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


```{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(1:7))

matrix_df <- matrix_df[-c(8:16),]

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

# ORIGINAL
groupColors <-c( "#1d4220", # conifer 
                 "#fc9d03", # grassland
                 "#56bf5f", # hardwood
                 "#397d3f", # hardwood-conifer 
                 "#7db7c7", # riparian 
                 "#f57fdf", # savannah
                 "#6e4f1e", # shrubland
                 "#f5e942", # cur ag
                 "#1d4220", # cur conifer
                 "#397d3f", # cur hdw-con
                 "#b0af9e", # developed
                 "#eb4034", # exotics
                 "#fc9d03", # grassland
                 "#56bf5f", # hardwood
                 "#7db7c7",
                 "#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>



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

library(tidyverse)

## read in data

raw_data <- read.csv("data/final_df_full.csv")


## filter, group and add helper columns

old_classes <- c("Late1", "Late2", "Late3")

old_growth_loss <- raw_data %>%
  filter(age_category %in% old_classes) %>%
  group_by(bps_name) %>%
  summarize(ref_percent = mean(ref_percent, na.rm = TRUE),
            cur_percent = mean(cur_percent, na.rm = TRUE),
            bps_acres = max(bps_acres)) %>%
  mutate(cur_percent = replace_na(cur_percent, 0)) %>%
  mutate(change = cur_percent - ref_percent,
       sign_change = (change >0)) 


# try arrow plot for top 10 BpS Names

old_growth_loss_10 <- old_growth_loss %>%
  top_n(n = 10, wt = bps_acres)


arrow_plot <- old_growth_loss_10 |> 
  ggplot(aes(
      x = ref_percent, xend = cur_percent, 
      y = reorder(bps_name, bps_acres), yend = bps_name,
      color = sign_change)) +
  geom_segment(
    arrow = arrow(angle = 30, length = unit(0.5, 'cm')),
    size = 3) +
  labs(
    x = 'Percent Change', 
    y = element_blank(),
    title = 'Changes in Late Succession Classes,  \nHistorical to ~2022',
    subtitle = 'Arrows in descending order by total extent of ecosystem'
  ) +
  scale_color_manual(
    values = c("#fcba03", "#10692c")) +
  theme_bw(base_size = 14) + 
  theme(legend.position = "none")


arrow_plot
```