From aee77e8bb528a15dcfdd079b01d13b99a67fae69 Mon Sep 17 00:00:00 2001
From: Elias Projahn <elias@johrpan.de>
Date: Sun, 14 Aug 2022 17:50:59 +0200
Subject: [PATCH] distance: Switch from median to density estimate

---
 R/method_adjacency.R | 24 ------------------------
 R/method_distance.R  |  2 +-
 R/utils.R            | 24 ++++++++++++++++++++++++
 man/densest.Rd       |  2 +-
 man/distance.Rd      |  2 +-
 5 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/R/method_adjacency.R b/R/method_adjacency.R
index 9fb73cf..ed0b925 100644
--- a/R/method_adjacency.R
+++ b/R/method_adjacency.R
@@ -1,27 +1,3 @@
-#' Find the densest value in the data.
-#'
-#' This function assumes that data represents a continuous variable and finds
-#' a single value with the highest estimated density. This can be used to
-#' estimate the mode of the data. If there is only one value that value is
-#' returned. If multiple density maxima with the same density exist, their mean
-#' is returned.
-#'
-#' @param data The input data.
-#'
-#' @return The densest value of data.
-#'
-#' @export
-densest <- function(data) {
-  as.numeric(if (length(data) <= 0) {
-    NULL
-  } else if (length(data) == 1) {
-    data
-  } else {
-    density <- stats::density(data)
-    mean(density$x[density$y == max(density$y)])
-  })
-}
-
 #' Score genes based on their proximity to the reference genes.
 #'
 #' In this case, the distance data that is available for one gene is first
diff --git a/R/method_distance.R b/R/method_distance.R
index e81bc35..217fa01 100644
--- a/R/method_distance.R
+++ b/R/method_distance.R
@@ -16,7 +16,7 @@
 distance <- function(id = "distance",
                      name = "Distance",
                      description = "Distance to telomeres",
-                     summarize = stats::median) {
+                     summarize = densest) {
   method(
     id = id,
     name = name,
diff --git a/R/utils.R b/R/utils.R
index 473059d..af4b9bb 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -40,6 +40,30 @@ num <- function(number, digits) {
   format(round(number, digits = digits), nsmall = digits)
 }
 
+#' Find the densest value in the data.
+#'
+#' This function assumes that data represents a continuous variable and finds
+#' a single value with the highest estimated density. This can be used to
+#' estimate the mode of the data. If there is only one value that value is
+#' returned. If multiple density maxima with the same density exist, their mean
+#' is returned.
+#'
+#' @param data The input data.
+#'
+#' @return The densest value of data.
+#'
+#' @export
+densest <- function(data) {
+  as.numeric(if (length(data) <= 0) {
+    NULL
+  } else if (length(data) == 1) {
+    data
+  } else {
+    density <- stats::density(data)
+    mean(density$x[density$y == max(density$y)])
+  })
+}
+
 # This is needed to make data.table's symbols available within the package.
 #' @import data.table
 NULL
diff --git a/man/densest.Rd b/man/densest.Rd
index 181a270..1dfee5a 100644
--- a/man/densest.Rd
+++ b/man/densest.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/method_adjacency.R
+% Please edit documentation in R/utils.R
 \name{densest}
 \alias{densest}
 \title{Find the densest value in the data.}
diff --git a/man/distance.Rd b/man/distance.Rd
index 2633ccb..3c54f8f 100644
--- a/man/distance.Rd
+++ b/man/distance.Rd
@@ -8,7 +8,7 @@ distance(
   id = "distance",
   name = "Distance",
   description = "Distance to telomeres",
-  summarize = stats::median
+  summarize = densest
 )
 }
 \arguments{