How to add environmental variable to DCA plot using ggplot2 - r

Question

I would like to plot an environmental variable on a ggplot2 version of a DCA plot.
I have some code where I extract species and data scores from vegan and then plot them up in ggplot2. I am having trouble trying to work out how I can get my environmental variable SWLI to plot as an arrow - something like this RDA's plots with ggvegan: How can I change text position for arrows text? (or see PCA example here https://www.rpubs.com/an-bui/vegan-cheat-sheet)
Can anybody help?
#DCA Plot
library(plyr)
library(vegan)
library(ggplot2)
library(cluster)
library(ggfortify)
library(factoextra)
#read in csv and remove variables you don't want to go through analysis
regforamcountsall<-read_csv("regionalforamcountsallnocalcs.csv")
swli<-read_csv("DCAenv.csv")
rownames(regforamcountsall)<-regforamcountsall$Sample
regforamcountsall$Sample = NULL
regforamcountsall$Site=NULL
regforamcountsall$SWLI=NULL
#check csv
regforamcountsall
#run ordination
ord<-decorana(regforamcountsall)
#get species scores
summary(ord)
#get DCA values of environmental variable
ord.fit <- envfit(ord ~ SWLI, data=swli, perm=999)
ord.fit
plot(ord, dis="site")
plot(ord.fit)
#use this summary code to get species scores for DCA1 and DCA2
#put species scores values in from ord plot summary stats
species.scores<-read.csv("speciescores.csv")
species.scores$species <- row.names(species.scores)
#Using the scores function from vegan to extract the sample scores and convert to a data.frame
data.scores <- as.data.frame(scores(ord))
# create a column of groupings/clusters, from the rownames of data.scores
data.scores$endgroup <- as.factor(pam(regforamcountsall, 3)$clustering)
#getting the convex hull of each unique point set
find_hull <- function(df) df[chull(data.scores$DCA1, data.scores$DCA2), ]
hulls <- NULL
for(i in 1:length(unique(data.scores$endgroup))){
endgroup_coords <- data.scores[data.scores$endgroup == i,]
hull_coords <- data.frame(
endgroup_coords[chull(endgroup_coords[endgroup_coords$endgroup == i,]$DCA1,
endgroup_coords[endgroup_coords$endgroup == i,]$DCA2),])
hulls <- rbind(hulls,hull_coords)
}
data.scores$numbers <- 1:length(data.scores$endgroup)
regforamcountsall<-read_csv("regionalforamcountsallnocalcs.csv")
rownames(regforamcountsall)<-regforamcountsall$Sample
data.scores$Site<-regforamcountsall$Site
data.scores$SWLI<-regforamcountsall$SWLI
data.scores
#DCA with species
data.scores$Site <- as.character(data.scores$Site)
library(scico)
dca <- ggplot() +
# add the point markers
geom_point(data=data.scores,aes(x=DCA1,y=DCA2,colour=SWLI,pch=Site),size=4) + geom_point(data=species.scores,aes(x=DCA1,y=DCA2),size=3,pch=3,alpha=0.8,colour="grey22") +
# add the hulls and labels - numbers position labels
geom_polygon(data = hulls,aes(x=DCA1,y=DCA2,fill=endgroup), alpha = 0.25) +
#geom_text(data=data.scores,aes(x=DCA1-0.03,y=DCA2,colour=endgroup, label = numbers))+
geom_text(data=species.scores,aes(x=DCA1+0.1,y=DCA2+0.1, label = species))+
#look this up
geom_segment(data=ord.fit,aes(x = 0, y = 0, xend=DCA1,yend=DCA2), arrow = arrow(length = unit(0.3, "cm")))+
theme_classic()+
scale_color_scico(palette = "lapaz")+
coord_fixed()
dca
#regforamcountsall data
structure(list(Sample = c("T3LB7.008", "T3LB7.18", "T3LB7.303",
"WAP 0 ST-2", "T3LB7.5", "LG120"), T.salsa = c(86.63793102, 68.5897436,
70.39274924, 5.199999999, 79.15057916, 44.40000001), H.wilberti = c(0,
0, 0, 0, 0.386100386, 9.399999998), Textularia = c(0, 0, 0, 0,
0, 0.4), T.irregularis = c(2.155172414, 10.25641026, 7.854984897,
0, 2.702702703, 0), P.ipohalina = c(0, 0, 0, 0, 0, 0), J.macrescens = c(4.741379311,
5.769230769, 4.833836859, 5.800000001, 8.108108107, 5.400000001
), T.inflata = c(6.465517244, 15.38461538, 16.918429, 83.2, 5.791505794,
40.4), S.lobata = c(0, 0, 0, 2.300000001, 0, 0), M.fusca = c(0,
0, 0, 3.499999999, 3.861003862, 0), A.agglutinans = c(0, 0, 0,
0, 0, 0), A.exiguus = c(0, 0, 0, 0, 0, 0), A.subcatenulatus = c(0,
0, 0, 0, 0, 0), P.hyperhalina = c(0, 0, 0, 0, 0, 0), SWLI = c(200,
197.799175, 194.497937, 192.034776, 191.746905, 190.397351),
Site = c("LSP", "LSP", "LSP", "WAP", "LSP", "LG")), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
#data.scores
structure(list(DCA1 = c(-1.88587476921648, -1.58550534382589,
-1.59816311314591, -0.0851161831632892, -1.69080448670088, -1.14488987340879
), DCA2 = c(0.320139736602921, 0.226662031865046, 0.230912045301637,
-0.0531232712001122, 0.272143119753744, 0.0696939776869396),
DCA3 = c(-0.755595015095353, -0.721144380683279, -0.675071834919103,
0.402339366526422, -0.731006052784081, 0.00474996849420783
), DCA4 = c(-1.10780013276303, -0.924265835490466, -0.957711953532202,
-0.434438970032073, -0.957873836258657, -0.508347000558056
), endgroup = structure(c(1L, 1L, 1L, 2L, 1L, 1L), .Label = c("1",
"2", "3"), class = "factor"), numbers = 1:6, Site = c("LSP",
"LSP", "LSP", "WAP", "LSP", "LG"), SWLI = c(200, 197.799175,
194.497937, 192.034776, 191.746905, 190.397351)), row.names = c(NA,
6L), class = "data.frame")
#species.scores
structure(list(species = c("1", "2", "3", "4", "5", "6"), DCA1 = c(-2.13,
-1.6996, -2.0172, -0.9689, 1.0372, -0.3224), DCA2 = c(0.342,
-0.8114, 0.3467, -0.3454, 2.0007, 0.9147)), row.names = c(NA,
6L), class = "data.frame")