---
title: "Invasive Reptiles in Florida"
output: 
  flexdashboard::flex_dashboard:
    theme: journal
    orientation: rows
    vertical_layout: fill
    social: ["twitter","facebook", "menu"]
    source_code: embed
   
---


```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(RColorBrewer)
library(ggvis)
library(googleVis)
library(lubridate)
library(data.table)
library(sp)
library(maptools)
library(ggplot2)
library(plyr)
library(tidyverse)
library(urbnmapr)
library(maps)
library(usmap)
library(cdlTools)
library(mapview)
library(googleVis)
```


```{r}
data = read.csv("C:/Users/Russe/Desktop/Helping with R/flex dashboard/repflexdat.csv")
data = na.omit(data)

```


```{r}

nb.cols <- 11
mycolors <- colorRampPalette(brewer.pal(8, "Reds"))(nb.cols)

```


Interactive Data Visualization{data-navmenu="Project Sections"}
==============================
Row
------------------------------

### Data Source:EDDMapS. 2020. Early Detection & Distribution Mapping System. The University of Georgia - Center for Invasive Species and Ecosystem Health. Available online at http://www.eddmaps.org/; last accessed May 10, 2020."

```{r}
valueBox(paste("Invasive Reptile Trends"),
color = "danger")

```

### Total Records in Florida

```{r}
valueBox(length(data$ComName),
icon = "fas fa-chart-bar")

```


### **Latitudinal HotSpots**

```{r}
gauge(round(mean(data$Latitude),
            digits = 2),
            min = min(data$Latitude),
            max = max(data$Latitude),
            sectors = gaugeSectors(success = c(29.6,30.9),
                          warning = c(26.9, 29.6),
                          danger = c(24.32054, 26.9),
                          colors = c('green','yellow','red')))
                          
                          
```


### North Florida
```{r}
valueBox(sum(data$region == "North"),
icon = "fas fa-chart-line ")

```

### Central Florida
```{r}
valueBox(sum(data$region == "Central"),
icon = "fas fa-chart-line")

```

### South Florida
```{r}
valueBox(sum(data$region == "South"),
icon = "fas fa-chart-line")

```


Row
-----------------------------------------

### Species by County

```{r}
p1 <- data %>%
  dplyr::group_by(Location) %>%
  dplyr::summarise(SciName = n()) %>%
  plot_ly(x = ~Location,
         y = ~SciName,
         type = 'bar') %>%
  layout(xaxis = list(title = "Invasive Reptiles per County"),
         yaxis = list(title = "Count"))
p1
```


### HotSpot Counties
```{r}

 piedat <- data %>%
  dplyr::group_by(Location) %>%
  dplyr::summarise(SciName = n()) %>%
  filter(SciName > 50)

nb.cols <- 11
gradient <- colorRampPalette(brewer.pal(8, "Spectral"))(nb.cols)
piedat <- piedat %>% arrange(desc(SciName))
piedat$colors <- gradient


p2 <- plot_ly(piedat, labels = ~Location, values = ~SciName,
              marker = list(colors = ~colors)) %>%
  add_pie(hole = 0.5) %>%
  layout(xaxis = list(zeroline = F,
                           showline = F,
                           showticklabels = F,
                           showgrid = F),
                      yaxis = list(zeroline = F,
                                   showline = F,
                                   showticklabels = F,
                                   showgrid = F))
p2

```

Row
-------------------------------------------------

### Invasive Reptile Records Per Year
```{r}

p4 <- data %>%
  dplyr::group_by(year) %>%
  dplyr::summarise(SciName = n()) %>%
  plot_ly(x = ~year,
         y = ~SciName,
         type = 'bar',
         name = "Records") %>%
   add_lines(y = ~fitted(loess(SciName ~ year)),
            name = "Trend",
            color = I("#FFC125"),
            showlegend = T,
            line = list(width = 5))%>%
  layout(xaxis = list(title = "Records per year"),
         yaxis = list(title = "'Count"))
  
p4

```



Maps{data-navmenu="Project Sections"}
========================================

Row
----------------------------------------

### HotSpot Map 
```{r}
require(ggplot2)

fldata <- data %>%
  dplyr::group_by(Location) %>%
  dplyr::summarise(SciName = n())

fldata$Location <- as.character(fldata$Location)
fldata$county <- tolower(fldata$Location)
#add FIPScode
fldata$county_fips <- usmap::fips("Florida", county = fldata$county)

# get shapefile
states <- map_data("state")

florida <- subset(states, region %in% c("florida"))

#convert to df
fl_df <- subset(states, region == "florida")

counties <- map_data("county")
fl_county <- subset(counties, region == "florida")

fl_base <- ggplot(data = fl_df, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + geom_polygon(color = "black", fill = "gray")

fl_county_map <- fl_base + theme_minimal() + 
  geom_polygon(data = fl_county, fill = NA, color = "white") +
  geom_polygon(color = "black", fill = NA)

# add fldata
fldata$county <-tolower(fldata$county)
fldata_tab <- dplyr::select(fldata, county, SciName, county_fips)
#change colname
colnames(fldata_tab) <- c("subregion", "total", "county_fips")

# combine fldata
fl_combined_counties <- inner_join(fldata_tab, fl_county, by = "subregion")

# drop axes and ticks
elements <- theme(
  axis.text = element_blank(),
  axis.line = element_blank(),
  axis.ticks = element_blank(),
  panel.border = element_blank(),
  panel.grid = element_blank(),
  axis.title = element_blank(),
  panel.background = element_blank()
)

# make a descriptive column
fl_combined_counties$observations <- cut(fl_combined_counties$total,breaks=c(0, 5, 10, 20, 40, 60, 80, 100, 1000, Inf),labels = c("0-5","50-10","10-20","20-40","40-60",                                                           "60-80","80-100","100-1000","1000+"))


flmap <- fl_base + 
  geom_polygon(data = fl_county, fill = "white", color = "black")+
  geom_polygon(color = "black", fill = NA) +
  geom_polygon(data = fl_combined_counties, aes(fill = observations), color = "black") +
  scale_color_manual(values = "#DA1616",
                     labels = "Observations") +
 scale_fill_brewer(palette = "Reds") +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(nrow=1, override.aes = list(alpha = 1), 
                             label.position="bottom",
                             label.hjust = .5)) +
  theme_minimal()+
  labs(fill="", color="")+
  theme(legend.position="none",
        legend.key.width = unit(1, "cm"), 
        legend.key.height = unit(0.5, "cm"),
        legend.key = element_blank(),
        legend.text = element_text(size=11))

# store the ggplotly object
ggplotly(flmap)


```


### Species Observations
```{r}

sp <- SpatialPoints(cbind(data$Longitude, data$Latitude),
                    proj4string = CRS("+init=epsg:4326"))

sp$Species <- data$SciName


mapview(sp, legend=FALSE)

```



Data Table{data-navmenu="Project Sections"}
=======================================================

```{r}

datatable(data,
          caption = "Invasive Reptile Data",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))

```



Species/Month{data-navmenu="Project Sections"}
========================================================

### Pivot Table: Monthly Activity per Species
```{r}

data$month <- factor(data$month, levels = month.name)

rpivotTable(data,
            aggregatorName = "Count",
            cols = "month",
            rows = "SciName",
            rendererName = "Heatmap")

```



Monthly Activity/County{data-navmenu="Project Sections"}
========================================================

### Pivot Table: Monthly Activity per County
```{r}

data$month <- factor(data$month, levels = month.name)

rpivotTable(data,
            aggregatorName = "Count",
            cols = "month",
            rows = "Location",
            rendererName = "Heatmap")

```

About{data-navmenu="Project Sections"}
========================================================

This Flex Dashboard was created by Russell J Gray.
Source code is open-source and available under the "</>Source Code" tab (top right)

For any questions comments or inquiries, please contact the author at rgrayherpetology@gmail.com

Data Source: EDDMapS. 2020. Early Detection & Distribution Mapping System. The University of Georgia - Center for Invasive Species and Ecosystem Health. Available online at http://www.eddmaps.org/; last accessed May 10, 2020."