Merge pull request #664 from cmu-delphi/ds/style

style: minor styling changes
cmu-delphi · Oct 2, 2023 · 31c8bf8 · 31c8bf8
2 parents fdbbb69 + a9afed7
commit 31c8bf8
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diff --git a/R-packages/evalcast/.lintr b/R-packages/evalcast/.lintr
@@ -0,0 +1,9 @@
+linters: linters_with_defaults(
+  line_length_linter(120),
+  cyclocomp_linter = NULL,
+  object_length_linter(length = 40L)
+  )
+exclusions: list(
+  "renv",
+  "venv"
+  )
diff --git a/R-packages/evalcast/R/get_predictions.R b/R-packages/evalcast/R/get_predictions.R
@@ -1,5 +1,5 @@
 #' Get predictions
-#' 
+#'
 #' For each of the provided forecast dates, runs a forecaster using the data
 #' that would have been available as of that given forecast date. Returns a list
 #' of "predictions cards", where each list element corresponds to a different
@@ -14,13 +14,12 @@
 #'   probabilities associated with quantile forecasts for that location and
 #'   ahead. If your forecaster produces point forecasts, then set `quantile=NA`.
 #'
-#'   One argument to `forecaster` must be named `df_list`. It will be
-#'   populated with the list of historical data returned by a call
-#'   to COVIDcast. The list will be the same length as the number of rows in
-#'   the `signals` tibble (see below).
-#'   The forecaster will also receive a single `forecast_date` as a named argument.
-#'   Any additional named arguments can be passed via the `forecaster_args`
-#'   argument below.
+#'   One argument to `forecaster` must be named `df_list`. It will be populated
+#'   with the list of historical data returned by a call to COVIDcast. The list
+#'   will be the same length as the number of rows in the `signals` tibble (see
+#'   below). The forecaster will also receive a single `forecast_date` as a
+#'   named argument. Any additional named arguments can be passed via the
+#'   `forecaster_args` argument below.
 #'
 #'   Thus, the forecaster should have a signature like
 #'   `forecaster(df_list = data, forecast_data = forecast_date, ...)`
@@ -29,49 +28,52 @@
 #' @template forecast_dates-template
 #' @template incidence_period-template
 #' @template apply_corrections-template
-#' @param response_data_source String indicating the `data_source` of the response.
-#'   This is used mainly for downstream evaluation. By default, this will be the
-#'   same as the `data_source` in the first row of the `signals` tibble.
+#' @param response_data_source String indicating the `data_source` of the
+#'   response. This is used mainly for downstream evaluation. By default, this
+#'   will be the same as the `data_source` in the first row of the `signals`
+#'   tibble.
 #' @param response_data_signal String indicating the `signal` of the response.
 #'   This is used mainly for downstream evaluation. By default, this will be the
 #'   same as the `signal` in the first row in the `signals` tibble.
 #' @param forecaster_args a list of additional named arguments to be passed
 #'   to `forecaster()`. A common use case would be to pass the period ahead
 #'   (e.g. predict 1 day, 2 days, ..., k days ahead). Note that `ahead` is a
 #'   required component of the forecaster output (see above).
-#' @param parallel_execution FALSE (default), TRUE, or a single positive integer. If TRUE,
-#'   executes each forecast date prediction in parallel on the number of
-#'   detected cores available minus 1. If FALSE, the code is run on a single
-#'   core. If integer, runs in parallel on that many cores, clipping to the
-#'   number of detected cores available if a greater number is requested. Uses
-#'   [`bettermc::mclapply`] for parallelism if using more than one core.
+#' @param parallel_execution FALSE (default), TRUE, or a single positive
+#'   integer. If TRUE, executes each forecast date prediction in parallel on the
+#'   number of detected cores available minus 1. If FALSE, the code is run on a
+#'   single core. If integer, runs in parallel on that many cores, clipping to
+#'   the number of detected cores available if a greater number is requested.
+#'   Uses [`bettermc::mclapply`] for parallelism if using more than one core.
 #' @param additional_mclapply_args a named list of additional arguments to pass
 #'   to [`bettermc::mclapply`] (besides `X`, `FUN`, and
 #'   `mc.cores`.)
-#' @param honest_as_of a boolean that, if true, ensures that the forecast_day, end_day,
-#' and as_of are all equal when downloading data. Otherwise, as_of is allowed to be freely
-#' set (and defaults to current date if not presented).
+#' @param honest_as_of a boolean that, if true, ensures that the forecast_day,
+#' end_day, and as_of are all equal when downloading data. Otherwise, as_of is
+#' allowed to be freely set (and defaults to current date if not presented).
 #' @param offline_signal_dir the directory that stores the cached data for each
-#' (signal, forecast day) pair. If this is null, no caching is done and the data is
-#' downloaded from covidcast.
+#' (signal, forecast day) pair. If this is null, no caching is done and the data
+#' is downloaded from covidcast.
 #'
 #' @template predictions_cards-template
 #'
 #' @examples \dontrun{
-#' baby_predictions = get_predictions(
+#' baby_predictions <- get_predictions(
 #'   baseline_forecaster, "baby",
 #'   tibble::tibble(
 #'     data_source = "jhu-csse",
 #'     signal = "deaths_incidence_num",
 #'     start_day = "2020-08-15",
 #'     geo_values = "mi",
-#'     geo_type = "state"),
+#'     geo_type = "state"
+#'   ),
 #'   forecast_dates = "2020-10-01",
 #'   incidence_period = "epiweek",
 #'   forecaster_args = list(
 #'     incidence_period = "epiweek",
 #'     ahead = 1:4
-#'   ))
+#'   )
+#' )
 #' }
 #'
 #' @export
@@ -89,15 +91,40 @@ get_predictions <- function(forecaster,
                             additional_mclapply_args = list(),
                             honest_as_of = TRUE,
                             offline_signal_dir = NULL) {
-
   assert_that(is_tibble(signals), msg = "`signals` should be a tibble.")
-  assert_that(xor(honest_as_of, "as_of" %in% names(signals)), msg = "`honest_as_of` should be set if and only if `as_of` is not set. Either remove as_of specification or set honest_as_of to FALSE.")
+  assert_that(
+    xor(honest_as_of, "as_of" %in% names(signals)),
+    msg = paste(
+      "`honest_as_of` should be set if and only if `as_of` is not set. Either",
+      " remove as_of specification or set honest_as_of to FALSE."
+    )
+  )
   incidence_period <- match.arg(incidence_period)
 
-  if (incidence_period == "epiweek") rlang::warn("incidence_period of 'epiweek' only selects weekly (Saturday) target end dates, actual 'epiweek' signals not supported.", "evalcast::get_predictions")
-  if ("time_type" %in% names(signals) && any(signals$time_type == "week" | signals$time_type == "epiweek")) rlang::abort("time_type in the signals arg can only be 'day'.", "evalcast::get_predictions")
-  if ("lag" %in% names(signals)) rlang::warn("lag in the signals arg will be ignored.", "evalcast::get_predictions")
-  if ("issues" %in% names(signals)) rlang::warn("issues in the signals arg will be ignored.", "evalcast::get_predictions")
+  if (incidence_period == "epiweek") {
+    rlang::warn(
+      paste(
+        "incidence_period of 'epiweek' only selects weekly (Saturday) target ",
+        "end dates, actual 'epiweek' signals not supported."
+      ),
+      "evalcast::get_predictions"
+    )
+  }
+  if (
+    "time_type" %in% names(signals) &&
+      any(signals$time_type == "week" | signals$time_type == "epiweek")
+  ) {
+    rlang::abort(
+      "time_type in the signals arg can only be 'day'.",
+      "evalcast::get_predictions"
+    )
+  }
+  if ("lag" %in% names(signals)) {
+    rlang::warn("lag in the signals arg will be ignored.", "evalcast::get_predictions")
+  }
+  if ("issues" %in% names(signals)) {
+    rlang::warn("issues in the signals arg will be ignored.", "evalcast::get_predictions")
+  }
 
   if (rlang::is_bool(parallel_execution) && parallel_execution == TRUE) {
     num_cores <- max(1L, parallel::detectCores() - 1L)
@@ -109,11 +136,14 @@ get_predictions <- function(forecaster,
     stop("parallel_execution argument is neither boolean nor integer.")
   }
 
-  assert_that(rlang::is_named2(additional_mclapply_args) &&
-              all(rlang::names2(additional_mclapply_args) %in% rlang::fn_fmls_names(bettermc::mclapply)))
+  assert_that(
+    rlang::is_named2(additional_mclapply_args) &&
+      all(rlang::names2(additional_mclapply_args) %in% rlang::fn_fmls_names(bettermc::mclapply))
+  )
 
   get_predictions_single_date_ <- function(forecast_date) {
-    preds <- do.call(get_predictions_single_date,
+    preds <- do.call(
+      get_predictions_single_date,
       list(
         forecaster = forecaster,
         signals = signals,
@@ -128,7 +158,14 @@ get_predictions <- function(forecaster,
   }
 
   if (num_cores > 1L) {
-    out <- rlang::inject(bettermc::mclapply(forecast_dates, get_predictions_single_date_, mc.cores = num_cores, !!!additional_mclapply_args)) %>% bind_rows()
+    out <- rlang::inject(
+      bettermc::mclapply(
+        forecast_dates,
+        get_predictions_single_date_,
+        mc.cores = num_cores,
+        !!!additional_mclapply_args
+      )
+    ) %>% bind_rows()
   } else {
     out <- lapply(forecast_dates, get_predictions_single_date_) %>% bind_rows()
   }
@@ -143,9 +180,10 @@ get_predictions <- function(forecaster,
       target_end_date = get_target_period(
         .data$forecast_date,
         incidence_period,
-        .data$ahead)$end,
+        .data$ahead
+      )$end,
       incidence_period = incidence_period
-      ) %>%
+    ) %>%
     relocate(.data$forecaster, .before = .data$forecast_date)
 
   class(out) <- c("predictions_cards", class(out))
@@ -159,7 +197,6 @@ get_predictions_single_date <- function(forecaster,
                                         forecaster_args,
                                         honest_as_of = TRUE,
                                         offline_signal_dir = NULL) {
-
   forecast_date <- lubridate::ymd(forecast_date)
   signals <- signal_listcols(signals, forecast_date)
 
@@ -189,9 +226,12 @@ get_predictions_single_date <- function(forecaster,
 
   out <- do.call(forecaster, forecaster_args)
   assert_that(all(c("ahead", "geo_value", "quantile", "value") %in% names(out)),
-              msg = paste("Your forecaster must return a data frame with",
-                          "(at least) the columnns `ahead`, `geo_value`,",
-                          "`quantile`, and `value`."))
+    msg = paste(
+      "Your forecaster must return a data frame with",
+      "(at least) the columnns `ahead`, `geo_value`,",
+      "`quantile`, and `value`."
+    )
+  )
 
   # make a predictions card
   out$forecast_date <- forecast_date