data-raw/ssd-fits/DATASET.R

In poissonconsulting/ssddata: Species Sensitivity Distribution Data

#    Copyright 2021 Province of British Columbia
#
#    Licensed under the Apache License, Version 2.0 (the "License");
#    you may not use this file except in compliance with the License.
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#       http://www.apache.org/licenses/LICENSE-2.1
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#    distributed under the License is distributed on an "AS IS" BASIS,
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#    See the License for the specific language governing permissions and
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library(readr)
library(usethis)
library(dplyr)
library(tidyr)

ssd_fits <- read_csv("data-raw/ssd-fits/ssd-fits.csv", 
  col_types = cols(
    Dataset = col_character(),
    Filter = col_character(),
    Software = col_character(),
    Version = col_character(),
    Distribution = col_character(),
    PC = col_integer(),
    Estimate = col_double(),
    SE = col_double(),
    Lower = col_double(),
    Upper = col_double(),
    Reference = col_character(),
    Notes = col_character()
  )
)


# Append fit data from May 7th 2021
load("data-raw/ssd-fits/ssd-fit-data/ssddata_ssdtools_v0.3.42021-09-02burrlioz_fits_batch.RData")
load("data-raw/ssd-fits/ssd-fit-data/ssddata_ssdtools_v0.3.42021-09-02shiny_default.RData")


all_ssd_fits[[1]]$hc_out
summary(all_ssd_fits[[1]]$dist[[1]])
names(all_ssd_fits)

new_fits[[1]]$hc_out
summary(new_fits[[1]]$dist[[1]])
names(new_fits)

all_fits_add <- bind_rows(lapply(all_ssd_fits, FUN = function(x) {
  x$hc_out
}), .id = "Dataset") %>%
  dplyr::mutate(
    Notes = "llogis-gamma-lnorm model averaged fit with 10000 bootstrap iterations. Used geometric mean of multiple species (if relevant)",
    Filter = NA,
    Software = "ssdtools",
    Version = "0.3.4",
    Distribution = "averaged",
    PC = 100 - percent,
    Estimate = est,
    SE = se,
    Lower = lcl,
    Upper = ucl,
    Reference = "data-raw/ssd-fits/ssd-fit-data/ssddata_ssdtools_v0.3.42021-09-shiny_default.RData"
  ) %>%
  dplyr::select(all_of(colnames(ssd_fits)))

new_fits_add <- cbind(do.call("rbind", lapply(new_fits, FUN = function(x) {
  x$hc_out
})), burrlioz_fits) %>%
  dplyr::mutate(
    Notes = paste(
      sapply(new_fits, FUN = function(x) {
        paste(names(x$dist), collapse = "-")
      }),
      " model averaged fit with 10000 bootstrap iterations. Used geometric mean of multiple species (if relevant)"
    ),
    Software = "ssdtools",
    Version = "0.3.4",
    Distribution = "averaged",
    Estimate = est,
    SE = se,
    Lower = lcl,
    Upper = ucl,
    Reference = "data-raw/ssd-fits/ssd-fit-data/ssddata_ssdtools_v0.3.42021-09-02burrlioz_fits_batch.RData"
  ) %>%
  dplyr::select(all_of(colnames(ssd_fits)))


updated_ssd_fits <- rbind(ssd_fits, all_fits_add, new_fits_add) %>%
  arrange(Dataset, Filter, PC) %>%
  distinct()

ssd_fits <- updated_ssd_fits

use_data(ssd_fits, overwrite = TRUE)