---
title: "Pine Beetle Models"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
    theme: flatly
  

     
---

```{r setup, include = FALSE}

library(flexdashboard)
library(readxl)
library(dplyr)
library(knitr)
library(ggplot2)
library(GGally)
library(tidymodels)
library(vip)

pine_table <- read_excel("Data_1993.xlsx", sheet = 1)

```
Data Description
=======================================================================

Column {data-width=400}
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### Data Description


From  1991  to  1996,  Jeffrey  pine  beetles (JPB) caused tree mor-tality throughout the Lake Tahoe Basin during a severedrought. Census data were collected annually on 10,721trees to assess patterns of JPB-caused mortality.
The data were collected during the Lake Tahoe Basin Jeffrey pine beetle outbreak in  1991-1996 from a 60-acre study area with 10,722 trees followed annually.

### Those tiny beetles(5 millimeters) can destroy large forest areas

```{r beetle-image, fig.width = 8, fig.height = 8}

image_url <- "http://t0.gstatic.com/licensed-image?q=tbn:ANd9GcRrTUh-9yo7qa8r-paTauCrJMC_Zj4F8miepv24zrr79paRUjnlJchmzoLaRMax7JN2mGjorwzRjMcgmlU"

include_graphics(image_url)

# Data prep
pine_table <-
 pine_table %>%
  mutate(Response = factor(Response,
                           labels = c("Not infested", "Infested")),
         BA_Inf_20th = BA_Inf_20th + 0.001)

```

### 


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### Introduction

Explain the map below and the dataset


### Infestation map


```{r inf-map, fig.width = 25, fig.height = 15}

ggplot(data = pine_table, aes(x = Easting, y = Northing)) +
  geom_point(aes(color = Infest_Serv1),
                 alpha = 0.9) +
  scale_color_gradient("",
                       low = "#009E73",
                       high = "red") +
  theme_gray() +
  xlab("UTM X") +
  ylab("UTM Y") +
  theme(text = element_text(size = 30))

```

Exploratory Analysis
=======================================================================

Column {data-width=500}
-----------------------------------------------------------------------
### 

### 




```{r tree-diam-hist, fig.width = 15, fig.height = 10}

pine_table %>%
  ggplot(aes(x = TreeDiam, fill = Response)) +
  geom_histogram(color="black", alpha = 0.6, position = "identity") +
  scale_fill_manual(values = c("#009E73", "#E69F00")) +
  labs(y = "", x = "Tree diameter", fill = "") +
theme_gray() +
  theme(text = element_text(size = 22))

```


Column {data-width=500}
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### 



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Statistical Models
=======================================================================

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