comparing.qmd

---
title: "Comparisons"
---

```{r include=FALSE, echo=FALSE, message=FALSE, warning=FALSE}
library(chorddiag)
library(htmlwidgets)
library(igraph)
library(readr)
library(tidygraph)
library(tidyverse)

```

### Comparing Past vs. Present Conditions

Using LANDFIRE’s BpS products, we explore two different ways to visualize past vs. current vegetation patterns. 

* First we present **changes in broad ecosystem types** using an interactive comparison diagram. 

* Second we compare **amounts of succession classes** (past and present) for the most prevalent ecosystems. 

## Comparing Broad Vegetation Trends
### Summary

* Broadly speaking, this subregion has not experienced significant ecosystem conversion to agricultural or urban land uses.  
* LANDFIRE mapped a fair amount of conversion from "past shrubland" to "conifer".  
* Nearly all of the land classified as present day “exotic vegetation” was converted from shrubland areas. 
* Note: number presented when hovering equals acres.

```{r chord, echo=FALSE, message=FALSE, warning=FALSE, include=FALSE}
# read in data
chord_df<- read_csv("data/bps2evt_chord.csv")
#view(histFireGVchord)

#convert to matrix
matrix_df <-as.matrix(as_adjacency_matrix(as_tbl_graph(chord_df),attr = "ACRES"))

#clean up matrix (could be cleaner!)
matrix_df = subset(matrix_df, select = -c(1:6))

matrix_df <- matrix_df[-c(7:15),]

#make a custom color pallet #eb4034 (redish) #b0af9e(grey)

# ORIGINAL
groupColors <-c( "#1d4220", # conifer 
                 "#fc9d03", # grassland
                 "#56bf5f", # hardwood
                 "#397d3f", # hardwood-conifer 
                 "#7db7c7", # riparian 
                 "#6e4f1e", # shrubland
                 "#f5e942", # cur ag
                 "#1d4220", # cur conifer
                 "#397d3f", # cur hdw-con
                 "#eb4034", # exotics
                 "#fc9d03", # grassland
                 "#56bf5f", # hardwood
                 "#b0af9e", # developed
                 "#7db7c7", #riparian
                 "#6e4f1e"# shrubland
                 

)



#make chord diagram
chord<-chorddiag(data = matrix_df,
                 type = "bipartite",
                 groupColors = groupColors,
                 groupnamePadding = 10,
                 groupPadding = 3,
                 groupnameFontsize = 12 ,
                 showTicks = FALSE,
                 margin=130,
                 tooltipGroupConnector = "    &#x25B6;    ",
                 chordedgeColor = "#363533"
)
chord 

#save then print to have white background
htmlwidgets::saveWidget(chord,
                        "chord.html",
                        background = "white",
                        selfcontained = TRUE
                        )
```



<iframe src="chord.html" height="720" width="720" style="border: 1px solid #464646;" allowfullscreen="" allow="autoplay" data-external=".5"></iframe>

<br>

## Succession classes for most dominant BpSs
- Succession classes have changed substantially in the Inter-Mountain Basins Montane Sagebrush Steppe - Mountain Big Sagebrush BpS, with significant overrepresentation of succession class C currently.
- Some BpSs appear to be missing succession classes on the landscape today, such as the Colorado Plateau Pinyon-Juniper Woodland which is missing succession class B.  


```{r scls chart, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10, fig.height=9}

BPS_SCLS2 <- read.csv("data/bpsScls2.csv")

bps_scls_3 <- BPS_SCLS2 %>%
  group_by(Var1) %>%
  mutate(total.count = sum(Freq)) %>%
  ungroup() %>%
  dplyr::filter(dense_rank(desc(total.count)) < 7) %>%
  dplyr::select(c("BpS_Name", "refLabel",  "currentPercent", "refPercent")) %>%
  pivot_longer(
    cols = c(`refPercent`, `currentPercent`), 
    names_to = "refCur", 
    values_to = "Percent"
    )

# order classes
bps_scls_3$refLabel <- factor(bps_scls_3$refLabel, levels= c(
  "Developed",
  "Agriculture",
  "UE",
  "UN",
  "E",
  "D",
  "C",
  "B",
  "A"))




sclasplot <-
  ggplot(bps_scls_3, aes(fill=factor(refCur), y=Percent, x=refLabel)) + 
  geom_col(width = 0.8, position = position_dodge()) +
  coord_flip() +
  facet_grid(. ~BpS) +
  scale_x_discrete(limits = (levels(bps_scls_3$refLabel))) +
  labs(
    title = "Succession Classes past and present",
    subtitle = "6 BpSs selected for illustration. Not all succession classes present in all BpSs",
    caption = "Data from landfire.gov; Chart © Randy Swaty",
    x = "",
    y = "Percent")+
  theme_minimal(base_size = 14)+
  theme(plot.title.position = "plot", #NEW parameter. Apply for subtitle too.
        plot.caption.position =  "plot") +
  scale_fill_manual(values = c("#3d4740", "#32a852" ), # present (grey), historical (green)
                    name = " ", 
                    labels = c("Present",
                               "Past")) +
  facet_wrap(~BpS_Name, nrow(3),labeller = labeller(BpS_Name = label_wrap_gen())) +
    theme(panel.spacing = unit(.05, "lines"),
        panel.border = element_rect(color = "black", fill = NA, size = 1), 
        strip.background = element_rect(color = "black", size = 1))

sclasplot

```