rabbit

The goal of rabbit is to …

Use package RcppRoll to optimise claulcations of metrics clculated in wolling windows, e.g. rolling mean, var, sd etc.

Installation

You can install the development version of rabbit from GitHub with:

# install.packages("devtools")
 devtools::install_github("traitecoevo/rabbit")

Example

This is a basic example:

library(rabbit)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

super-fast version of the rolling window calculations:

example file is less than one hour of a bilby called piccolo:

df <- arrow::read_parquet("tests/testthat/raw_Pic2Jan_50000.parquet")
nrow(df)
#> [1] 50000

the new version is pretty fast:

system.time(dat <- moving_window_calcs_2(df))
#>    user  system elapsed 
#>   0.132   0.010   0.161

Identifying high sumVDBA times

sumVDBA is the best measure we have of heat-generating movement or activities:

library(ggplot2)
dat %>%
  ggplot(aes(x = time, y = sumVDBA)) +
  geom_point(size = 0.2) + theme_classic()

Now can classify all these movements based on a pre-built classifier from a zoo animal:

# load a classifcation object
MSOM = readRDS("tests/testthat/MSOM_8by7_small.rds")

# make predictions
nighttime_activities <- classify_behaviors(dat, MSOM)

we can order the activities by their estimated energy use / heat production:

nighttime_activities <- 
  nighttime_activities %>%
  filter(!is.na(behavior)&!is.na(sumVDBA)) %>%
  mutate(behavior = forcats::fct_reorder(behavior, sumVDBA, .fun = median, na.rm = TRUE))

and we can plot energy use and estimated activity through time

nighttime_activities %>%
  ggplot(aes(x = time, y = sumVDBA, col = behavior)) +
  geom_point(alpha = 0.7, size = 0.2) + theme_classic()

the data seems to at a temporal resolution of 25 readings per second which might be a lot to handle for various graphing applications. Here is a function to take the mode of the categorical variables at a given resolution.

window_in_minutes <- 1

minute_summary <- summary_by_time(nighttime_activities,
                                      time_col = "time",
                                      behavior_col = "behavior",
                                      window_minutes=window_in_minutes
                                      )

minute_summary %>%
ggplot(aes(x = window, y = mode_behavior, color = mode_behavior)) +
    geom_point(size = 3) +
    labs(
      x = "Time",
      y = "Mode Behavior",
      title = paste0("Most Common Behavior Within Every ", window_in_minutes, " Minute(s)")
    ) +
    theme_minimal() -> plotting
  print(plotting)