robschick / dive_eda_reproducibility

Josh's pre-post code

Quick Start Guide

To get up and rolling with the code just to see what happens, you need to do these 5 things:

1. clone the dive_eda repo from GitHub
2. make sure you have all the packages installed (see below for list)
3. In RStudio, type targets::tar_make() into the R console and run the entire analysis pipeline (it is structured to run using the targets package in R)
4. Load the output with targets::tar_load(eda_results). This will load a 12-element list that contains the summary output for each tag (note that you can view the summary manifest with targets::tar_load(eda_results_manifest).
5. View the results as a summary table of the significant results, e.g. eda_results[[7]]$df where the 7 is the index in the eda_results_manifest list

These packages are required to run the analysis pipeline:

• targets
• future
• future.batchtools
• dplyr
• lubridate
• ggplot2
• ggthemes
• stringr
• tidyr

A note on run time - as of this writing, the pipeline takes about 76 minutes to run over the 8 example tags, each of which contains approximately 14 days’ worth of diving data at 5-minute intervals.

Processing was performed and timed using a 2019 MacBook Pro with a 2.3GHz 8-Core Intel Core i9 with 64GB of RAM. This can be sped up with use of multiple cores; e.g. targets::tar_make_future(workers = 4) see targets::tar_make_future() for guidance. See the end of the document for full session info, including the R version, and R packages version.

Assuming you are in the R project, and have the targets library loaded, in Rcode, you would source this code to run the analysis:

targets::tar_make()
targets::tar_load(eda_results) 
eda_results

And that summary table of the conditional tests for an example tag (e.g., ZcTag095_DUML) looks as follows:

stat	0–6h	0–3h	3–6h	0–1h	1–2h	2–3h	3–4h	4–5h	5–6h
prop_surface
iddi_obs				0.09 (-)
prop_upward				0.09 (-)					0.08 (-)
prop_downward
prop_no_change				0.05 (+)					0.08 (+)
total_absolute
total_upward
total_downward
depth_seq			0.05 (+)						0.02 (+)
depth_diff_seq			0.06 (+)
type_seq									0.1 (+)
direction_change_seq				0.07 (-)

Table 1. Results from one example whale (ZcTag095_DUML, conditional) indicates sigificance for each test-statistic across the 9 temporal windows. Results are significant at the 0.1 level if printed in table. The conditional tests represent pre-post pairs, where the ‘pre’ window must match the context of the diving state of the animal at the time of the exposure. See Section 2.2 of the manuscript for details.

The eda_results_manifest allows you to glimpse the different animals under the different conditions:

• To get the results for unconditional tests for ZcTag069, type eda_results[[2]]$df
• To get conditional results for ZcTag097_DUML, type eda_results[[11]]$df
• The full manifest for the example tags is:

targets::tar_load(eda_results_manifest)
eda_results_manifest

##         tag conditional index
## 1  ZcTag069        TRUE     1
## 2  ZcTag069       FALSE     2
## 3  ZcTag085        TRUE     3
## 4  ZcTag085       FALSE     4
## 5  ZcTag093        TRUE     5
## 6  ZcTag093       FALSE     6
## 7  ZcTag095        TRUE     7
## 8  ZcTag095       FALSE     8
## 9  ZcTag096        TRUE     9
## 10 ZcTag096       FALSE    10
## 11 ZcTag097        TRUE    11
## 12 ZcTag097       FALSE    12

Data

The code base is set up to run using the targets package. We describe the structure of the targets-based workflow, as well as the analytical R code. We assume you have cloned the dive_eda repo and opened up the project in R. We wrote this code to work with series data that arise from a SPLASH-10 tag from WildLife Computers. In our case, the tags were programmed to record the depth every 5 minutes for approximately 2 weeks:

dive_dat <- read_csv(here::here('data/sattag', 'ZcTag084_DUML_series_20200108.csv'))

dive_dat %>% 
  select(DeployID, Day, Time, Depth, DRange)

## # A tibble: 3,878 × 5
##    DeployID Day         Time   Depth DRange
##    <chr>    <chr>       <time> <dbl>  <dbl>
##  1 ZcTag084 23-May-2019 12:50    267   27.5
##  2 ZcTag084 23-May-2019 12:55    267   27.5
##  3 ZcTag084 23-May-2019 13:00    170   26.5
##  4 ZcTag084 23-May-2019 13:05     12   12.5
##  5 ZcTag084 23-May-2019 13:10    534   54.5
##  6 ZcTag084 23-May-2019 13:15   1165  110. 
##  7 ZcTag084 23-May-2019 13:20   1360  112. 
##  8 ZcTag084 23-May-2019 13:25   1360  112. 
##  9 ZcTag084 23-May-2019 13:30   1360  112. 
## 10 ZcTag084 23-May-2019 13:35   1360  112. 
## # … with 3,868 more rows

Thus, we are concerned with the time series of values in the Depth column, which as described in the User Manual for the tags, is the center of a depth bin (in meters); DRange is the +/- of the error (also in meters). In the manuscript, we run the code over a series of animals, e.g. 8 tags; the code is structured to run over all of these tags.

References

Quick, N. J., Cioffi, W. R., Shearer, J., & Read, A. J. (2019). Mind the gap—Optimizing satellite tag settings for time series analysis of foraging dives in Cuvier’s beaked whales (Ziphius cavirostris). Animal Biotelemetry, 7(1), 5. https://doi.org/10.1186/s40317-019-0167-5

Shearer, J. M., Quick, N. J., Cioffi, W. R., Baird, R. W., Webster, D. L., Foley, H. J., Swaim, Z. T., Waples, D. M., Bell, J. T., & Read, A. J. (2019). Diving behaviour of Cuvier’s beaked whales (Ziphius cavirostris) off Cape Hatteras, North Carolina. R Soc Open Sci, 6(2), 181728. https://doi.org/10.1098/rsos.181728

R Environment

The current R environment on which this was run is here:

sessionInfo()

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] targets_0.6.0   forcats_0.5.1   stringr_1.4.0   dplyr_1.0.7    
##  [5] purrr_0.3.4     readr_2.0.2     tidyr_1.1.3     tibble_3.1.5   
##  [9] ggplot2_3.3.5   tidyverse_1.3.1 here_1.0.1     
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.7        lubridate_1.7.10  ps_1.6.0          assertthat_0.2.1 
##  [5] rprojroot_2.0.2   digest_0.6.28     utf8_1.2.2        R6_2.5.1         
##  [9] cellranger_1.1.0  backports_1.2.1   reprex_2.0.1      evaluate_0.14    
## [13] httr_1.4.2        highr_0.9         pillar_1.6.3      rlang_0.4.11     
## [17] readxl_1.3.1      rstudioapi_0.13   data.table_1.14.0 callr_3.7.0      
## [21] rmarkdown_2.9     bit_4.0.4         igraph_1.2.6      munsell_0.5.0    
## [25] broom_0.7.9       compiler_4.1.0    modelr_0.1.8      xfun_0.26        
## [29] pkgconfig_2.0.3   htmltools_0.5.1.1 tidyselect_1.1.1  codetools_0.2-18 
## [33] fansi_0.5.0       crayon_1.4.1      tzdb_0.1.2        dbplyr_2.1.1     
## [37] withr_2.4.2       grid_4.1.0        jsonlite_1.7.2    gtable_0.3.0     
## [41] lifecycle_1.0.1   DBI_1.1.1         magrittr_2.0.1    scales_1.1.1     
## [45] vroom_1.5.5       cli_3.0.1         stringi_1.7.5     fs_1.5.0         
## [49] xml2_1.3.2        ellipsis_0.3.2    generics_0.1.0    vctrs_0.3.8      
## [53] tools_4.1.0       bit64_4.0.5       glue_1.4.2        hms_1.1.1        
## [57] parallel_4.1.0    processx_3.5.2    yaml_2.2.1        colorspace_2.0-2 
## [61] rvest_1.0.1       knitr_1.36        haven_2.4.3