change name and add features

DESCRIPTION

@@ -1,19 +1,20 @@
-Package: huebreaker
+Package: caIRA
 Type: Package
-Title: Collecting the Cluster Based Tree and Metadata
-Version: 0.1.2
+Title: Collecting and Identification from Outbreak Cluster Based Tree and Metadata
+Version: 0.1.3
 Author: Dhihram Tenrisau, Stéphane Hué
 Maintainer: Dhihram Tenrisau <[email protected]>
 Description: This package is used to find the cluster-based tree and metadata. The cluster-based tree is built based on the clusters in the data. The metadata contains information about the data used to build the tree.
 License: file LICENSE
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Imports:
     ape,
     dplyr,
-    stringr
+    stringr,
+    progress
 Suggests: 
     knitr,
     rmarkdown,

NAMESPACE

@@ -1,6 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
-export(huebreaker)
+export(genclus)
 importFrom(ape,extract.clade)
 importFrom(ape,getMRCA)
 importFrom(dplyr,"%>%")
@@ -15,5 +15,5 @@ importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
 importFrom(dplyr,slice)
 importFrom(dplyr,ungroup)
+importFrom(progress,progress_bar)
 importFrom(stringr,str_split)
-importFrom(utils,globalVariables)

NEWS.md

@@ -1,3 +1,15 @@
+# MyPackage 0.1.3
+
+## New Features
+- Change name package to caIRA.
+
+## Improvements
+- Loading bar.
+
+## Bug Fixes
+- Making function faster than before.
+
+
 # MyPackage 0.1.2
 
 ## New Features

R/huebreaker.R → R/caIRA.R

@@ -1,25 +1,28 @@
 #' Collecting the Cluster Based Tree and Metadata
 #'
-#' @param tree tree file
-#' @param metat metadata dataframe contains `label`, `location`, and `date`
-#' @param bootstrap_threshold Numeric, the bootstrap threshold
-#' @param date_range Numeric, date range of the tip (days)
-#' @param samearea Logical, whether to consider only groups from the same area
+#' This function identifies monophyletic groups within a phylogenetic tree based on provided criteria, such as bootstrap threshold, date range, and geographic location.
 #'
-#' @return a data frame containing the monophyletic groups that meet the criteria
-#' @export
+#' @param tree A phylogenetic tree object (likely of class `phylo` from the `ape` package).
+#' @param metat A metadata dataframe containing at least the columns `label`, `location`, and `date`.
+#' @param bootstrap_threshold Numeric value specifying the minimum bootstrap support required for a clade to be considered.
+#' @param date_range Numeric value specifying the range of dates (in days) within which tips must fall to be considered as part of the same group.
+#' @param samearea Logical flag indicating whether only groups from the same geographic area should be considered.
 #'
+#' @return A dataframe containing the monophyletic groups that meet the specified criteria.
+#' @export
 #' @importFrom ape getMRCA extract.clade
-#' @importFrom dplyr %>% rowwise mutate ungroup filter select inner_join arrange desc group_by slice bind_rows
+#' @importFrom dplyr %>% filter select mutate rowwise ungroup inner_join arrange desc group_by slice bind_rows
 #' @importFrom stringr str_split
-#' @importFrom utils globalVariables
+#' @importFrom progress progress_bar
 #'
 #' @examples
 #' # Load necessary packages
 #' library(ape)
 #' library(dplyr)
 #' library(stringr)
-#'  # Generate a random tree with 20 tips
+#' library(progress)
+#'
+#' # Generate a random tree with 20 tips
 #' tree <- rtree(n = 20)
 #' # Generate random bootstrap values for each node
 #' set.seed(666)
@@ -33,9 +36,10 @@
 #' random_dates <- sample(seq(start_date, end_date, by = "day"), size = length(tree$tip.label), replace = TRUE)
 #' # Create a data frame with the metadata
 #' metadata <- data.frame(label = tree$tip.label, location = areas, date = random_dates)
-#' # Run the huebreaker function
-#' huebreaker(tree, metadata, bootstrap_threshold = 90, date_range = 30, samearea = TRUE)
-huebreaker <- function(tree, metat, bootstrap_threshold, date_range, samearea) {
+#' # Run the genclus function
+#' genclus(tree, metadata, bootstrap_threshold = 90, date_range = 30, samearea = TRUE)
+genclus <- function(tree, metat, bootstrap_threshold, date_range, samearea) {
+
   bootstrap_threshold <- as.numeric(bootstrap_threshold)
   date_range <- as.numeric(date_range)
   samearea <- as.logical(samearea)
@@ -45,22 +49,56 @@ huebreaker <- function(tree, metat, bootstrap_threshold, date_range, samearea) {
     stop("The tree does not contain bootstrap values.")
   }
 
-  # Extract monophyletic groups
-  groups <- list()
+  # Identify the root node (usually it's the first node after the tips)
+  root_node <- length(tree$tip.label) + 1
+
+  # Calculate the total number of iterations for progress tracking
   tip_count <- length(tree$tip.label)
+  total_iterations <- (tip_count * (tip_count - 1)) / 2
+
+  # Set up the progress bar
+  pb <- progress_bar$new(
+    format = "(:spin) [:bar] :percent [Elapsed time: :elapsedfull || Estimated time remaining: :eta]",
+    total = total_iterations,
+    complete = "=",   # Completion bar character
+    incomplete = "-", # Incomplete bar character
+    current = ">",    # Current bar character
+    clear = FALSE,    # If TRUE, clears the bar when finish
+    width = 100       # Width of the progress bar
+  )
+
+  # Extract monophyletic groups with bootstrap value filtering first
+  groups <- list()
+
   for (i in 1:(tip_count - 1)) {
     for (j in (i + 1):tip_count) {
+      # Update progress
+      pb$tick()
+
       common_ancestor <- getMRCA(tree, c(tree$tip.label[i], tree$tip.label[j]))
-      clade <- extract.clade(tree, common_ancestor)
-      if (!is.null(clade) && all(clade$tip.label %in% tree$tip.label)) {
-        group <- sort(clade$tip.label)
-        group_name <- paste(group, collapse = ", ")
-        if (!group_name %in% names(groups)) {
-          groups[[group_name]] <- list(
-            tips = group,
-            bootstrap_value = tree$node.label[common_ancestor - length(tree$tip.label)],
-            parent_node = common_ancestor
-          )
+
+      # Determine bootstrap value, set to NA if the node is the root
+      if (common_ancestor == root_node) {
+        bootstrap_value <- NA
+      } else {
+        bootstrap_value <- tree$node.label[common_ancestor - length(tree$tip.label)]
+      }
+
+      # Proceed only if the bootstrap value is NA (root) or meets the threshold
+      if (is.na(bootstrap_value) || bootstrap_value >= bootstrap_threshold) {
+        clade <- extract.clade(tree, common_ancestor)
+
+        if (!is.null(clade) && all(clade$tip.label %in% tree$tip.label)) {
+          group <- sort(clade$tip.label)
+          group_name <- paste(group, collapse = ", ")
+
+          if (!group_name %in% names(groups)) {
+            groups[[group_name]] <- list(
+              tips = group,
+              bootstrap_value = bootstrap_value,
+              parent_node = common_ancestor
+            )
+          }
         }
       }
     }
@@ -99,18 +137,18 @@ huebreaker <- function(tree, metat, bootstrap_threshold, date_range, samearea) {
     ) %>%
     ungroup()
 
-  # Convert Bootstrap to numeric
+  # Convert Bootstrap to numeric (though already filtered above, it's good to ensure this)
   monophyletic_df <- monophyletic_df %>%
     mutate(Bootstrap = as.numeric(Bootstrap))
 
-  # Filter the DataFrame based on bootstrap values, same area if required, and date range within given days
+  # Filter the DataFrame based on the same area if required and date range within given days
   if (samearea) {
     filtered_df <- monophyletic_df %>%
-      filter(Bootstrap >= bootstrap_threshold, AllSameArea == TRUE, diff <= date_range) %>%
+      filter(AllSameArea == TRUE, diff <= date_range) %>%
       select(Group, Tips, Bootstrap, ParentNode, AreaName, MinDate, MaxDate, diff)
   } else {
     filtered_df <- monophyletic_df %>%
-      filter(Bootstrap >= bootstrap_threshold, diff <= date_range) %>%
+      filter(diff <= date_range) %>%
       select(Group, Tips, Bootstrap, ParentNode, AreaName, MinDate, MaxDate, diff)
   }
 
@@ -160,3 +198,4 @@ huebreaker <- function(tree, metat, bootstrap_threshold, date_range, samearea) {
 
 # Declare global variables to avoid R CMD check warnings
 utils::globalVariables(c("Tips", "TipList", "Areas", "AllSameArea", "Dates", "MaxDate", "MinDate", "Bootstrap", "Group", "ParentNode", "AreaName", "ID", "NumTips"))
+

docs/index.Rmd

@@ -1,8 +1,8 @@
 ---
-title: "Collecting the Cluster with huebreaker"
+title: "Collecting and Identification the Outbreak Cluster"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Collecting the Cluster with huebreaker}
+  %\VignetteIndexEntry{Collecting and Identification the Outbreak Cluster}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -14,33 +14,39 @@ knitr::opts_chunk$set(
 )
 ```
 
-The goal of huebreaker is to find the cluster based tree and metadata of the data. The package is based on the paper:
+The goal of Collecting and Identification the Outbreak Cluster (caIRA) is to find the cluster based tree and metadata of the data. The package is based on the paper:
 
 Ragonnet-Cronin, M., Hodcroft, E., Hué, S. et al. Automated analysis of phylogenetic clusters. BMC Bioinformatics 14, 317 (2013). <https://doi.org/10.1186/1471-2105-14-317>
 
 Hall M, Woolhouse M, Rambaut A (2015) Epidemic Reconstruction in a Phylogenetics Framework: Transmission Trees as Partitions of the Node Set. PLoS Comput Biol 11(12): e1004613. <https://doi.org/10.1371/journal.pcbi.1004613>
 
-This package is the part of Dhihram Tenrisau, MSc Health Data Science summer project, 'Phylodynamic of Norovirus in UK 2003-2023'. The project is supervised by Stéphane Hué
+This package is the part of Dhihram Tenrisau, MSc Health Data Science summer project, 'Phylodynamic of Norovirus in UK 2003-2023'. The project is supervised by Stéphane Hué.
+
+By using this package, the hope will be emerging (not the the disease emerging) like the lyrics of the song 'Ah Ça Ira':
+
+*Ah ça ira, réjouis-toi!* (It'll be fine, rejoice!)
+
+*Ah ça ira, le bon temps viendra* (It'll be fine, good times will come)
 
 ## Installation
 
 You can install the development version of huebreaker from [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("devtools")
-devtools::install_github("Dhihram/huebreaker")
+devtools::install_github("Dhihram/caIRA")
 ```
 
 ## Example
 
 This package needs the additional package `tidyverse`, `ape`, `treeio`, and `dplyr`
 
 ```{r, warning=FALSE, message=FALSE}
-library(huebreaker)
 library(tidyverse)
 library(ape)
 library(dplyr)
 library(treeio)
+library(caIRA)
 ```
 
 ### Data
@@ -75,15 +81,12 @@ gg
 
 ### Package Utilization
 
-This package will utilize:
-1. Finding and clustering the monophylectic groups in the tree
-2. Add the parameter of the clusters: `bootstrap_treshold`, `data_range`, and `samearea`
-3. Keep the maximum monophylectic groups in the cluster identify
+This package will utilize: 1. Finding and clustering the monophylectic groups in the tree 2. Add the parameter of the clusters: `bootstrap_treshold`, `data_range`, and `samearea` 3. Keep the maximum monophylectic groups in the cluster identify
 
 the `bootstrap_treshold` is the minimum bootstrap value to be considered as a cluster. The `data_range` is the range of the days to be considered as a cluster. The `samearea` is the boolean value to consider the same area as a cluster.
 
 ```{r}
-res <- huebreaker(tree, metat, bootstrap_threshold = 80, date_range = 30, samearea = TRUE)
+res <- genclus(tree, metat, bootstrap_threshold = 80, date_range = 30, samearea = TRUE)
 knitr::kable(res)
 ```