appease the gods of linting

epiforecasts · Oct 3, 2024 · 8f4d680 · 8f4d680
1 parent f1be943
commit 8f4d680
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Showing 8 changed files with 24 additions and 23 deletions.
diff --git a/R/class-forecast-binary.R b/R/class-forecast-binary.R
@@ -23,7 +23,7 @@ as_forecast_binary <- function(data,
 #' @importFrom cli cli_abort
 #' @keywords validate-forecast-object
 assert_forecast.forecast_binary <- function(
-    forecast, forecast_type = NULL, verbose = TRUE, ...
+  forecast, forecast_type = NULL, verbose = TRUE, ...
 ) {
   forecast <- assert_forecast_generic(forecast, verbose)
   assert_forecast_type(forecast, actual = "binary", desired = forecast_type)

diff --git a/R/class-forecast-nominal.R b/R/class-forecast-nominal.R
@@ -2,7 +2,7 @@
 #' @keywords check-forecasts
 #' @importFrom checkmate assert_names assert_set_equal test_set_equal
 assert_forecast.forecast_nominal <- function(
-    forecast, forecast_type = NULL, verbose = TRUE, ...
+  forecast, forecast_type = NULL, verbose = TRUE, ...
 ) {
   forecast <- assert_forecast_generic(forecast, verbose)
   assert(check_columns_present(forecast, "predicted_label"))

diff --git a/R/class-forecast-point.R b/R/class-forecast-point.R
@@ -3,7 +3,7 @@
 #' @importFrom cli cli_abort
 #' @keywords validate-forecast-object
 assert_forecast.forecast_point <- function(
-    forecast, forecast_type = NULL, verbose = TRUE, ...
+  forecast, forecast_type = NULL, verbose = TRUE, ...
 ) {
   forecast <- assert_forecast_generic(forecast, verbose)
   assert_forecast_type(forecast, actual = "point", desired = forecast_type)

diff --git a/R/class-forecast-quantile.R b/R/class-forecast-quantile.R
@@ -94,7 +94,7 @@ score.forecast_quantile <- function(forecast, metrics = get_metrics(forecast), .
 #' @rdname assert_forecast
 #' @keywords validate-forecast-object
 assert_forecast.forecast_quantile <- function(
-    forecast, forecast_type = NULL, verbose = TRUE, ...
+  forecast, forecast_type = NULL, verbose = TRUE, ...
 ) {
   forecast <- assert_forecast_generic(forecast, verbose)
   assert_forecast_type(forecast, actual = "quantile", desired = forecast_type)
@@ -186,12 +186,13 @@ get_pit.forecast_quantile <- function(forecast, by, ...) {
   quantile_coverage <-
     forecast[, .(quantile_coverage = mean(quantile_coverage)),
              by = c(unique(c(by, "quantile_level")))]
-  quantile_coverage <- quantile_coverage[order(quantile_level),
-                                         .(
-                                           quantile_level = c(quantile_level, 1),
-                                           pit_value = diff(c(0, quantile_coverage, 1))
-                                         ),
-                                         by = c(get_forecast_unit(quantile_coverage))
+  quantile_coverage <- quantile_coverage[
+    order(quantile_level),
+    .(
+      quantile_level = c(quantile_level, 1),
+      pit_value = diff(c(0, quantile_coverage, 1))
+    ),
+    by = c(get_forecast_unit(quantile_coverage))
   ]
   return(quantile_coverage[])
 }

diff --git a/R/class-forecast-sample.R b/R/class-forecast-sample.R
@@ -2,7 +2,7 @@
 #' @rdname assert_forecast
 #' @keywords validate-forecast-object
 assert_forecast.forecast_sample <- function(
-    forecast, forecast_type = NULL, verbose = TRUE, ...
+  forecast, forecast_type = NULL, verbose = TRUE, ...
 ) {
   forecast <- assert_forecast_generic(forecast, verbose)
   assert_forecast_type(forecast, actual = "sample", desired = forecast_type)
@@ -61,10 +61,10 @@ is_forecast_sample <- function(x) {
 #' @importFrom checkmate assert_numeric
 #' @export
 as_forecast_quantile.forecast_sample <- function(
-    data,
-    probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
-    type = 7,
-    ...
+  data,
+  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
+  type = 7,
+  ...
 ) {
   forecast <- copy(data)
   assert_forecast(forecast, verbose = FALSE)
@@ -175,9 +175,10 @@ get_pit.forecast_sample <- function(forecast, by, n_replicates = 100, ...) {
   forecast <- as.data.table(forecast)
 
   # if prediction type is not quantile, calculate PIT values based on samples
-  forecast_wide <- data.table::dcast(forecast,
-                                     ... ~ paste0("InternalSampl_", sample_id),
-                                     value.var = "predicted"
+  forecast_wide <- data.table::dcast(
+    forecast,
+    ... ~ paste0("InternalSampl_", sample_id),
+    value.var = "predicted"
   )
 
   pit <- forecast_wide[, .(pit_value = pit_sample(

diff --git a/R/get-coverage.R b/R/get-coverage.R
@@ -69,7 +69,7 @@ get_coverage <- function(forecast, by = "model") {
   # convert to wide interval format and compute interval coverage --------------
   interval_forecast <- quantile_to_interval(forecast, format = "wide")
   interval_forecast[,
-                    interval_coverage := (observed <= upper) & (observed >= lower)
+    interval_coverage := (observed <= upper) & (observed >= lower)
   ][, c("lower", "upper", "observed") := NULL]
   interval_forecast[, interval_coverage_deviation :=
                       interval_coverage - interval_range / 100]

diff --git a/R/get-duplicate-forecasts.R b/R/get-duplicate-forecasts.R
@@ -17,9 +17,9 @@
 #' example <- rbind(example_quantile, example_quantile[1000:1010])
 #' get_duplicate_forecasts(example)
 get_duplicate_forecasts <- function(
-    data,
-    forecast_unit = NULL,
-    counts = FALSE
+  data,
+  forecast_unit = NULL,
+  counts = FALSE
 ) {
   assert_data_frame(data)
   data <- ensure_data.table(data)

diff --git a/R/utils.R b/R/utils.R
@@ -217,4 +217,3 @@ get_protected_columns <- function(data = NULL) {
 
   return(protected_columns)
 }
-
Original file line number	Diff line number	Diff line change
Expand Up		@@ -217,4 +217,3 @@ get_protected_columns <- function(data = NULL) {

		return(protected_columns)
		}