Dev nh

NAMESPACE

@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(BRI)
+export(BRI_)
 export(CSI)
 export(IBI)
 export(LRM)
@@ -38,6 +39,7 @@ importFrom(dplyr,rename)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
 importFrom(dplyr,summarize)
+importFrom(lubridate,ymd)
 importFrom(plyr,rbind.fill)
 importFrom(purrr,map)
 importFrom(purrr,map_dfr)

R/BRI.R

@@ -5,30 +5,26 @@
 #'   the BRI score, the more degraded the benthic community represented by the sample.
 #'
 #' @details
-#'   The BRI is the abundance weighted pollution tolerance score of the organisms present in a benthic sample. The higher
+#'   The BRI is the 4th root relative abundance weighted pollution tolerance score of the organisms present in a benthic sample. The higher
 #'   the BRI score, the more degraded the benthic community represented by the sample.
 #'
 #'   Two types of data are needed to calculate the BRI:
 #'
-#'   (1) the abundance of each species and
-#'   (2) its pollution tolerance score, P.
-#'
-#'   The P values are available for most species present in the assemblage. Only species for which P values are avialable
-#'   are used in the BRI calculations. P values showld be obtained for the appropriate habitat and from the most
-#'   up-to-date list available.
+#'   (1) the abundance of each species
+#'   (2) species-specific pollution tolerance score (aka, P Value)
 #'
-#'   The first step in the BRI calculation is to compute the 4th root of the abundance of each taxon in the sample for
-#'   which P values are available. The next step is to multiply the 4th root abundance value by the P value, for each
-#'   taxon.
+#'   Tolerance Values are stored in the Southern California SQO Species List provided with this coded. Species names are periodically
+#'   updated by benthic experts.
 #'
-#'   Next, separately sum all of the 4th roots of the abundances and all of the products of the 4th roots of abundance
-#'   and P values. Taxa that lack P values are not included in either sum. The next step is to calculate the BRI score
-#'   as:
+#'   The BRI is only calculated from those taxa with a tolerance score. The first step in the BRI calculation is to compute the 4th root
+#'   of the abundance of each taxon in the sample that have an associated tolerance score
+#'   The next step is to multiply the 4th root abundance value by the tolerance score for each taxon.
+#'   The next step is to sum all of the 4th root abundance values in a given sample.
+#'   The actual BRI score is calculated as:
 #'
 #'   \deqn{ \frac{\sum \left(\sqrt[p]{\textrm{Abundance}} \right) \times P}{\sum \sqrt[p]{\textrm{Abundance}}} }
 #'
-#'   The last step is to compare the BRI score to the BRI threshold values in Table 5 to determine the BRI category and
-#'   category score.
+#'   The last step is to convert the BRI score to condition category using the category thresholds listed in Table 5.
 #'
 #'   <Table 5. To be included in R markdown file>
 #'
@@ -64,7 +60,6 @@
 #'
 #' @export
 
-
 BRI <- function(BenthicData, logfile = file.path(getwd(), 'logs', format(Sys.time(), "%Y-%m-%d_%H:%M:%S"), 'log.txt' ), verbose = T)
 {
 
@@ -128,16 +123,16 @@ BRI <- function(BenthicData, logfile = file.path(getwd(), 'logs', format(Sys.tim
     # CASQO Technical Manual 3rd Edition Page 72 - Table 4.24
     mutate(
       Category = case_when( (Score < 39.96) ~ "Reference",
-                                (Score >= 39.96 & Score < 49.15) ~ "Low Disturbance",
-                                (Score >= 49.15 & Score < 73.27) ~ "Moderate Disturbance",
-                                (Score >= 73.27) ~ "High Disturbance"
-    )) %>%
+                            (Score >= 39.96 & Score < 49.15) ~ "Low Disturbance",
+                            (Score >= 49.15 & Score < 73.27) ~ "Moderate Disturbance",
+                            (Score >= 73.27) ~ "High Disturbance"
+      )) %>%
     # Output the BRI category score given the category for thresholds for Southern CA Marine Bays
     mutate(
       `Category Score` = case_when( (Category == "Reference") ~ 1,
-                                      (Category == "Low Disturbance") ~ 2,
-                                      (Category == "Moderate Disturbance") ~ 3,
-                                      (Category == "High Disturbance") ~ 4 )
+                                    (Category == "Low Disturbance") ~ 2,
+                                    (Category == "Moderate Disturbance") ~ 3,
+                                    (Category == "High Disturbance") ~ 4 )
     ) %>%
     dplyr::mutate(Index = "BRI")
 
@@ -150,5 +145,3 @@ BRI <- function(BenthicData, logfile = file.path(getwd(), 'logs', format(Sys.tim
 
   return(out)
 }
-
-

R/SQO BRI - generic.R

@@ -48,23 +48,34 @@
 #'        NoOrganismsPresent with 0 abundance;
 #'
 #' @usage
-#' BRI(benthic_data)
+#' BRI_(benthic_data)
 #'
 #' @examples
 #' data(benthic_sampledata) # load sample data
-#' BRI(benthic_sampledata) # see the output
+#' BRI_(benthic_sampledata) # see the output
 #'
 #' @import vegan
 #' @import reshape2
 #' @importFrom dplyr left_join filter rename select mutate group_by summarize summarise case_when
+#' @importFrom lubridate ymd
 #'
 #' @export
 
-
-BRI <- function(BenthicData) #BenthicData will need to be the species abundances for each sample in the correct format noted above and in support material
+BRI_ <- function(BenthicData) #BenthicData will need to be the species abundances for each sample in the correct format noted above and in support material
 {
+
+
+  # I put these in the import statements in the roxygen comments
+  # I think the only function that may have came from tidyverse that this function needed was ymd from lubridate
+  # If others come up we can add them as needed, or if it becomes too much we can import the whole tidyverse package (@import tidyverse)
+  # - Robert 06.10.2024
+
   #loading in packages needed to run function
-  require(tidyverse)
+  # require(tidyverse)
+
+  # It appears that this version of the function works with all lowercase column names - Robert 06.10.2024
+  names(BenthicData) <- names(BenthicData) %>% tolower()
+
   #loading in SQO species list that contains p codes, amongst other things
 
   load("data/SoCal SQO LU 4_7_20.RData")

R/SQOUnified.R

@@ -72,7 +72,13 @@ SQOUnified <- function(benthic = NULL, chem = NULL, tox = NULL, logfile = file.p
 
   # ---- Chemistry ----
   if (!is.null(chem)) {
-    chem <- chem.sqo(chem, logfile = file.path( dirname(logfile), 'Chemistry', 'log.txt' ), verbose = verbose) %>%
+
+    chemlogfile <- file.path( dirname(logfile), 'Chemistry', 'chemlog.Rmd' )
+    chemlibs <- c('tidyverse', 'DT', 'knitr', 'rmarkdown', 'SQOUnified')
+
+    init.log(chemlogfile, base.func.name = sys.call(), type = 'RMarkdown', current.time = Sys.time(), is.base.func = length(sys.calls()) == 1, verbose = verbose, libraries = chemlibs)
+
+    chem <- chem.sqo(chem, logfile = chemlogfile, verbose = verbose) %>%
       mutate(LOE = 'Chemistry') %>%
       select(StationID, LOE, Index, Score, Category, `Category Score`)
   } else {