Modernise package

.github/workflows/R-CMD-check.yaml

@@ -30,7 +30,7 @@ jobs:
       R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: r-lib/actions/setup-pandoc@v2
 

.github/workflows/test-coverage.yaml

@@ -16,7 +16,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v5
+      - uses: actions/checkout@v6
 
       - uses: r-lib/actions/setup-r@v2
         with:
@@ -56,7 +56,7 @@ jobs:
 
       - name: Upload test results
         if: failure()
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
         with:
           name: coverage-test-failures
           path: ${{ runner.temp }}/package

DESCRIPTION

@@ -5,7 +5,7 @@ Description: Provides functionality to infer trajectories from single-cell data,
   represent them into a common format, and adapt them. Other biological information
   can also be added, such as cellular grouping, RNA velocity and annotation.
   Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.
-Version: 1.2.5
+Version: 1.3.0
 Authors@R: c(
     person(
       "Robrecht", 
@@ -26,11 +26,11 @@ URL: https://github.com/dynverse/dynwrap
 BugReports: https://github.com/dynverse/dynwrap/issues
 License: MIT + file LICENSE
 LazyData: TRUE
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.3
 Roxygen: list(markdown = TRUE)
 Encoding: UTF-8
 Depends:
-  R (>= 3.0.0)
+  R (>= 4.1.0)
 Imports:
   assertthat,
   babelwhale,
@@ -40,7 +40,6 @@ Imports:
   dynparam,
   igraph,
   glue,
-  magrittr,
   Matrix,
   methods,
   purrr (>= 1.0.0),

NAMESPACE

@@ -122,10 +122,6 @@ importFrom(igraph,graph_from_data_frame)
 importFrom(igraph,is_directed)
 importFrom(igraph,layout_with_fr)
 importFrom(igraph,neighborhood)
-importFrom(magrittr,"%$%")
-importFrom(magrittr,"%<>%")
-importFrom(magrittr,set_colnames)
-importFrom(magrittr,set_rownames)
 importFrom(methods,as)
 importFrom(methods,formalArgs)
 importFrom(methods,is)

NEWS.md

@@ -1,3 +1,21 @@
+# dynwrap 1.3.0
+
+* MODERNISATION: Replace `%>%` pipes with native `|>` pipe throughout.
+
+* MODERNISATION: Remove `magrittr` dependency; replace `set_rownames()`/`set_colnames()` with base R assignments.
+
+* MODERNISATION: Replace deprecated `dplyr::do()` with `reframe()`.
+
+* MODERNISATION: Replace deprecated `mutate_at()` with `mutate(across())`.
+
+* FIX: Add missing `"id"` to `globalVariables()` to silence R CMD check NOTE about no visible binding for global variable `id`.
+
+* FIX: Replace relative cross-vignette URLs (`../create_ti_method_xxx`) with proper file-relative links to silence R CMD check NOTE.
+
+* MINOR CHANGE: Update GitHub Actions workflows to use `actions/checkout@v6` and `actions/upload-artifact@v7`.
+
+* MINOR CHANGE: Add `pkgdown.yaml` GitHub Actions workflow for website deployment.
+
 # dynwrap 1.2.5
 
 * BUG FIX `convert_definition()`: Fix for purrr having deprecated `invoke` in favour of `exec` (PR #166).

R/adapt_orient_topology.R

@@ -25,45 +25,45 @@ flip_edges <- function(
       paste0(trajectory$milestone_network$from, trajectory$milestone_network$to)
   ), msg = "All edges in the milestone_network_toflip should also be present in the trajectory milestone network")
 
-  milestone_network_toflip <- milestone_network_toflip %>%
+  milestone_network_toflip <- milestone_network_toflip |>
     select(from, to)
 
   # flip edge if from is later than to
-  trajectory$milestone_network <- trajectory$milestone_network %>%
-    left_join(milestone_network_toflip %>% mutate(flip = TRUE), c("from", "to")) %>%
+  trajectory$milestone_network <- trajectory$milestone_network |>
+    left_join(milestone_network_toflip |> mutate(flip = TRUE), c("from", "to")) |>
     mutate(flip = ifelse(is.na(flip), FALSE, flip))
 
   # flip milestone network & progressions
-  trajectory$progressions <- trajectory$progressions %>%
-    left_join(trajectory$milestone_network %>% select(from, to, flip), c("from", "to")) %>%
+  trajectory$progressions <- trajectory$progressions |>
+    left_join(trajectory$milestone_network |> select(from, to, flip), c("from", "to")) |>
     mutate(
       from2 = from,
       from = ifelse(flip, to, from),
       to = ifelse(flip, from2, to),
       percentage = ifelse(flip, 1-percentage, percentage)
-    ) %>%
+    ) |>
     select(-flip, -from2)
 
   if (!is.null(trajectory$dimred_segment_progressions)) {
     trajectory$dimred_segment_progressions <-
-      trajectory$dimred_segment_progressions %>%
-      left_join(trajectory$milestone_network %>% select(from, to, flip), c("from", "to")) %>%
+      trajectory$dimred_segment_progressions |>
+      left_join(trajectory$milestone_network |> select(from, to, flip), c("from", "to")) |>
       mutate(
         from2 = from,
         from = ifelse(flip, to, from),
         to = ifelse(flip, from2, to),
         percentage = ifelse(flip, 1-percentage, percentage)
-      ) %>%
+      ) |>
       select(-flip, -from2)
   }
 
-  trajectory$milestone_network <- trajectory$milestone_network %>%
+  trajectory$milestone_network <- trajectory$milestone_network |>
     mutate(
       from2 = from,
       from = ifelse(flip, to, from),
       to = ifelse(flip, from2, to),
       directed = TRUE
-    ) %>%
+    ) |>
     select(-flip, -from2)
 
   trajectory

R/add_regulatory_network.R

@@ -58,7 +58,7 @@ add_regulatory_network <- function(dataset, regulatory_network, regulatory_netwo
     regulatory_network_sc$target <- factor(regulatory_network_sc$target, targets)
   }
 
-  dataset <- dataset %>% extend_with(
+  dataset <- dataset |> extend_with(
     "dynwrap::with_regulatory_network",
     regulatory_network = regulatory_network,
     regulatory_network_sc = regulatory_network_sc,

R/calculate_average.R

@@ -21,16 +21,16 @@
 #' @rdname calculate_average
 calculate_average_by_group <- function(x, cell_grouping) {
   milestone_percentages <-
-    cell_grouping %>%
-    mutate(percentage = 1) %>%
+    cell_grouping |>
+    mutate(percentage = 1) |>
     rename(milestone_id = group_id)
 
   calculate_average_by_milestone_percentages(x, milestone_percentages)
 }
 
 calculate_average_by_milestone_percentages <- function(x, milestone_percentages) {
   # cast milestone percentages to matrix
-  milpct_m <- milestone_percentages %>%
+  milpct_m <- milestone_percentages |>
     reshape2::acast(cell_id ~ milestone_id, value.var = "percentage", fill = 0, fun.aggregate = sum)
 
   stat <- colSums(milpct_m)

R/calculate_geodesic_distances.R

@@ -70,7 +70,7 @@ calculate_geodesic_distances_ <- function(
     waypoint_ids <- unique(c(waypoint_ids, waypoint_milestone_percentages$waypoint_id))
     milestone_percentages <- bind_rows(
       milestone_percentages,
-      waypoint_milestone_percentages %>% rename(cell_id = waypoint_id)
+      waypoint_milestone_percentages |> rename(cell_id = waypoint_id)
     )
   }
 
@@ -80,20 +80,19 @@ calculate_geodesic_distances_ <- function(
 
   # rename milestones to avoid name conflicts between cells and milestones
   milestone_trafo_fun <- function(x) paste0("MILESTONE_", x)
-  milestone_network <- milestone_network %>% mutate(from = milestone_trafo_fun(from), to = milestone_trafo_fun(to))
-  milestone_ids <- milestone_ids %>% milestone_trafo_fun()
-  milestone_percentages <- milestone_percentages %>% mutate(milestone_id = milestone_trafo_fun(milestone_id))
-  divergence_regions <- divergence_regions %>% mutate(milestone_id = milestone_trafo_fun(milestone_id))
+  milestone_network <- milestone_network |> mutate(from = milestone_trafo_fun(from), to = milestone_trafo_fun(to))
+  milestone_ids <- milestone_ids |> milestone_trafo_fun()
+  milestone_percentages <- milestone_percentages |> mutate(milestone_id = milestone_trafo_fun(milestone_id))
+  divergence_regions <- divergence_regions |> mutate(milestone_id = milestone_trafo_fun(milestone_id))
 
   # add 'extra' divergences for transitions not in a divergence
   extra_divergences <-
-    milestone_network %>%
-    # filter(from != to) %>% # filter self edges
-    rowwise() %>%
-    mutate(in_divergence = divergence_regions %>% group_by(divergence_id) %>% summarise(match = all(c(from, to) %in% milestone_id)) %>% {any(.$match)}) %>%
-    filter(!in_divergence) %>%
-    do({tibble(divergence_id = paste0(.$from, "__", .$to), milestone_id = c(.$from, .$to), is_start = c(T, F))}) %>%
-    ungroup() %>%
+    milestone_network |>
+    # filter(from != to) |> # filter self edges
+    rowwise() |>
+    mutate(in_divergence = divergence_regions |> group_by(divergence_id) |> summarise(match = all(c(from, to) %in% milestone_id)) |> (\(x) any(x$match))()) |>
+    filter(!in_divergence) |>
+    reframe(tibble(divergence_id = paste0(from, "__", to), milestone_id = c(from, to), is_start = c(TRUE, FALSE))) |>
     distinct(divergence_id, milestone_id, .keep_all = TRUE)
 
   divergence_regions <- bind_rows(
@@ -111,32 +110,32 @@ calculate_geodesic_distances_ <- function(
   # calculate cell-cell distances for pairs of cells that are in the same transition, i.e. an edge or a divergence region
   cell_in_tent_distances <-
     map_df(divergence_ids, function(did) {
-      dir <- divergence_regions %>% filter(divergence_id == did)
-      mid <- dir %>% filter(is_start) %>% .$milestone_id
+      dir <- divergence_regions |> filter(divergence_id == did)
+      mid <- dir |> filter(is_start) |> pull(milestone_id)
       tent <- dir$milestone_id
 
       tent_nomid <- setdiff(tent, mid)
       tent_distances <- igraph::distances(mil_gr, v = mid, to = tent, mode = "out", weights = igraph::E(mil_gr)$length)
 
       relevant_pct <-
-        milestone_percentages %>%
-        group_by(cell_id) %>%
-        filter(all(milestone_id %in% tent)) %>%
+        milestone_percentages |>
+        group_by(cell_id) |>
+        filter(all(milestone_id %in% tent)) |>
         ungroup()
 
       if (nrow(relevant_pct) <= 1) {
         return(NULL)
       }
 
       scaled_dists <-
-        relevant_pct %>%
+        relevant_pct |>
         mutate(dist = percentage * tent_distances[mid, milestone_id])
 
       pct_mat <-
         bind_rows(
-          scaled_dists %>% select(from = cell_id, to = milestone_id, length = dist),
-          tent_distances %>% as.data.frame() %>% gather(from, length) %>% mutate(to = from)
-        ) %>%
+          scaled_dists |> select(from = cell_id, to = milestone_id, length = dist),
+          tent_distances |> as.data.frame() |> gather(from, length) |> mutate(to = from)
+        ) |>
         reshape2::acast(from ~ to, value.var = "length", fill = 0)
 
       wp_cells <- rownames(pct_mat)[rownames(pct_mat) %in% waypoint_ids]
@@ -150,7 +149,7 @@ calculate_geodesic_distances_ <- function(
       if (!isFALSE(directed)) {
         # calculate the sign of the distance
         # distance is negative if the cell is closer to the beginning than the waypoint
-        begin <- dir %>% filter(is_start) %>% pull(milestone_id)
+        begin <- dir |> filter(is_start) |> pull(milestone_id)
 
         signs <- sign(-outer(distances[, begin], distances[begin, ], "-"))
         signs[is.na(signs)] <- 1 # when disconnected, sign will be NaN, so that distance remains + Inf
@@ -159,10 +158,10 @@ calculate_geodesic_distances_ <- function(
         distances <- distances * signs
       }
 
-      distances <- distances %>%
-        as.matrix() %>%
-        reshape2::melt(varnames = c("from", "to"), value.name = "length") %>%
-        mutate_at(c("from", "to"), as.character) %>%
+      distances <- distances |>
+        as.matrix() |>
+        reshape2::melt(varnames = c("from", "to"), value.name = "length") |>
+        mutate(across(c("from", "to"), as.character)) |>
         filter(from != to)
 
       distances
@@ -181,26 +180,26 @@ calculate_geodesic_distances_ <- function(
       cell_in_tent_distances$from2,
       cell_in_tent_distances$to
     )
-    cell_in_tent_distances <- cell_in_tent_distances %>% select(-from2)
+    cell_in_tent_distances <- cell_in_tent_distances |> select(-from2)
     cell_in_tent_distances$length <- abs(cell_in_tent_distances$length)
 
     # add reverse edges if distance approx. zero
     # this is necessary because the direction will be taken into account
     cell_in_tent_distances <- bind_rows(
       cell_in_tent_distances,
-      cell_in_tent_distances %>%
-        filter(length <= 1e-20) %>%
-        mutate(from2 = from, from = to, to = from2) %>%
+      cell_in_tent_distances |>
+        filter(length <= 1e-20) |>
+        mutate(from2 = from, from = to, to = from2) |>
         select(-from2)
     )
   }
 
   # combine all networks into one graph
   gr <-
-    bind_rows(milestone_network, cell_in_tent_distances) %>%
-    group_by(from, to) %>%
-    summarise(length = min(length)) %>%
-    ungroup() %>%
+    bind_rows(milestone_network, cell_in_tent_distances) |>
+    group_by(from, to) |>
+    summarise(length = min(length)) |>
+    ungroup() |>
     igraph::graph_from_data_frame(directed = directed, vertices = unique(c(milestone_ids, cell_ids_trajectory, waypoint_ids)))
 
   # compute cell-to-cell distances across entire graph
@@ -209,7 +208,7 @@ calculate_geodesic_distances_ <- function(
     directed == "reverse" ~ "in",
     TRUE ~ "all"
   )
-  out <- gr %>%
+  out <- gr |>
     igraph::distances(
       v = waypoint_ids,
       to = cell_ids_trajectory,