Cleaning before v 0.51

DESCRIPTION

@@ -71,6 +71,7 @@ Suggests:
     testthat (>= 3.0.0),
     tibble,
     tidyr,
+    tidytree,
     treeio,
     treemapify,
     utils, 

R/controls.R

@@ -24,7 +24,7 @@ search_exact_seq_pq <- function(physeq, sequences) {
   for (i in seq_along(sequences)) {
     original <- sequences[[i]]
     rev <- reverse(sequences[[i]])
-    rev_comp <-  Biostrings::reverseComplement(sequences[[i]])
+    rev_comp <- Biostrings::reverseComplement(sequences[[i]])
     comp <- complement(sequences[[i]])
 
     original_count <- sum(grepl(original, physeq@refseq))

R/dada_phyloseq.R

@@ -1784,13 +1784,15 @@ tbl_sum_samdata <- function(physeq, remove_col_unique_value = TRUE, ...) {
 #' @author Adrien Taudière
 #' @examples
 #' df <- subset_taxa_pq(data_fungi, taxa_sums(data_fungi) > 5000)
+#' \dontrun{
 #' df <- add_funguild_info(df,
 #'   taxLevels = c(
 #'     "Domain", "Phylum", "Class", "Order",
 #'     "Family", "Genus", "Species"
 #'   )
 #' )
 #' sort(table(df@tax_table[, "guild"]), decreasing = TRUE)
+#' }
 #' @details
 #' This function is mainly a wrapper of the work of others.
 #'   Please make a reference to `FUNGuildR::funguild_assign()` if you
@@ -1846,6 +1848,7 @@ add_funguild_info <- function(physeq,
 #' @export
 #' @author Adrien Taudière
 #' @examples
+#' \dontrun{
 #' df <- subset_taxa_pq(data_fungi, taxa_sums(data_fungi) > 5000)
 #' df <- add_funguild_info(df,
 #'   taxLevels = c(
@@ -1863,6 +1866,7 @@ add_funguild_info <- function(physeq,
 #'     levels_order = p$data$Guild[order(p$data$nb_seq)]
 #'   ) +
 #'     ylab("") + theme(axis.text.y = element_blank()))
+#' }
 #' @seealso [add_funguild_info()]
 
 plot_guild_pq <-

tests/testthat/test_tuckey.R

@@ -1,8 +1,9 @@
 data("GlobalPatterns", package = "phyloseq")
-GlobalPatterns@sam_data[, "Soil_logical"] <- ifelse(GlobalPatterns@sam_data[, "SampleType"] == "Soil", "Soil", "Not Soil")
+GlobalPatterns@sam_data[, "Soil_logical"] <-
+  ifelse(GlobalPatterns@sam_data[, "SampleType"] == "Soil", "Soil", "Not Soil")
 test_that("hill_tuckey_pq function works fine with GlobalPatterns dataset", {
-  expect_silent(hill_tuckey_pq(GlobalPatterns, "Soil_logical"))
-  expect_silent(hill_tuckey_pq(GlobalPatterns, "SampleType"))
+  expect_silent(suppressMessages(hill_tuckey_pq(GlobalPatterns, "Soil_logical")))
+  expect_silent(suppressMessages(hill_tuckey_pq(GlobalPatterns, "SampleType")))
   expect_message(hill_tuckey_pq(GlobalPatterns, "SampleType", silent = FALSE))
   expect_s3_class(hill_tuckey_pq(GlobalPatterns, "SampleType"), "ggplot")
   expect_error(hill_tuckey_pq(GlobalPatterns, "SampleTYPE"))

vignettes/MiscMetabar.Rmd

@@ -7,6 +7,15 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  message = FALSE
+)
+```
+
 # Introduction to MiscMetabar : an R packages to facilitate visualization and reproducibility in metabarcoding analysis
 
 ## Raison d'être
@@ -26,7 +35,7 @@ For developers, I also have a vignette describing som [rules of codes](https://a
 
 ### Summarize a physeq object
 
-```{r example, message=FALSE}
+```{r example}
 library("MiscMetabar")
 library("phyloseq")
 library("magrittr")

vignettes/Reclustering.Rmd

@@ -11,11 +11,13 @@ bibliography: bibliography.bib
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  message = FALSE
 )
 ```
 
-```{r setup, message=FALSE}
+
+```{r setup}
 library(MiscMetabar)
 ```
 
@@ -48,7 +50,7 @@ summary_plot_pq(otu)
 
 Another post-clustering transformation method is implemented in `lulu_pq()`, which uses @froslev2017's method for curation of DNA amplicon data. The aim is more to clean non-biological information than to make explicitly less clusters.
 
-```{r, message=FALSE, results="hide"}
+```{r, results="hide"}
 library("lulu")
 data(data_fungi_sp_known)
 lulu_res <- lulu_pq(data_fungi_sp_known)
@@ -70,4 +72,4 @@ track_wkflow(list(
 ))
 ```
 
-# References
+# References

vignettes/alpha-div.Rmd

@@ -12,7 +12,8 @@ editor_options:
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  message = FALSE
 )
 ```
 

vignettes/beta-div.Rmd

@@ -7,10 +7,12 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
+
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  message = FALSE
 )
 ```
 

vignettes/bibliography.bib

@@ -313,6 +313,13 @@ @article{smith2019
   journal   = {Journal of Open Source Software}
 }
 
+@manual{smith2023,
+  title  = {phylosmith: Functions to help analyze data as phyloseq objects},
+  author = {Schuyler Smith},
+  year   = {2023},
+  note   = {R package version 1.0.7},
+  url    = {https://schuyler-smith.github.io/phylosmith/}
+}
  @misc{taberlet2012,
   doi       = {10.1002/(issn)2637-4943},
   title     = {Environmental dna},

vignettes/filter.Rmd

@@ -10,7 +10,8 @@ vignette: >
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  message = FALSE
 )
 ```
 

vignettes/import_export_track.Rmd

@@ -8,11 +8,11 @@ vignette: >
 ---
 
 
-
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  message = FALSE
 )
 ```
 
@@ -51,4 +51,4 @@ In bioinformatic pipeline, we often need to track the number of samples, sequenc
 
 ```{r}
 track_wkflow(list(data_fungi, data_fungi_sp_known))
-```
+```

vignettes/tengeler.Rmd

@@ -8,6 +8,15 @@ vignette: >
 ---
 This tutorial explore the dataset from Tengeler et al. (2020) available in the `mia` package.
 
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  message = FALSE
+)
+```
+
 # Load library 
 
 ```{r}

vignettes/tree_visualization.Rmd

@@ -8,12 +8,30 @@ vignette: >
 bibliography: bibliography.bib
 ---
 
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  message = FALSE,
+  warning = FALSE,
+  fig.width = 10,
+  fig.height = 8
+)
+
+quiet <- function(x) {
+  sink(tempfile())
+  on.exit(sink())
+  invisible(force(x))
+}
+```
+
 ```{r}
+library("tidytree") # first load to disable warning about phylo class
 library("MiscMetabar")
 library("phangorn")
 df <- subset_taxa_pq(data_fungi, taxa_sums(data_fungi) > 9000)
-df_tree <- build_phytree_pq(df, nb_bootstrap = 5)
-data_fungi_tree <- merge_phyloseq(df, df_tree$ML$tree)
+df_tree <- quiet(build_phytree_pq(df, nb_bootstrap = 5))
+data_fungi_tree <- merge_phyloseq(df, phyloseq::phy_tree(df_tree$ML$tree))
 ```
 
 
@@ -27,17 +45,18 @@ ggdensitree(df_tree$ML_bs, alpha = .3, colour = "steelblue") +
   geom_tiplab(size = 3) + hexpand(.35)
 ggtree(as.treedata(df_tree$ML)) +
   geom_text(aes(x = branch, label = AA_subs, vjust = -.5), size = 1)
-tax_tab <- as_tibble(data_fungi_tree@tax_table) |>
-  dplyr::mutate(.otu = as.character(.otu))
+```
+
+```{r}
+tax_tab <- as.data.frame(data_fungi_tree@tax_table)
+tax_tab <- data.frame("OTU" = rownames(tax_tab), tax_tab)
 p <- ggtree(as.treedata(data_fungi_tree@phy_tree)) %<+%
-  as_tibble(data_fungi_tree@tax_table)
+  tax_tab
 p + geom_tippoint(aes(color = Class, shape = Phylum)) +
   geom_text(aes(label = Genus), hjust = -0.2, size = 2)
 ggtree(as.treedata(data_fungi_tree@phy_tree), branch.length = "none") %<+%
-  as_tibble(data_fungi_tree@tax_table) +
+  tax_tab +
   geom_tippoint(aes(color = Class, shape = Phylum), size = 2)
-
-# removeClass("phylo", where = "package:tidytree")
 ```