From ceec784515ea5fc80329db8e27593f6ed8a97a2e Mon Sep 17 00:00:00 2001
From: "Zachary S.L Foster" <zacharyfoster1989@gmail.com>
Date: Tue, 1 May 2018 15:05:01 -0700
Subject: [PATCH] fixed a few examples

---
 R/heat_tree.R       | 2 +-
 R/parsers.R         | 5 +++--
 R/primersearch.R    | 7 +++----
 man/heat_tree.Rd    | 2 +-
 man/primersearch.Rd | 7 +++----
 5 files changed, 11 insertions(+), 12 deletions(-)

diff --git a/R/heat_tree.R b/R/heat_tree.R
index 5ed46e4..4fe56c8 100644
--- a/R/heat_tree.R
+++ b/R/heat_tree.R
@@ -363,7 +363,7 @@ heat_tree.Taxmap <- function(.input, ...) {
 #' #  You can plot up to 4 quantative variables use node/edge size/color, but it
 #' #  is usually best to use 2 or 3. The plot below uses node size for number of
 #' #  OTUs and color for number of reads and edge size for number of samples
-#' x$data$taxon_counts <- calc_n_samples(x, dataset = "taxon_counts", append = TRUE)
+#' x$data$n_samples <- calc_n_samples(x, dataset = "taxon_counts")
 #' heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = total,
 #'           edge_color = n_samples)
 #' 
diff --git a/R/parsers.R b/R/parsers.R
index 76997a2..b564bc4 100644
--- a/R/parsers.R
+++ b/R/parsers.R
@@ -573,16 +573,17 @@ parse_silva_fasta <- function(file = NULL, input = NULL, include_seqs = TRUE) {
   raw_headers <- names(raw_data)
   
   # Make classifications easier to parse
-  name_chars <- "A-Za-z0-9._+ \\-"
+  name_chars <- "A-Za-z0-9._+ ='\"\\-"
   parts <- stringr::str_match(raw_headers,
                               paste0("^(.+;)([", name_chars, "]+)(\\(?.*\\)?)$"))
   parts <- as.data.frame(parts[, -1], stringsAsFactors = FALSE)
   colnames(parts) <- c("tax", "binom", "common")
   parts$binom <- sub(parts$binom, pattern = "sp\\. ", replacement = "sp\\._")
   parts$binom <- sub(parts$binom, pattern = "uncultured ", replacement = "uncultured_")
-  parts$binom <- gsub(pattern = " ", replacement = ";", parts$binom) 
+  # parts$binom <- gsub(pattern = " ", replacement = ";", parts$binom) 
   parts$binom <- sub(pattern = ";$", replacement = " ", parts$binom)
   headers <- apply(parts, MARGIN = 1, paste0, collapse = "")
+  headers <- gsub(headers, pattern = "\\[|\\]", replacement = "")
   
   # Create taxmap object
   output <- taxa::extract_tax_data(tax_data = headers,
diff --git a/R/primersearch.R b/R/primersearch.R
index 89410cd..c48520c 100644
--- a/R/primersearch.R
+++ b/R/primersearch.R
@@ -210,10 +210,9 @@ parse_primersearch <- function(file_path) {
 #' 
 #' # Visualize which taxa were amplified
 #' #  This work because only amplicons are returned by `primersearch`
-#' n_amplified <- obj$obs_apply("pcr",
-#'                              function(x) length(unique(x)),
-#'                              value = "input",
-#'                              simplify = TRUE)
+#' n_amplified <- unlist(obj$obs_apply("pcr",
+#'                                     function(x) length(unique(x)),
+#'                                     value = "input"))
 #' prop_amped <- n_amplified / obj$n_obs()
 #' heat_tree(obj,
 #'           node_label = taxon_names, 
diff --git a/man/heat_tree.Rd b/man/heat_tree.Rd
index 710c523..784c814 100644
--- a/man/heat_tree.Rd
+++ b/man/heat_tree.Rd
@@ -412,7 +412,7 @@ heat_tree(x, node_label = taxon_names, node_size = total, node_color = total)
 #  You can plot up to 4 quantative variables use node/edge size/color, but it
 #  is usually best to use 2 or 3. The plot below uses node size for number of
 #  OTUs and color for number of reads and edge size for number of samples
-x$data$taxon_counts <- calc_n_samples(x, dataset = "taxon_counts", append = TRUE)
+x$data$n_samples <- calc_n_samples(x, dataset = "taxon_counts")
 heat_tree(x, node_label = taxon_names, node_size = n_obs, node_color = total,
           edge_color = n_samples)
 
diff --git a/man/primersearch.Rd b/man/primersearch.Rd
index 4b7cd17..1a47c9c 100644
--- a/man/primersearch.Rd
+++ b/man/primersearch.Rd
@@ -156,10 +156,9 @@ obj$data$pcr$taxon_id <- obj$data$tax_data$taxon_id[pcr_result$input]
 
 # Visualize which taxa were amplified
 #  This work because only amplicons are returned by `primersearch`
-n_amplified <- obj$obs_apply("pcr",
-                             function(x) length(unique(x)),
-                             value = "input",
-                             simplify = TRUE)
+n_amplified <- unlist(obj$obs_apply("pcr",
+                                    function(x) length(unique(x)),
+                                    value = "input"))
 prop_amped <- n_amplified / obj$n_obs()
 heat_tree(obj,
           node_label = taxon_names,