updating after rebase

R/arx_classifier.R

@@ -370,5 +370,3 @@ print.arx_class <- function(x, ...) {
   name <- "ARX Classifier"
   NextMethod(name = name, ...)
 }
-
-# this is a trivial change to induce a check

R/utils-latency.R

@@ -183,12 +183,11 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
   }
   itval <- epiprocess:::guess_period(c(x$time_value, end_date), "time_value")
   # get the time values we need to fill in
-  completed_time_values <-
-    x %>%
+  completed_time_values <- x %>%
     group_by(across(all_of(groups))) %>%
     summarise(
       time_value = list2(
-        time_value = seq_null_swap(from = max(time_value) + itval, to = end_date, by = itval)
+        time_value = seq_forward(from = max(time_value) + itval, to = end_date, by = itval)
       )
     ) %>%
     unnest("time_value") %>%
@@ -197,6 +196,7 @@ pad_to_end <- function(x, groups, end_date, columns_to_complete = NULL) {
   grouped_and_arranged <- x %>%
     arrange(across(all_of(c("time_value", groups)))) %>%
     group_by(across(all_of(groups)))
+
   values_to_fill <- grouped_and_arranged %>%
     slice(min(across(all_of(columns_to_complete), count_single_column)):n())
   filled_values <- values_to_fill %>%
@@ -222,7 +222,7 @@ count_single_column <- function(col) {
 
 #' seq, but returns null if from is larger
 #' @keywords internal
-seq_null_swap <- function(from, to, by) {
+seq_forward <- function(from, to, by) {
   if (from > to) {
     return(NULL)
   }

R/utils-misc.R

@@ -75,3 +75,14 @@ is_classification <- function(trainer) {
 is_regression <- function(trainer) {
   get_parsnip_mode(trainer) %in% c("regression", "unknown")
 }
+
+
+enlist <- function(...) {
+  # converted to thin wrapper around
+  rlang::dots_list(
+    ...,
+    .homonyms = "error",
+    .named = TRUE,
+    .check_assign = TRUE
+  )
+}

tests/testthat/test-get_test_data.R

@@ -1,4 +1,4 @@
-library(dplyr)
+suppressPackageStartupMessages(library(dplyr))
 test_that("return expected number of rows and returned dataset is ungrouped", {
   r <- epi_recipe(case_death_rate_subset) %>%
     step_epi_ahead(death_rate, ahead = 7) %>%

tests/testthat/test-step_epi_naomit.R

@@ -1,6 +1,6 @@
-library(dplyr)
-library(parsnip)
-library(workflows)
+suppressPackageStartupMessages(library(dplyr))
+suppressPackageStartupMessages(library(parsnip))
+suppressPackageStartupMessages(library(workflows))
 
 # Random generated dataset
 x <- tibble(