---
title: "Bird Inventory by year. Sustainable Cattle Ranching Project"
output: 
  flexdashboard::flex_dashboard:
    theme: journal
    storyboard: true
    social: menu
    source: embed
---

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


```{r loadpackages, include=FALSE, warning=FALSE, message=FALSE}
library(readxl) # lee excel
library(tidyverse)# manipula datos eficientemente
library(sp) # spatial polygon
library(magrittr)
library(raster)
library(rasterVis)
library(sf) # new spatial package
library(mapview) # plotting maps in html
library(knitr)
library(colorspace)
library(DT)
library(leaflet)
library(lubridate)
library(tmap)
library(tmaptools)
library(vegan)
library(mobsim)
library(plotly)
library(kableExtra)



```


```{r data, message=FALSE, warning=FALSE}
###################################
###
### AVES
###
###################################

aves_atlantico <- as.data.frame(read_excel("C:/Users/diego.lizcano/Box Sync/CodigoR/Tablas_GCS/data/GAICA_Aves_2013.xlsx", sheet = "ATLANTICO"))

aves_quindio <- as.data.frame(read_excel("C:/Users/diego.lizcano/Box Sync/CodigoR/Tablas_GCS/data/GAICA_Aves_2013.xlsx", sheet = "QUINDIO"))

aves_santander <- as.data.frame(read_excel("C:/Users/diego.lizcano/Box Sync/CodigoR/Tablas_GCS/data/GAICA_Aves_2013.xlsx", sheet = "SANTANDER"))

aves_meta <- as.data.frame(read_excel("C:/Users/diego.lizcano/Box Sync/CodigoR/Tablas_GCS/data/GAICA_Aves_2013.xlsx", sheet = "META"))

aves_full_2013 <- rbind(aves_atlantico, aves_quindio, aves_santander, aves_meta)

aves_full_2013$LOC <- as.numeric(aves_full_2013$LOC)
aves_full_2013$Fecha <- as.POSIXct(paste(
      aves_full_2013$YEAR, 
      aves_full_2013$MES,
      aves_full_2013$DIA,
      sep="-"),format = c("%Y-%m-%d"), tz="UTC")

rm(aves_atlantico)
rm(aves_quindio)
rm(aves_santander)
rm(aves_meta)

aves_full_2016 <- as.data.frame(read_excel("C:/Users/diego.lizcano/Box Sync/CodigoR/Tablas_GCS/data/AVES_GAICA_FINAL_2016_2017.xlsx", sheet = "INVENTARIO"))

aves_full_2016$YEAR <- lubridate::year(aves_full_2016$Fecha)
aves_full_2016$MES <- as.character(lubridate::month(aves_full_2016$Fecha))
aves_full_2016$DIA <- as.character(lubridate::day(aves_full_2016$Fecha))
aves_full <- bind_rows (aves_full_2016, aves_full_2013)


```


### Inventories were carried out in farms part of the sustainable cattle ranching project using standardized methods. In this map we depict the farms and dates of sampling.

```{r map}
# make sp spatial point
coordinates(aves_full) = ~LOC+LAT
geo<- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
proj4string(aves_full) <-  geo

# make sf
sampling.sf = st_as_sf(aves_full)


mapview(sampling.sf, 
        zcol = c("YEAR", "SITIO" ),
         map.types = c("OpenStreetMap.DE", "Esri.WorldImagery" ),
        leafletHeight = 8, 
        burst = TRUE, hide = TRUE)
 


```

***

Five ecoregions were sampled looking for birds using:

- Point counts along the landscape

- Mist netting 

- The sampling covered forest, pastures and silvopastoral systems



- You can change the display options to show yeas or sampled fams.


### Summary of species counts by Departamento by year 

```{r sp_count, warning=FALSE, message=FALSE}

# select by uniques
site_species_all <- as.data.frame(sampling.sf) %>% dplyr::select(DEPARTAMENTO, YEAR,  Fecha, NOMBRE_CIENTIFICO, NUMERO_DE_INDIVIDUOS) # %>%  distinct() # igual a site_dept_loc<-

site_species_g<- site_species_all %>% filter(DEPARTAMENTO == "Guajira")

site_species_G_y1 <- site_species_g %>% filter(YEAR == unique(site_species_g$YEAR)[1]) # yr 2017

site_species_G_y2 <- site_species_g %>% filter(YEAR == unique(site_species_g$YEAR)[2]) # yr 2013

auto_mat_gy <- as.data.frame(sampling.sf) %>%
  group_by (YEAR, DEPARTAMENTO) %>%
  # count ()
  summarize_at("NOMBRE_CIENTIFICO", n_distinct) %>% 
  # spread(DEPARTAMENTO, NOMBRE_CIENTIFICO, fill=0) %>% 
  as.data.frame() 

names(auto_mat_gy) <- c("YEAR","DEPARTAMENTO", "SPECIES")

########  acum by yr2
# get species
sp_gy2 <- unique (site_species_G_y2$SPECIES)
# get sites

########  acum by yr1
# get species
sp_gy1 <- unique (site_species_G_y1$SPECIES)
# get sites



g <- ggplot(auto_mat_gy, aes(YEAR, SPECIES))
# Number of cars in each class:
g2 <- g + geom_col() + facet_wrap(vars(DEPARTAMENTO), nrow = 5) + 
  labs(y = "Species count")

ggplotly(g2)

```

***

Species accumulation curves showed a plateau exept Santander 


### Species list for Atlantico

```{r Atlantico}

table_by_depto <- function(site_species_all, DEPARTAMENTO) {
  
  site_species_a<- site_species_all %>% filter(DEPARTAMENTO == DEPARTAMENTO)
  
  site_species_A_y1 <- site_species_a %>% 
                      dplyr::select(NOMBRE_CIENTIFICO, YEAR) %>% 
                      filter(YEAR == unique(site_species_a$YEAR)[1]) %>%  # yr 2013
                      dplyr::count(NOMBRE_CIENTIFICO)  # yr 2017
  site_species_A_y1$n <- 1
  
  site_species_A_y2 <- site_species_a %>% 
                      dplyr::select(NOMBRE_CIENTIFICO, YEAR) %>% 
                      filter(YEAR == unique(site_species_a$YEAR)[2]) %>%  # yr 2013
                      dplyr::count(NOMBRE_CIENTIFICO)
  
  site_species_A_y2$n <- 1
  
  depto_sp <- merge(x=site_species_A_y1, y=site_species_A_y2,by="NOMBRE_CIENTIFICO",all=TRUE)
  
  names(depto_sp) <- c("Species", unique(site_species_a$YEAR)[1], unique(site_species_a$YEAR)[2])

  return(depto_sp)
  }

# 
# 
# site_species_a<- site_species_all %>% filter(DEPARTAMENTO == "Atlantico")
# 
# site_species_A_y1 <- site_species_a %>% 
#                     dplyr::select(NOMBRE_CIENTIFICO, YEAR) %>% 
#                     filter(YEAR == unique(site_species_a$YEAR)[1]) %>%  # yr 2013
#                     dplyr::count(NOMBRE_CIENTIFICO)  # yr 2017
# site_species_A_y1$n <- 1
# 
# site_species_A_y2 <- site_species_a %>% 
#                     dplyr::select(NOMBRE_CIENTIFICO, YEAR) %>% 
#                     filter(YEAR == unique(site_species_a$YEAR)[2]) %>%  # yr 2013
#                     dplyr::count(NOMBRE_CIENTIFICO)
# 
# site_species_A_y2$n <- 1
# 
# atlantico_sp <- merge(x=site_species_A_y1, y=site_species_A_y2,by="NOMBRE_CIENTIFICO",all=TRUE)
# 
# names(atlantico_sp) <- c("Species", unique(site_species_a$YEAR)[1], unique(site_species_a$YEAR)[2])

atlantico_sp <- table_by_depto(site_species_all = site_species_all, "Atlantico")

kable(atlantico_sp) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))



```



***

Species accumulation curves showed a plateau exept Santander 



### Species list for Guajira

```{r Guajira}
Guajira_sp <- table_by_depto(site_species_all = site_species_all, "Guajira")

kable(Guajira_sp) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

***

Species accumulation curves showed a plateau exept Santander 



### Species list for Quindio

```{r Quindio}
Quindio_sp <- table_by_depto(site_species_all = site_species_all, "Quindio")

kable(Quindio_sp) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```


***

Species accumulation curves showed a plateau exept Santander 



### Species list for Meta

```{r Meta}

  site_species_m<- site_species_all %>% filter(DEPARTAMENTO == "Meta")
  
  site_species_M_y1 <- site_species_m %>% 
                      dplyr::select(NOMBRE_CIENTIFICO, YEAR) %>% 
                      filter(YEAR == unique(site_species_m$YEAR)[1]) %>%  # yr 2013
                      dplyr::count(NOMBRE_CIENTIFICO)  # yr 2017
  site_species_M_y1$n <- 1
  
  site_species_M_y2 <- site_species_m %>% 
                      dplyr::select(NOMBRE_CIENTIFICO, YEAR) %>% 
                      filter(YEAR == unique(site_species_m$YEAR)[2]) %>%  # yr 2013
                      dplyr::count(NOMBRE_CIENTIFICO)
  
  site_species_M_y2$n <- 1
  
    site_species_M_y3 <- site_species_m %>% 
                      dplyr::select(NOMBRE_CIENTIFICO, YEAR) %>% 
                      filter(YEAR == unique(site_species_m$YEAR)[3]) %>%  # yr 2013
                      dplyr::count(NOMBRE_CIENTIFICO)
  
  site_species_M_y3$n <- 1
  
  Meta_sp1 <- merge(x=site_species_M_y1, y=site_species_M_y2, by="NOMBRE_CIENTIFICO",all=TRUE)
  
    Meta_sp <- merge(x=Meta_sp1, y=site_species_M_y3, by="NOMBRE_CIENTIFICO",all=TRUE)
  
  names(Meta_sp) <- c("Species", unique(site_species_m$YEAR)[1], unique(site_species_m$YEAR)[2], unique(site_species_m$YEAR)[3])

kable(Meta_sp) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

***

Species accumulation curves showed a plateau exept Santander