Migrate package pipelines from magrittr %>% to native R |>

DESCRIPTION

@@ -11,11 +11,10 @@ Description: Process and summarize DAS data files.
     and includes a PDF with the DAS data format requirements expected by the package.
 URL: https://swfsc.github.io/swfscDAS/, https://github.com/swfsc/swfscDAS/
 BugReports: https://github.com/swfsc/swfscDAS/issues/
-Depends: R (>= 4.0.0)
+Depends: R (>= 4.1)
 Imports:
     dplyr (>= 1.1.0), 
     lubridate, 
-    magrittr,
     methods, 
     parallel, 
     purrr, 

NAMESPACE

@@ -88,7 +88,6 @@ importFrom(lubridate,day)
 importFrom(lubridate,month)
 importFrom(lubridate,tz)
 importFrom(lubridate,year)
-importFrom(magrittr,"%>%")
 importFrom(methods,setOldClass)
 importFrom(parallel,clusterExport)
 importFrom(parallel,detectCores)

NEWS.md

@@ -18,6 +18,8 @@
 
 * Added the SpCodes.dat file (from [`CruzPlot`](https://github.com/SWFSC/CruzPlot)) to 'inst' to have available as a default
 
+* Changed to using R native pip, and updated package dependency to require R >= 4.1
+
 
 # swfscDAS 0.6.4
 

R/das_check.R

@@ -133,7 +133,7 @@ das_check <- function(
   x.lines <- substr(do.call(c, x.lines.list), 4, 39)
 
   message("Processing DAS file")
-  x.proc <- suppressWarnings(das_process(x)) %>%
+  x.proc <- suppressWarnings(das_process(x)) |>
     left_join(select(x, "file_das", "line_num", "idx"),
               by = c('file_das', "line_num"))
   x.proc <- as_das_df(x.proc)
@@ -188,7 +188,7 @@ das_check <- function(
 
 
   ### Check lat/lon coordinates - NA events are ignored
-  # x.proc.ll <- x.proc %>% filter(!(Event %in% c("?", 1:8)))
+  # x.proc.ll <- x.proc |> filter(!(Event %in% c("?", 1:8)))
   lat.which <- which(!between(x.proc$Lat, -90, 90))
   lon.which <- which(!between(x.proc$Lat, -180, 1800))
 
@@ -242,23 +242,23 @@ das_check <- function(
   }
 
   # Create data frame with prev_columns
-  x.proc.prev <- x.proc %>%
+  x.proc.prev <- x.proc |>
     mutate(Event_prev = lag(.data$Event),
            OnEffort_prev = lag(.data$OnEffort))
 
   # 2) All R events occur while off effort, or after a B event that occurs while off effort
-  br.r.which <- x.proc.prev %>%
+  br.r.which <- x.proc.prev |>
     filter((.data$Event == "R" & .data$OnEffort_prev & .data$Event_prev != "B") |
-             (.data$Event == "B" & .data$OnEffort_prev)) %>%
-    select("idx") %>%
-    unlist() %>%
+             (.data$Event == "B" & .data$OnEffort_prev)) |>
+    select("idx") |>
+    unlist() |>
     unname()
 
   # 3) All E events occur while on effort
-  e.which <- x.proc.prev %>%
-    filter(.data$Event == "E" & !.data$OnEffort_prev) %>%
-    select("idx") %>%
-    unlist() %>%
+  e.which <- x.proc.prev |>
+    filter(.data$Event == "E" & !.data$OnEffort_prev) |>
+    select("idx") |>
+    unlist() |>
     unname()
 
 
@@ -293,11 +293,11 @@ das_check <- function(
     Extra_data = substr(x.lines, 100, max(nchar(x.lines))),
     idx = seq_along(x.lines),
     stringsAsFactors = FALSE
-  ) %>%
-    filter(!(.data$Event %in% c("C", "*", "#"))) %>%
+  ) |>
+    filter(!(.data$Event %in% c("C", "*", "#"))) |>
     mutate(Extra_data = trimws(.data$Extra_data, which = "both"))
 
-  x.tmp.filt.data <- x.tmp.filt %>% select(starts_with("Data"))
+  x.tmp.filt.data <- x.tmp.filt |> select(starts_with("Data"))
 
   x.tmp.which <- lapply(1:ncol(x.tmp.filt.data), function(i) {
     x1 <- trimws(x.tmp.filt.data[[i]], which = "left")

R/das_chop_condition.R

@@ -179,9 +179,9 @@ das_chop_condition.das_df <- function(x, conditions, seg.min.km = 0.1,
   segdata <- data.frame(
     do.call(rbind, lapply(eff.chop.list, function(i) i[["das.df.segdata"]])),
     stringsAsFactors = FALSE
-  ) %>%
+  ) |>
     mutate(segnum = seq_along(.data$seg_idx),
-           dist = round(.data$dist, 4)) %>%
+           dist = round(.data$dist, 4)) |>
     select("segnum", "seg_idx", everything())
 
   ### Segment lengths
@@ -191,8 +191,8 @@ das_chop_condition.das_df <- function(x, conditions, seg.min.km = 0.1,
   x.eff <- data.frame(
     do.call(rbind, lapply(eff.chop.list, function(i) i[["das.df"]])),
     stringsAsFactors = FALSE
-  ) %>%
-    left_join(segdata[, c("seg_idx", "segnum")], by = "seg_idx") %>%
+  ) |>
+    left_join(segdata[, c("seg_idx", "segnum")], by = "seg_idx") |>
     select(-"dist_to_next")
 
   ### Message about segments that were combined
@@ -261,11 +261,11 @@ das_chop_condition.das_df <- function(x, conditions, seg.min.km = 0.1,
   #   even if the last segment is < seg.min.km.
   #   Because of indexing method, the last break point will still be
   #   removed to join the final two segments if necessary
-  d.pre <- das.df %>%
-    group_by(.data$effort_seg_pre) %>%
+  d.pre <- das.df |>
+    group_by(.data$effort_seg_pre) |>
     summarise(idx_start = min(.data$idx),
               idx_end = max(.data$idx),
-              dist_length = sum(.data$dist_to_next)) %>%
+              dist_length = sum(.data$dist_to_next)) |>
     slice(-n())
 
   # == 0 check is here in case seg.min.km is 0
@@ -283,15 +283,15 @@ das_chop_condition.das_df <- function(x, conditions, seg.min.km = 0.1,
   effort.seg <- rep(FALSE, nrow(das.df))
   effort.seg[cond.idx] <- TRUE
 
-  das.df <- das.df %>%
-    select(-c("effort_seg_pre", "idx")) %>%
+  das.df <- das.df |>
+    select(-c("effort_seg_pre", "idx")) |>
     mutate(seg_idx = paste(i, cumsum(effort.seg), sep = "_"))
 
 
   #------------------------------------------------------
   ### Calculate lengths of effort segments
-  das.df.dist.summ <- das.df %>%
-    group_by(.data$seg_idx) %>%
+  das.df.dist.summ <- das.df |>
+    group_by(.data$seg_idx) |>
     summarise(sum_dist = sum(.data$dist_to_next))
 
   seg.lengths <- das.df.dist.summ$sum_dist

R/das_chop_equallength.R

@@ -251,16 +251,16 @@ das_chop_equallength.das_df <- function(x,
   segdata <- data.frame(
     do.call(rbind, lapply(eff.chop.list, function(i) i[["das.df.segdata"]])),
     stringsAsFactors = FALSE
-  ) %>%
+  ) |>
     mutate(segnum = seq_along(.data$file),
-           dist = round(.data$dist, 4)) %>%
+           dist = round(.data$dist, 4)) |>
     select("segnum", everything())
 
   ### Each das data point, along with segnum
   x.eff <- data.frame(
     do.call(rbind, lapply(eff.chop.list, function(i) i[["das.df"]])),
     stringsAsFactors = FALSE
-  ) %>%
+  ) |>
     left_join(segdata[, c("seg_idx", "segnum")], by = "seg_idx")
 
   ### Message about segments with length 0

R/das_chop_section.R

@@ -80,15 +80,15 @@ das_chop_section.das_df <- function(x, conditions, distance.method = NULL,
     randpicks = NA
   )
 
-  x.summ <- x %>%
+  x.summ <- x |>
     mutate(ces_dup = duplicated(.data$cont_eff_section),
-           dist_from_prev_sect = ifelse(.data$ces_dup, .data$dist_from_prev, NA)) %>%
-    group_by(.data$cont_eff_section) %>%
+           dist_from_prev_sect = ifelse(.data$ces_dup, .data$dist_from_prev, NA)) |>
+    group_by(.data$cont_eff_section) |>
     summarise(dist_sum = sum(.data$dist_from_prev_sect, na.rm = TRUE))
 
   # Call das_chop_equallength using max section length + 1
   das_chop_equallength(
-    x %>% select(-"cont_eff_section"),
+    x |> select(-"cont_eff_section"),
     conditions = conditions,
     seg.km = max(x.summ$dist_sum) + 1, randpicks.load = randpicks.df,
     num.cores = num.cores

R/das_comments.R

@@ -59,5 +59,5 @@ das_comments.das_dfr <- function(x) {
     paste(na.omit(i), collapse = "")
   })
 
-  x.c %>% mutate(comment_str = unname(x.c.c))
+  x.c |> mutate(comment_str = unname(x.c.c))
 }

R/das_effort.R

@@ -209,7 +209,7 @@ das_effort.das_df <- function(
   # Prep for chop functions
 
   # Remove comments if specified
-  if (is.null(event.touse) & comment.drop) x <- x %>% filter(.data$Event != "C")
+  if (is.null(event.touse) & comment.drop) x <- x |> filter(.data$Event != "C")
 
   # Add index column for adding back in ? and 1:8 events, and extract those events
   x$idx_eff <- seq_len(nrow(x))
@@ -230,9 +230,9 @@ das_effort.das_df <- function(
 
   x.oneff.all <- x[x.oneff.which, ]
 
-  x.oneff <- x.oneff.all %>% filter(!(.data$Event %in% event.tmp) )
-  x.oneff.tmp <- x.oneff.all %>%
-    filter(.data$Event %in% event.tmp) %>%
+  x.oneff <- x.oneff.all |> filter(!(.data$Event %in% event.tmp) )
+  x.oneff.tmp <- x.oneff.all |>
+    filter(.data$Event %in% event.tmp) |>
     mutate(cont_eff_section = NA, dist_from_prev = NA, seg_idx = NA, segnum = NA)
 
   rownames(x.oneff) <- rownames(x.oneff.tmp) <- NULL
@@ -248,12 +248,12 @@ das_effort.das_df <- function(
     stop("Error in row numbers - please report this as an issue")
 
   # Filter for specified events, if applicable
-  if (!is.null(event.touse)) x.oneff <- x.oneff %>% filter(.data$Event %in% event.touse)
+  if (!is.null(event.touse)) x.oneff <- x.oneff |> filter(.data$Event %in% event.touse)
 
 
   # Verbosely remove remaining data without Lat/Lon/DateTime info
   if (any(is.na(x.oneff$Lat) | is.na(x.oneff$Lon) | is.na(x.oneff$DateTime))) {
-    x.nacheck <- x.oneff %>%
+    x.nacheck <- x.oneff |>
       mutate(ll_dt_na = is.na(.data$Lat) | is.na(.data$Lon) | is.na(.data$DateTime),
              eff_na = .data$ll_dt_na & (.data$Event %in% c("R", "E")),
              sight_na = .data$ll_dt_na & (.data$Event %in% c("S", "K", "M", "G", "t", "A")))
@@ -273,7 +273,7 @@ das_effort.das_df <- function(
            .print_file_line(x.nacheck$file_das, x.nacheck$line_num, which(x.nacheck$sight_na)))
 
     # Remove events with NA lat/lon/dt info
-    x.oneff <- x.oneff %>% filter(!is.na(.data$Lat) & !is.na(.data$Lon) & !is.na(.data$DateTime))
+    x.oneff <- x.oneff |> filter(!is.na(.data$Lat) & !is.na(.data$Lon) & !is.na(.data$DateTime))
     message(paste0("There were ", sum(x.nacheck$ll_dt_na), " on effort ",
                    ifelse(comment.drop, "(non-C) ", ""), "events ",
                    "with NA Lat/Lon/DateTime values that will ignored ",
@@ -309,17 +309,17 @@ das_effort.das_df <- function(
   # If specified, verbosely remove cont eff sections with length <=0.1,
   # and no sighting events
   if (seg0.drop) {
-    x.ces.summ <- x.oneff %>%
-      group_by(.data$cont_eff_section) %>%
+    x.ces.summ <- x.oneff |>
+      group_by(.data$cont_eff_section) |>
       summarise(dist_sum = sum(.data$dist_from_prev[-1]),
                 has_sight = any(c("S", "K", "M", "G", "t") %in% .data$Event),
                 line_min = min(.data$line_num))
 
-    ces.keep <- x.ces.summ %>%
-      filter(.data$has_sight | .data$dist_sum > 0.1) %>%
+    ces.keep <- x.ces.summ |>
+      filter(.data$has_sight | .data$dist_sum > 0.1) |>
       pull(cont_eff_section)
 
-    x.oneff <- x.oneff %>%
+    x.oneff <- x.oneff |>
       filter(.data$cont_eff_section %in% ces.keep)
 
     # Recalculate cont eff section index
@@ -359,17 +359,17 @@ das_effort.das_df <- function(
 
   # Add strata info to segdata as needed - easiest to do this here
   if (!is.null(strata.files)) {
-    x.strata.summ <- x.eff %>%
-      group_by(.data$segnum) %>%
+    x.strata.summ <- x.eff |>
+      group_by(.data$segnum) |>
       summarise(strata_which = unique(.data$strata_which),
                 stratum = ifelse(.data$strata_which == 0, NA,
-                                 names(strata.files)[.data$strata_which])) %>%
+                                 names(strata.files)[.data$strata_which])) |>
       select("segnum", "stratum")
     if (nrow(x.strata.summ) != nrow(segdata))
       stop("Error processing strata and segdata - please report this as an issue")
 
-    segdata <- segdata %>% left_join(x.strata.summ, by = "segnum")
-    x.eff <- x.eff %>% select(-!!c(names(strata.files), "strata_which"))
+    segdata <- segdata |> left_join(x.strata.summ, by = "segnum")
+    x.eff <- x.eff |> select(-!!c(names(strata.files), "strata_which"))
   }
 
   # Check that things are as expected
@@ -388,33 +388,33 @@ das_effort.das_df <- function(
   # Only for sightinfo groupsizes, and thus no segdata info doesn't matter
   if (nrow(x.oneff.tmp) > 0) x.eff <- bind_rows(x.eff, x.oneff.tmp)
 
-  x.eff.all <- x.eff %>%
-    arrange(.data$idx_eff) %>%
+  x.eff.all <- x.eff |>
+    arrange(.data$idx_eff) |>
     select(-"idx_eff")
 
 
   #----------------------------------------------------------------------------
   #----------------------------------------------------------------------------
   # Summarize sightings
-  sightinfo <- x.eff.all %>%
+  sightinfo <- x.eff.all |>
     left_join(select(segdata, "segnum", "mlat", "mlon"),
-              by = "segnum") %>%
-    das_sight(returnformat = "default") %>%
+              by = "segnum") |>
+    das_sight(returnformat = "default") |>
     mutate(included = (.data$Bft <= 5 & .data$OnEffort & .data$ObsStd),
-           included = ifelse(is.na(.data$included), FALSE, .data$included)) %>%
+           included = ifelse(is.na(.data$included), FALSE, .data$included)) |>
     select(-c("dist_from_prev", "cont_eff_section"))
 
   # Clean and return
-  segdata <- segdata %>% select(-"seg_idx")
+  segdata <- segdata |> select(-"seg_idx")
   # If seg0.drop, then change '0' distances to 0.1
   if (seg0.drop) {
-    segdata <- segdata %>%
+    segdata <- segdata |>
       mutate(dist = if_else(dist < 0.1, 0.1, dist))
   }
 
-  sightinfo <- sightinfo %>%
-    mutate(year = year(.data$DateTime)) %>%
-    select(-"seg_idx") %>%
+  sightinfo <- sightinfo |>
+    mutate(year = year(.data$DateTime)) |>
+    select(-"seg_idx") |>
     select("segnum", "mlat", "mlon", "Event", "DateTime", "year", everything())
 
   list(segdata = segdata, sightinfo = sightinfo, randpicks = randpicks)

R/das_effort_sight.R

@@ -70,7 +70,7 @@ das_effort_sight <- function(x.list, sp.codes, sp.events = c("S", "G", "K", "M",
 
   ### Prep
   segdata <- x.list$segdata
-  sightinfo <- x.list$sightinfo %>% filter(.data$Event %in% sp.events)
+  sightinfo <- x.list$sightinfo |> filter(.data$Event %in% sp.events)
   randpicks <- x.list$randpicks
 
   ### Processing
@@ -92,15 +92,15 @@ das_effort_sight <- function(x.list, sp.codes, sp.events = c("S", "G", "K", "M",
 
   segdata.col1 <- select(segdata, "seg_idx")
   sightinfo.forsegdata.list <- lapply(sp.codes, function(i, sightinfo, d1) {
-    d0 <- sightinfo %>%
-      filter(.data$included, .data$SpCode == i) %>%
-      group_by(.data$seg_idx) %>%
+    d0 <- sightinfo |>
+      filter(.data$included, .data$SpCode == i) |>
+      group_by(.data$seg_idx) |>
       summarise(nSI = n(),
                 ANI = sum(.data$GsSegment))
 
     names(d0) <- c("seg_idx", paste(names(d0)[-1], i, sep = "_"))
 
-    z <- full_join(d1, d0, by = "seg_idx") %>% select(-"seg_idx")
+    z <- full_join(d1, d0, by = "seg_idx") |> select(-"seg_idx")
     z[is.na(z)] <- 0
 
     z
@@ -110,12 +110,12 @@ das_effort_sight <- function(x.list, sp.codes, sp.events = c("S", "G", "K", "M",
 
 
   ### Clean up and return
-  segdata <- segdata %>%
-    left_join(sightinfo.forsegdata.df, by = "seg_idx") %>%
+  segdata <- segdata |>
+    left_join(sightinfo.forsegdata.df, by = "seg_idx") |>
     select(-"seg_idx")
 
-  sightinfo <- sightinfo %>%
-    mutate(included = ifelse(.data$SpCode %in% sp.codes, .data$included, FALSE)) %>%
+  sightinfo <- sightinfo |>
+    mutate(included = ifelse(.data$SpCode %in% sp.codes, .data$included, FALSE)) |>
     select(-c("seg_idx", "GsSegment"))
 
   list(segdata = segdata, sightinfo = sightinfo, randpicks = randpicks)