vallenderlab · sdhutchins · Feb 27, 2019 · Jan 24, 2019 · Jan 28, 2019 · Jan 28, 2019
diff --git a/.Rbuildignore b/.Rbuildignore
@@ -5,3 +5,5 @@
 ^\.travis\.yml$
 ^.*\.Rproj$
 ^\.Rproj\.user$
+CNAME
+^pkgdown$
diff --git a/.travis.yml b/.travis.yml
@@ -10,15 +10,15 @@ branches:
   only:
   - master
   - dev-master
-  - build-fixes
-
 
 env:
   global:
     - _R_CHECK_FORCE_SUGGESTS_=false
     - R_REMOTES_NO_ERRORS_FROM_WARNINGS=true
+    - CODECOV_TOKEN=2ff74b01-00e8-4e2f-9cc7-1f90ca17e480
 
 r:
+- oldrel
 - release
 - devel
 

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,9 +1,10 @@
 Package: MicrobiomeR
 Title: Analyze Microbiome Data
-Version: 0.4.1
+Version: 0.5.1
 Authors@R: c(
     person("Robert", "Gilmore", email = "rgilmore@umc.edu", role = "cre"),
     person("Shaurita", "Hutchins", email = "shutchins2@umc.edu", role = "aut"))
+Maintainer: Rob Gilmore <rgilmore@umc.edu>
 License: MIT + file LICENSE
 biocViews: Metagenomics, Microbiome, Sequencing, SystemsBiology
 Description: An R package for microbiome analysis combining functions from phyloseq, metacodeR, 
@@ -16,48 +17,50 @@ Remotes:
     github::grunwaldlab/metacoder,
     github::joey711/phyloseq,
     github::microbiome/microbiome,
+    github::r-lib/covr,
     github::jonclayden/shades
 Imports:
     ape,
+    biomformat,
+    crayon,
     DT,
     data.table,
     diptest,
     dplyr,
-    rlang,
+    forcats,
+    ggplot2,
+    ggpubr,
+    ggthemes,
+    ggrepel,
     glue,
+    htmlwidgets,
+    htmltools,
+    leaflet,
+    magrittr,
     metacoder,
-    modes,
     microbiome (>= 1.5.27),
+    modes,
     openxlsx,
     phyloseq,
     plotly,
     purrr,
+    rlang,
+    rstudioapi,
+    scales,
     scico,
+    shades,
+    stringr,
     taxa,
     tibble,
     tidyr,
-    viridis,
-    yaml,
-    ggplot2,
-    stringr,
-    rstudioapi,
-    magrittr,
-    ggpubr,
-    ggthemes,
-    crayon,
-    biomformat,
-    forcats,
-    ggrepel,
-    scales,
     vegan,
-    leaflet,
-    htmlwidgets,
-    shades
+    viridis,
+    yaml
 Suggests:
+    covr,
     knitr,
-    testthat,
     rmarkdown,
-    covr
+    testthat
 Additional_repositories:
     http://bioconductor.org/packages/release/bioc/,
     http://bioconductor.org/packages/release/data/annotation,
@@ -66,6 +69,6 @@ Additional_repositories:
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 6.1.1
-URL: https://github.com/vallenderlab/MicrobiomeR
+URL: https://github.com/vallenderlab/MicrobiomeR, https://microbiomer.vallenderlab.science/
 BugReports: https://github.com/vallenderlab/MicrobiomeR/issues
 VignetteBuilder: knitr
diff --git a/NAMESPACE b/NAMESPACE
@@ -1,7 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
-export(agglomerate_metacoder)
+export(agglomerate_taxmap)
 export(alpha_diversity_measures)
 export(alpha_diversity_plot)
 export(as_MicrobiomeR_format)
@@ -15,9 +15,9 @@ export(correlation_data)
 export(correlation_plot)
 export(correlation_plots)
 export(cov_filter)
-export(create_metacoder)
 export(create_phyloseq)
 export(create_pub_table)
+export(create_taxmap)
 export(get_color_palette)
 export(heat_tree_parameters)
 export(heat_tree_plots)
@@ -59,7 +59,6 @@ export(viridis_magma_palette)
 export(viridis_palette)
 export(vlookup)
 export(which_format)
-import(scales)
 import(vegan)
 importFrom(ape,Ntip)
 importFrom(ape,is.rooted)
@@ -155,6 +154,8 @@ importFrom(purrr,modify_at)
 importFrom(rlang,enquos)
 importFrom(rlang,eval_tidy)
 importFrom(rlang,is_quosure)
+importFrom(scales,percent)
+importFrom(scales,pretty_breaks)
 importFrom(scico,scico)
 importFrom(shades,saturation)
 importFrom(shades,scalefac)

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,32 @@
+# MicrobiomeR 0.5.1
+* A specific bug was introduced that prevented local machines and Travis CI servers from building 
+the package.
+  * This bug is attributed to a dplyr update (0.7.8 -> 0.8.0.1)
+  * See [Joss branch #79](https://github.com/vallenderlab/MicrobiomeR/pull/79)
+  * See [Joss debug branch #82](https://github.com/vallenderlab/MicrobiomeR/pull/82)
+  * See [dplyr issue #4213](https://github.com/tidyverse/dplyr/issues/4213)
+  * See [Travis CI forum post](https://travis-ci.community/t/travis-build-ignoring-r-package-version-in-description/2431)
+* Updated the paper for submission
+
+
+# MicrobiomeR 0.5.0
+
+* Added the JOSS paper and draft vignette
+* Changed correlation plots
+  * Removed color from background
+  * Removed hard coded plot limits
+  * Added "1:1" line and Average lines
+  * Added a `trans` parameter to transform the x and y axis
+* Changed function names
+  * From `create_metacoder` to `create_taxmap`
+  * From `agglomerate_metacoder` to `agglomerate_taxmap`
+  * From `melt_metacoder` to `melt_taxmap`
+* Bugs
+  * Fixed `output_dir` bug where error should have been a warning
+  * Fixed correlation plot bug where treatments were on the wrong axis.
+  * Fixed output message for heat tree plots.
+* Fixed build-check Notes and Warnings
+
 # MicrobiomeR 0.4.1
 
 * Fixed wilcoxon pvalue in analysis vignette.
@@ -8,7 +37,7 @@
 ## Renamed functions
 
 * `get_alpha_diversity_measures` to `alpha_diversity_measures`
-  
+
 ## Added functions
 
 * Added `stacked_barplots`, `alpha_diversity_plots`, `ordination_plots`

diff --git a/R/alpha-diversity.R b/R/alpha-diversity.R
@@ -1,6 +1,6 @@
 #' @title Alpha Diveristy Measues
 #' @description This function generates various alpha diversity measures include Shannon, Fisher, Coverage, Gini Simpson, and Inverse Simpson.
-#' @param obj An object to be converted to a metacoder object with \code{\link[MicrobiomeR]{create_metacoder}}.
+#' @param obj An object to be converted to a Taxmap object with \code{\link[MicrobiomeR]{create_taxmap}}.
 #' @param group The "TreatmentGroup" or similar grouping from your metadata to denote sample groups, Default: 'TreatmentGroup'
 #' @return Returns a list of alpha diversity measures with metadata.
 #' @pretty_print TRUE
@@ -22,11 +22,11 @@
 #' @importFrom utils combn
 alpha_diversity_measures <- function(obj, group = "TreatmentGroup") {
   metacoder_object <- validate_MicrobiomeR_format(
-    obj = create_metacoder(obj),
+    obj = create_taxmap(obj),
     valid_formats = c("analyzed_format")
   )
 
-  # Convert metacoder object to a phyloseq object.
+  # Convert Taxmap object to a phyloseq object.
   phyloseq_object <- metacoder::as_phyloseq(metacoder_object, otu_table = "otu_abundance", phy_tree = "phy_tree")
 
   # Get all of the diversities.
@@ -52,7 +52,7 @@ alpha_diversity_measures <- function(obj, group = "TreatmentGroup") {
 
 #' @title Alpha Diversity Plot
 #' @description Plot the alpha diversity using a violin plot. `alpha_diversity_plots` generates plots for all alpha diversity measures.
-#' @param obj An object to be converted to a metacoder object with \code{\link[MicrobiomeR]{create_metacoder}}.
+#' @param obj An object to be converted to a Taxmap object with \code{\link[MicrobiomeR]{create_taxmap}}.
 #' @param measure Select an alpha diversity measure such as shannon, gini simpson, and inverse simpson, Default: 'shannon'
 #' @param group The "TreatmentGroup" or similar grouping or column from your metadata to denote sample groups, Default: 'TreatmentGroup'
 #' @param select_otu_table Choose an otu table to analyze, Default: 'otu_proportions'
@@ -65,7 +65,9 @@ alpha_diversity_measures <- function(obj, group = "TreatmentGroup") {
 #' if (interactive()) {
 #'   library(MicrobiomeR)
 #'   data <- analyzed_silva
-#'   plot <- alpha_diversity_plot(obj = data, measure = "shannon", select_otu_table = "otu_proportions")
+#'   plot <- alpha_diversity_plot(obj = data,
+#'                                measure = "shannon",
+#'                                select_otu_table = "otu_proportions")
 #'   plot
 #' }
 #' }
@@ -81,7 +83,7 @@ alpha_diversity_measures <- function(obj, group = "TreatmentGroup") {
 alpha_diversity_plot <- function(obj, measure = "shannon", group = "TreatmentGroup", select_otu_table = "otu_proportions", title = NULL) {
   # Validate data format
   metacoder_object <- validate_MicrobiomeR_format(
-    obj = create_metacoder(obj),
+    obj = create_taxmap(obj),
     valid_formats = c("analyzed_format")
   )
   metacoder_object$data$sample_data[[measure]] <- vegan::diversity(metacoder_object$data[[select_otu_table]][, metacoder_object$data$sample_data$X.SampleID],

diff --git a/R/barplot.R b/R/barplot.R
@@ -1,8 +1,10 @@
+#' @title Melt Taxmap
+#' @param obj An object to be converted to a taxmap object with \code{\link[MicrobiomeR]{create_taxmap}}.
 #' @importFrom dplyr right_join setdiff
 #' @importFrom tidyr gather_
 #' @family Formatting
-#' @rdname stacked_barplot
-melt_metacoder <- function(obj) {
+#' @rdname melt_taxmap
+melt_taxmap <- function(obj) {
   sd <- data.frame(obj$data$sample_data)
   TT <- data.frame(obj$data$otu_annotations, stringsAsFactors = FALSE)
   otu.table <- data.frame(obj$data$otu_proportions, check.names = FALSE, stringsAsFactors = FALSE)
@@ -13,12 +15,13 @@ melt_metacoder <- function(obj) {
     rename(SampleID = `X.SampleID`) %>%
     rename(OTU = `otu_id`)
 }
-
+#' @title Convert Proportions
 #' @param melted_df A "melted" dataframe from the metacoder object's data.
+#' @param tax_level The taxonomic level, Default: 'Phylum'
 #' @importFrom dplyr filter group_by summarize mutate enquo quo_name
 #' @importFrom stats na.omit
 #' @family Data Manipulators
-#' @rdname stacked_barplot
+#' @rdname convert_proportions
 convert_proportions <- function(melted_df, tax_level) {
   t <- dplyr::enquo(tax_level)
   tax_level.abund <- paste0(dplyr::quo_name(t), ".Abundance")
@@ -34,7 +37,7 @@ convert_proportions <- function(melted_df, tax_level) {
 #' @title Stacked Barplot
 #' @description Create a stacked barplot to show relative abundance of taxa. `convert_proportions` converts the dataframe abundance values to percent 100 and returns a transformed dataframe.
 #' `melt_metacoder` melts the metacoder or phyloseq tables into a dataframe and returns a melted dataframe. `stacked_barplots` creates a stacked barplots for multiple taxonomic levels and returns a list of stacked barplots.
-#' @param obj An object to be converted to a metacoder object with \code{\link[MicrobiomeR]{create_metacoder}}.
+#' @param obj An object to be converted to a taxmap object with \code{\link[MicrobiomeR]{create_taxmap}}.
 #' @param tax_level The taxonomic level, Default: 'Phylum'
 #' @param fill The taxonomic level by which the bars are filled, Default: 'Phylum'
 #' @param xlabel The label of the x axis, Default: 'Samples'
@@ -55,8 +58,8 @@ convert_proportions <- function(melted_df, tax_level) {
 #' @importFrom ggplot2 ggplot aes annotate geom_bar ylab element_blank element_rect xlab annotate
 #' @importFrom magrittr %>%
 #' @importFrom dplyr mutate
+#' @importFrom scales pretty_breaks
 #' @importFrom shades scalefac saturation
-#' @import scales
 #' @import vegan
 #'
 #' @inheritParams convert_proportions
@@ -66,13 +69,13 @@ convert_proportions <- function(melted_df, tax_level) {
 #' @export
 stacked_barplot <- function(obj, tax_level = "Phylum", fill = "Phylum", xlabel = "Samples", faceted = FALSE, title = NULL, palette_values = NULL) {
   metacoder_object <- validate_MicrobiomeR_format(
-    obj = create_metacoder(obj),
+    obj = create_taxmap(obj),
     valid_formats = c("analyzed_format")
   )
 
   # Start by melting the data in the "standard" way using psmelt.
   # Also, transform the abundance data to relative abundance
-  mdf <- convert_proportions(melt_metacoder(metacoder_object), tax_level)
+  mdf <- convert_proportions(melt_taxmap(metacoder_object), tax_level)
   mdf <- dplyr::mutate(mdf, !!sym(tax_level) := factor(!!sym(tax_level), levels = unique(mdf[[tax_level]])))
 
   # Build the plot data structure
@@ -116,12 +119,13 @@ stacked_barplot <- function(obj, tax_level = "Phylum", fill = "Phylum", xlabel =
   return(p)
 }
 
-
+#' @title Stacked Barplots
 #' @param tax_levels The taxonomic levels, Default: 'c("Phylum", "Class", "Order")'
+#' @param obj An object to be converted to a taxmap object with \code{\link[MicrobiomeR]{create_taxmap}}.
 #' @family Visualizations
-#' @rdname stacked_barplot
+#' @rdname stacked_barplots
 #' @export
-stacked_barplots <- function(obj, tax_levels = c("Phylum", "Class", "Order"), group = "TreatmentGroup", select_otu_table = "otu_proportions") {
+stacked_barplots <- function(obj, tax_levels = c("Phylum", "Class", "Order")) {
   if (is.null(tax_levels)) {
     tax_levels <- c("Phylum", "Class", "Order")
   } else if (length(tax_levels) < 2) {