Enable new linters and tweak some suboptimal patterns

.lintr

@@ -1,4 +1,10 @@
 linters: linters_with_defaults(
     line_length_linter = line_length_linter(120L),
+    any_is_na_linter(),
+    any_duplicated_linter(),
+    outer_negation_linter(),
+    nzchar_linter(),
+    is_numeric_linter(),
+    fixed_regex_linter(),
     object_name_linter = object_name_linter(styles = c("snake_case", "symbols", "CamelCase", "SNAKE_CASE"))
   )

R/AnnDataView.R

@@ -368,7 +368,7 @@ convert_to_indices <- function(
       )
     }
     which(subset)
-  } else if (is.integer(subset) || is.numeric(subset)) {
+  } else if (is.numeric(subset)) {
     subset <- as.integer(subset)
     if (any(subset < 1 | subset > max_length)) {
       cli_abort(
@@ -385,7 +385,7 @@ convert_to_indices <- function(
       ))
     }
     indices <- match(subset, names_vector)
-    if (any(is.na(indices))) {
+    if (anyNA(indices)) {
       # nolint start: object_usage_linter
       missing_names <- subset[is.na(indices)]
       # nolint end: object_usage_linter

R/ReticulateAnnData.R

@@ -350,7 +350,7 @@ ReticulateAnnData <- R6::R6Class(
           # Set proper index names if they don't exist or are auto-generated
           if (
             is.null(rownames(obs)) ||
-              all(rownames(obs) == "") ||
+              !any(nzchar(rownames(obs))) ||
               all(rownames(obs) == as.character(seq_len(nrow(obs))))
           ) {
             py_obs$index <- reticulate::r_to_py(paste0(
@@ -360,7 +360,7 @@ ReticulateAnnData <- R6::R6Class(
           }
           if (
             is.null(rownames(var)) ||
-              all(rownames(var) == "") ||
+              !any(nzchar(rownames(var))) ||
               all(rownames(var) == as.character(seq_len(nrow(var))))
           ) {
             py_var$index <- reticulate::r_to_py(paste0(

R/as_Seurat.R

@@ -211,7 +211,7 @@ as_Seurat <- function(
   } else if (!is.null(adata$X)) {
     layers_mapping[["X"]] <- NA
   }
-  if (any(duplicated(names(layers_mapping)))) {
+  if (anyDuplicated(names(layers_mapping)) > 0) {
     cli_abort(
       "{.arg layers_mapping} or {.arg x_mapping} must not contain any duplicate names",
       "i" = "Found duplicate names: {.val {names(layers_mapping)[duplicated(names(layers_mapping))]}}"

R/as_SingleCellExperiment.R

@@ -216,7 +216,7 @@ as_SingleCellExperiment <- function(
   } else if (!is.null(adata$X)) {
     assays_mapping[["X"]] <- NA
   }
-  if (any(duplicated(names(assays_mapping)))) {
+  if (anyDuplicated(names(assays_mapping)) > 0) {
     cli_abort(
       "{.arg assays_mapping} or {.arg x_mapping} must not contain any duplicate names",
       "i" = "Found duplicate names: {.val {names(assays_mapping)[duplicated(names(assays_mapping))]}}"

R/check_requires.R

@@ -41,7 +41,7 @@ check_python_packages <- function(what, requires) {
 
   is_available <- map_lgl(requires, reticulate::py_module_available)
 
-  if (any(!is_available)) {
+  if (!all(is_available)) {
     missing <- requires[!is_available]
     # nolint start object_usage_linter
     missing_str <- format_package_list(missing)
@@ -73,7 +73,7 @@ check_python_packages <- function(what, requires) {
 check_r_packages <- function(what, requires, where) {
   is_available <- map_lgl(requires, requireNamespace, quietly = TRUE)
 
-  if (any(!is_available)) {
+  if (!all(is_available)) {
     missing <- requires[!is_available]
     # nolint start object_usage_linter
     missing_str <- format_package_list(missing)

R/from_Seurat.R

@@ -527,7 +527,7 @@ from_Seurat <- function(
       next
     }
 
-    dest_name <- gsub(paste0(assay_name, "_"), "", graph_name)
+    dest_name <- gsub(paste0(assay_name, "_"), "", graph_name, fixed = TRUE)
 
     obsp_mapping[dest_name] <- graph_name
   }

R/from_SingleCellExperiment.R

@@ -53,15 +53,15 @@ from_SingleCellExperiment <- function(
   assay_names <- SummarizedExperiment::assayNames(sce)
   if (
     length(SummarizedExperiment::assays(sce)) > 0 &&
-      (is.null(assay_names) || any(assay_names == ""))
+      (is.null(assay_names) || !all(nzchar(assay_names)))
   ) {
     if (is.null(assay_names)) {
       assay_names <- paste0(
         "assay",
         seq_along(SummarizedExperiment::assays(sce))
       )
     } else {
-      empty_names <- which(assay_names == "")
+      empty_names <- which(!nzchar(assay_names))
       assay_names[empty_names] <- paste0("assay", empty_names)
     }
 

R/generate_dataframe.R

@@ -18,5 +18,5 @@ generate_dataframe <- function(num_rows, types = names(vector_generators)) {
 
   data <- lapply(types, generate_vector, n = num_rows)
   names(data) <- types
-  as.data.frame(data)
+  list2DF(data)
 }

R/generate_dataset.R

@@ -226,14 +226,14 @@ generate_dataset <- function(
       factor(NA_character_, ordered = TRUE)
     } else if (uns_type == "scalar_logical_with_nas") {
       NA_real_
-    } else if (grepl("scalar_", uns_type)) {
-      generate_vector(1L, gsub("scalar_", "", uns_type))
-    } else if (grepl("vec_", uns_type)) {
-      generate_vector(10L, gsub("vec_", "", uns_type))
-    } else if (grepl("df_", uns_type)) {
-      generate_dataframe(10L, gsub("df_", "", uns_type))
-    } else if (grepl("mat_", uns_type)) {
-      generate_matrix(10L, 10L, gsub("mat_", "", uns_type))
+    } else if (grepl("scalar_", uns_type, fixed = TRUE)) {
+      generate_vector(1L, gsub("scalar_", "", uns_type, fixed = TRUE))
+    } else if (grepl("vec_", uns_type, fixed = TRUE)) {
+      generate_vector(10L, gsub("vec_", "", uns_type, fixed = TRUE))
+    } else if (grepl("df_", uns_type, fixed = TRUE)) {
+      generate_dataframe(10L, gsub("df_", "", uns_type, fixed = TRUE))
+    } else if (grepl("mat_", uns_type, fixed = TRUE)) {
+      generate_matrix(10L, 10L, gsub("mat_", "", uns_type, fixed = TRUE))
     } else {
       cli_abort("Unknown {.field uns} type: {.val {uns_type}}")
     }

R/utils.R

@@ -78,8 +78,8 @@ to_R_matrix <- function(mat) {
 self_name <- function(x) {
   if (is.null(names(x))) {
     x <- setNames(x, x)
-  } else if (any(names(x) == "")) {
-    is_missing <- names(x) == ""
+  } else if (!all(nzchar(names(x)))) {
+    is_missing <- !nzchar(names(x))
     names(x)[is_missing] <- x[is_missing]
   }
 

R/write_h5ad_helpers.R

@@ -61,15 +61,15 @@ write_h5ad_element <- function(
       if (length(value) == 1 && !is.matrix(value)) {
         write_h5ad_numeric_scalar
       } else if (
-        is.integer(value) && any(is.na(value)) && length(dim(value)) <= 1
+        is.integer(value) && anyNA(value) && length(dim(value)) <= 1
       ) {
         write_h5ad_nullable_integer
       } else {
         write_h5ad_dense_array
       }
     } else if (is.logical(value)) {
       # Logical values
-      if (any(is.na(value))) {
+      if (anyNA(value)) {
         write_h5ad_nullable_boolean
       } else if (length(value) == 1) {
         # Single Booleans should be written as numeric scalars
@@ -214,7 +214,7 @@ write_h5ad_dense_array <- function(
     value <- as.matrix(value)
   }
 
-  if (is.matrix(value) && any(is.na(value))) {
+  if (is.matrix(value) && anyNA(value)) {
     # is.na(value) <- NaN gets ignored
     na_indices <- is.na(value)
     value[na_indices] <- NaN

R/write_zarr_helpers.R

@@ -63,14 +63,14 @@ write_zarr_element <- function(
       # Numeric values
       if (length(value) == 1 && !is.matrix(value)) {
         write_zarr_numeric_scalar
-      } else if (is.integer(value) && any(is.na(value))) {
+      } else if (is.integer(value) && anyNA(value)) {
         write_zarr_nullable_integer
       } else {
         write_zarr_dense_array
       }
     } else if (is.logical(value)) {
       # Logical values
-      if (any(is.na(value))) {
+      if (anyNA(value)) {
         write_zarr_nullable_boolean
       } else if (length(value) == 1) {
         # Single Booleans should be written as numeric scalars

tests/testthat/test-HDF5-read.R

@@ -63,7 +63,7 @@ test_that("reading 1D sparse numeric arrays works", {
 test_that("reading 1D nullable arrays works", {
   array_1d <- read_h5ad_nullable_integer(file, "obs/IntNA")
   expect_vector(array_1d, ptype = integer(), size = 50)
-  expect_true(any(is.na(array_1d)))
+  expect_true(anyNA(array_1d))
 
   array_1d <- read_h5ad_dense_array(file, "obs/FloatNA")
   expected <- array(rep(42.42, 50))
@@ -72,11 +72,11 @@ test_that("reading 1D nullable arrays works", {
 
   array_1d <- read_h5ad_nullable_boolean(file, "obs/Bool")
   expect_vector(array_1d, ptype = logical(), size = 50)
-  expect_false(any(is.na(array_1d)))
+  expect_false(anyNA(array_1d))
 
   array_1d <- read_h5ad_nullable_boolean(file, "obs/BoolNA")
   expect_vector(array_1d, ptype = logical(), size = 50)
-  expect_true(any(is.na(array_1d)))
+  expect_true(anyNA(array_1d))
 })
 
 test_that("reading string scalars works", {

tests/testthat/test-Zarr-read.R

@@ -74,7 +74,7 @@ for (zarr_version in c("v2", "v3")) {
   test_that(paste("reading Zarr", zarr_version, "1D nullable arrays works"), {
     array_1d <- read_zarr_nullable_integer(store, "obs/IntNA")
     expect_vector(array_1d, ptype = integer(), size = 50)
-    expect_true(any(is.na(array_1d)))
+    expect_true(anyNA(array_1d))
 
     array_1d <- read_zarr_dense_array(store, "obs/FloatNA")
     expected <- array(rep(42.42, 50))
@@ -83,7 +83,7 @@ for (zarr_version in c("v2", "v3")) {
 
     array_1d <- read_zarr_nullable_boolean(store, "obs/BoolNA")
     expect_vector(array_1d, ptype = logical(), size = 50)
-    expect_true(any(is.na(array_1d)))
+    expect_true(anyNA(array_1d))
   })
 
   test_that(paste("reading Zarr", zarr_version, "string scalars works"), {

vignettes/articles/development_status.Rmd

@@ -28,7 +28,7 @@ source <- list.files(proj_root, pattern = "*.R$", recursive = TRUE)
 # look for trackstatus comments
 status_lines <- map_df(source, function(path) {
   lines <- readLines(paste0(proj_root, "/", path))
-  line_numbers <- grep("# trackstatus:", lines)
+  line_numbers <- grep("# trackstatus:", lines, fixed = TRUE)
 
   map_df(line_numbers, function(line_number) {
     tryCatch(
@@ -200,7 +200,7 @@ for (i in seq_along(data)) {
     " * To investigate: ", data[[i]]$to_investigate, "\n",
     " * To fix: ", data[[i]]$to_fix, "\n\n",
     "### Error message\n\n",
-    paste(paste0("    ", strsplit(data[[i]]$error_message, "\n")[[1]], "\n"), collapse = ""), "\n\n",
+    paste(paste0("    ", strsplit(data[[i]]$error_message, "\n", fixed = TRUE)[[1]], "\n"), collapse = ""), "\n\n",
     "### Proposed solution\n\n",
     data[[i]]$proposed_solution, "\n\n"
   )