diff --git a/DESCRIPTION b/DESCRIPTION
index 06ae2e6..97c977e 100644
--- a/DESCRIPTION
+++ b/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, 
diff --git a/NAMESPACE b/NAMESPACE
index 0b65ac8..fe19f55 100644
--- a/NAMESPACE
+++ b/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)
diff --git a/NEWS.md b/NEWS.md
index 65f38a1..9d3cdc9 100644
--- a/NEWS.md
+++ b/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
 
diff --git a/R/das_check.R b/R/das_check.R
index 6d2b1d6..912a495 100644
--- a/R/das_check.R
+++ b/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")
diff --git a/R/das_chop_condition.R b/R/das_chop_condition.R
index 9a94042..b129ce3 100644
--- a/R/das_chop_condition.R
+++ b/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$sum_dist
diff --git a/R/das_chop_equallength.R b/R/das_chop_equallength.R
index 482c74c..4ed666b 100644
--- a/R/das_chop_equallength.R
+++ b/R/das_chop_equallength.R
@@ -251,16 +251,16 @@ das_chop_equallength.das_df <- function(x,
   segdata <- data.frame(
     do.call(rbind, lapply(eff.chop.list, function(i) i[["das.df.segdata"]])),
     stringsAsFactors = FALSE
-  ) %>%
+  ) |>
     mutate(segnum = seq_along(.data$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
diff --git a/R/das_chop_section.R b/R/das_chop_section.R
index 5ed889f..b123989 100644
--- a/R/das_chop_section.R
+++ b/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
diff --git a/R/das_comments.R b/R/das_comments.R
index 393fd76..1b2fa45 100644
--- a/R/das_comments.R
+++ b/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))
 }
diff --git a/R/das_effort.R b/R/das_effort.R
index a384a63..c67fc44 100644
--- a/R/das_effort.R
+++ b/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)
diff --git a/R/das_effort_sight.R b/R/das_effort_sight.R
index 86537e5..5d411b9 100644
--- a/R/das_effort_sight.R
+++ b/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)
diff --git a/R/das_effort_strata.R b/R/das_effort_strata.R
index 9ab606a..58bfe68 100644
--- a/R/das_effort_strata.R
+++ b/R/das_effort_strata.R
@@ -69,9 +69,9 @@ das_effort_strata.das_df <- function(x, strata.files, ...) {
     strata.list.lines <-  lapply(strata.list.poly, st_cast, "LINESTRING")
 
     pts.toadd <- lapply(rev(strata.new.todo), function(i, das.df, strata.list.lines) {
-      das.df.curr <- das.df %>% slice(i-1, i)
-      pts.sf <- das.df.curr %>%
-        mutate(Lon_sf = .data$Lon, Lat_sf = .data$Lat) %>%
+      das.df.curr <- das.df |> slice(i-1, i)
+      pts.sf <- das.df.curr |>
+        mutate(Lon_sf = .data$Lon, Lat_sf = .data$Lat) |>
         st_as_sf(coords = c("Lon_sf", "Lat_sf"), crs = 4326, agr = "constant")
 
       # Convert both effort and strata poly to lines
@@ -90,19 +90,19 @@ das_effort_strata.das_df <- function(x, strata.files, ...) {
       # 'New' point will have same data as i-1 b/c we haven't made it to i yet
       idx.eff <- das.df.curr$idx_eff[1]
       df.out2 <- bind_cols(
-        das.df.curr %>% select("Event":"line_num") %>% slice(1),
-        das.df.curr %>% select("idx_eff":"strata_which") %>% slice(2)
+        das.df.curr |> select("Event":"line_num") |> slice(1),
+        das.df.curr |> select("idx_eff":"strata_which") |> slice(2)
       )
 
-      das.df.curr %>%
-        slice(1) %>%
-        bind_rows(df.out2) %>%
+      das.df.curr |>
+        slice(1) |>
+        bind_rows(df.out2) |>
         mutate(Event = c("strataE", "strataR"), Lon = das.poly.coords[1], Lat = das.poly.coords[2],
                idx_eff = c(idx.eff+0.4, idx.eff+0.5))
     }, das.df = x.strata, strata.list.lines = strata.list.lines)
 
     for (i.idx in seq_along(pts.toadd)) {
-      x.strata <- x.strata %>%
+      x.strata <- x.strata |>
         add_row(pts.toadd[[i.idx]], .before = rev(strata.new.todo)[i.idx])
     }
 
diff --git a/R/das_intersects_strata.R b/R/das_intersects_strata.R
index 00e16b6..011edf3 100644
--- a/R/das_intersects_strata.R
+++ b/R/das_intersects_strata.R
@@ -92,7 +92,7 @@ das_intersects_strata.list <- function(x, strata.files, ...) {
 
   x1 <- das_intersects_strata(x$segdata, strata.files, "mlon", "mlat")
   names.new <- base::setdiff(names(x1), names(x$segdata))
-  x1.tojoin <- x1 %>% select("segnum", !!names.new)
+  x1.tojoin <- x1 |> select("segnum", !!names.new)
 
   x2 <- left_join(x$sightinfo, x1.tojoin, by = "segnum")
   # x2 <- das_intersects_strata(x$sightinfo, strata.files, "mlat", "mlon")
diff --git a/R/das_process.R b/R/das_process.R
index ae11bcf..3dafbb3 100644
--- a/R/das_process.R
+++ b/R/das_process.R
@@ -251,22 +251,22 @@ das_process.das_dfr <- function(x, days.gap = 20, reset.event = TRUE,
 
   #----------------------------------------------------------------------------
   ### Extract GMT Offsets for dates + cruise numbers
-  x.offset <- x %>%
-    filter(.data$Event == "B") %>%
+  x.offset <- x |>
+    filter(.data$Event == "B") |>
     mutate(Date = as.Date(.data$DateTime, tz = ""),
            Cruise = as.numeric(.data$Data1),
-           OffsetGMT = as.integer(.data$Data3)) %>%
-    select("Date", "Cruise", "OffsetGMT") %>%
+           OffsetGMT = as.integer(.data$Data3)) |>
+    select("Date", "Cruise", "OffsetGMT") |>
     distinct()
 
-  x.offset.summ <- x.offset %>%
-    group_by(.data$Date, .data$Cruise) %>%
+  x.offset.summ <- x.offset |>
+    group_by(.data$Date, .data$Cruise) |>
     summarise(offsetgmt_uq = n_distinct(.data$OffsetGMT))
 
 
 
   if (any(x.offset.summ$offsetgmt_uq != 1)) {
-    x.offset.mult <- x.offset.summ %>% filter(.data$offsetgmt_uq > 1)
+    x.offset.mult <- x.offset.summ |> filter(.data$offsetgmt_uq > 1)
     warning("The following dates + cruise numbers have multiple OffsetGMT ",
             "values (Field 3 of B events), and thus the OffsetGMT column ",
             "will contain only NAs in the output data frame:\n",
@@ -429,19 +429,19 @@ das_process.das_dfr <- function(x, days.gap = 20, reset.event = TRUE,
     event.tmp <- c("?", 1:8)
     x$a_idx <- cumsum(x$Event == "A")
     x$idx <- seq_along(x$Event)
-    x.key <- x %>%
-      filter(.data$Event == "A") %>%
+    x.key <- x |>
+      filter(.data$Event == "A") |>
       select("a_idx", "DateTime", "Lat", "Lon")
-    x.tmp <- x %>%
-      filter(.data$Event %in% event.tmp) %>%
-      select(-c("DateTime", "Lat", "Lon")) %>%
-      left_join(x.key, by = "a_idx") %>%
+    x.tmp <- x |>
+      filter(.data$Event %in% event.tmp) |>
+      select(-c("DateTime", "Lat", "Lon")) |>
+      left_join(x.key, by = "a_idx") |>
       select(!!names(x))
 
-    x <- x %>%
-      filter(!(.data$Event %in% event.tmp)) %>%
-      bind_rows(x.tmp) %>%
-      arrange(.data$idx) %>%
+    x <- x |>
+      filter(!(.data$Event %in% event.tmp)) |>
+      bind_rows(x.tmp) |>
+      arrange(.data$idx) |>
       select(-c("idx", "a_idx"))
     rm(x.key, x.tmp)
   }
@@ -458,10 +458,10 @@ das_process.das_dfr <- function(x, days.gap = 20, reset.event = TRUE,
     paste0("Data", 1:12), "EffortDot", "EventNum", "file_das", "line_num"
   )
 
-  data.frame(x, tmp, stringsAsFactors = FALSE) %>%
-    mutate(Date = as.Date(.data$DateTime, tz = "")) %>%
-    left_join(x.offset, by = c("Cruise", "Date")) %>%
-    select(!!cols.tokeep) %>%
+  data.frame(x, tmp, stringsAsFactors = FALSE) |>
+    mutate(Date = as.Date(.data$DateTime, tz = "")) |>
+    left_join(x.offset, by = c("Cruise", "Date")) |>
+    select(!!cols.tokeep) |>
     as_das_df()
 }
 
diff --git a/R/das_segdata.R b/R/das_segdata.R
index a3a73ca..0454ba2 100644
--- a/R/das_segdata.R
+++ b/R/das_segdata.R
@@ -112,9 +112,9 @@ das_segdata.das_df <- function(x, conditions, segdata.method = c("avg", "maxdist
             "continuous effort section ", section.id, ":\n",
             paste(df.out1.cols, collapse  = ", "))
 
-  df.out1 <- x %>%
-    select(!!df.out1.cols) %>%
-    select(file = "file_das", everything()) %>%
+  df.out1 <- x |>
+    select(!!df.out1.cols) |>
+    select(file = "file_das", everything()) |>
     slice(n()) #use n() instead of 1 b/c some vars may be NA in first line
 
 
@@ -125,7 +125,7 @@ das_segdata.das_df <- function(x, conditions, segdata.method = c("avg", "maxdist
   )
 
   #----------------------------------------------------------------------------
-  segdata.all %>%
+  segdata.all |>
     select("seg_idx", "section_id", "section_sub_id", "file", "stlin", "endlin",
            "lat1", "lon1", "DateTime1", "lat2", "lon2", "DateTime2",
            "mlat", "mlon", "mDateTime", "dist", "year", "month", "day", "mtime",
@@ -223,11 +223,11 @@ das_segdata.das_df <- function(x, conditions, segdata.method = c("avg", "maxdist
         mlat = startpt.curr[1], mlon = startpt.curr[2], mDateTime = mean(c(startdt.curr, enddt.curr)),
         dist = 0,
         stringsAsFactors = FALSE
-      ) %>%
+      ) |>
         mutate(mtime = strftime(.data$mDateTime, format = "%H:%M:%S",
                                 tz = tz(.data$mDateTime)),
                year = year(.data$mDateTime), month = month(.data$mDateTime),
-               day = day(.data$mDateTime)) %>%
+               day = day(.data$mDateTime)) |>
         bind_cols(df.out1, conditions.list.df)
 
     } else {
@@ -326,8 +326,8 @@ das_segdata.das_df <- function(x, conditions, segdata.method = c("avg", "maxdist
 
                 } else {
                   #.segdata_aggr() throws an error if not character or numeric
-                  tmp <- k.list[[k]] %>%
-                    filter(!is.na(.data$val)) %>%
+                  tmp <- k.list[[k]] |>
+                    filter(!is.na(.data$val)) |>
                     mutate(val_frac = .data$val * .data$dist)
 
                   if (nrow(tmp) == 0) NA else sum(tmp$val_frac) / sum(tmp$dist)
@@ -341,10 +341,10 @@ das_segdata.das_df <- function(x, conditions, segdata.method = c("avg", "maxdist
           } else if (segdata.method == "maxdist") {
             conditions.list.df <- data.frame(
               lapply(names(conditions.list), function(k, k.list) {
-                tmp <- k.list[[k]] %>%
-                  filter(!is.na(.data$val)) %>%
-                  group_by(.data$val) %>%
-                  summarise(dist_sum = sum(as.numeric(.data$dist))) %>%
+                tmp <- k.list[[k]] |>
+                  filter(!is.na(.data$val)) |>
+                  group_by(.data$val) |>
+                  summarise(dist_sum = sum(as.numeric(.data$dist))) |>
                   arrange(desc(.data$dist_sum), .data$val)
 
                 if (nrow(tmp) == 0) NA else tmp$val[1]
@@ -373,11 +373,11 @@ das_segdata.das_df <- function(x, conditions, segdata.method = c("avg", "maxdist
             mlat = midpt.curr[1], mlon = midpt.curr[2], mDateTime = mean(c(startdt.curr, enddt.curr)),
             dist = seg.lengths[subseg.curr],
             stringsAsFactors = FALSE
-          ) %>%
+          ) |>
             mutate(mtime = strftime(.data$mDateTime, format = "%H:%M:%S",
                                     tz = tz(.data$mDateTime)),
                    year = year(.data$mDateTime), month = month(.data$mDateTime),
-                   day = day(.data$mDateTime)) %>%
+                   day = day(.data$mDateTime)) |>
             bind_cols(df.out1, conditions.list.df)
 
           segdata.all <- rbind(segdata.all, segdata)
@@ -426,7 +426,7 @@ das_segdata.das_df <- function(x, conditions, segdata.method = c("avg", "maxdist
 
   #--------------------------------------------------------------------------
   # Ensure longitudes are between -180 and 180, and return
-  segdata.all %>%
+  segdata.all |>
     mutate(lon1 = ifelse(.greater(.data$lon1, 180), .data$lon1 - 360, .data$lon1),
            lon1 = ifelse(.less(.data$lon1, -180), .data$lon1 + 360, .data$lon1),
            lon2 = ifelse(.greater(.data$lon2, 180), .data$lon2 - 360, .data$lon2),
diff --git a/R/das_sight.R b/R/das_sight.R
index 4037bae..a5eafb5 100644
--- a/R/das_sight.R
+++ b/R/das_sight.R
@@ -226,8 +226,8 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
   event.sight <- c("S", "K", "M", "G", "s", "k", "m", "g", "t", "p", "F")
   event.sight.info <- c("A", "?", 1:8)
 
-  sight.df <- x %>%
-    filter(.data$Event %in% c(event.sight, event.sight.info)) %>%
+  sight.df <- x |>
+    filter(.data$Event %in% c(event.sight, event.sight.info)) |>
     mutate(sight_cumsum = cumsum(.data$Event %in% event.sight))
 
 
@@ -260,8 +260,8 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
   #--------------------------------------------------------
   ### Data that is in all sighting events
   # SightNo is left as character because of entries such as "408A"
-  sight.info.all <- sight.df %>%
-    filter(.data$Event %in% event.sight) %>%
+  sight.info.all <- sight.df |>
+    filter(.data$Event %in% event.sight) |>
     mutate(SightNo = case_when(.data$Event %in% c("S", "K", "M") ~ .data$Data1,
                                .data$Event %in% c("G", "g") ~ .data$Data1,
                                .data$Event %in% c("s", "k", "m") ~ .data$Data1),
@@ -298,33 +298,33 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
                .data$Event == "g" ~ .data$Data4,
                .data$Event == "t" ~ .data$Data4,
                .data$Event == "p" ~ .data$Data4,
-               .data$Event == "F" ~ .data$Data3))) %>%
-    group_by(day(.data$DateTime)) %>%
+               .data$Event == "F" ~ .data$Data3))) |>
+    group_by(day(.data$DateTime)) |>
     mutate(SightNoDaily = paste(base::format(.data$DateTime, "%Y%m%d"),
                                 cumsum(.data$Event %in% c("S", "K", "M", "G")),
                                 sep = "_"),
            SightNoDaily = if_else(.data$Event %in% c("S", "K", "M", "G"),
-                                  .data$SightNoDaily, NA_character_)) %>%
-    ungroup() %>%
+                                  .data$SightNoDaily, NA_character_)) |>
+    ungroup() |>
     select("sight_cumsum", "SightNo", "Subgroup", "SightNoDaily",
            "Obs", "ObsStd", "Bearing", "Reticle", "DistNm")
 
 
   #--------------------------------------------------------
   ### Marine mammal (+subgroup) sightings; Events S, K, M, G
-  sight.info.skmg1 <- sight.df %>%
-    filter(.data$Event %in% c("S", "K", "M", "G")) %>%
+  sight.info.skmg1 <- sight.df |>
+    filter(.data$Event %in% c("S", "K", "M", "G")) |>
     mutate(Cue = if_else(.data$Event == "G", NA_real_, as.numeric(.data$Data3)),
            Method = as.numeric(.data$Data4),
            CalibSchool = toupper(.data$Data10),
            PhotosAerial = toupper(.data$Data11),
-           Biopsy = toupper(.data$Data12)) %>%
+           Biopsy = toupper(.data$Data12)) |>
     select("sight_cumsum", "Cue", "Method",
            "CalibSchool", "PhotosAerial", "Biopsy")
 
   # Data from A row
-  sight.info.skmg2 <- sight.df %>%
-    filter(.data$Event =="A") %>%
+  sight.info.skmg2 <- sight.df |>
+    filter(.data$Event =="A") |>
     mutate(Photos = toupper(.data$Data3),
            Birds = toupper(.data$Data4),
            nSp = unlist(
@@ -332,15 +332,15 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
                       function(d5, d6, d7, d8) {
                         sum(!is.na(c(d5, d6, d7, d8)))
                       })),
-           Mixed = .data$nSp > 1) %>%
+           Mixed = .data$nSp > 1) |>
     select("sight_cumsum", "Photos", "Birds", "nSp", "Mixed",
            SpCode1 = "Data5", SpCode2 = "Data6",
            SpCode3 = "Data7", SpCode4 = "Data8")
 
   # Data from ? row, if any
-  sight.info.skmg3 <- sight.df %>%
-    filter(.data$Event %in% c("?")) %>%
-    group_by(.data$sight_cumsum) %>%
+  sight.info.skmg3 <- sight.df |>
+    filter(.data$Event %in% c("?")) |>
+    group_by(.data$sight_cumsum) |>
     reframe(Prob = TRUE,
             SpCodeProb1 = .data$Data5, SpCodeProb2 = .data$Data6,
             SpCodeProb3 = .data$Data7, SpCodeProb4 = .data$Data8)
@@ -348,27 +348,27 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
 
   # Data from numeric events (groupsize and composition estimates)
   sight.info.skmg4 <- if (return.format == "complete") {
-    sight.df %>%
-      filter(.data$Event %in% as.character(1:8)) %>%
+    sight.df |>
+      filter(.data$Event %in% as.character(1:8)) |>
       mutate(SpPerc1 = as.numeric(.data$Data5),
              SpPerc2 = as.numeric(.data$Data6),
              SpPerc3 = as.numeric(.data$Data7),
              SpPerc4 = as.numeric(.data$Data8),
              GsSchoolBest = as.numeric(.data$Data2),
              GsSchoolHigh = as.numeric(.data$Data3),
-             GsSchoolLow = as.numeric(.data$Data4)) %>%
+             GsSchoolLow = as.numeric(.data$Data4)) |>
       select("sight_cumsum", ObsEstimate = "Data1",
              "SpPerc1", "SpPerc2", "SpPerc3", "SpPerc4",
              "GsSchoolBest", "GsSchoolHigh", "GsSchoolLow")
 
   } else {
-    sight.df %>%
-      filter(.data$Event %in% as.character(1:8)) %>%
+    sight.df |>
+      filter(.data$Event %in% as.character(1:8)) |>
       mutate(Data2 = as.numeric(.data$Data2), Data3 = as.numeric(.data$Data3),
              Data4 = as.numeric(.data$Data4), Data5 = as.numeric(.data$Data5),
              Data6 = as.numeric(.data$Data6), Data7 = as.numeric(.data$Data7),
-             Data8 = as.numeric(.data$Data8)) %>%
-      group_by(.data$sight_cumsum) %>%
+             Data8 = as.numeric(.data$Data8)) |>
+      group_by(.data$sight_cumsum) |>
       reframe(ObsEstimate = list(.data$Data1),
               SpPerc1 = mean_narm(.data$Data5),
               SpPerc2 = mean_narm(.data$Data6),
@@ -413,11 +413,11 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
     sum(duplicated(sight.info.skmg3$sight_cumsum)) == 0
   )
 
-  sight.info.skmg <- sight.info.skmg1 %>%
-    left_join(sight.info.skmg2, by = "sight_cumsum") %>%
-    left_join(sight.info.skmg3, by = "sight_cumsum") %>%
-    left_join(sight.info.skmg4, by = "sight_cumsum") %>%
-    mutate(Prob = ifelse(is.na(.data$Prob), FALSE, .data$Prob)) %>%
+  sight.info.skmg <- sight.info.skmg1 |>
+    left_join(sight.info.skmg2, by = "sight_cumsum") |>
+    left_join(sight.info.skmg3, by = "sight_cumsum") |>
+    left_join(sight.info.skmg4, by = "sight_cumsum") |>
+    mutate(Prob = ifelse(is.na(.data$Prob), FALSE, .data$Prob)) |>
     select("sight_cumsum", "Cue", "Method", "Photos", "Birds", "CalibSchool",
            "PhotosAerial", "Biopsy", "Prob", "nSp", "Mixed", "ObsEstimate",
            starts_with("SpCode"), starts_with("SpCodeProb"),
@@ -431,40 +431,40 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
 
   #--------------------------------------------------------
   ### Marine mammal (+subgroup) resights; Events s, k, m
-  sight.info.resight <- sight.df %>%
-    filter(.data$Event %in% c("s", "k", "m")) %>%
-    mutate(CourseSchool = as.numeric(.data$Data5)) %>%
+  sight.info.resight <- sight.df |>
+    filter(.data$Event %in% c("s", "k", "m")) |>
+    mutate(CourseSchool = as.numeric(.data$Data5)) |>
     select("sight_cumsum", "CourseSchool")
 
 
   #--------------------------------------------------------
   ### Turtle sightings; Events t
-  sight.info.t <- sight.df %>%
-    filter(.data$Event == "t") %>%
+  sight.info.t <- sight.df |>
+    filter(.data$Event == "t") |>
     mutate(TurtleSp = .data$Data2,
            TurtleGs = as.numeric(.data$Data5),
            TurtleJFR = .data$Data6,
            TurtleAge = toupper(.data$Data8),
-           TurtleCapt = toupper(.data$Data9)) %>%
+           TurtleCapt = toupper(.data$Data9)) |>
     select("sight_cumsum", "TurtleSp", "TurtleGs",
            "TurtleJFR", "TurtleAge", "TurtleCapt")
 
 
   #--------------------------------------------------------
   ### Pinnipeds; event p
-  sight.info.p <- sight.df %>%
-    filter(.data$Event == "p") %>%
+  sight.info.p <- sight.df |>
+    filter(.data$Event == "p") |>
     mutate(PinnipedSp = .data$Data2,
-           PinnipedGs = as.numeric(.data$Data5)) %>%
+           PinnipedGs = as.numeric(.data$Data5)) |>
     select("sight_cumsum", "PinnipedSp", "PinnipedGs")
 
 
   #--------------------------------------------------------
   ### Fishing boats; Events F
-  sight.info.f <- sight.df %>%
-    filter(.data$Event == "F") %>%
+  sight.info.f <- sight.df |>
+    filter(.data$Event == "F") |>
     mutate(BoatType = .data$Data5,
-           BoatGs = as.numeric(.data$Data6)) %>%
+           BoatGs = as.numeric(.data$Data6)) |>
     select("sight_cumsum", "BoatType", "BoatGs")
 
 
@@ -473,16 +473,16 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
 
   #--------------------------------------------------------
   ### Formatting for all options - now done for wide and complete
-  to.return <- sight.df %>%
-    filter(.data$Event %in% event.sight) %>%
+  to.return <- sight.df |>
+    filter(.data$Event %in% event.sight) |>
     select(-c("Data1", "Data2", "Data3", "Data4", "Data5", "Data6",
-              "Data7", "Data8", "Data9", "Data10", "Data11", "Data12")) %>%
-    left_join(sight.info.all, by = "sight_cumsum") %>%
-    left_join(sight.info.skmg, by = "sight_cumsum") %>%
-    left_join(sight.info.resight, by = "sight_cumsum") %>%
-    left_join(sight.info.t, by = "sight_cumsum") %>%
-    left_join(sight.info.p, by = "sight_cumsum") %>%
-    left_join(sight.info.f, by = "sight_cumsum") %>%
+              "Data7", "Data8", "Data9", "Data10", "Data11", "Data12")) |>
+    left_join(sight.info.all, by = "sight_cumsum") |>
+    left_join(sight.info.skmg, by = "sight_cumsum") |>
+    left_join(sight.info.resight, by = "sight_cumsum") |>
+    left_join(sight.info.t, by = "sight_cumsum") |>
+    left_join(sight.info.p, by = "sight_cumsum") |>
+    left_join(sight.info.f, by = "sight_cumsum") |>
     select(-"sight_cumsum")
 
 
@@ -492,9 +492,9 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
     # Split multi-species sightings into multiple rows as necessary
     to.return$idx <- seq_len(nrow(to.return))
 
-    to.return.multi <- to.return %>%
-      filter(.data$Event %in% c("S", "K", "M", "G")) %>%
-      group_by(.data$idx) %>%
+    to.return.multi <- to.return |>
+      filter(.data$Event %in% c("S", "K", "M", "G")) |>
+      group_by(.data$idx) |>
       summarise(Sp1_list = list(c(.data$SpCode1, .data$SpCodeProb1, .data$GsSpBest1,
                                   .data$GsSpHigh1, .data$GsSpLow1)),
                 Sp2_list = list(c(.data$SpCode2, .data$SpCodeProb2, .data$GsSpBest2,
@@ -502,17 +502,17 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
                 Sp3_list = list(c(.data$SpCode3, .data$SpCodeProb3, .data$GsSpBest3,
                                   .data$GsSpHigh3, .data$GsSpLow3)),
                 Sp4_list = list(c(.data$SpCode4, .data$SpCodeProb4, .data$GsSpBest4,
-                                  .data$GsSpHigh4, .data$GsSpLow4))) %>%
+                                  .data$GsSpHigh4, .data$GsSpLow4))) |>
       pivot_longer(c("Sp1_list", "Sp2_list", "Sp3_list", "Sp4_list"),
                    names_to = "sp_list_name", values_to = "sp_list",
-                   values_drop_na = TRUE) %>%
+                   values_drop_na = TRUE) |>
       mutate(SpCode = map_chr(.data$sp_list, function(i) i[1]),
              SpCodeProb = map_chr(.data$sp_list, function(i) i[2]),
              GsSpBest = as.numeric(map_chr(.data$sp_list, function(i) i[3])),
              GsSpHigh = as.numeric(map_chr(.data$sp_list, function(i) i[4])),
-             GsSpLow = as.numeric(map_chr(.data$sp_list, function(i) i[5]))) %>%
-      filter(!is.na(.data$SpCode)) %>%
-      select("idx", "SpCode", "SpCodeProb", "GsSpBest", "GsSpHigh", "GsSpLow") %>%
+             GsSpLow = as.numeric(map_chr(.data$sp_list, function(i) i[5]))) |>
+      filter(!is.na(.data$SpCode)) |>
+      select("idx", "SpCode", "SpCodeProb", "GsSpBest", "GsSpHigh", "GsSpLow") |>
       arrange(.data$idx)
 
     # Names and order of columns to return
@@ -528,16 +528,16 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
     )
 
     # Finalize return data frame, consolidating columns as possible
-    to.return <- to.return %>%
+    to.return <- to.return |>
       select(-c("SpCode1", "SpCode2", "SpCode3", "SpCode4",
                 "SpCodeProb1", "SpCodeProb2", "SpCodeProb3", "SpCodeProb4",
                 "SpPerc1", "SpPerc2", "SpPerc3", "SpPerc4",
                 "GsSpBest1", "GsSpBest2", "GsSpBest3", "GsSpBest4",
                 "GsSpHigh1", "GsSpHigh2", "GsSpHigh3", "GsSpHigh4",
-                "GsSpLow1", "GsSpLow2", "GsSpLow3", "GsSpLow4")) %>%
-      full_join(to.return.multi, by = "idx") %>%
-      arrange(.data$idx) %>%
-      select(!!sight.names) %>%
+                "GsSpLow1", "GsSpLow2", "GsSpLow3", "GsSpLow4")) |>
+      full_join(to.return.multi, by = "idx") |>
+      arrange(.data$idx) |>
+      select(!!sight.names) |>
       mutate(SpCode = case_when(.data$Event %in% c("S", "K", "M", "G") ~ .data$SpCode,
                                 .data$Event == "t" ~ .data$TurtleSp,
                                 .data$Event == "p" ~ .data$PinnipedSp,
@@ -549,7 +549,7 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
                                       .data$Event == "F" ~ .data$BoatGs,
                                       TRUE ~ NA_real_),
              GsSpBest = if_else(.data$Event %in% c("t", "p", "F"),
-                                .data$GsSchoolBest, .data$GsSpBest)) %>%
+                                .data$GsSchoolBest, .data$GsSpBest)) |>
       select(-c("TurtleSp", "TurtleGs", "PinnipedSp", "PinnipedGs",
                 "BoatType", "BoatGs"))
   }
@@ -557,7 +557,7 @@ das_sight.das_df <- function(x, return.format = c("default", "wide", "complete")
 
   #--------------------------------------------------------
   ### Calculate perp dist and return
-  to.return %>%
-    mutate(PerpDistKm = abs(sin(.data$Bearing*pi/180) * .data$DistNm) * 1.852) %>%
+  to.return |>
+    mutate(PerpDistKm = abs(sin(.data$Bearing*pi/180) * .data$DistNm) * 1.852) |>
     filter(.data$Event %in% return.events)
 }
diff --git a/R/randpicks_convert.R b/R/randpicks_convert.R
index b75dc63..48ac9af 100644
--- a/R/randpicks_convert.R
+++ b/R/randpicks_convert.R
@@ -22,12 +22,12 @@
 #' @export
 randpicks_convert <- function(x.randpicks, x.segdata, seg.km) {
   # For each continuous effort section, determine the number of segments
-  x.segdata.summ <- x.segdata %>%
-    mutate(cont_eff_sect = cumsum(.data$stlin == 1)) %>%
-    group_by(.data$cont_eff_sect) %>%
+  x.segdata.summ <- x.segdata |>
+    mutate(cont_eff_sect = cumsum(.data$stlin == 1)) |>
+    group_by(.data$cont_eff_sect) |>
     summarise(count = n(),
               dist_sum = sum(.data$dist))
-  x.summ.check <- x.segdata.summ %>%
+  x.summ.check <- x.segdata.summ |>
     mutate(dist_max = .data$count * seg.km + 0.5 * seg.km,
            dist_check = .data$dist_max >= .data$dist_sum)
 
@@ -44,14 +44,14 @@ randpicks_convert <- function(x.randpicks, x.segdata, seg.km) {
 
 
   # Prep segdata summary info for joining with randpicks
-  rand.out <- x.segdata.summ %>%
-    filter(.data$dist_sum > seg.km) %>%
-    bind_cols(x.randpicks) %>%
+  rand.out <- x.segdata.summ |>
+    filter(.data$dist_sum > seg.km) |>
+    bind_cols(x.randpicks) |>
     mutate(pos_value = ceiling(.data$RandPick * .data$count))
 
   # 'Expand' randpicks data to include all continuous effort sections
-  x.segdata.summ %>%
-    select(effort_section = "cont_eff_sect") %>%
-    left_join(rand.out, by = c("effort_section" = "cont_eff_sect")) %>%
+  x.segdata.summ |>
+    select(effort_section = "cont_eff_sect") |>
+    left_join(rand.out, by = c("effort_section" = "cont_eff_sect")) |>
     select("effort_section", randpicks = "pos_value")
 }
diff --git a/R/swfscDAS-internal.R b/R/swfscDAS-internal.R
index 7f1a8ed..2cbb78e 100644
--- a/R/swfscDAS-internal.R
+++ b/R/swfscDAS-internal.R
@@ -48,14 +48,14 @@
       identical(is.na(y.df[[1]]), is.na(y.df[[2]]))
     )
 
-    y.df <- y.df %>% select(c(1, 2))
+    y.df <- y.df |> select(c(1, 2))
     names(y.df) <- c("lon", "lat")
 
     if (anyNA(y.df$lon)) {
-      obj.list <- y.df %>%
-        mutate(na_sum = cumsum(is.na(.data$lon) & is.na(.data$lat))) %>%
-        filter(!is.na(.data$lon) & !is.na(.data$lat)) %>%
-        group_by(.data$na_sum) %>%
+      obj.list <- y.df |>
+        mutate(na_sum = cumsum(is.na(.data$lon) & is.na(.data$lat))) |>
+        filter(!is.na(.data$lon) & !is.na(.data$lat)) |>
+        group_by(.data$na_sum) |>
         summarise(temp = list(
           st_polygon(list(matrix(c(.data$lon, .data$lat), ncol = 2)))
         ))
diff --git a/R/swfscDAS-package.R b/R/swfscDAS-package.R
index baadd72..b20989c 100644
--- a/R/swfscDAS-package.R
+++ b/R/swfscDAS-package.R
@@ -14,7 +14,6 @@
 #'
 #' @importFrom dplyr add_row arrange between bind_cols bind_rows case_when desc distinct everything filter full_join group_by if_else lag left_join mutate n n_distinct reframe right_join select slice starts_with summarise ungroup
 #' @importFrom lubridate year month day tz
-#' @importFrom magrittr %>%
 #' @importFrom methods setOldClass
 #' @importFrom parallel clusterExport detectCores parLapplyLB stopCluster
 #' @importFrom readr cols col_character fwf_cols fwf_positions read_fwf
diff --git a/data-raw/das_sample.R b/data-raw/das_sample.R
index 23fe960..ba3ffe8 100644
--- a/data-raw/das_sample.R
+++ b/data-raw/das_sample.R
@@ -44,9 +44,9 @@ source("data-raw/das_sample_funcs.R")
 idx1 <- head(which(x.orig$Event == "B"), 1)
 idx2 <- tail(which(x.orig$Event == "E"), 1)
 
-x <- x.orig %>%
+x <- x.orig |>
   # Slice from first B event to before last E event
-  slice(idx1:idx2) %>%
+  slice(idx1:idx2) |>
   # Adjust dates and lat/lons
   mutate(DateTime = DateTime + days(round(runif(1, min = 5, max = 10) * 365, 0)),
          Lat = Lat + runif(1, min = 30, max = 40),
@@ -63,8 +63,8 @@ x <- x.orig %>%
 
 # Set cruise number and remove a specific comments
 stopifnot(x$Event[which(grepl("j3", x$Data3))] == "C") #x[165, ]
-x <- x %>%
-  mutate(Data1 = ifelse(Event == "B", 1000, Data1)) %>%
+x <- x |>
+  mutate(Data1 = ifelse(Event == "B", 1000, Data1)) |>
   slice(-which(grepl("j3", x$Data3)))
 
 
diff --git a/data-raw/das_sample_funcs.R b/data-raw/das_sample_funcs.R
index 433422b..133035c 100644
--- a/data-raw/das_sample_funcs.R
+++ b/data-raw/das_sample_funcs.R
@@ -70,7 +70,7 @@ raw_das_fwf <- function(x, file, data9len = 100) {
 
   ### Process output of das_read
   na.paste <- c("NA", "NANA", "NANANA")
-  x.proc <- x %>%
+  x.proc <- x |>
     mutate(EffortDot = ifelse(EffortDot, ".", " "),
            tm_hms = paste0(chr_z(hour(DateTime)), chr_z(minute(DateTime)),
                            chr_z(second(DateTime))),
diff --git a/data-raw/das_unit.R b/data-raw/das_unit.R
index c0561e5..c4774fd 100644
--- a/data-raw/das_unit.R
+++ b/data-raw/das_unit.R
@@ -34,9 +34,9 @@ names.cols <- c(
 x <- data.frame(
   Event = "B", EffortDot = TRUE, DateTime = dt1, Lat = lat1, Lon = lon1,
   Data1 = "1", Data2 = "C", Data3 = "7", Data4 = "Y", Data5 = NA, Data6 = NA, Data7 = NA, Data8 = NA, Data9 = NA
-) %>%
+) |>
   add_row(Event = "R", EffortDot = TRUE, DateTime = dt1, Lat = lat1, Lon = lon1,
-          Data1 = "1", Data2 = "C", Data3 = "7", Data4 = "Y", Data5 = NA, Data6 = NA, Data7 = NA, Data8 = NA, Data9 = NA) %>%
+          Data1 = "1", Data2 = "C", Data3 = "7", Data4 = "Y", Data5 = NA, Data6 = NA, Data7 = NA, Data8 = NA, Data9 = NA) |>
   mutate(EventNum = NA, file_das = "das_unit.das", line_num = seq_along(.))
 
 
diff --git a/tests/testthat/test-zero_sighting_events.R b/tests/testthat/test-zero_sighting_events.R
index 44eb508..d74a594 100644
--- a/tests/testthat/test-zero_sighting_events.R
+++ b/tests/testthat/test-zero_sighting_events.R
@@ -12,8 +12,8 @@ test_that("The column classes of the das_sight 'default' output are the same whe
   y.sight <- dplyr::filter(das_sight(y.proc, return.format = "default"),
                                Event == "ZZ")
 
-  y.sight0 <- y.proc %>%
-    filter(!(Event %in% event.sight)) %>%
+  y.sight0 <- y.proc |>
+    filter(!(Event %in% event.sight)) |>
     das_sight(return.format = "default")
 
   expect_identical(y.sight, y.sight0)
@@ -24,8 +24,8 @@ test_that("The column classes of the das_sight 'wide' output, are the same wheth
   y.sight <- dplyr::filter(das_sight(y.proc, return.format = "wide"),
                                Event == "ZZ")
 
-  y.sight0 <- y.proc %>%
-    filter(!(Event %in% event.sight)) %>%
+  y.sight0 <- y.proc |>
+    filter(!(Event %in% event.sight)) |>
     das_sight(return.format = "wide")
 
   expect_identical(y.sight, y.sight0)
@@ -36,8 +36,8 @@ test_that("The column classes of the das_sight 'complete' output are the same wh
   y.sight <- dplyr::filter(das_sight(y.proc, return.format = "complete"),
                                Event == "ZZ")
 
-  y.sight0 <- y.proc %>%
-    filter(!(Event %in% event.sight)) %>%
+  y.sight0 <- y.proc |>
+    filter(!(Event %in% event.sight)) |>
     das_sight(return.format = "complete")
 
   expect_identical(y.sight, y.sight0)
diff --git a/vignettes/swfscDAS.Rmd b/vignettes/swfscDAS.Rmd
index b06a62f..941a552 100644
--- a/vignettes/swfscDAS.Rmd
+++ b/vignettes/swfscDAS.Rmd
@@ -71,9 +71,9 @@ table(y.proc$Event)
 table(y.proc$Bft)
 
 # Filter for R and E events to extract lat/lon points
-y.proc %>% 
-  filter(Event %in% c("R", "E")) %>% 
-  select(Event, Lat, Lon, Cruise, Mode, EffType) %>% 
+y.proc |> 
+  filter(Event %in% c("R", "E")) |> 
+  select(Event, Lat, Lon, Cruise, Mode, EffType) |> 
   head()
 ```
 
@@ -83,18 +83,18 @@ The `swfscDAS` package does contain specific functions for extracting and/or sum
 
 ```{r sight}
 y.sight <- das_sight(y.proc, return.format = "default")
-y.sight %>% 
-  select(Event, SightNo:PerpDistKm) %>% 
+y.sight |> 
+  select(Event, SightNo:PerpDistKm) |> 
   glimpse()
 
 y.sight.wide <- das_sight(y.proc, return.format = "wide")
-y.sight.wide %>% 
-  select(Event, SightNo:PerpDistKm) %>% 
+y.sight.wide |> 
+  select(Event, SightNo:PerpDistKm) |> 
   glimpse()
 
 y.sight.complete <- das_sight(y.proc, return.format = "complete")
-y.sight.complete %>% 
-  select(Event, SightNo:PerpDistKm) %>% 
+y.sight.complete |> 
+  select(Event, SightNo:PerpDistKm) |> 
   glimpse()
 ```
 
@@ -104,7 +104,7 @@ You can also easily filter or subset the sighting data for the desired event cod
 y.sight.sg <- das_sight(y.proc, return.events = c("S", "G"))
 
 # Note that this is equivalent to:
-y.sight.sg2 <- das_sight(y.proc) %>% filter(Event %in% c("S", "G"))
+y.sight.sg2 <- das_sight(y.proc) |> filter(Event %in% c("S", "G"))
 ```
 
 ## Effort
@@ -173,6 +173,6 @@ In addition, you can use `das_comments` to generate comment strings. This is par
 y.comm <- das_comments(y.proc)
 glimpse(select(y.comm, Event, line_num, comment_str))
 
-y.comm %>% 
+y.comm |> 
   filter(str_detect(comment_str, "gear")) #Could also use grepl() here
 ```