inst/doc/c_warbleR_workflow_02.R

In warbleR: Streamline Bioacoustic Analysis

params <-
list(EVAL = FALSE)

## ----echo = FALSE, message = FALSE------------------------------------------------------------------------------------------------------------------

# remove all objects
rm(list = ls())

# unload all non-based packages
out <- sapply(paste("package:", names(sessionInfo()$otherPkgs), sep = ""), function(x) try(detach(x, unload = FALSE, character.only = TRUE), silent = TRUE))

# load packages
X <- c("warbleR", "knitr")
invisible(lapply(X, library, character.only = TRUE))
# library(kableExtra)

options(knitr.table.format = "html")
# opts_chunk$set(comment = "")
knitr::opts_chunk$set(
  comment = "",
  fig.width = 5, 
  fig.height = 3.5,
  dpi = 40,
  out.width = "80%"
)
# opts_knit$set(root.dir = tempdir())
options(width = 150, max.print = 100)

# from https://stackoverflow.com/questions/28961431/computationally-heavy-r-vignettes, so that vignettes will be built upon installation, but not executed during R CMD check (which is contributing to the /doc being too large)
is_check <- ("CheckExEnv" %in% search()) || any(c(
  "_R_CHECK_TIMINGS_",
  "_R_CHECK_LICENSE_"
) %in% names(Sys.getenv()))
knitr::opts_chunk$set(eval = !is_check, comment = "")

# for vignette checking and image file output
# setwd("~/Desktop/R/warbleR_example2/")

# website to fix gifs
# https://ezgif.com/optimize

## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------
# 
# library(warbleR)
# 
# # set your working directory appropriately
# # setwd("/path/to/working directory")
# 
# # run this if you have restarted RStudio between vignettes without saving your workspace (assuming that you are in your /home/username directory)
# setwd(file.path(getwd(), "warbleR_example"))
# 
# # Check your location
# getwd()

## ----echo = TRUE, eval=FALSE------------------------------------------------------------------------------------------------------------------------
# 
# # The package must be loaded in your working environment
# ls("package:warbleR")

## ----echo=TRUE, eval=FALSE--------------------------------------------------------------------------------------------------------------------------
# 
# # To run this example:
# # Open Phae_hisnr.csv and modify the start coordinate of the first selection and the end coordinate of the second selection so that the signals overlap
# 
# Phae.hisnr <- read.csv("Phae_hisnr.csv", header = TRUE)
# str(Phae.hisnr)
# head(Phae.hisnr, n = 15)
# 
# # yields a data frame with an additional column (ovlp.sels) that indicates which selections overlap
# Phae.hisnr <- overlapping_sels(X = Phae.hisnr, max.ovlp = 0)
# 
# # run the function again but this time retain only the signals that don't overlap
# Phae.hisnr <- overlapping_sels(X = Phae.hisnr, max.ovlp = 0, drop = TRUE)

## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------
# 
# spectrograms(Phae.hisnr, wl = 300, flim = c(2, 10), it = "jpeg", res = 150, osci = TRUE, ovlp = 90)

## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------
# 
# # remove selections after deleting corresponding image files
# Phae.hisnr2 <- filter_sels(Phae.hisnr, it = "jpeg", incl.wav = TRUE)
# nrow(Phae.hisnr2)

## ----echo=TRUE, eval=FALSE--------------------------------------------------------------------------------------------------------------------------
# 
# # if selections can be read, "OK" will be printed to check.res column
# checksels(Phae.hisnr2, check.header = FALSE)

## ----eval=FALSE, echo=FALSE-------------------------------------------------------------------------------------------------------------------------
# 
# # ### Cut selections into individual sound files
# #
# # Listening to signals complements visual inspection and classification. The function `cut_sels` can be very useful for aural comparison of selected signals. Selected signals can be played as individual sounds rather than having to open up entire sound files. As a word of caution, generating cuts of sound files will also propagate any naming errors present in your original files.
# #
# # `cut_sels` can also be used to your advantage if your original recordings are long (over 10-15 minutes). Some _warbleR_ functions, so it's helpful to use shorter duration sound files. You can make selections of shorter pieces of long original recordings, either in _Raven_ or _Syrinx_, and use `cut_sels` to generate shorter segments for smoother signal detection in `warbleR`.
# 
# cut_sels(X = Phae.hisnr2, mar = 0.01, labels = c("sound.files", "selec"))
# 
# # bug in the above cut_sels code
# 
# # Error in apply(X[, sapply(X, is.factor)], 2, as.character) :
# #   dim(X) must have a positive length
# 
# # cut_sels(selec.table) # this works!

## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------
# 
# tailor_sels(Phae.hisnr2, wl = 300, flim = c(2, 10), wn = "hanning", mar = 0.1, osci = TRUE, title = c("sound.files", "selec"), auto.next = TRUE)
# 
# # Read in tailor_sels output after renaming the csv file
# Phae.hisnrt <- read.csv("Phae_hisnrt.csv", header = TRUE)
# str(Phae.hisnrt)

## ----eval=TRUE, echo=FALSE--------------------------------------------------------------------------------------------------------------------------

Phae.hisnrt <- read.csv("Phae_hisnrt.csv", header = TRUE)
str(Phae.hisnrt)

## ----eval=FALSE, echo=TRUE--------------------------------------------------------------------------------------------------------------------------
# 
# # highlight selected signals
# full_spectrograms(Phae.hisnrt, wl = 300, flim = c(2, 10), ovlp = 10, sxrow = 6, rows = 15, it = "jpeg")
# 
# # concatenate full_spectrograms image files into a single PDF per recording
# # full_spectrograms images must be jpegs
# full_spectrograms2pdf(keep.img = FALSE, overwrite = TRUE)

## ----eval=FALSE, echo=FALSE-------------------------------------------------------------------------------------------------------------------------
# 
# # Note for later...full_spectrograms2pdf works on auto_detec files in the working directory too...maybe including a suffix argument would help

## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------
# 
# # we will use Phaethornis songs and selections from the warbleR package
# data(list = c("Phae.long1", "lbh_selec_table"))
# writeWave(Phae.long1, "Phae.long1.wav") # save sound files
# 
# # subset selection table
# # already contains the frequency range for these signals
# st <- lbh_selec_table[lbh_selec_table$sound.files == "Phae.long1.wav", ]
# 
# # read wave file as an R object
# sgnl <- read_sound_file(as.character(st$sound.files[1]))
# 
# # create color column
# st$colors <- c("red2", "blue", "green")
# 
# # highlight selections
# color_spectro(wave = sgnl, wl = 300, ovlp = 90, flim = c(1, 8.6), collevels = seq(-90, 0, 5), dB = "B", X = st, col.clm = "colors", base.col = "skyblue", t.mar = 0.07, f.mar = 0.1)

## ----eval = FALSE, echo = FALSE---------------------------------------------------------------------------------------------------------------------
# 
# # was getting bugs using the xeno-canto recordings
# # but code sort of works for the following code:
# # problem is that code takes a while to run and then shows the whole long spectrogram
# # suggestion for color spectro - an argument to zoom in on section of x-axis?
# 
# X <- Phae.hisnrt[Phae.hisnrt$sound.files == "Phaethornis-longirostris-154072.wav", ]
# X$colors <- c("red2", "blue", "green", "yellow", "orange")
# 
# X2 <- frange(X)
# # View(X2)
# 
# color_spectro(
#   wave = read_sound_file("Phaethornis-longirostris-154072.wav"), wl = 300, ovlp = 90, flim = c(1, 8.6), collevels = seq(-90, 0, 5),
#   dB = "B", X = X2, col.clm = "colors", base.col = "skyblue", t.mar = 0.07, f.mar = 0.1
# )

## ----eval=FALSE, echo=FALSE-------------------------------------------------------------------------------------------------------------------------
# 
# # spec_param takes a single selection from the selection table as input
# tweak_spectro(Phae.hisnrt[1, ], length.out = 5, ovlp = 90, wl = c(150, 900), wn = c("hanning", "bartlett"), collev.min = c(-60, -30), pal = "reverse.gray.colors.2", path = NULL, rm.axes = TRUE, cex = 0.45, flim = c(2, 10))

## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------
# 
# # create a column of recording IDs for friendlier catalog labels
# rec_ID <- sapply(1:nrow(Phae.hisnrt), function(x) {
#   gsub(x = strsplit(as.character(Phae.hisnrt$sound.files[x]), split = "-")[[1]][3], pattern = ".wav$", replacement = "")
# })
# rec_ID
# 
# Phae.hisnrt$rec_ID <- rec_ID
# str(Phae.hisnrt)
# 
# # set color palette
# # alpha controls transparency for softer colors
# cmc <- function(n) cm.colors(n, alpha = 0.8)
# 
# catalog(X = Phae.hisnrt, flim = c(2, 10), nrow = 4, ncol = 3, height = 10, width = 10, tag.pal = list(cmc), cex = 0.8, same.time.scale = TRUE, mar = 0.01, wl = 300, gr = FALSE, labels = "rec_ID", tags = "rec_ID", hatching = 1, group.tag = "rec_ID", spec.mar = 0.4, lab.mar = 0.8, max.group.cols = 5)
# 
# catalog2pdf(keep.img = FALSE, overwrite = TRUE)
# 
# # assuming we are working from the warbleR_example directory
# # the ~/ format does not apply to Windows
# # make sure you have already moved or deleted all other pdf files
# move_images(from = ".", it = "pdf", create.folder = TRUE, folder.name = "Catalog_image_files")

## ----eval = FALSE, echo = FALSE---------------------------------------------------------------------------------------------------------------------
# 
# # suggestion for move_imgs
# # add argument for regex so as not to delete/move all image files of a given type
# # and be able to move just "Cat*.pdf"...etc

## ----eval=FALSE-------------------------------------------------------------------------------------------------------------------------------------
# # now create a catalog without labels, tags, groups or axes
# Phae.hisnrt$no_label <- ""
# 
# # catalog(X = Phae.hisnrt, flim = c(1, 10), nrow = 4, ncol = 3, height = 10, width = 10, cex = 0.8, same.time.scale = TRUE, mar = 0.01, wl = 300, spec.mar = 0.4, rm.axes = TRUE, labels = "no_label", lab.mar = 0.8, max.group.cols = 5, img.suffix = "nolabel")
# 
# catalog(X = Phae.hisnrt, flim = c(1, 10), nrow = 4, ncol = 3, height = 10, width = 10, tag.pal = list(cmc), cex = 0.8, same.time.scale = TRUE, mar = 0.01, wl = 300, gr = FALSE, labels = "no_label", spec.mar = 0.4, lab.mar = 0.8, max.group.cols = 5, img.suffix = "nolabels")
# 
# catalog2pdf(keep.img = FALSE, overwrite = TRUE)