knitr::opts_chunk$set(echo = TRUE) library(wastdr)
drake::loadd("wastd_data") rt <- . %>% reactable::reactable( ., filterable = T, sortable = T, searchable = T, defaultColDef = reactable::colDef(html = TRUE) )
Data was accessed on r wastd_data$downloaded_on
.
We filter AnimalEncounters to those with turtles in calendar years 2016-18 and split them by live and dead turtles. We exclude tagged turtles (which can also record damages/injuries).
ani <- wastd_data$animals %>% dplyr::mutate(calendar_year = datetime %>% lubridate::year()) %>% dplyr::filter(calendar_year>=2016, calendar_year<=2018) %>% dplyr::filter(taxon=="Cheloniidae") ani_dead <- ani %>% filter_dead() ani_alive <- ani %>% filter_alive() %>% dplyr::filter(health!="na") dmg <- wastd_data$turtle_dmg %>% dplyr::filter(calendar_year>=2016, calendar_year<=2018) morph <- wastd_data$turtle_morph %>% dplyr::filter(calendar_year>=2016, calendar_year<=2018)
Both alive and dead turtles are exported to CSV and RDS (to open in R), plus damages and morphometrics for combined dead and alive animals.
dest <- "inst/reports/strandings_2016_2018" ani_alive %>% saveRDS(here::here(dest, "alive.rds")) ani_alive %>% readr::write_csv(here::here(dest, "alive.csv")) ani_dead %>% saveRDS(here::here(dest, "dead.rds")) ani_dead %>% readr::write_csv(here::here(dest, "dead.csv")) dmg %>% saveRDS(here::here(dest, "dmg.rds")) dmg %>% readr::write_csv(here::here(dest, "dmg.csv")) morph %>% saveRDS(here::here(dest, "morph.rds")) morph %>% readr::write_csv(here::here(dest, "morph.csv"))
This map provides a quick overview of stranding locations.
ani_dead %>% map_mwi()
The following table previews the contents of the CSV "ani_dead.csv".
ani_dead %>% rt()
This map provides a quick overview of rescue/non-mortalities locations.
ani_alive %>% map_mwi()
The following table previews the contents of the CSV "ani_alive.csv".
ani_alive %>% rt()
dmg %>% rt
morph %>% rt
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