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custom-checklist.Rmd
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custom-checklist.Rmd
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---
title: "Generating custom checklists with rWCVP"
author: "Matilda Brown"
date: "24/05/2022"
---
<!-- GENERATED BY vignettes/articles/precompile.R -->
As well as allowing users to create checklists from the [World Checklist of Vascular Plants (WCVP)](https://wcvp.science.kew.org), rWCVP provides functionality for modifying a checklist output to generate a customised report. I'll demonstrate this here by **generating a list of species that are endemic (or near-endemic) to Sierra Leone**.
In addition to `rWCVP`, we'll use the `tidyverse` packages for data manipulation and plotting, the `gt` package for rendering nice tables, and the `sdpep` package to find bordering regions.
```r
library(rWCVP)
library(tidyverse)
library(gt)
library(spdep)
```
In this example we use the pipe operator (`%>%`),`dplyr` and `ggplot` - if these are unfamiliar we'd suggest checking out https://tidyverse.org/ and some of the help pages therein, or this code might be difficult to interpret.
Now, let's get started!
## A checklist of endemic species
We start by generating a checklist of the species that occur in Sierra Leone. Remembering and/or finding the appropriate WGSRPD Level 3 codes is a pain, so we can use `get_wgsrpd3_codes("Sierra Leone")` to do that work for us in the function call.
```r
sl_code <- get_wgsrpd3_codes("Sierra Leone")
#> i Matches to input geography found at Area (Level 3) and Country (Gallagher)
sl_species <- wcvp_checklist(area=sl_code, synonyms = FALSE)
#> i No taxon specified. Generating checklist for all species.
#> i Generating a checklist of accepted species names only. Use `synonyms = TRUE` to include all names
```
How many species occur in Sierra Leone, and how many are endemic? We can use the `endemic` column here, so it's really simple.
```r
#enclosed in parentheses so that the output is printed as well as assigned
(endemic_summary <- sl_species %>%
distinct(taxon_name, endemic) %>%
group_by(endemic) %>%
summarise(number.of.sp = n()))
#> # A tibble: 2 x 2
#> endemic number.of.sp
#> <lgl> <int>
#> 1 FALSE 3303
#> 2 TRUE 45
```
Easy! For a list of endemic species, we can simply filter our checklist using the `endemic` column, but what about **near-endemics**?
## A checklist of near-endemic species
Depending on how we define near-endemics, there are two ways we can approach the filtering step.
1. We define near-endemics as those species that occur in Sierra Leone and one other WGSPRD3 Area (L3). From a data perspective, this means filtering out species that have >2 rows in `sl_species` (because each row is a species-area occurrence).
2. Alternatively, we can consider near-endemics as those species that might occur across a border, so are functionally endemic. To do this, we need to a) identify the neighbouring WGSPRD3 Areas and b) filter our species list accordingly.
### 1. Species in Sierra Leone plus 1 other area
We can just directly remove any species from our list of species from Sierra Leone that occur in more than 2 areas.
```r
sl_near_endemics1 <- sl_species %>%
group_by(plant_name_id, taxon_name) %>%
filter(n() < 3) %>%
ungroup()
```
### 2. Species in Sierra Leone and neighbouring areas
First, we need to identify which WGSRPD Areas share a border with Sierra Leone.
We could do this by looking at a map, but we'll do this programmitically using the shape files.
To do so, we take the WGSRPD level 3 polygons and find all the areas that border each other.
```r
sf_use_s2(FALSE)
area_polygons <- rWCVPdata::wgsrpd3
area_neighbours <- poly2nb(area_polygons)
```
**Note, we had to turn off spherical coordinates in `sf` for this using `sf_use_s2(FALSE)`.**
Now we have a list of neighbouring areas, we need to find the ones that border Sierra Leone.
```r
#which polygon/s is/are Sierra Leone?
sl_index <- which(area_polygons$LEVEL3_COD %in% sl_code)
#find neighbouring polygons
sl_neighbours_index <- area_neighbours[[sl_index]]
#get the polygons for Sierra Leone plus its neighbours
sl_plus_neighbours <- area_polygons[c(sl_index, sl_neighbours_index),]
```
We can map the region to sanity-check this automated neighbour detection before we generate our final checklist.
```r
#get a sensible bounding box for our plot
bounding_box <- st_bbox(sl_plus_neighbours)
xmin <- bounding_box["xmin"] - 2
xmax <- bounding_box["xmax"] + 2
ymin <- bounding_box["ymin"] - 2
ymax <- bounding_box["ymax"] + 2
#plot the map
ggplot(area_polygons) +
#world polygons first, for context
geom_sf(fill="white", colour="grey") +
#add polygons of interest
geom_sf(data=sl_plus_neighbours, fill="#a4dba2", colour="gray20")+
#bounding box we sey up above
coord_sf(xlim=c(xmin, xmax), ylim=c(ymin, ymax))+
#add country name labels
geom_sf_label(data=sl_plus_neighbours,aes(label=sl_plus_neighbours$LEVEL3_NAM))+
#add ocean background
theme(panel.background = element_rect(fill="#b6badb")) +
#remove x and y from axes
xlab(NULL) +
ylab(NULL)
```
Of course, we could have just identified Guinea and Liberia as neighbouring countries from a map, then found the codes using `get_wgsrpd3_codes("Liberia")` and `get_wgsrpd3_codes("Guinea")`, but that's not nearly as much fun!
Next, we can identify near-endemics as those species that *only* have occurrences in Sierra Leone, Guinea or Liberia.
```r
sl_near_endemics2 <- sl_species %>%
# filtering for each species
group_by(plant_name_id) %>%
# only keep those where all points are in the threee countries
filter(all(area_code_l3 %in% sl_plus_neighbours$LEVEL3_COD)) %>%
ungroup()
```
And finally we filter our list to only species that occur in Sierra Leone + one neighbour, like we did in Option 1. Looking at the map it seems plausible that a species could occur right at the triple junction between the three countries, but for this example we will exclude those species.
```r
sl_near_endemics2 <- sl_near_endemics2 %>%
group_by(plant_name_id, taxon_name) %>%
filter(n() < 3) %>%
ungroup()
```
## Creating our formatted report
Now we can do something a bit fancy - turn our checklist data frame into a formatted report. To do this, we plug it into a template file called "custom_checklist.Rmd" that is stored in the `rWCVP` package folder (specifically, the "rmd" subfolder). We pass the data (as well as some other information) using `params`, and need to specify a file name using `output_file`.
```r
library(rmarkdown)
#informative test to include in our html
checklist_description <- "Checklist of species that are endemic to Sierra Leone (or near-endemic, based on neighbouring countries)"
#for file saving
wd <- getwd()
#do the rendering
render(system.file("rmd", "custom_checklist.Rmd", package = "rWCVP"),
quiet = TRUE,
params=list( version = "New Phytologist Special Issue",
mydata = sl_nearendemics2,
description = checklist_description),
output_file = paste0(wd,"/Sierra_Leone_endemics_and_near_endemics.html"))
```
And here is our file (screenshot here, because the report has left the building)!
____
