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.

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:
::kable(as.data.frame(baskets_fruits_weights[1:6, 1:6]),
knitrcentering = TRUE,
caption = "Species x assemblages matrix based on the **fruits** dataset")
```

apple | apricot | banana | currant | blackberry | blueberry | |
---|---|---|---|---|---|---|

basket_1 | 400 | 0 | 100 | 0 | 0 | 0 |

basket_2 | 200 | 0 | 400 | 0 | 0 | 0 |

basket_3 | 200 | 0 | 500 | 0 | 0 | 0 |

basket_4 | 300 | 0 | 0 | 0 | 0 | 0 |

basket_5 | 200 | 0 | 0 | 0 | 0 | 0 |

basket_6 | 100 | 0 | 200 | 0 | 0 | 0 |

Let’s call the traits dataframe:

```
# Load data:
data("fruits_traits", package = "mFD")
# Remove fuzzy traits in this tutorial:
<- fruits_traits[ , -c(6:8)]
fruits_traits # Display the table:
::kable(head(fruits_traits),
knitrcaption = "Species x traits data frame")
```

Size | Plant | Climate | Seed | Sugar | |
---|---|---|---|---|---|

apple | 5-10cm | tree | temperate | pip | 103.9 |

apricot | 3-5cm | tree | temperate | pit | 92.4 |

banana | 10-20cm | tree | tropical | none | 122.3 |

currant | 0-1cm | shrub | temperate | pip | 73.7 |

blackberry | 1-3cm | shrub | temperate | pip | 48.8 |

blueberry | 0-1cm | forb | temperate | pip | 100.0 |

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[-c(6:8), ]
fruits_traits_cat # Thus remove the "fuzzy_name" column:
<- fruits_traits_cat[ , -3]
fruits_traits_cat # Display the table:
::kable(head(fruits_traits_cat),
knitrcaption = "Traits types based on **fruits & baskets** dataset")
```

trait_name | trait_type |
---|---|

Size | O |

Plant | N |

Climate | O |

Seed | O |

Sugar | Q |

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**

```
<- mFD::funct.dist(
sp_dist_fruits 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:
<- mFD::quality.fspaces(
fspaces_quality_fruits 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):
<- fspaces_quality_fruits$"details_fspaces"$"sp_pc_coord" sp_faxes_coord_fruits
```

Compute alpha FD indices (here only Functional Richness but if other indices have to be plotted, then compute them):

**USAGE**

```
<- mFD::alpha.fd.multidim(
alpha_fd_indices_fruits 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!

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.

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_faxes_coord_fruits)
range_sp_coord
# Based on the range of species coordinates values, compute a nice range ...
# ... for functional axes:
<- range_sp_coord +
range_faxes c(-1, 1) * (range_sp_coord[2] - range_sp_coord[1]) * 0.05
range_faxes
```

`## [1] -0.5214338 0.4850924`

**USAGE** Plot
background for PC1 and PC2 plot

```
# get species coordinates along the two studied axes:
<- sp_faxes_coord_fruits[, c("PC1", "PC2")]
sp_faxes_coord_xy
# Plot background with grey backrgound:
<- mFD::background.plot(range_faxes = range_faxes,
plot_k faxes_nm = c("PC1", "PC2"),
color_bg = "grey95")
plot_k
```

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:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_xy,
vert 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)

```
<- mFD::pool.plot(ggplot_bg = plot_k,
plot_sp_vert 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
;)

```
<- mFD::pool.plot(ggplot_bg = plot_k,
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
```

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:
<- mFD::sp.filter(asb_nm = c("basket_1"),
sp_filter_basket1 sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_1):
<- sp_filter_basket1$`species coordinates`
sp_faxes_coord_basket1
# basket_6:
## filter species from basket_6:
<- mFD::sp.filter(asb_nm = c("basket_6"),
sp_filter_basket6 sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_6):
<- sp_filter_basket6$`species coordinates`
sp_faxes_coord_basket6
# basket_10:
## filter species from basket_10:
<- mFD::sp.filter(asb_nm = c("basket_10"),
sp_filter_basket10 sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_10):
<- sp_filter_basket10$`species coordinates` sp_faxes_coord_basket10
```

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`

```
## PC1 PC2
## apple 0.0055715265 0.0350421604
## banana 0.4180172546 -0.1414728845
## cherry -0.0180809780 0.2978695529
## lemon 0.1067949113 0.0007714157
## melon -0.1493941692 -0.2420723462
## passion_fruit 0.1101264243 -0.1062790540
## pear -0.0005886084 0.0297927029
## strawberry -0.2917242495 -0.0898440618
```

**USAGE** Retrieve
the names of species being vertices of basket_1, basket_6 and
basket_10:

```
# basket_1:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket1,
vert_nm_basket1 order_2D = TRUE,
check_input = TRUE)
# basket_6:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket6,
vert_nm_basket6 order_2D = TRUE,
check_input = TRUE)
# basket_10:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket10,
vert_nm_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

**c**onvex-**h**ull 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

```
<- mFD::fric.plot(ggplot_bg = plot_k,
plot_try 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

```
<- mFD::fric.plot(ggplot_bg = plot_k,
plot_try 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

```
<- mFD::fric.plot(ggplot_bg = plot_k,
plot_try 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

```
<- mFD::fric.plot(ggplot_bg = plot_k,
plot_try 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! ;)

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_faxes_coord_fruits)
range_sp_coord
## Based on the range of species coordinates values, compute a nice range ...
## ... for functional axes:
<- range_sp_coord +
range_faxes 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:
<- list()
plot_FRic
####### Compute all the combiantion we can get and the number of plots
<- utils::combn(c("PC1", "PC2", "PC3", "PC4"), 2)
axes_plot <- ncol(axes_plot)
plot_nb
######## Loop on all pairs of axes:
# for each combinaison of two axis:
for (k in (1:plot_nb)) {
# get names of axes to plot:
<- axes_plot[1:2, k]
xy_k
####### Steps previously showed
# a - Background:
# get species coordinates along the two studied axes:
<- sp_faxes_coord_fruits[, xy_k]
sp_faxes_coord_xy
# Plot background with grey backrgound:
<- mFD::background.plot(range_faxes = range_faxes,
plot_k 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:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_xy,
vert order_2D = FALSE,
check_input = TRUE)
<- mFD::pool.plot(ggplot_bg = plot_k,
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:
<- mFD::sp.filter(asb_nm = c("basket_1"),
sp_filter_basket1 sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_1):
<- sp_filter_basket1$`species coordinates`
sp_faxes_coord_basket1
# basket_6:
## filter species from basket_6:
<- mFD::sp.filter(asb_nm = c("basket_6"),
sp_filter_basket6 sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_6):
<- sp_filter_basket6$`species coordinates`
sp_faxes_coord_basket6
# basket_10:
## filter species from basket_10:
<- mFD::sp.filter(asb_nm = c("basket_10"),
sp_filter_basket10 sp_faxes_coord = sp_faxes_coord_xy,
asb_sp_w = baskets_fruits_weights)
## get species coordinates (basket_10):
<- sp_filter_basket10$`species coordinates`
sp_faxes_coord_basket10
# Step 1 follow-up Vertices names:
# basket_1:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket1,
vert_nm_basket1 order_2D = TRUE,
check_input = TRUE)
# basket_6:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket6,
vert_nm_basket6 order_2D = TRUE,
check_input = TRUE)
# basket_10:
<- mFD::vertices(sp_faxes_coord = sp_faxes_coord_basket10,
vert_nm_basket10 order_2D = TRUE,
check_input = TRUE)
# Step 2: plot convex-hulls and species of studied assemblages:
<- mFD::fric.plot(ggplot_bg = plot_k,
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_k
plot_FRic[[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`

`## [[1]]`

```
##
## [[2]]
```

```
##
## [[3]]
```

```
##
## [[4]]
```

```
##
## [[5]]
```

```
##
## [[6]]
```

`patchwork`

packageWe 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

```
<- (plot_FRic[[1]] + patchwork::plot_spacer() + patchwork::plot_spacer() +
patchwork_FRic 2]] + plot_FRic[[4]] + patchwork::plot_spacer() +
plot_FRic[[3]] + plot_FRic[[5]] + plot_FRic[[6]]) +
plot_FRic[[::plot_layout(byrow = TRUE, heights = rep(1, 3),
patchworkwidths = 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!