Update documentation

R/dplyr_methods.R

@@ -226,7 +226,6 @@ distinct.SpatialExperiment <- function(.data, ..., .keep_all=FALSE) {
         dplyr::distinct(..., .keep_all=.keep_all)
 }
 
-
 #' Subset rows using column values
 #'
 #'

R/ggplot2_methods.R

@@ -43,15 +43,12 @@
 #'
 #' @export
 #' @examples
-#' \dontrun{
-#' library(ggplot2)
 #' `%>%` <- magrittr::`%>%`
 #' example(read10xVisium)
 #' 
 #' spe %>%
-#`    ggplot(aes(x = .cell, y = array_row)) +
-#`    geom_point()
-#' }
+#'    ggplot(ggplot2::aes(x = .cell, y = array_row)) +
+#'    ggplot2::geom_point()
 NULL
 
 #' @export

R/pillar_utilities.R

@@ -1,5 +1,9 @@
 NBSP <- "\U00A0"
 
+#' pillar format comment
+#' 
+#' @keywords internal
+#' @noRd
 pillar___format_comment = function (x, width)
 {
   if (length(x) == 0L) {
@@ -8,14 +12,21 @@ pillar___format_comment = function (x, width)
   map_chr(x, pillar___wrap, prefix = "# ", width = min(width, cli::console_width()))
 }
 
+#' pillar string wrap
+#' 
 #' @importFrom fansi strwrap_ctl
+#' @keywords internal
+#' @noRd
 pillar___strwrap2 = function (x, width, indent)
 {
   fansi::strwrap_ctl(x, width = max(width, 0), indent = indent,
                      exdent = indent + 2)
 }
 
-
+#' pillar wrap
+#' 
+#' @keywords internal
+#' @noRd
 pillar___wrap = function (..., indent = 0, prefix = "", width)
 {
   x <- paste0(..., collapse = "")

R/plotly_methods.R

@@ -95,44 +95,15 @@
 #'
 #' @return A plotly
 #' @export
+#' 
 #' @examples
-#' \dontrun{
-#' # plot_ly() tries to create a sensible plot based on the information you
-#' # give it. If you don't provide a trace type, plot_ly() will infer one.
-#' plot_ly(economics, x=~pop)
-#' plot_ly(economics, x=~date, y=~pop)
-#' # plot_ly() doesn't require data frame(s), which allows one to take
-#' # advantage of trace type(s) designed specifically for numeric matrices
-#' plot_ly(z=~volcano)
-#' plot_ly(z=~volcano, type="surface")
-#'
-#' # plotly has a functional interface: every plotly function takes a plotly
-#' # object as it's first input argument and returns a modified plotly object
-#' add_lines(plot_ly(economics, x=~date, y=~ unemploy / pop))
-#'
-#' # To make code more readable, plotly imports the pipe operator from magrittr
-#' economics %>%
-#'     plot_ly(x=~date, y=~ unemploy / pop) %>%
-#'     add_lines()
-#'
-#' # Attributes defined via plot_ly() set 'global' attributes that
-#' # are carried onto subsequent traces, but those may be over-written
-#' plot_ly(economics, x=~date, color=I("black")) %>%
-#'     add_lines(y=~uempmed) %>%
-#'     add_lines(y=~psavert, color=I("red"))
-#'
-#' # Attributes are documented in the figure reference -> https://plot.ly/r/reference
-#' # You might notice plot_ly() has named arguments that aren't in this figure
-#' # reference. These arguments make it easier to map abstract data values to
-#' # visual attributes.
-#' p <- plot_ly(iris, x=~Sepal.Width, y=~Sepal.Length)
-#' add_markers(p, color=~Petal.Length, size=~Petal.Length)
-#' add_markers(p, color=~Species)
-#' add_markers(p, color=~Species, colors="Set1")
-#' add_markers(p, symbol=~Species)
-#' add_paths(p, linetype=~Species)
-#' }
+#' `%>%` <- magrittr::`%>%`
+#' example(read10xVisium)
+#' 
+#' spe %>%
+#'   plot_ly(x=~array_col, y=~ array_row)
 #'
+
 plot_ly <- function(data=data.frame(), ..., type=NULL, name=NULL,
     color=NULL, colors=NULL, alpha=NULL,
     stroke=NULL, strokes=NULL, alpha_stroke=1,

R/utilities.R

@@ -325,6 +325,8 @@ get_special_datasets <- function(SpatialExperiment_object, n_dimensions_to_retur
 
         mat
     })
+    # Create list of special columns for SpatialExperiment
+
 }
 
 get_needed_columns <- function(.data) {