bats2020.Rmd

---
title: "bats2020"
author: "Patrick Lorch"
date: "9/3/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Bats 2020

Using R to process files recorded using WildlifeAcoustics SM4BAT-FS recorder (Unit Serial number:  S4U08764) to classify Bat calls.

## Testing bioacoustics package

```{r bioacoustics}
# install.packages("bioacoustics")
library(bioacoustics)
# install.packages("devtools")
# devtools::install_github("riggsd/guano-r", subdir="guano")
library(guano)

data_dir = file.path(getwd(), "../Bats 2020/SCR forest Management")
S4U08764_20200819_173700 = read_audio(file.path(data_dir, "../Bats 2020/S4U08764_20200819_173700.wav"))
metadata(S4U08764_20200819_173700)

# returns empty list
read.guano(file.path(data_dir, "S4U08764_20200819_173700.wav"))
```

## Move images to species folders

This will need to be run once per location directory

```{r move}
library(readr)
library(dplyr)

# Change these dirs for other directories
# data_dir = file.path(getwd(), "../Bats 2020/Johnson's Creek")
# Kaliedescope_output_dir = file.path(getwd(), 
#                                     "../Bats 2020/Johnson's Creek output")
# data_dir = file.path(getwd(), "../Bats 2020/SCR forest Management")
# Kaliedescope_output_dir = file.path(getwd(), 
#                                     "../Bats 2020/SCR forest Management output")
data_dir = file.path(getwd(), "../Bats 2020/Williams")
Kaliedescope_output_dir = file.path(getwd(),
                                    "../Bats 2020/Williams output")

id = read_csv(file.path(Kaliedescope_output_dir, 
                         "id.csv"), 
     col_types = cols(DATE = col_date(format = "%Y-%m-%d"), 
         `DATE-12` = col_date(format = "%Y-%m-%d"), 
         `MANUAL ID` = col_character(), ORGID = col_character(), 
         `REVIEW ORGID` = col_character(), 
         `REVIEW USERID` = col_character(), 
         TIME = col_time(format = "%H:%M:%S"), 
         `TIME-12` = col_time(format = "%H:%M:%S"), 
         USERID = col_character()))

dirs_to_create = id$`AUTO ID*` %>%
    unique()
dirs_to_create = file.path(data_dir,dirs_to_create)
sapply(dirs_to_create, FUN = dir.create, recursive = TRUE)

files_to_copy = list.files(data_dir, 
                           pattern = ".wav")
copy_from_files = id$`IN FILE`[order(id$`IN FILE`)]
if(all.equal(files_to_copy, copy_from_files)){
    copy_from_f = file.path(data_dir,
                                id$`IN FILE`)
    copy_to_files = file.path(data_dir,
                              id$`AUTO ID*`,
                                id$`IN FILE`)
    file.copy(copy_from_f, copy_to_files)

} else {print("Lists are not equal. Not copying.")}
table(id$`AUTO ID*`)
spp_totals = function (dd = data_dir){
    dirs_list = list.dirs(dd,full.names = F)[-1]
    dir_counts = sapply(file.path(dd, dirs_list),
                        FUN = function(x) {
                            length(list.files(x))
                            }
                        )
    names(dir_counts) = dirs_list
    dir_counts
}
spp_totals()

for (d in dirs_list){
    id.sub = id[id$`AUTO ID*` == d,]
    write.csv(id.sub, 
              file.path(data_dir, 
                        d,
                        paste("id",d,"csv",
                              sep = ".")))
}

# If the two tables above match, you can run the command below 
#  to remove original copies, 
# but BE CAREFUL!
# file.remove(copy_from_f)

# summary
spp_totals1 =spp_totals(dd = file.path(getwd(), "../Bats 2020/Johnson's Creek"))
spp_totals2 =spp_totals(dd = file.path(getwd(), "../Bats 2020/SCR forest Management"))
spp_totals3 =spp_totals(dd = file.path(getwd(), "../Bats 2020/Williams"))

spp_sum_site = bind_rows(JohnsonsCreek_2020 = spp_totals1,
                         SCRforestManagement_2020 = spp_totals2,
                         Williams_2020 = spp_totals3,
                         .id = "Site")
write.csv(spp_sum_site,"spp_sum_site.csv")
```