run styler::style_pkg(), add action for doc-and-style-r

.github/.gitignore

@@ -1 +1,2 @@
 *.html
+*.rds

.github/workflows/call-doc-and-style-r.yml

@@ -0,0 +1,10 @@
+# document and style R code using a reusable workflow
+name: call-doc-and-style-r
+# on specifies the build triggers. See more info at https://docs.github.com/en/actions/learn-github-actions/events-that-trigger-workflows
+on:
+  push:
+    branches: [main]
+jobs:
+  call-workflow:
+    uses: nmfs-fish-tools/ghactions4r/.github/workflows/doc-and-style-r.yml@main
+

R/calc_index_areas.R

@@ -44,13 +44,14 @@
 #' list("test" = c(39, 38))
 #' # run the function
 #' index <- calc_index_areas(
-#'   data, fit, grid, dir = getwd(), boundaries = list("test" = c(39, 38))
+#'   data, fit, grid,
+#'   dir = getwd(), boundaries = list("test" = c(39, 38))
 #' )
 #' # look at the index, which will be in "test" because that is what we
 #' # named the boundary
 #' index[["test"]][["index"]]
 #' }
-#' 
+#'
 calc_index_areas <- function(data,
                              fit,
                              prediction_grid,

R/data.R

@@ -33,4 +33,4 @@
 #' * South of Cape Mendocino
 #' * North of Monterey Bay
 #' * South of Monterey Bay
-"boundaries_data"
+"boundaries_data"

R/diagnose.R

@@ -81,7 +81,7 @@ diagnose <- function(dir,
     fit[["data"]][["residuals2"]] <- stats::residuals(
       fit,
       model = 2,
-      type = "mle-mvn"  
+      type = "mle-mvn"
     )
   }
 

R/filter_grid.R

@@ -6,7 +6,7 @@
 #' @return A data frame with the same structure as `grid`.
 #' @author Kelli F. Johnson
 filter_grid <- function(grid, boundary_north, boundary_south) {
-  sub_grid <- grid %>% 
+  sub_grid <- grid %>%
     dplyr::filter(latitude < boundary_north) %>%
-    dplyr::filter(latitude >= boundary_south)  
+    dplyr::filter(latitude >= boundary_south)
 }

R/filter_odfw.R

@@ -1,13 +1,11 @@
-
-
 # ##### AT SEA OBSERVER CPUE DATA PREP/FILTERING
 
-# # created by A. Whitman 
-# # updated 06/21/2021 
+# # created by A. Whitman
+# # updated 06/21/2021
 
 # # last update to exclude 2020 due to sample size.
 
-# # purpose: explore drift level data for the At Sea charter observer CPUE index 
+# # purpose: explore drift level data for the At Sea charter observer CPUE index
 
 # # load some libraries  - not sure if I'll need all these.
 
@@ -51,29 +49,29 @@
 # dat<-dat[!is.na(dat$Total_lingcod),]
 # summary(dat)
 
-# # adding a p/a variable for lingcod 
+# # adding a p/a variable for lingcod
 # dat$ling.pa<-ifelse(dat$Total_lingcod==0,0,1)
 
-# # adding a megaregion variable 
+# # adding a megaregion variable
 # dat$Megaregion<-as.factor(ifelse(dat$Port<30,"North","South"))
 
-# # positive rate of overall dataset 
-# dim(dat)[1] # total records 
-# length(which(dat$ling.pa==1)) # total positive drifts 
-# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate 
+# # positive rate of overall dataset
+# dim(dat)[1] # total records
+# length(which(dat$ling.pa==1)) # total positive drifts
+# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate
 
 # # positive rate by year without any filtering
-# pos_rate_year<-dat %>% 
+# pos_rate_year<-dat %>%
 #   dplyr::group_by(Year)%>%
-#   dplyr::summarise(No.Drifts=n(),Pos.Drifts=round((sum(ling.pa)/n()),3)) %>% 
+#   dplyr::summarise(No.Drifts=n(),Pos.Drifts=round((sum(ling.pa)/n()),3)) %>%
 #   as.data.frame()
-# pos_rate_year  
+# pos_rate_year
 
-# # ouch on 2020, might need to throw that year out, otherwise, annual rate 
+# # ouch on 2020, might need to throw that year out, otherwise, annual rate
 # # ranges from approx. 0.203 to 0.611
 
-# #looking at potential filters 
-# # drift minutes, drifts without depth, depth bins, closed season or depths 
+# #looking at potential filters
+# # drift minutes, drifts without depth, depth bins, closed season or depths
 # # look at drifts by megaregion and by depth bin, filter by percent midwater
 
 # ### FILTER: DRIFT MINUTES
@@ -91,7 +89,7 @@
 # dat<-dat[dat$DriftMinutes<=28,]
 
 # #'
-# #' 
+# #'
 # filter_odfw_drift_quantile <- function(x, percent = 0.95, verbose = FALSE) {
 #   limit <- stats::quantile(x, percent)
 #   remove <- which(x > limit)
@@ -108,110 +106,110 @@
 # }
 # test %>%
 #   dplyr::mutate(filter_drift = filter_odfw_drift_quantile(DriftMinutes))
-# # positive rate of overall dataset 
-# dim(dat)[1] # total records 
-# length(which(dat$ling.pa==1)) # total positive drifts 
-# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate 
+# # positive rate of overall dataset
+# dim(dat)[1] # total records
+# length(which(dat$ling.pa==1)) # total positive drifts
+# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate
 
 # ### FILTER: DEPTHS
 
 # summary(dat$SDEPTH_fm)
-# # remove the NAs 
+# # remove the NAs
 # dat<-dat[!is.na(dat$SDEPTH_fm),]
 # histogram(dat$SDEPTH_fm)
-# quantile(dat$SDEPTH_fm,0.99) #99 percentile is 60, so I will cut it off there 
+# quantile(dat$SDEPTH_fm,0.99) #99 percentile is 60, so I will cut it off there
 # dat<-dat[dat$SDEPTH_fm<=60,]
 
-# # positive rate of overall dataset 
-# dim(dat)[1] # total records 
-# length(which(dat$ling.pa==1)) # total positive drifts 
-# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate 
+# # positive rate of overall dataset
+# dim(dat)[1] # total records
+# length(which(dat$ling.pa==1)) # total positive drifts
+# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate
 
 # #creating a depth bin from the start depth in FMs
-# dat<- dat %>% 
-#   mutate(DepthBin=cut(dat$SDEPTH_fm,breaks = c(seq(0,60,by = 5)))) %>% 
+# dat<- dat %>%
+#   mutate(DepthBin=cut(dat$SDEPTH_fm,breaks = c(seq(0,60,by = 5)))) %>%
 #   as.data.frame()
 
-# # look at drifts by depth bin 
+# # look at drifts by depth bin
 # drifts_by_depth<-dat %>%
-#   group_by(DepthBin) %>% 
+#   group_by(DepthBin) %>%
 #   summarise(drifts=n())
 # drifts_by_depth
 # histogram(dat$DepthBin)
 
-# ### FILTER: PERCENT MIDWATER FISH 
+# ### FILTER: PERCENT MIDWATER FISH
 
 # summary(dat$percent_midwater)
 # quantile(dat$percent_midwater)  # so 50% is 0.75 midwater, and 75% is 1.0! this would be a pretty heavy filter
 # histogram(dat$percent_midwater) # lots of zeros and lots of 1.0, no clear break in between
-# length(which(dat$percent_midwater<=0.75))  
+# length(which(dat$percent_midwater<=0.75))
 
-# #just to check, are there lingcod caught on any 100% midwater trips? 
+# #just to check, are there lingcod caught on any 100% midwater trips?
 # length(which(dat$percent_midwater==1.0 & dat$ling.pa==1)) # yay
 
-# # if I filter out all the 1.0's, what do the quantiles look like then? 
+# # if I filter out all the 1.0's, what do the quantiles look like then?
 # dat_test<-dat[dat$percent_midwater<1.0,]
 # dim(dat_test)[1]
 # quantile(dat_test$percent_midwater,0.99) # that's 0.92 percent midwater, so I think that's reasonable
 # # it's also similar to the last round (which was 95% midwater)
 
 # dat<-dat[dat$percent_midwater<=0.92,]
 
-# # positive rate of overall dataset 
-# dim(dat)[1] # total records 
-# length(which(dat$ling.pa==1)) # total positive drifts 
-# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate 
+# # positive rate of overall dataset
+# dim(dat)[1] # total records
+# length(which(dat$ling.pa==1)) # total positive drifts
+# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate
 
-# #### CHECKING OPEN DEPTHS 
+# #### CHECKING OPEN DEPTHS
 # summary(dat$GF_OpenDepth)
 
-# # so lingcod is not a LL species, so those get recategorized 
+# # so lingcod is not a LL species, so those get recategorized
 # levels(dat$GF_OpenDepth)[levels(dat$GF_OpenDepth)=="<30 fm & >40 fm (LL)"] <- "<30 fm"
 # levels(dat$GF_OpenDepth)[levels(dat$GF_OpenDepth)=="<40 fm & >40 fm (LL)"] <- "<40 fm"
 # histogram(dat$GF_OpenDepth)
 # table(dat$GF_OpenDepth,dat$ling.pa > 0) # a few trips outside of 40fm
 
-# #checking if fishing in legal depths 
+# #checking if fishing in legal depths
 # #add a legal depth from the GF_OpenDepth
 # dat$legal.dep<-as.numeric(ifelse(dat$GF_OpenDepth=="All Depth",1000,substring(dat$GF_OpenDepth,2,3)))
 # dat$dep.diff<-dat$legal.dep-dat$SDEPTH_fm # so if it's negative, they were fishing in depths that were deeper than legal
 # length(which(dat$dep.diff<0))
 
-# #### FILTER: FISHING OUTSIDE LEGAL DEPTHS 
+# #### FILTER: FISHING OUTSIDE LEGAL DEPTHS
 
-# # remove drifts outside legal depths, with a buffer 
+# # remove drifts outside legal depths, with a buffer
 # dat<-dat[dat$dep.diff>-5,]
-  
-# # positive rate of overall dataset 
-# dim(dat)[1] # total records 
-# length(which(dat$ling.pa==1)) # total positive drifts 
-# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate 
+
+# # positive rate of overall dataset
+# dim(dat)[1] # total records
+# length(which(dat$ling.pa==1)) # total positive drifts
+# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate
 
 # # positive rate by year without any filtering
-# pos_rate_year<-dat %>% 
+# pos_rate_year<-dat %>%
 #   dplyr::group_by(Year)%>%
-#   dplyr::summarise(No.Drifts=n(),Pos.Drifts=sum(ling.pa),Pos.Perc=round((sum(ling.pa)/n()),3)) %>% 
+#   dplyr::summarise(No.Drifts=n(),Pos.Drifts=sum(ling.pa),Pos.Perc=round((sum(ling.pa)/n()),3)) %>%
 #   as.data.frame()
-# pos_rate_year  
+# pos_rate_year
 
-# ### FILTER - REMOVE 2020 BECAUSE OF SAMPLE SIZE 
+# ### FILTER - REMOVE 2020 BECAUSE OF SAMPLE SIZE
 
 # dat<-dat[dat$Year %in% c(2001,2003:2019),]
 
-# # positive rate of overall dataset 
-# dim(dat)[1] # total records 
-# length(which(dat$ling.pa==1)) # total positive drifts 
-# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate 
+# # positive rate of overall dataset
+# dim(dat)[1] # total records
+# length(which(dat$ling.pa==1)) # total positive drifts
+# length(which(dat$ling.pa==1))/dim(dat)[1] # positive rate
 
-# pos_rate_year<-dat %>% 
+# pos_rate_year<-dat %>%
 #   dplyr::group_by(Year)%>%
-#   dplyr::summarise(No.Drifts=n(),Pos.Drifts=sum(ling.pa),Pos.Perc=round((sum(ling.pa)/n()),3)) %>% 
+#   dplyr::summarise(No.Drifts=n(),Pos.Drifts=sum(ling.pa),Pos.Perc=round((sum(ling.pa)/n()),3)) %>%
 #   as.data.frame()
 # pos_rate_year
 
-# # okay, I think that's it for filters. 
+# # okay, I think that's it for filters.
 
-# # exporting final dataset and positive rate table 
+# # exporting final dataset and positive rate table
 
 # #write.csv(dat,"Final_filtered_ORAtSea_updated.csv",row.names = F)
 # #write.csv(pos_rate_year,"PositivesByYear_updated.csv",row.names = F)

R/format_data.R

@@ -46,7 +46,7 @@
 format_data <- function(data, ...) {
   if (all(
     c("Latitude_dd", "cpue_kg_per_ha_der", "Datetime_utc_iso") %in%
-    colnames(data)
+      colnames(data)
   )) {
     class(data) <- c("nwfscSurvey", class(data))
   }

R/format_strata.R

@@ -9,7 +9,7 @@
 #' format_strata(nwfscSurvey::GetStrata.fn("sablefish"))
 format_strata <- function(strata = nwfscSurvey::GetStrata.fn("coast"),
                           min_depth = 55,
-                          max_depth = 1280){
+                          max_depth = 1280) {
   strata_temp <- strata %>%
     tidyr::separate(name, into = c("dname", "STRATA"), sep = "_") %>%
     dplyr::group_by(STRATA) %>%

R/lookup_grid.R

@@ -50,7 +50,7 @@ lookup_grid <- function(x,
 
   out <- dplyr::mutate(
     .data = data,
-    area_km2 = {{column}},
+    area_km2 = {{ column }},
     vessel_year = "0",
     depth_scaled = scale(depth, center = mean_depth, scale = sd_depth),
     depth_scaled_squared = depth_scaled^2

R/map_base.R

@@ -27,11 +27,11 @@ map_base <- function(x_min = -155,
   # Crop the polygon for plotting and efficiency:
   # sf::st_bbox(map_data) to find the rough coordinates
   coast <- suppressWarnings(suppressMessages(
-      sf::st_crop(
-        map_data,
-        c(xmin = x_min, ymin = y_min, xmax = x_max, ymax = y_max)
-      )
-    ))
+    sf::st_crop(
+      map_data,
+      c(xmin = x_min, ymin = y_min, xmax = x_max, ymax = y_max)
+    )
+  ))
   coast_proj <- sf::st_transform(coast, crs = utm_zone_10)
   gg <- ggplot2::ggplot(coast_proj) +
     ggplot2::geom_sf() +

R/map_density.R

@@ -34,7 +34,6 @@ map_density <- function(predictions,
                         n_col = 2,
                         column_grep = "^est[2]*$",
                         tile_size = c(2000, 2000)) {
-
   column <- grep(column_grep, colnames(predictions), value = TRUE)
   predictions <- dplyr::rename(
     .data = predictions,
@@ -66,7 +65,7 @@ map_density <- function(predictions,
   )
   names(x) <- split_names
 
- gg <- map_base() +
+  gg <- map_base() +
     ggplot2::geom_tile(
       data = purrr::list_rbind(x, names_to = "year"),
       mapping = aes(x * 1000, y * 1000, fill = layer)