vert = NA, fill_vert = NA) plot_try
USAGE Add the convex-hulls of the three studied assemblages: only convex hulls with coloured surroundings, no species plotted
plot_try <- mFD::fric.plot(ggplot_bg = plot_k, asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1, "basket_6" = sp_faxes_coord_basket6, "basket_10" = sp_faxes_coord_basket10), asb_vertices_nD = list("basket_1" = vert_nm_basket1, "basket_6" = vert_nm_basket6, "basket_10" = vert_nm_basket10), plot_sp = FALSE, color_ch = c("basket_1" = "#7a0177", "basket_6" = "#c51b8a", "basket_10" = "#fa9fb5"), fill_ch = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), alpha_ch = c("basket_1" = 0.4, "basket_6" = 0.4, "basket_10" = 0.4), shape_sp = NA, size_sp = NA, color_sp = NA, fill_sp = NA, shape_vert = NA, size_vert = NA, color_vert = NA, fill_vert = NA) plot_try
USAGE Add the convex-hulls of the three studied assemblages: only convex hulls with transparent surroundings, species plotted but with no differences between non-vertices and vertices species
plot_try <- mFD::fric.plot(ggplot_bg = plot_k, asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1, "basket_6" = sp_faxes_coord_basket6, "basket_10" = sp_faxes_coord_basket10), asb_vertices_nD = list("basket_1" = vert_nm_basket1, "basket_6" = vert_nm_basket6, "basket_10" = vert_nm_basket10), plot_sp = TRUE, color_ch = NA, fill_ch = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), alpha_ch = c("basket_1" = 0.4, "basket_6" = 0.4, "basket_10" = 0.4), shape_sp = c("basket_1" = 21, "basket_6" = 22, "basket_10" = 24), size_sp = c("basket_1" = 2, "basket_6" = 2, "basket_10" = 2), color_sp = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), fill_sp = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), shape_vert = c("basket_1" = 21, "basket_6" = 22, "basket_10" = 24), size_vert = c("basket_1" = 2, "basket_6" = 2, "basket_10" = 2), color_vert = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), fill_vert = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6")) plot_try
USAGE Add the convex-hulls of the three studied assemblages: only convex hulls with transparent surroundings, species plotted but with different colour between non-vertices and vertices species
plot_try <- mFD::fric.plot(ggplot_bg = plot_k, asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1, "basket_6" = sp_faxes_coord_basket6, "basket_10" = sp_faxes_coord_basket10), asb_vertices_nD = list("basket_1" = vert_nm_basket1, "basket_6" = vert_nm_basket6, "basket_10" = vert_nm_basket10), plot_sp = TRUE, color_ch = NA, fill_ch = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), alpha_ch = c("basket_1" = 0.4, "basket_6" = 0.4, "basket_10" = 0.4), shape_sp = c("basket_1" = 21, "basket_6" = 22, "basket_10" = 24), size_sp = c("basket_1" = 2, "basket_6" = 2, "basket_10" = 2), color_sp = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), fill_sp = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), shape_vert = c("basket_1" = 21, "basket_6" = 22, "basket_10" = 24), size_vert = c("basket_1" = 2, "basket_6" = 2, "basket_10" = 2), color_vert = c("basket_1" = "#7a0177", "basket_6" = "#c51b8a", "basket_10" = "#fa9fb5"), fill_vert = c("basket_1" = "#7a0177", "basket_6" = "#c51b8a", "basket_10" = "#fa9fb5")) plot_try
Now that we have seen the basic workflow of how to add graph components with
only two functional axis using the mFD functions which relies on the
ggplot2 package, let's compute as many graph as wanted! ;)
Plotting functional convex-hulls for more than two assemblages for several pair of axis
For this part, we will shamelessly use a for loop as follow:
USAGE Structure of the loop to compute as many FRic plots as wanted (combination of functional axes): here plots for PC1, PC2, PC3 and PC4
####### Preliminary steps: ## Compute the range of functional axes: range_sp_coord <- range(sp_faxes_coord_fruits) ## Based on the range of species coordinates values, compute a nice range ... ## ... for functional axes: range_faxes <- range_sp_coord + c(-1, 1) * (range_sp_coord[2] - range_sp_coord[1]) * 0.05 ####### Create a list that will contains plots for each combination of axis: plot_FRic <- list() ####### Compute all the combiantion we can get and the number of plots axes_plot <- utils::combn(c("PC1", "PC2", "PC3", "PC4"), 2) plot_nb <- ncol(axes_plot) ######## Loop on all pairs of axes: # for each combinaison of two axis: for (k in (1:plot_nb)) { # get names of axes to plot: xy_k <- axes_plot[1:2, k] ####### Steps previously showed # a - Background: # get species coordinates along the two studied axes: sp_faxes_coord_xy <- sp_faxes_coord_fruits[, xy_k] # Plot background with grey backrgound: plot_k <- mFD::background.plot(range_faxes = range_faxes, faxes_nm = c(xy_k[1], xy_k[2]), color_bg = "grey95") # b - Global convex-hull: # Retrieve vertices coordinates along the two studied functional axes: vert <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_xy, order_2D = FALSE, check_input = TRUE) plot_k <- mFD::pool.plot(ggplot_bg = plot_k, sp_coord2D = sp_faxes_coord_xy, vertices_nD = vert, plot_pool = FALSE, color_pool = NA, fill_pool = NA, alpha_ch = 0.8, color_ch = "white", fill_ch = "white", shape_pool = NA, size_pool = NA, shape_vert = NA, size_vert = NA, color_vert = NA, fill_vert = NA) # c - Assemblages convex-hulls and species: # Step 1: Species coordinates: # basket_1: ## filter species from basket_1: sp_filter_basket1 <- mFD::sp.filter(asb_nm = c("basket_1"), sp_faxes_coord = sp_faxes_coord_xy, asb_sp_w = baskets_fruits_weights) ## get species coordinates (basket_1): sp_faxes_coord_basket1 <- sp_filter_basket1$`species coordinates` # basket_6: ## filter species from basket_6: sp_filter_basket6 <- mFD::sp.filter(asb_nm = c("basket_6"), sp_faxes_coord = sp_faxes_coord_xy, asb_sp_w = baskets_fruits_weights) ## get species coordinates (basket_6): sp_faxes_coord_basket6 <- sp_filter_basket6$`species coordinates` # basket_10: ## filter species from basket_10: sp_filter_basket10 <- mFD::sp.filter(asb_nm = c("basket_10"), sp_faxes_coord = sp_faxes_coord_xy, asb_sp_w = baskets_fruits_weights) ## get species coordinates (basket_10): sp_faxes_coord_basket10 <- sp_filter_basket10$`species coordinates` # Step 1 follow-up Vertices names: # basket_1: vert_nm_basket1 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket1, order_2D = TRUE, check_input = TRUE) # basket_6: vert_nm_basket6 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket6, order_2D = TRUE, check_input = TRUE) # basket_10: vert_nm_basket10 <- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket10, order_2D = TRUE, check_input = TRUE) # Step 2: plot convex-hulls and species of studied assemblages: plot_k <- mFD::fric.plot(ggplot_bg = plot_k, asb_sp_coord2D = list("basket_1" = sp_faxes_coord_basket1, "basket_6" = sp_faxes_coord_basket6, "basket_10" = sp_faxes_coord_basket10), asb_vertices_nD = list("basket_1" = vert_nm_basket1, "basket_6" = vert_nm_basket6, "basket_10" = vert_nm_basket10), plot_sp = TRUE, color_ch = NA, fill_ch = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), alpha_ch = c("basket_1" = 0.4, "basket_6" = 0.4, "basket_10" = 0.4), shape_sp = c("basket_1" = 21, "basket_6" = 22, "basket_10" = 24), size_sp = c("basket_1" = 2, "basket_6" = 2, "basket_10" = 2), color_sp = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), fill_sp = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), shape_vert = c("basket_1" = 21, "basket_6" = 22, "basket_10" = 24), size_vert = c("basket_1" = 2, "basket_6" = 2, "basket_10" = 2), color_vert = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6"), fill_vert = c("basket_1" = "#1c9099", "basket_6" = "#67a9cf", "basket_10" = "#d0d1e6")) ####### Save the plot on the plot list: plot_FRic[[k]] <- plot_k }
Let's now have a look at the plot_FRic list which contains the FRic plots for
the three studied assemblages (basket_1, basket_6 and basket_10). It contains as
many element as there are combination of two axis with the four studied axis:
plot_FRic
Gathering plots together using the patchwork package
We are now close to the final graph! Using the patchwork package, we then combine the plots altogether:
USAGE Combine Fric plots into a single graph using the patchwork package
patchwork_FRic <- (plot_FRic[[1]] + patchwork::plot_spacer() + patchwork::plot_spacer() + plot_FRic[[2]] + plot_FRic[[4]] + patchwork::plot_spacer() + plot_FRic[[3]] + plot_FRic[[5]] + plot_FRic[[6]]) + patchwork::plot_layout(byrow = TRUE, heights = rep(1, 3), widths = rep(1, 3), ncol = 3, nrow = 3, guides = "collect") patchwork_FRic
You can now play with colours, shapes and add as many assemblages as wanted!
Plotting species differently according to one trait value (coming soon...)
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