demo_report.Rmd

---
title: "LANDFIRE-Powered Report for LTA `r params$MAP_UNIT_S` "
author: "Randy Swaty"
date: "2023-02-20"
output: 
  html_document:
    theme: flatly
    toc: yes
    toc_float:
      collapsed: true

params:
  MAP_UNIT_SYMBOL: "223Ba01"
---



```{r setup, message=FALSE, warning=FALSE, include=FALSE}
library(tidyverse)
library(stringr)
library(crosstalk)
library(ggsci)
library(scales)
library(plotly)

data <- read_csv("bps_evt_ltas_hoosier.csv") %>%
  mutate(ACRES = Count*0.222)

bps_transitions <- read.csv("bps_transitions.csv")

bps_scls_ref_cur_ltas <- read_csv("data/bps_scls_ref_cur_ltas.csv")

```

## General Background to this report

Using data from the [LANDFIRE](https://landfire.gov/) program we drafted these LTA-specific reports that depict:

* How many acres of the most prevalent historical ecosystems (called "Biophysical Settings", BpS) were present just prior to European colonization
* How many acres of the most prevalent current ecosystems (called "Existing Vegetation Types") were present ca2020
* Past vs. current succession class amounts for 3 most prevalent BpSs

*These charts provided as a demo only.  Local review and interpretation will be key to success!*

<br>

## Top 10 Biophysical Settings 

```{r bps chart, message=FALSE, warning=FALSE, echo=FALSE, fig.width=10, fig.height=10}
bpsname <- data %>%
  filter(MAP_UNIT_SYMBOL == params$MAP_UNIT_SYMBOL) %>%
  group_by(BPS_NAME) %>%
  summarize(ACRES = sum(ACRES)) %>%
  arrange(desc(ACRES)) %>%
  top_n(n = 10, wt = ACRES)

# plot
bpsChart <- 
  ggplot(data = bpsname, aes(x = BPS_NAME, y = ACRES)) +
  geom_bar(stat = "identity", fill = "#183d1f") +
  labs(
    subtitle = "Represents dominant vegetation systems pre-European colonization",
    caption = "Data from landfire.gov.",
    x = "",
    y = "Acres") +
  scale_x_discrete(limits = rev(bpsname$BPS_NAME),
                   labels = function(x) str_wrap(x, width = 18)) +
  scale_y_continuous(labels = comma) +
  coord_flip() +
  theme_bw(base_size = 14)


bpsChart


```

<br>

**Learn more about Biophysical Settings:**

* LANDFIRE description of the [Spatial Data](https://landfire.gov/vegetation/bps)
* LANDFIRE description of the  [Descriptions and Models](https://landfire.gov/vegetation/bps-models)
* Blankenship et al., (2021) [paper](https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.3484) describing development of the models and descriptions.


<br>

## Top 10 Existing Vegetation Types


```{r evt chart, message=FALSE, warning=FALSE, echo=FALSE, fig.width=10, fig.height=10}
evtname <- data %>%
  filter(MAP_UNIT_SYMBOL == params$MAP_UNIT_SYMBOL) %>%
  group_by(EVT_NAME) %>%
  summarize(ACRES = sum(ACRES)) %>%
  arrange(desc(ACRES)) %>%
  top_n(n = 10, wt = ACRES)

# plot
evtChart <- 
  ggplot(data = evtname, aes(x = EVT_NAME, y = ACRES)) +
  geom_bar(stat = "identity", fill = "#0e1624") +
  labs(
    subtitle = "Represents dominant vegetation systems ~2020",
    caption = "Data from landfire.gov.",
    x = "",
    y = "Acres") +
  scale_x_discrete(limits = rev(evtname$EVT_NAME),
                   labels = function(x) str_wrap(x, width = 18)) +
  scale_y_continuous(labels = comma) +
  coord_flip() +
  theme_bw(base_size = 14)


evtChart


```

<br>

**Learn more about Existing Vegetation Types:**

* LANDFIRE description of the [concept and spatial data](https://landfire.gov/vegetation/evt)
* [Descriptions](https://landfire.gov/sites/default/files/documents/LANDFIRE_Ecological_Systems_Descriptions_CONUS.pdf) of the Ecological Systems mapped in this dataset



## Reference vs. current succession classes for most prevalent Biophysical Settings

The following chart illustrates modeled "Reference" percentages compared to mapped current percentages of [LANDFIRE Succession Classes](https://landfire.gov/vegetation/sclass) for the top 3 Biophysical Settings in the LTA.  The charts are illustrative only.  To understand what the succession classes represent it is necessary to read the descriptions that are downloadable [here](https://landfirereview.org/search.php).  

<br>

```{r scls chart, message=FALSE, warning=FALSE, echo=FALSE, fig.width=10, fig.height=6}


one_lta_wrangled <- bps_scls_ref_cur_ltas %>%
  filter(MAP_UNIT_SYMBOL == params$MAP_UNIT_SYMBOL) %>%
  group_by(bps_aoi) %>%
  mutate(total.count = sum(Count)) %>%
  ungroup() %>%
  dplyr::filter(dense_rank(desc(total.count)) < 4) %>%
  dplyr::select(c("BpS_Name", "refLabel",  "currentPercent", "refPercent")) %>%
  pivot_longer(
    cols = c(`refPercent`, `currentPercent`), 
    names_to = "refCur", 
    values_to = "Percent"
  )


  
  
  # order classes
  one_lta_wrangled$refLabel <- factor(one_lta_wrangled$refLabel, levels= c(
    "Developed",
    "Agriculture",
    "UE",
    "UN",
    "E",
    "D",
    "C",
    "B",
    "A"))
  
  
  
  
  sclasplot <-
    ggplot(one_lta_wrangled, 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(one_lta_wrangled$refLabel))) +
    labs(
      title = "Succession Classes past and present",
      subtitle = "3 BpSs selected for illustration. Not all succession classes present in all BpSs",
      caption = "\nData from landfire.gov.",
      x = "",
      y = "Percent")+
    theme_minimal(base_size = 12)+
    theme(plot.caption = element_text(hjust = 0, face= "italic"), #Default is hjust=1
          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

  
```

<br>

To learn more about succession class comparisons:

* Swaty et al., (2021) [paper](https://www.mdpi.com/2073-445X/11/1/28/pdf) describing application of model results in calculation of Vegetation Departure
* LANDFIRE description of [succession classes](https://landfire.gov/vegetation/sclass)


<br>

![](images/banner_2021.jpg)