05_Demo.Rmd

---
title: "Appendix 1 - sPlotOpen - Demo"
author: "Francesco Maria Sabatini, Jonathan Lenoir, Helge Bruelheide"
date: "07/04/2021"
output: html_document
---

Appendix to the paper: Sabatini, Lenoir et al., sPlotOpen – An environmentally-balanced, open-access, global dataset of vegetation plots. *Global Ecology and Biogeography*.
<br>

This demo illustrates how to import and manipulate sPlotOpen data to create some basic graphics or tables together with a reference list. As a worked example, the code below will:  

1. select all plots containing at least a species of *Quercus* from sPlotOpen's resampled iteration #1  
2. show some summary at biome level  
3. graph the distribution of the community weighted mean of a selected functional trait  
4. show the geographical location of all selected plots  
5. create a reference list based on the plots effectively selected.  

<br>

```{r, message=F, warning=F}
#load libraries
library(tidyverse)
library(sf)
library(raster)
library(rnaturalearth)
library(RefManageR)
```

## Import data
```{r}
load("_sPlotOpenDB/sPlotOpen.RData")
ls()
```
## Extract all plots containing at least a *Quercus* species 
Use only the first resampled iteration of sPlotOpen
```{r}
#select only the first resample
header.oa1 <- header.oa %>% 
  filter(Resample_1 == T)
DT2.oa1 <- DT2.oa %>% 
  filter(PlotObservationID %in% header.oa1$PlotObservationID)
CWM_CWV.oa1 <- CWM_CWV.oa %>% 
  filter(PlotObservationID %in% header.oa1$PlotObservationID)
```

```{r}
#get all plots containing at least one Quercus species
plotlist.quercus <- DT2.oa1 %>% 
  filter(str_detect(Species, "^Quercus")) %>% 
  distinct(PlotObservationID) %>% 
  pull(PlotObservationID)

header.quercus <- header.oa1 %>% 
  filter(PlotObservationID %in% plotlist.quercus & 
           Resample_1 == T)

DT2.quercus <- DT2.oa1 %>% 
  filter(PlotObservationID %in% plotlist.quercus)

CWM_CWV.quercus <- CWM_CWV.oa1 %>% 
  mutate(Quercus=ifelse(PlotObservationID %in% plotlist.quercus, T, F))

```
There are `r length(plotlist.quercus)` plots containing at least a *Quercus* species in sPlotOpen's resampled iteration 1.  
<br>

## Number of plots with *Quercus* across biomes
Summarize the number of plots containing at least one *Quercus* species across biomes  

```{r}
header.quercus %>% 
  group_by(Biome) %>% 
  summarize(n = n())
```
\pagebreak

## Compare Community Weighted Means
Compare the distribution of the community weighted means of Stem density, between plots containing and not containing a *Quercus* species.

```{r, warning=F}
ggplot(data = CWM_CWV.quercus) + 
  geom_density(aes(x = StemDens_CWM, fill = Quercus), col = NA, alpha = 0.5) + 
  theme_bw()
```
\pagebreak

## Geographical distribution of plots containing a *Quercus* species  
<br>
Download some spatial data of the world and create a template map using the r package `rnaturalearth`, first. Transform all geographical data to Eckert IV projection.
```{r, message=F, warning=F, results="hide"}
countries <- ne_countries(returnclass = "sf") %>% 
  st_transform(crs = "+proj=eck4") %>% 
  st_geometry()
graticules <- ne_download(type = "graticules_15", category = "physical",
                          returnclass = "sf") %>% 
  st_transform(crs = "+proj=eck4") %>% 
  st_geometry()
bb <- ne_download(type = "wgs84_bounding_box", category = "physical",
                  returnclass = "sf") %>% 
  st_transform(crs = "+proj=eck4") %>% 
  st_geometry()
```

Template of Global map - with country borders
```{r, results="hide", warning=F, message=F}
w3a <- ggplot() +
  geom_sf(data = bb, col = "grey20", fill = "white") +
  geom_sf(data = graticules, col = "grey20", lwd = 0.1) +
  geom_sf(data = countries, fill = "grey90", col = NA, lwd = 0.3) +
  coord_sf(crs = "+proj=eck4") +
  theme_minimal() +
  theme(axis.text = element_blank(), 
        legend.title = element_text(size=12), 
        legend.text = element_text(size=12),
        legend.background = element_rect(size = 0.1, linetype = "solid", colour = 1), 
        legend.key.height = unit(1.1, "cm"), 
        legend.key.width = unit(1.1, "cm"))
```
Project selected plots to Eckert IV and transform them to sf, before plotting.
```{r, results="hide", warning=F, message=F}
header.quercus.sf <- SpatialPointsDataFrame(coords = header.quercus %>%
                                      dplyr::select(Longitude, Latitude), 
                                    proj4string = CRS("+init=epsg:4326"), 
                                    data=header.quercus %>%
                                      dplyr::select(-Longitude, -Latitude)) %>% 
  st_as_sf() %>% 
  st_transform(crs = "+proj=eck4")
```

Show all plots containing at least one *Quercus* species. Color code based on biomes.
```{r, fig.width=8, fig.height=9, fig.align="center", warning=F, message=F}
(Figure1a <- w3a + 
  geom_sf(data = header.quercus.sf, aes(color = Biome), 
          pch = 16, size = 0.8, alpha = 0.8) + 
  geom_sf(data = countries, col = "grey20", fill=NA, lwd = 0.3) + 
  theme(legend.position = "bottom", 
        legend.title = element_blank()) + 
  guides(color = guide_legend(ncol = 2,
                            override.aes = list(size = 2))))
```
\pagebreak

# Create a reference list for selected plots
Create reference list as BibText
```{r}
sPlotOpen_citation(IDs=plotlist.quercus, level = "database", 
                   out.file = "_output/demo.bib")
# show first few lines of output file
read_lines("_output/demo.bib", n_max = 25)
```

Convert to reference list
```{r, warning=F}
mybib <- RefManageR::ReadBib("_output/demo.bib", check = FALSE) 
mybib
```

## sessionInfo()
```{r}
sessionInfo()
```