Error if grouped dataframes are used in functions

NAMESPACE

@@ -14,24 +14,34 @@ S3method(cal_apply,cal_object)
 S3method(cal_apply,data.frame)
 S3method(cal_apply,tune_results)
 S3method(cal_estimate_beta,data.frame)
+S3method(cal_estimate_beta,grouped_df)
 S3method(cal_estimate_beta,tune_results)
 S3method(cal_estimate_isotonic,data.frame)
+S3method(cal_estimate_isotonic,grouped_df)
 S3method(cal_estimate_isotonic,tune_results)
 S3method(cal_estimate_isotonic_boot,data.frame)
+S3method(cal_estimate_isotonic_boot,grouped_df)
 S3method(cal_estimate_isotonic_boot,tune_results)
 S3method(cal_estimate_linear,data.frame)
+S3method(cal_estimate_linear,grouped_df)
 S3method(cal_estimate_linear,tune_results)
 S3method(cal_estimate_logistic,data.frame)
+S3method(cal_estimate_logistic,grouped_df)
 S3method(cal_estimate_logistic,tune_results)
 S3method(cal_estimate_multinomial,data.frame)
+S3method(cal_estimate_multinomial,grouped_df)
 S3method(cal_estimate_multinomial,tune_results)
 S3method(cal_plot_breaks,data.frame)
+S3method(cal_plot_breaks,grouped_df)
 S3method(cal_plot_breaks,tune_results)
 S3method(cal_plot_logistic,data.frame)
+S3method(cal_plot_logistic,grouped_df)
 S3method(cal_plot_logistic,tune_results)
 S3method(cal_plot_regression,data.frame)
+S3method(cal_plot_regression,grouped_df)
 S3method(cal_plot_regression,tune_results)
 S3method(cal_plot_windowed,data.frame)
+S3method(cal_plot_windowed,grouped_df)
 S3method(cal_plot_windowed,tune_results)
 S3method(cal_validate_beta,resample_results)
 S3method(cal_validate_beta,rset)

R/cal-estimate-beta.R

@@ -82,6 +82,18 @@ cal_estimate_beta.tune_results <- function(.data,
     )
 }
 
+#' @export
+#' @rdname cal_estimate_beta
+cal_estimate_beta.grouped_df <- function(.data,
+                                         truth = NULL,
+                                         shape_params = 2,
+                                         location_params = 1,
+                                         estimate = NULL,
+                                         parameters = NULL,
+                                         ...) {
+  abort_if_grouped_df()
+}
+
 #' @rdname required_pkgs.cal_object
 #' @keywords internal
 #' @export

R/cal-estimate-isotonic.R

@@ -83,6 +83,16 @@ cal_estimate_isotonic.tune_results <- function(.data,
     )
 }
 
+#' @export
+#' @rdname cal_estimate_isotonic
+cal_estimate_isotonic.grouped_df <- function(.data,
+                                             truth = NULL,
+                                             estimate = NULL,
+                                             parameters = NULL,
+                                             ...) {
+  abort_if_grouped_df()
+}
+
 #------------------ >>  Bootstrapped Isotonic Regression------------------------
 #' Uses a bootstrapped Isotonic regression model to calibrate probabilities
 #' @param times Number of bootstraps.
@@ -166,6 +176,17 @@ cal_estimate_isotonic_boot.tune_results <- function(.data,
     )
 }
 
+#' @export
+#' @rdname cal_estimate_isotonic_boot
+cal_estimate_isotonic_boot.grouped_df <- function(.data,
+                                                  truth = NULL,
+                                                  estimate = NULL,
+                                                  times = 10,
+                                                  parameters = NULL,
+                                                  ...) {
+  abort_if_grouped_df()
+}
+
 #------------------------------ Implementation ---------------------------------
 cal_isoreg_impl <- function(.data,
                             truth,

R/cal-estimate-linear.R

@@ -1,8 +1,8 @@
 #------------------------------- Methods ---------------------------------------
 #' Uses a linear regression model to calibrate numeric predictions
 #' @inheritParams cal_estimate_logistic
-#' @param .data A `data.frame` object, or `tune_results` object, that contains
-#' predictions and probability columns.
+#' @param .data Am ungrouped  `data.frame` object, or `tune_results` object,
+#' that contains a prediction column.
 #' @param truth The column identifier for the observed outcome data (that is
 #' numeric). This should be an unquoted column name.
 #' @param estimate Column identifier for the predicted values
@@ -125,6 +125,17 @@ cal_estimate_linear.tune_results <- function(.data,
     )
 }
 
+#' @export
+#' @rdname cal_estimate_linear
+cal_estimate_linear.grouped_df <- function(.data,
+                                           truth = NULL,
+                                           estimate = NULL,
+                                           smooth = TRUE,
+                                           parameters = NULL,
+                                           ...) {
+  abort_if_grouped_df()
+}
+
 #' @rdname required_pkgs.cal_object
 #' @keywords internal
 #' @export

R/cal-estimate-logistic.R

@@ -1,7 +1,7 @@
 #------------------------------- Methods ---------------------------------------
 #' Uses a logistic regression model to calibrate probabilities
-#' @param .data A `data.frame` object, or `tune_results` object, that contains
-#' predictions and probability columns.
+#' @param .data An ungrouped `data.frame` object, or `tune_results` object,
+#' that contains predictions and probability columns.
 #' @param truth The column identifier for the true class results
 #' (that is a factor). This should be an unquoted column name.
 #' @param estimate A vector of column identifiers, or one of `dplyr` selector
@@ -102,6 +102,18 @@ cal_estimate_logistic.tune_results <- function(.data,
     )
 }
 
+#' @export
+#' @rdname cal_estimate_logistic
+cal_estimate_logistic.grouped_df <- function(.data,
+                                             truth = NULL,
+                                             estimate = NULL,
+                                             smooth = TRUE,
+                                             parameters = NULL,
+                                             ...) {
+  abort_if_grouped_df()
+}
+
+
 #' @rdname required_pkgs.cal_object
 #' @keywords internal
 #' @export

R/cal-estimate-multinom.R

@@ -107,6 +107,17 @@ cal_estimate_multinomial.tune_results <-
     )
 }
 
+#' @export
+#' @rdname cal_estimate_multinomial
+cal_estimate_multinomial.grouped_df <- function(.data,
+                                                truth = NULL,
+                                                estimate = NULL,
+                                                smooth = TRUE,
+                                                parameters = NULL,
+                                                ...) {
+  abort_if_grouped_df()
+}
+
 #' @rdname required_pkgs.cal_object
 #' @keywords internal
 #' @export

R/cal-plot-breaks.R

@@ -14,7 +14,8 @@
 #' If the predictions are well calibrated, the fitted curve should align with
 #' the diagonal line.
 #'
-#' @param .data A data.frame object containing predictions and probability columns.
+#' @param .data An ungrouped data frame object containing predictions and
+#' probability columns.
 #' @param truth The column identifier for the true class results
 #' (that is a factor). This should be an unquoted column name.
 #' @param estimate A vector of column identifiers, or one of `dplyr` selector
@@ -74,13 +75,11 @@
 #' combined <- bind_rows(mutate(segment_logistic, source = "original"), gl)
 #'
 #' combined %>%
-#'   group_by(source) %>%
-#'   cal_plot_logistic(Class, .pred_good)
+#'   cal_plot_logistic(Class, .pred_good, group = source)
 #'
 #' # The grouping can be faceted in ggplot2
 #' combined %>%
-#'   group_by(source) %>%
-#'   cal_plot_logistic(Class, .pred_good) +
+#'   cal_plot_logistic(Class, .pred_good, group = source) +
 #'   facet_wrap(~source) +
 #'   theme(legend.position = "")
 #' @seealso [cal_plot_logistic()], [cal_plot_windowed()]
@@ -163,6 +162,21 @@ cal_plot_breaks.tune_results <- function(.data,
   )
 }
 
+#' @export
+#' @rdname cal_plot_breaks
+cal_plot_breaks.grouped_df <- function(.data,
+                                       truth = NULL,
+                                       estimate = NULL,
+                                       num_breaks = 10,
+                                       conf_level = 0.90,
+                                       include_ribbon = TRUE,
+                                       include_rug = TRUE,
+                                       include_points = TRUE,
+                                       event_level = c("auto", "first", "second"),
+                                       ...) {
+  abort_if_grouped_df()
+}
+
 #--------------------------- >> Implementation ---------------------------------
 
 cal_plot_breaks_impl <- function(.data,

R/cal-plot-logistic.R

@@ -109,6 +109,20 @@ cal_plot_logistic.tune_results <- function(.data,
   )
 }
 
+#' @export
+#' @rdname cal_plot_logistic
+cal_plot_logistic.grouped_df <- function(.data,
+                                         truth = NULL,
+                                         estimate = NULL,
+                                         conf_level = 0.90,
+                                         smooth = TRUE,
+                                         include_rug = TRUE,
+                                         include_ribbon = TRUE,
+                                         event_level = c("auto", "first", "second"),
+                                         ...) {
+  abort_if_grouped_df()
+}
+
 #--------------------------- >> Implementation ---------------------------------
 cal_plot_logistic_impl <- function(.data,
                                    truth = NULL,

R/cal-plot-regression.R

@@ -6,7 +6,8 @@
 #' is shown. If the predictions are well calibrated, the fitted curve should align with
 #' the diagonal line.
 #'
-#' @param .data A data.frame object containing prediction and truth columns.
+#' @param .data An ungrouped data frame object containing a prediction
+#' column.
 #' @param truth The column identifier for the true results
 #' (numeric). This should be an unquoted column name.
 #' @param estimate The column identifier for the predictions.
@@ -85,6 +86,16 @@ cal_plot_regression.tune_results <- function(.data,
   )
 }
 
+#' @export
+#' @rdname cal_plot_regression
+cal_plot_regression.grouped_df <- function(.data,
+                                           truth = NULL,
+                                           estimate = NULL,
+                                           smooth = TRUE,
+                                           ...) {
+  abort_if_grouped_df()
+}
+
 regression_plot_impl <- function(.data, truth, estimate, group,
                                  smooth, ...) {
   truth <- enquo(truth)

R/cal-plot-windowed.R

@@ -123,6 +123,22 @@ cal_plot_windowed.tune_results <- function(.data,
   )
 }
 
+#' @export
+#' @rdname cal_plot_windowed
+cal_plot_windowed.grouped_df <- function(.data,
+                                         truth = NULL,
+                                         estimate = NULL,
+                                         window_size = 0.1,
+                                         step_size = window_size / 2,
+                                         conf_level = 0.90,
+                                         include_ribbon = TRUE,
+                                         include_rug = TRUE,
+                                         include_points = TRUE,
+                                         event_level = c("auto", "first", "second"),
+                                         ...) {
+  abort_if_grouped_df()
+}
+
 #--------------------------- >> Implementation ---------------------------------
 cal_plot_windowed_impl <- function(.data,
                                    truth = NULL,

R/utils.R

@@ -79,3 +79,13 @@ get_group_argument <- function(group, .data, call = rlang::env_parent()) {
 
   return(group)
 }
+
+abort_if_grouped_df <- function(call = rlang::caller_env()) {
+  cli::cli_abort(
+    c(
+      "x" = "This function does not work with grouped data frames.",
+      "i" = "Apply {.fn dplyr::ungroup} and use the {.arg .by} argument."
+    ),
+    call = call
+  )
+}