Plot coverage (#46)

* Add a function to plot the coverage

* Add tests for coverage plots

* Add function to `{pkgdown}` site

* Update NEWS

NAMESPACE

@@ -35,6 +35,9 @@ S3method(mutation_prevalence,geno_tbl)
 S3method(mutation_prevalence,ref_alt_cov_tbl)
 S3method(plot,mut_freq)
 S3method(plot,mut_prev)
+S3method(plot_coverage,cov_tbl)
+S3method(plot_coverage,default)
+S3method(plot_coverage,ref_alt_cov_tbl)
 S3method(tbl_sum,alt_tbl)
 S3method(tbl_sum,cov_tbl)
 S3method(tbl_sum,geno_tbl)
@@ -65,6 +68,7 @@ export(miplicorn_example)
 export(mutation_frequency)
 export(mutation_prevalence)
 export(plot_chromoMap)
+export(plot_coverage)
 export(plot_karyoploteR)
 export(plot_mutation_frequency)
 export(plot_mutation_prevalence)

NEWS.md

@@ -1,5 +1,7 @@
 # miplicorn (development version)
 
+- New `plot_coverage()` function can be used to plot the average coverage across
+  a given grouping variable (#46).
 - The column created by `label_mutations()` has been renamed from
   `ans_der_indel` to `mutation_label` (#44).
 - `label_mutations()` more clearly signals when the computation can not be

R/plot_coverage.R

@@ -0,0 +1,67 @@
+#------------------------------------------------
+#' Plot coverage
+#'
+#' Create a bar chart visualizing the average coverage across a given grouping
+#' variable. The color of each bar represents the magnitude of the mean
+#' coverage.
+#'
+#' @param data A data frame, data frame extension (e.g. a tibble), or a lazy
+#'   data frame (e.g. from dbplyr or dtplyr).
+#' @param group_by The grouping variable. The average coverage is computed for
+#'   each unique value of the variable.
+#'
+#' @export
+#' @examples
+#' # Read example data
+#' data <- read_tbl_coverage(miplicorn_example("coverage_AA_table.csv"))
+#'
+#' # Plot coverage grouped by gene
+#' plot_coverage(data, gene)
+#' @export
+plot_coverage <- function(data, group_by) {
+  UseMethod("plot_coverage")
+}
+
+#' @export
+plot_coverage.default <- function(data, group_by) {
+  cli_abort(c(
+    "Cannot plot the coverage with this data object.",
+    "i" = "Object must be a coverage table or a reference, alternate, coverage table."
+  ))
+}
+
+# Plotting function
+plot_coverage_fn <- function(data, group_by) {
+  # Ensure inputs exist
+  rlang::check_required(data)
+  rlang::check_required(group_by)
+
+  # Group and create stats
+  plot_data <- data %>%
+    dplyr::group_by({{ group_by }}) %>%
+    dplyr::summarise(mean_coverage = mean(.data$coverage, na.rm = TRUE))
+
+  # Plot
+  ggplot2::ggplot(
+    data = plot_data,
+    mapping = ggplot2::aes(
+      x = {{ group_by }},
+      y = .data$mean_coverage,
+      fill = .data$mean_coverage
+    )
+  ) +
+    ggplot2::geom_col() +
+    default_theme() +
+    ggplot2::theme(
+      axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1),
+      legend.position = "none"
+    )
+}
+
+#' @rdname plot_coverage
+#' @export
+plot_coverage.cov_tbl <- plot_coverage_fn
+
+#' @rdname plot_coverage
+#' @export
+plot_coverage.ref_alt_cov_tbl <- plot_coverage_fn

_pkgdown.yml

@@ -51,6 +51,7 @@ reference:
       These functions are used to visualize data.
     contents:
       - chromosome-map
+      - plot_coverage
       - plot_mutation_prevalence
       - plot_mutation_frequency
       - theme_miplicorn

tests/testthat/_snaps/plot_coverage.md

@@ -0,0 +1,5 @@
+# data must have mut_prev class
+
+    Cannot plot the coverage with this data object.
+    i Object must be a coverage table or a reference, alternate, coverage table.
+