lint: formatting

Author: dshemetov

R/forecasters/data_transforms.R

@@ -153,7 +153,6 @@ extend_ahead <- function(epi_data, ahead) {
   return(list(epi_data, effective_ahead))
 }
 
-
 #' get the Taylor expansion coefficients for a vector of values
 #' @param values the vector of values to interpolate
 #' @param degree the degree of the polynomial
@@ -233,7 +232,6 @@ calculate_whitening_params <- function(
   return(learned_params)
 }
 
-
 #' scale so that every data source has the same 95th quantile
 data_whitening <- function(epi_data, colname, learned_params, nonlin_method = c("quart_root", "none"), join_cols = NULL) {
   if (is.null(learned_params)) {
@@ -273,7 +271,6 @@ data_coloring <- function(epi_data, colname, learned_params, nonlin_method = c("
   res %>% select(-ends_with("_center"), -ends_with("_scale"))
 }
 
-
 #' the distance between two integers/dates, mod m e.g. mod_dist(1,9,10) = 2
 mod_dist <- function(a, b, m) {
   pmin(as.integer(a - b) %% m, as.integer(b - a) %% m)
@@ -354,7 +351,6 @@ climate_median <- function(epi_data, target, ahead, window_size = 3, recent_wind
     )
 }
 
-
 #' add the first principal component for each season_week to epi_data, shifted
 #' by ahead
 #' @description

R/forecasters/epipredict_utilities.R

@@ -154,9 +154,6 @@ get_oversized_test_data <- function(full_data, test_data_interval, preproc, sour
     arrange(time_value)
 }
 
-
-
-
 epi_as_of <- function(epi_dataframe) {
   attributes(epi_dataframe)$metadata$as_of
 }

R/forecasters/forecaster_baseline_linear.R

@@ -8,7 +8,6 @@ forecaster_baseline_linear <- function(epi_data, ahead, log = FALSE, sort = FALS
     left_join(population_data, by = "geo_value") %>%
     mutate(value = value / population * 10**5)
 
-
   if (log) {
     df_processed <- df_processed %>% mutate(value = log(value))
   }

R/forecasters/forecaster_flatline.R

@@ -58,7 +58,6 @@ flatline_fc <- function(epi_data,
   if (is.null(true_forecast_date)) {
     true_forecast_date <- max(epi_data$time_value)
   }
-  res$predictions
   pred <- format_storage(res$predictions, true_forecast_date)
   pred %<>% mutate(forecast_date = true_forecast_date)
   # (geo_value, forecast_date, target_end_date, quantile, value)

R/forecasters/formatters.R

@@ -14,9 +14,7 @@
 #' @export
 format_storage <- function(pred, true_forecast_date, target_end_date) {
   pred %>%
-    mutate(
-      .dstn = nested_quantiles(.pred_distn)
-    ) %>%
+    mutate(.dstn = nested_quantiles(.pred_distn)) %>%
     unnest(.dstn) %>%
     select(-any_of(c(".pred_distn", ".pred", "time_value"))) %>%
     rename(quantile = quantile_levels, value = values) %>%

scripts/covid_hosp_prod.R

@@ -1,6 +1,4 @@
 # The COVID Hospitalization Production Forecasting Pipeline.
-#
-
 source("scripts/targets-common.R")
 
 submission_directory <- Sys.getenv("COVID_SUBMISSION_DIRECTORY", "cache")
@@ -31,18 +29,8 @@ forecaster_fns <- list2(
 )
 
 rlang::list2(
-  tar_target(
-    aheads,
-    command = {
-      -1:3
-    }
-  ),
-  tar_target(
-    forecasters,
-    command = {
-      seq_along(forecaster_fns)
-    }
-  ),
+  tar_target(aheads, command = -1:3),
+  tar_target(forecasters, command = seq_along(forecaster_fns)),
   tar_map(
     values = tidyr::expand_grid(
       tibble(