In FlukeAndFeather/breath: Analyze whale breathing behavior

knitr::opts_chunk$set(echo = FALSE,
                      warning = FALSE)
library(breath)
library(tidyverse)

Read data from the new database and find breaths/dives

goldcon <- DBI::dbConnect(
    RSQLite::SQLite(),
    "/Volumes/COPYCATSdat/CATS/Database/gold.sqlite"
  )
depid <- "bb190304-18" 
bb190304_18_data <- tbl(goldcon, "prh") %>% 
  filter(depid == {{ depid }}) %>% 
  select(dt0 = dt_lcl,
         p = depth) %>% 
  collect() %>% 
  mutate(dt = as.POSIXct(dt0, tz = "Etc/GMT+3", origin = "1970-01-01")) %>% 
  select(-dt0)
bb190304_18_fs <- 10

bb190304_18_1Hz <- list(
  data = bb190304_18_data,
  fs = bb190304_18_fs
) %>% 
  smooth_p(1) %>% 
  decimate(1)

bb190304_18_dives <- find_breaths_dives(
  bb190304_18_1Hz,
  interval = 10,
  surface = 2,
  ibi_thr = 300
)

Plot a few dives (note: no lunges because I haven't imported them yet)

summarize_dives(bb190304_18_dives)
plot_dive_event(bb190304_18_dives, diveid = 2, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 5, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 10, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 15, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 20, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 25, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 30, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 35, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 40, buffer = 30)
plot_dive_event(bb190304_18_dives, diveid = 43, buffer = 30)

From Mar 04 20:33 until the end of the deployment this animal was in a travel mode taking shallow, brief dives (none exceeding five minutes). What's the distribution of breathing rates in 15 minute intervals?

travel_start <- ymd_hm("2019-03-04 20:33", tz = tz(bb190304_18_dives$data$dt))
breathing_rate <- bb190304_18_dives$data %>% 
  filter(dt >= travel_start) %>% 
  mutate(interval = floor(as.numeric(dt - min(dt), units = "mins") / 15)) %>% 
  group_by(interval) %>% 
  summarize(n_breaths = sum(is_breath)) %>% 
  ungroup() %>% 
  slice(1:(nrow(.) - 1)) %>% 
  mutate(breaths_hr = n_breaths * 4)
ggplot(breathing_rate, aes(x = breaths_hr)) +
  geom_histogram(binwidth = 10, boundary = 0, color = "black", fill = "white") +
  labs(x = "Breaths per hour",
       y = "Count of 15-min intervals") +
  theme_minimal()