---
title: "Resultados del Monitoreo Acústico Awake"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
#    storyboard: true
    social: menu
    source: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
```

Hitos
=======================================================================

Column {data-width=300}
-----------------------------------------------------------------------


### chart1

```{r}
valueBox(260, caption = "Horas de capacitación", icon="fa-graduation-cap")


```


### Chart 2

```{r}
valueBox(27, caption = "Anfitriones participando", 
         icon="fa-users", color= "warning")


```


### Chart 3

```{r}
valueBox(167, caption = "Sitios instalados", 
         icon="fa-map-marker", color= "info")


```


### Chart 3

```{r}
valueBox(526.332, caption = "Archivos de Audio", 
         icon="fa-file-audio-o", color= "success")


```

### Chart 3

```{r}
valueBox(217, caption = "Especies validadas", 
         icon="fa-check-square-o", color= "info")


```


Column {data-width=300}
-----------------------------------------------------------------------


### Anfitriones subiendo información a la plataforma

```{r}
gauge(95, min = 0, max = 100, symbol = '%',
      #label="Anfitriones subiendo información a la plataforma",
      gaugeSectors(
  success = c(70, 100), warning = c(40, 69), danger = c(0, 39)
  ))

```

### Participación de mujeres

```{r}
gauge(45, min = 0, max = 100, symbol = '%',
      #label="Anfitriones subiendo información a la plataforma",
      gaugeSectors(
  success = c(70, 100), warning = c(40, 69), danger = c(0, 39)
  ))

```

### Chart 3

```{r}
valueBox(42, caption = "Patrones de reconocimiento", 
         icon="fa fa-cog fa-spin fa-3x fa-fw", color= "success")


```

### Chart 3

```{r}
valueBox(18, caption = "Especies priorizadas", 
         icon="fa-list-alt", color= "success")


```



Column {data-width=400}
-----------------------------------------------------------------------

### Resumen de metodos y resultados

Capacitaciones

- Realizamos talleres presenciales con los anfitriones en Choco, Cauca, Putumayo Valle del Cauca, Meta, y Casanare.

- Realizamos capacitaciones virtuales con los anfitriones para resolver dudas y hacer sueguimiento al proceso de programación, configuración y subida de informacion a la plataforma Arbimon.

- Visitas de campo a los 27 anfitriones.

Grabadoras

- Instalamos 100 grabadoras del tipo Audiomoth, de la mano de 27 anfitriones de Awake.

- Las grabadoras capturaron mas de medio millon de archivos de audio que corresponden a 1.5 Teras de información.

- Se desarrolló e implementó un protocolo de monitoreo, que puede ser replicado en cualquier otro proyecto de turismo de naturaleza.

Detalles adicionales 

- [Monitoreo Acústico](https://monitoreo-acustico.netlify.app)
- [AwakeBIO](https://awake.travel/awakebio)


Mapa
=======================================================================

Column {data-width=650}
-----------------------------------------------------------------------

### Localizacion de especies identificadas




```{r}
#### Codigo mapa
library(readxl)
library(tidyverse)
library(viridis)
library(patchwork)
library(hrbrthemes)
library(circlize)
library(sf)
library(mapview)
library(networkD3)

awake <- read_excel("D:/BoxFiles/Box Sync/CodigoR/Awake_datos/data/Plantilla_de_registros_3.5_aves_cleaned_AnfitrionFixed.xlsx")

awake_sf = st_as_sf(awake, coords = c("decimalLongitude", "decimalLatitude"), crs = 4326)


# df %>% group_by(id) %>% mutate(csum = cumsum(value))

awake_2 <- awake_sf %>%
  group_by(verbatimLocality) %>%
  # mutate(csum = cumsum(occurrenceID))
  summarise(recording = n_distinct(occurrenceID )) 

mapview(awake_2, cex = "recording", alpha = 0.1,
        zcol = "verbatimLocality", map.types	
="CartoDB.DarkMatter" ) #zcol = "verbatimLocality


```
Column {.tabset data-width=350}
-----------------------------------------------------------------------

### Metodología

Grabaciones

- Instalamos 100 grabadoras del tipo Audiomoth, de la mano de 27 anfitriones de Awake.

- Cada anfitrión instaló de una a cuatro grabadoras en su reserva.

- Las grabadoras estuvieron activas de una semana a un mes en cada sitio de instalación.

- En cada región un especialista identifico acústicamente algunas especies.

- Las especies identificadas se usaron para construir patrones de reconocimiento con algoritmos de inteligencia artificial. 

- El tamaño del circulo en el mapa representa el número de grabaciones de cada reserva que se lograron identificar hasta especie.


Detalles adicionales 

- [Monitoreo Acústico](https://monitoreo-acustico.netlify.app)
- [AwakeBIO](https://awake.travel/awakebio)



Especies y anfitriones
=======================================================================

Column {data-width=650}
-----------------------------------------------------------------------

### Red de especies y anfitriones

```{r}
#Codigo para red bipartita

awake_3 <- awake_sf %>%
  select(scientificName, eventDate, stateProvince,
         order, family, genus, occurrenceID, verbatimLocality) %>% 
   count(scientificName, verbatimLocality, sort = TRUE)
  #filter(stateProvince=="Cauca") %>%
  #group_by(verbatimLocality) %>%
   #mutate(csum = sum(scientificName))
  #summarise(recording = n_distinct(occurrenceID ))

#### put italica
#awake_3$scientificName <- paste("_",awake_3$scientificName, "_", sep="")

# From these flows we need to create a node data frame: it lists every entities involved in the flow
nodes <- data.frame(name=c(
            as.character(awake_3$scientificName),
            as.character(awake_3$verbatimLocality)) %>%
                      unique())

awake_3$scientificName=match(awake_3$scientificName, nodes$name)-1 
awake_3$verbatimLocality=match(awake_3$verbatimLocality, nodes$name)-1

# prepare colour scale
ColourScal ='d3.scaleOrdinal() .range(["#FDE725FF","#B4DE2CFF","#6DCD59FF","#35B779FF","#1F9E89FF","#26828EFF","#31688EFF","#3E4A89FF","#482878FF","#440154FF"])'


# Make the Network
sankeyNetwork(Links = awake_3, Nodes = nodes,
                     Source = "scientificName", 
                    Target = "verbatimLocality",
                     Value = "n", NodeID = "name", 
                     sinksRight=FALSE, colourScale=ColourScal, nodeWidth=40, fontSize=10, nodePadding=4, width=1000)


# ="CartoDB.DarkMatter" ) #zcol = "verbatimLocality

#library(dygraphs)
#dygraph(nhtemp, main = "New Haven Temperatures") %>% 
#  dyRangeSelector(dateWindow = c("1920-01-01", "1960-01-01"))
```

Column {data-width=350}
-----------------------------------------------------------------------

### Metodología

- Se seleccionaron 18 especies con muy buenos patrones de reconocimiento en Arbimon y se buscaron con algoritmos de inteligencia artificial en todas las grabaciones. 

- En la grafica se muestra cuales especies se encuentran en la reserva de cual anfitrion.

- El ancho de la banda gris representa el número de grabaciones de esa especie en la reserva.


Detalles adicionales 

- [Monitoreo Acústico](https://monitoreo-acustico.netlify.app)
- [AwakeBIO](https://awake.travel/awakebio)


Taxonomia
=======================================================================

Column {data-width=600}
-----------------------------------------------------------------------


### Especies monitoreadas y sus cantos en porcentajes

```{r}
library(plotly)

#p <- ggplot(data = diamonds, aes(x = cut, fill = clarity)) +
#            geom_bar(position = "dodge")
#ggplotly(p)

awake_circular <- awake %>%
  select(scientificName,vernacularName,
         order, family, genus, verbatimLocality) %>% 
   count(scientificName, order, family, vernacularName, sort = TRUE)
  #arrange(family)
  #dplyr::summarize ()
# awake_circular$id <- 1:18


# make smaller n to 100
awake_circular$n <- awake_circular$n *.1

# Set a number of 'empty bar' to add at the end of each order
empty_bar <- 2
to_add <- data.frame( matrix(NA, empty_bar*nlevels(factor(awake_circular$order)), ncol(awake_circular)) )
colnames(to_add) <- colnames(awake_circular)
to_add$order <- rep(levels(factor(awake_circular$order)), each=empty_bar)
awake_circular <- rbind(awake_circular, to_add)
awake_circular <- awake_circular %>% arrange(order)
awake_circular$id <- seq(1, nrow(awake_circular))

# Get the name and the y position of each label
label_data <- awake_circular
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar     # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)



# prepare a data frame for base lines
base_data <- awake_circular %>% 
  dplyr::group_by(order) %>% 
  dplyr::summarize (start=min(id), end=max(id) - empty_bar) %>% 
  rowwise() %>% 
  dplyr::mutate(title=mean(c(start, end)))

# prepare a data frame for grid (scales)
grid_data <- base_data
grid_data$end <- grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
grid_data$start <- grid_data$start - 1
grid_data <- grid_data[-1,]


# Make the plot
p <- ggplot(awake_circular, aes(x=as.factor(id), y=as.numeric(n), fill=order)) +       # Note that id is a factor. If x is numeric, there is some space between the first bar
  
  geom_bar(aes(x=as.factor(id), y=as.numeric(n), fill=order), stat="identity", alpha=0.5) +
  
  # Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
  geom_segment(data=grid_data, aes(x = end, y = 80, xend = start, yend = 80), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 60, xend = start, yend = 60), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 40, xend = start, yend = 40), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 20, xend = start, yend = 20), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 100, xend = start, yend = 100), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
  
  # Add text showing the value (n) of each 100/75/50/25 lines
  annotate("text", x = rep(max(awake_circular$id),5), y = c(20, 40, 60, 80, 100), label = c("20", "40", "60", "80", "100") , color="grey", size=3 , angle=0, fontface="bold", hjust=1) +
  
  geom_bar(aes(x=as.factor(id), y=as.numeric(n), fill=order), stat="identity", alpha=0.5) +
  ylim(-100,120) +
  theme_minimal() +
  theme(
    legend.position = "none",
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm") 
  ) +
  coord_polar() + 
  geom_text(data=label_data, aes(x=id, y=as.numeric(n)-10, label=scientificName, hjust=hjust), color="black", fontface="italic",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) +
  
  # Add base line information
  geom_segment(data=base_data, aes(x = start, y = -7, xend = end, yend = -5), colour = "black", alpha=0.7, size=0.6 , inherit.aes = FALSE )  +
  geom_text(data=base_data, aes(x = as.numeric(title), y = -18, label=order), hjust=c(0,0,1,0,0,0,0), colour = "black", alpha=0.8, size=2, fontface="bold", inherit.aes = FALSE)

p



q <- ggplot(awake_circular, aes(x=scientificName, y=as.numeric(n), fill=order)) +       # Note that id is a factor. If x is numeric, there is some space between the first bar
  
  geom_bar(aes(x=scientificName, y=as.numeric(n), fill=order), stat="identity", alpha=0.5) + 
  
geom_text(data=awake_circular, aes(x=scientificName, y=-4, label=vernacularName), color="black", fontface="italic",alpha=0.6, size=3, angle= 90, inherit.aes = FALSE ) 
  



```


Column {data-width=400}
-----------------------------------------------------------------------



### Nombres de las especies

```{r}
library(kableExtra)

sp_table <- awake %>%
  select(scientificName,vernacularName,
         order, family, genus) %>% 
   count(vernacularName, scientificName, family, order,  sort = TRUE)

names(sp_table) <- c("Nombre comun", "Nombre cientifico", 
                     "familia", "orden", "cantos")
  
sp_table %>%
  kbl() %>%
  kable_paper("hover", full_width = T, font_size = 9)
  #kable_styling(bootstrap_options = "striped", font_size = 8)

```