Merge branch 'develop' of https://github.com/craddm/eegUtils into dev…

…elop

NAMESPACE

@@ -73,6 +73,8 @@ S3method(eeg_reference,default)
 S3method(eeg_reference,eeg_ICA)
 S3method(eeg_reference,eeg_data)
 S3method(eeg_reference,eeg_epochs)
+S3method(eeg_summarise,default)
+S3method(eeg_summarise,eeg_epochs)
 S3method(electrode_locations,data.frame)
 S3method(electrode_locations,eeg_data)
 S3method(electrode_locations,eeg_epochs)
@@ -226,6 +228,7 @@ export(eeg_downsample)
 export(eeg_epochs)
 export(eeg_filter)
 export(eeg_reference)
+export(eeg_summarise)
 export(electrode_locations)
 export(epoch_data)
 export(epoch_stats)

R/plot_gfp.R

@@ -9,6 +9,7 @@
 #'   separately for different conditions.
 #' @param keep_trials Calculate GFP for each epoch separately, then average over
 #'   epochs.
+#' @author Matt Craddock \email{matt@@mattcraddock.com}
 #' @export
 #' @examples
 #' plot_gfp(demo_spatial)

R/summarise_eeg.R

@@ -0,0 +1,145 @@
+#' Calculate simple summary statistics for `eeg_*` objects
+#'
+#' Calculate the timepoint-by-timepoint mean, standard deviation, standard
+#' error, or variance `eeg_epochs` objects.
+#'
+#' @param data An `eegUtils` object.
+#' @param ... Various arguments passed to specific functions
+#' @return A tibble
+#' @export
+eeg_summarise <- function(data,
+                          ...) {
+  UseMethod("eeg_summarise", data)
+}
+
+#' @export
+eeg_summarise.default <- function(data,
+                                  ...) {
+  stop("Not implemented for objects of class ",
+       paste(class(data), collapse = "/"))
+}
+
+#' @param statistic The statistic to calculate at each timepoint. Defaults to
+#'   "sem"
+#' @param conditions Conditions to group the data by.
+#' @param time_lim Timepoint(s) to summarise. Can be a range, for which a
+#'   summary statistic will be provided for each timepoint, or a list of
+#'   individual times. If none is supplied, the function will calculate a
+#'   summary for every timepoint.
+#' @describeIn eeg_summarise Calculate summary statistics for `eeg_epochs`
+#'   objects
+#' @export
+eeg_summarise.eeg_epochs <- function(data,
+                                     statistic = c("sem",
+                                                   "mean",
+                                                   "sd",
+                                                   "var"),
+                                     conditions = NULL,
+                                     time_lim = NULL,
+                                     ...) {
+
+  if (!is.null(conditions)) {
+    groups <- c(conditions, "time")
+  } else {
+    groups <- "time"
+  }
+
+  chans <- channel_names(data)
+  data <- as.data.frame(data)
+
+  if (!is.null(time_lim)) {
+    data <- select_times(data,
+                         time_lim)
+  }
+
+  data <- dplyr::group_by(data,
+                          dplyr::across({{ groups }}))
+
+  summary_function <-
+    switch(statistic,
+           mean = function(x) mean(x),
+           sem = function(x) sd(x) / length(x),
+           sd = function(x) sd(x),
+           var = function(x) var(x)
+           )
+
+  data <- dplyr::summarise(
+    data,
+    dplyr::across(chans,
+                  summary_function,
+                  .names = paste(statistic,
+                                 sep = "_",
+                                 "{.col}")
+                  )
+    )
+  data
+}
+
+#' Calculate the standard error of the mean
+#'
+#' @param data An `eegUtils` object
+#' @param time_lim A vector of time-points to calculate SEM over.
+#' @param conditions Split the calculation across multiple conditions
+#' @keywords internal
+
+calculate_sem <- function(data,
+                          time_lim = NULL,
+                          conditions = NULL) {
+
+  conditions <- rlang::enquo(conditions)
+  chans <- channel_names(data)
+
+  if (is.list(time_lim)) {
+    all_sems <- lapply(time_lim,
+                       function(x) calculate_sem(data,
+                                                 time_lim = x,
+                                                 conditions = {{conditions}}))
+    return(
+      do.call("rbind",
+              all_sems)
+    )
+
+  } else {
+    data <- select_times(data,
+                         time_lim)
+  }
+  data <- as.data.frame(data)
+
+  if (!is.null(conditions)) {
+    data <- group_by(data,
+                     {{conditions}},
+                     .data$epoch)
+  }
+  data <- dplyr::summarise(
+    data,
+    dplyr::across(chans,
+                  mean))
+  data <- dplyr::summarise(
+    data,
+    count = length(unique(.data$epoch)),
+    dplyr::across(chans,
+                  list(sem = ~sd(.) / sqrt(count))))
+  data$time_lim <- list(time_lim)
+  data
+}
+
+calculate_sd <- function(data,
+                         time_lim,
+                         conditions = NULL) {
+  conditions <- rlang::enquo(conditions)
+  chans <- channel_names(data)
+  data <- select_times(data,
+                       time_lim)
+  data <- as.data.frame(data)
+  if (!is.null(conditions)) {
+    data <- group_by(data,
+                     {{conditions}})
+  }
+  data <- dplyr::summarise(
+    data,
+    count = length(unique(.data$epoch)),
+    dplyr::across(chans,
+                  list(sd = ~sd(.))))
+  data$time_lim <- list(time_lim)
+  data
+}

R/topoplot.R

@@ -83,7 +83,7 @@ topoplot.default <- function(data,
 #' @import tidyr
 #' @family scalp-based maps
 #' @describeIn topoplot Topographical plotting of data.frames and other non
-#'   eeg_data objects.
+#'   `eeg_data` objects.
 #' @export
 
 topoplot.data.frame <- function(data,