Detecting 55 kHz USVs with bedding

Stress-amphetamine

Author

Published

Invalid Date

Code

cns <- consolidate(path = "/media/m/Backup Plus/Campo abierto CUS/",
                   dest.path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/",
                   parallel = 10)

Code

fix_wavs(samp.rate = 200, bit.depth = 16)

Code

ohun::feature_acoustic_data(path = .Options$warbleR$path)

ssf <- ohun::split_acoustic_data(sgmt.dur = 5 * 60, cores = 10, path = .Options$warbleR$path)

write.csv(ssf, file.path(.Options$warbleR$path, "/5min_clip_info.csv"), row.names = FALSE)

Code

warbleR_options(wav.path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/5min_clips/")

detection <- energy_detector(
  path = .Options$warbleR$path,
  thinning = 0.5,
  bp = c(35, 90),
  smooth = 1,
  threshold = 2.5,
  hold.time = 3,
  min.duration = 1,
  max.duration = 200,
  cores = 5
)

saveRDS(
  detection,
  "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/detection_55kHz_with_bedding_CUIDO_MATERNO.RDS"
)

1 Random forest classification

Code

detection <- readRDS("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/detection_55kHz_with_bedding_CUIDO_MATERNO.RDS")

# measure spectrographic parameters
spectral_parameters <- spectro_analysis(detection, bp = c(35, 85), fast = TRUE, ovlp = 70, parallel = 22)

# leer el modelo 
rf_model <- readRDS("/home/m/Dropbox/Projects/rat_ultrasonic_vocalizations/data/processed/random_forest_55kHz_with_bedding.RDS")

sapply(spectral_parameters, function(x) sum(is.na(x)))

detection <- detection[!is.na(spectral_parameters$meandom), ]

spectral_parameters <- spectral_parameters[!is.na(spectral_parameters$meandom), ]

write.csv(spectral_parameters, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/spectral_parameters.csv", row.names = FALSE)

# aplicarlo sobre las detecciones nuevas
detection$class <-predict(object = rf_model, data = spectral_parameters)$predictions

# remover los sonidos clasificados como ruido de fondo 
filtered_detection <- detection[detection$class == "true.positive", ]

saveRDS(filtered_detection, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/random_forest_filtered_detection_55kHz_with_bedding.RDS")  

write.csv(filtered_detection, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/filtered_detection_55kHz_with_bedding.csv", row.names = FALSE)

counts <- aggregate(selec ~ sound.files, filtered_detection, length)
names(counts)[2] <- "55khz.call.count"

write.csv(counts, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/counts_per_sound_files_detection_55kHz_with_bedding.csv", row.names = FALSE)

1.1 Summarized

1.1.1 USV counts per minute

Code

filtered_detection_55 <- readRDS("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/random_forest_filtered_detection_55kHz_with_bedding.RDS")  

orgf <- read.csv("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/5min_clip_info.csv")

reass_detec <- reassemble_detection(detection = filtered_detection_55, Y = orgf, pb = FALSE)

# counts per minute
count_min <- vocal_rate(X = reass_detec, time.window = 60, time.resolution = 60, path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/", files = list.files("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/", pattern = "\\.wav$"))

# convert to minute rate
count_min$minute <- count_min$start / 60 + 1

wide_count_min <- reshape(count_min[, c("counts", "minute", "sound.files")], direction = "wide", idvar = "sound.files", timevar = "minute")

names(wide_count_min) <- c("sound.files", paste("min", 1:max(count_min$minute)))

print_kable(wide_count_min)

sound.files	min 1	min 2	min 3	min 4	min 5	min 6	min 7	min 8	min 9	min 10	min 11	min 12	min 13	min 14	min 15	min 16	min 17	min 18	min 19	min 20	min 21	min 22	min 23	min 24
T0000001-1.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
T0000001-2.wav	1	0	0	0	0	6	0	0	0	1	0	2	2	4	4	0	0
T0000001-3.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000001-4.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000002-1.wav	0	0	0	0	0	0	2	0	0	2	1	0	2	3	0	0	0
T0000002-2.wav	25	45	50	44	46	0	0	0	0	0	22	27	16	4	0	0	0
T0000002-3.wav	0	0	0	0	0	18	16	12	4	20	18	47	20	19	37	19	0
T0000002-4.wav	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000003-1.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000003-2.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000003-3.wav	0	0	0	1	9	28	119	71	59	26	68	100	6	3	30	0
T0000003-4.wav	0	0	0	0	1	0	22	3	0	0	0	0	0	0	0	0
T0000004-1.wav	0	1	0	0	2	1	3	2	1	0	2	10	2	1	1	1	0	0	0	0	0	0	0	0
T0000004-2.wav	1	8	47	66	63	67	65	24	22	41	58	32	21	30	21	1	0	0	0	0	0	0	0	0
T0000004-3.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
T0000004-4.wav	0	0	0	0	0	0	0	0	2	3	5	3	12	3	2	0	0	0	0	0	0	0	2	0
T0000005-1.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000005-2.wav	0	0	1	3	1	0	0	0	0	0	1	2	7	7	1	0
T0000005-3.wav	0	3	21	74	52	31	3	0	0	1	5	0	0	0	0	0
T0000005-4.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000006-1.wav	2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000006-2.wav	0	0	0	0	0	0	0	0	0	1	3	8	1	1	0	0
T0000006-3.wav	0	0	0	0	13	10	4	2	4	1	2	1	0	0	0	0
T0000006-4.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000007-1.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
T0000007-2.wav	0	4	24	48	76	39	9	71	27	137	11	60	89	29	0	7
T0000007-3.wav	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
T0000007-4.wav	0	0	0	0	0	1	4	3	9	13	24	18	25	51	18	0
T0000008-1.wav	19	32	29	110	80	36	93	148	144	37	13	80	86	33	100	75	5
T0000008-2.wav	0	0	0	1	1	0	0	0	10	5	1	0	0	0	1	0	0
T0000008-3.wav	0	2	11	27	11	28	29	83	52	29	9	32	60	19	33	12	3
T0000008-4.wav	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

1.1.2 Minute with highest USV count

Code

vr <- vocal_rate(X = reass_detec, time.window = 60, time.resolution = 1, path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/")

# rate per minute
vr$rate <- vr$rate * 60

# get highest per sound file
sub_vr <- vr[vr$duration == 60, ]

# get the row with the highest rate per sound file
highes_min <- do.call(rbind, lapply(split(sub_vr, sub_vr$sound.files), function(x) x[which.max(x$rate), ]))

print_kable(highes_min)

sound.files	start	end	counts	duration	rate
T0000001-1.wav	912	972	3	60	3
T0000001-2.wav	271	331	6	60	6
T0000002-1.wav	737	797	3	60	3
T0000002-2.wav	47	107	61	60	61
T0000002-3.wav	634	694	51	60	51
T0000002-4.wav	54	114	1	60	1
T0000003-3.wav	383	443	143	60	143
T0000003-4.wav	357	417	22	60	22
T0000004-1.wav	676	736	12	60	12
T0000004-2.wav	335	395	102	60	102
T0000004-3.wav	909	969	1	60	1
T0000004-4.wav	752	812	15	60	15
T0000005-2.wav	726	786	9	60	9
T0000005-3.wav	200	260	88	60	88
T0000006-1.wav	0	60	2	60	2
T0000006-2.wav	614	674	8	60	8
T0000006-3.wav	261	321	15	60	15
T0000007-2.wav	541	601	140	60	140
T0000007-3.wav	572	632	1	60	1
T0000007-4.wav	775	835	57	60	57
T0000008-1.wav	410	470	165	60	165
T0000008-2.wav	500	560	15	60	15
T0000008-3.wav	419	479	84	60	84

Session information

R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_CR.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=es_CR.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=es_CR.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_CR.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Costa_Rica
tzcode source: system (glibc)

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggplot2_3.5.2           ohun_1.0.3              DT_0.33                
 [4] kableExtra_1.4.0        ranger_0.17.0           readxl_1.4.5           
 [7] svMisc_1.4.3            rfigshare_0.3.8         viridis_0.6.5          
[10] viridisLite_0.4.2       Rraven_1.0.14           pbapply_1.7-4          
[13] bioacoustics_0.2.8.9000 warbleR_1.1.35          NatureSounds_1.0.5     
[16] knitr_1.50              seewave_2.2.3           tuneR_1.4.7            
[19] devtools_2.4.5          usethis_3.1.0          

loaded via a namespace (and not attached):
 [1] DBI_1.2.3           bitops_1.0-9        gridExtra_2.3      
 [4] remotes_2.5.0       testthat_3.2.3      rlang_1.1.6        
 [7] magrittr_2.0.3      e1071_1.7-16        compiler_4.5.0     
[10] systemfonts_1.2.3   vctrs_0.6.5         stringr_1.5.1      
[13] profvis_0.4.0       pkgconfig_2.0.3     crayon_1.5.3       
[16] fastmap_1.2.0       backports_1.5.0     ellipsis_0.3.2     
[19] promises_1.3.3      rmarkdown_2.29      sessioninfo_1.2.3  
[22] purrr_1.0.4         xfun_0.52           cachem_1.1.0       
[25] jsonlite_2.0.0      later_1.4.2         R6_2.6.1           
[28] stringi_1.8.7       RColorBrewer_1.1-3  pkgload_1.4.0      
[31] brio_1.1.5          cellranger_1.1.0    Rcpp_1.1.0         
[34] httpuv_1.6.16       Matrix_1.7-3        igraph_2.1.4       
[37] tidyselect_1.2.1    rstudioapi_0.17.1   yaml_2.3.10        
[40] dtw_1.23-1          miniUI_0.1.2        curl_6.4.0         
[43] pkgbuild_1.4.8      lattice_0.22-7      tibble_3.3.0       
[46] shiny_1.10.0        withr_3.0.2         evaluate_1.0.3     
[49] signal_1.8-1        moments_0.14.1      sf_1.0-21          
[52] sketchy_1.0.5       units_0.8-7         proxy_0.4-27       
[55] urlchecker_1.0.1    xml2_1.3.8          pillar_1.11.0      
[58] packrat_0.9.2       KernSmooth_2.23-26  checkmate_2.3.2    
[61] generics_0.1.4      RCurl_1.98-1.17     scales_1.4.0       
[64] xtable_1.8-4        class_7.3-23        glue_1.8.0         
[67] tools_4.5.0         xaringanExtra_0.8.0 fs_1.6.6           
[70] XML_3.99-0.18       grid_4.5.0          RJSONIO_2.0.0      
[73] cli_3.6.5           svglite_2.1.3       dplyr_1.1.4        
[76] gtable_0.3.6        fftw_1.0-9          digest_0.6.37      
[79] classInt_0.4-11     rjson_0.2.23        htmlwidgets_1.6.4  
[82] farver_2.1.2        memoise_2.0.1       htmltools_0.5.8.1  
[85] lifecycle_1.0.4     httr_1.4.7          mime_0.13          
[88] MASS_7.3-65

--- title: Detecting 55 kHz USVs with bedding subtitle: Stress-amphetamine author: <center><font size="4"><a href="https://marce10.github.io/">Marcelo Araya-Salas, PhD</a></font></center> date: <center>`r format(Sys.Date(), "%d-%m-%Y")`</center> toc: true toc-depth: 2 toc-location: left number-sections: true highlight-style: pygments format: html: df-print: kable code-fold: true code-tools: true css: qmd.css editor_options: chunk_output_type: console --- ```{r packages, message = FALSE, warning = FALSE, echo = FALSE, eval = TRUE, include = FALSE} ## add 'developer/' to packages to be installed from github x <- c("devtools", "maRce10/warbleR", "bioacoustics", "pbapply", "Rraven", "parallel", "viridis", "rfigshare", "svMisc", "readxl", "ranger", "kableExtra", "DT", "maRce10/ohun", "ggplot2") sketchy::load_packages(x) ``` ```{r functions and global parameters, eval = TRUE, echo = FALSE} warbleR_options( wav.path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/" ) opts_knit$set(root.dir = "..") options(knitr.kable.NA = '') vocal_rate <- function(X, time.window = 60, time.resolution = 1, path = ".", files = unique(X$sound.files)) { # counted if midpoint is within the segment file_durations <- warbleR::duration_sound_files(files = files, path = path) rate_df_list <- lapply(files, function(x){ X <- X[X$sound.files == x, ] X$mid.point <- (X$end + X$start) / 2 # get duration for each sound file duration <- file_durations$duration[file_durations$sound.files == x] # get position in time where to sample sampling_times <- seq(0, duration, by = time.resolution) # get the number of calls in a minute with a sliding window of 1 second across each sound file counts <- sapply(sampling_times, function(t) { # get the calls in the time window sum(X$mid.point >= t & X$mid.point < (t + time.window)) }) # add duration as last value if not the same if (sampling_times[length(sampling_times)] != duration) { sampling_times <- c(sampling_times, duration) } else { # remove last one if last value is the same as duration counts <- counts[-length(counts)] } # create a data frame with the time and the number of calls rate_df <- data.frame( sound.files = x, start = sampling_times[-length(sampling_times)], end = ifelse(sampling_times[-length(sampling_times)] + time.window > duration, duration , sampling_times[-length(sampling_times)] + time.window), counts = counts ) return(rate_df) }) results_df <- do.call(rbind, rate_df_list) results_df$duration <- results_df$end - results_df$start results_df$rate <- results_df$counts / results_df$duration return(results_df) } print_kable <- function(x) { kb <- kable(x, row.names = FALSE, digits = 4, "html") kb <- kable_styling(kb, bootstrap_options = c("striped", "hover", "condensed", "responsive")) scroll_box(kb, width = "100%") } ``` ```{r, eval = FALSE} cns <- consolidate(path = "/media/m/Backup Plus/Campo abierto CUS/", dest.path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/", parallel = 10) ``` ```{r, eval = FALSE} fix_wavs(samp.rate = 200, bit.depth = 16) ``` ```{r, eval = FALSE} ohun::feature_acoustic_data(path = .Options$warbleR$path) ssf <- ohun::split_acoustic_data(sgmt.dur = 5 * 60, cores = 10, path = .Options$warbleR$path) write.csv(ssf, file.path(.Options$warbleR$path, "/5min_clip_info.csv"), row.names = FALSE) ``` ```{r, eval = FALSE} warbleR_options(wav.path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/5min_clips/") detection <- energy_detector( path = .Options$warbleR$path, thinning = 0.5, bp = c(35, 90), smooth = 1, threshold = 2.5, hold.time = 3, min.duration = 1, max.duration = 200, cores = 5 ) saveRDS( detection, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/detection_55kHz_with_bedding_CUIDO_MATERNO.RDS" ) ``` # Random forest classification ```{r measure acoustic parameters and run random forest, eval = FALSE} detection <- readRDS("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/detection_55kHz_with_bedding_CUIDO_MATERNO.RDS") # measure spectrographic parameters spectral_parameters <- spectro_analysis(detection, bp = c(35, 85), fast = TRUE, ovlp = 70, parallel = 22) # leer el modelo rf_model <- readRDS("/home/m/Dropbox/Projects/rat_ultrasonic_vocalizations/data/processed/random_forest_55kHz_with_bedding.RDS") sapply(spectral_parameters, function(x) sum(is.na(x))) detection <- detection[!is.na(spectral_parameters$meandom), ] spectral_parameters <- spectral_parameters[!is.na(spectral_parameters$meandom), ] write.csv(spectral_parameters, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/spectral_parameters.csv", row.names = FALSE) # aplicarlo sobre las detecciones nuevas detection$class <-predict(object = rf_model, data = spectral_parameters)$predictions # remover los sonidos clasificados como ruido de fondo filtered_detection <- detection[detection$class == "true.positive", ] saveRDS(filtered_detection, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/random_forest_filtered_detection_55kHz_with_bedding.RDS") write.csv(filtered_detection, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/filtered_detection_55kHz_with_bedding.csv", row.names = FALSE) counts <- aggregate(selec ~ sound.files, filtered_detection, length) names(counts)[2] <- "55khz.call.count" write.csv(counts, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/counts_per_sound_files_detection_55kHz_with_bedding.csv", row.names = FALSE) ``` ## Summarized ### USV counts per minute ```{r} filtered_detection_55 <- readRDS("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/random_forest_filtered_detection_55kHz_with_bedding.RDS") orgf <- read.csv("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/5min_clip_info.csv") reass_detec <- reassemble_detection(detection = filtered_detection_55, Y = orgf, pb = FALSE) # counts per minute count_min <- vocal_rate(X = reass_detec, time.window = 60, time.resolution = 60, path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/", files = list.files("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/", pattern = "\\.wav$")) # convert to minute rate count_min$minute <- count_min$start / 60 + 1 wide_count_min <- reshape(count_min[, c("counts", "minute", "sound.files")], direction = "wide", idvar = "sound.files", timevar = "minute") names(wide_count_min) <- c("sound.files", paste("min", 1:max(count_min$minute))) print_kable(wide_count_min) ``` ### Minute with highest USV count ```{r} vr <- vocal_rate(X = reass_detec, time.window = 60, time.resolution = 1, path = "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/") # rate per minute vr$rate <- vr$rate * 60 # get highest per sound file sub_vr <- vr[vr$duration == 60, ] # get the row with the highest rate per sound file highes_min <- do.call(rbind, lapply(split(sub_vr, sub_vr$sound.files), function(x) x[which.max(x$rate), ])) print_kable(highes_min) ``` ```{r random forest results plots, eval = FALSE, echo = FALSE} filtered_detection_55 <- readRDS("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/random_forest_filtered_detection_55kHz_with_bedding.RDS") clip_num <- sapply(filtered_detection_55$sound.files, FUN = function(x) strsplit(x, "-")[[1]][3]) filtered_detection_55$clip_num <- as.numeric(ifelse(is.na(clip_num), 1, gsub(".wav", "", clip_num))) cons <- read.csv("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/file_names_info.csv") orgf <- read.csv("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/5min_clip_info.csv") filtered_detection_55$org <- sapply(filtered_detection_55$sound.files, function(x) orgf$original.sound.files[orgf$sound.files == x] ) # filtered_detection_55$folder <- sapply(filtered_detection_55$org, function(x) # basename(cons$original_dir[cons$old_name == x]) # ) filtered_detection_55$time <- (filtered_detection_55$end + filtered_detection_55$start) / 2 filtered_detection_55$time <- filtered_detection_55$time + ((filtered_detection_55$clip_num - 1) * 5 * 60) aggregate(time ~ clip_num, filtered_detection_55, range) aggregate(time ~ clip_num, filtered_detection_55, range) filtered_detection_55$minute <- round((filtered_detection_55$time) / 60, 0) -1 agg_55 <- aggregate(selec ~ org + minute, data = filtered_detection_55, length) ragg <- reshape(agg_55, direction = "wide", idvar = "minute", timevar = "org") ragg <- t(ragg) ragg <- as.data.frame(ragg) ragg <- ragg[-1, ] ragg$sound.files <- rownames(ragg) ragg$sound.files <- gsub("selec.", "", ragg$sound.files) fls <- list.files("/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/", pattern = "\\.wav") fls <- setdiff(fls, ragg$sound.files) flm <- as.data.frame(cbind(fls, matrix(nrow = length(fls), ncol= max(filtered_detection_55$minute) + 1))) colnames(flm) <- c("sound.files", paste("min", 0:max(filtered_detection_55$minute))) colnames(ragg) <- c(paste("min", 0:(ncol(ragg) - 2)), "sound.files") miss_cols <- setdiff(names(flm), names(ragg)) ragg_miss <- as.data.frame(matrix(nrow = nrow(ragg), ncol = length(miss_cols))) names(ragg_miss) <- miss_cols ragg_all <- cbind(ragg, ragg_miss) ragg <- rbind(ragg_all, flm) rownames(ragg) <- 1:nrow(ragg) ragg$tanda <- sapply(ragg$sound.files, function(x) basename(cons$original_dir[cons$new_name == x])) # ragg$tanda[28] <- "Tanda02" # ragg$tanda <- unlist(ragg$tanda) # colnames(ragg) <- c(paste("min", 0:max(filtered_detection_55$minute)), "sound.files", "tanda") ragg <- ragg[, c("tanda", "sound.files", paste("min", 0:max(filtered_detection_55$minute)))] ragg[is.na(ragg)] <- 0 ragg$total <- apply(ragg[, paste("min", 0:max(filtered_detection_55$minute))], 1, function(x) sum(as.numeric(x))) ragg <- ragg[order(ragg$tanda, ragg$sound.files), ] nrow(ragg) write.csv(ragg, "/media/m/Backup Plus/Campo abierto CUS/consolidated_sound_files/counts.per.minute.csv", row.names = FALSE) # summ # ary(Indometh) # data in long format # # ## long to wide (direction = "wide") requires idvar and timevar at a minimum # reshape(Indometh, direction = "wide", idvar = "Subject", timevar = "time") # # ## can also explicitly specify name of combined variable # wide <- reshape(Indometh, direction = "wide", idvar = "Subject", # timevar = "time", v.names = "conc", sep= "_") # wide # # # unstack() # filtered_detection_22 <- readRDS("./data/processed/random_forest_filtered_detection_22kHz_no_bedding.RDS") # # agg_22 <- aggregate(selec ~ sound.files, data = filtered_detection_22, length) # # wvs <- list.files(.Options$warbleR$path) # out <- lapply(wvs, function(x) { # # # cnt_22 <- agg_22$selec[agg_22$sound.files == x] # # if (length(cnt_22) == 0) cnt_22 <- 0 # cnt_55 <- agg_55$selec[agg_55$sound.files == x] # if (length(cnt_55) == 0) cnt_55 <- 0 # data.frame(sound.files = x, type_55_kHz = cnt_55) # } # ) # # output <- do.call(rbind, out) # # # cor(output$type_22_kHz, output$type_55_kHz) # # plot(output$type_22_kHz, output$type_55_kHz) # # # output$folder <- paste0("AL_TANDA", gsub(".wav|0000", "", sapply(output$sound.files, function(x) strsplit(x, "_")[[1]][7]))) # # output <- output[, c(4, 1:3)] # # # output$total <- output$type_22_kHz + output$type_55_kHz # # write.csv(output, "usv_type_count_alcohol_sensitization.csv", row.names = FALSE) ``` --- <font size="4">Session information</font> ```{r session info, echo=F, eval = TRUE} sessionInfo() ```