Moved recent changes to fix-structure-plot branch.

DESCRIPTION

@@ -1,8 +1,8 @@
 Encoding: UTF-8
 Type: Package
 Package: fastTopics
-Version: 0.7-4
-Date: 2025-01-29
+Version: 0.7-03
+Date: 2024-01-29
 Title: Fast Algorithms for Fitting Topic Models and Non-Negative
     Matrix Factorizations to Count Data
 Authors@R: c(person("Peter","Carbonetto",role=c("aut","cre"),
@@ -47,9 +47,7 @@ Imports:
     gtools,
     quadprog,
     irlba,
-    rlang,
     dplyr,
-    tidyr,
     Rtsne,
     uwot,
     ashr,

NAMESPACE

@@ -72,24 +72,16 @@ importFrom(Rtsne,Rtsne)
 importFrom(ashr,ash)
 importFrom(cowplot,plot_grid)
 importFrom(cowplot,theme_cowplot)
-importFrom(dplyr,arrange)
-importFrom(dplyr,coalesce)
-importFrom(dplyr,group_by)
-importFrom(dplyr,join_by)
-importFrom(dplyr,lag)
-importFrom(dplyr,left_join)
-importFrom(dplyr,mutate)
 importFrom(dplyr,select)
-importFrom(dplyr,ungroup)
 importFrom(ggplot2,aes_q)
 importFrom(ggplot2,aes_string)
 importFrom(ggplot2,after_stat)
 importFrom(ggplot2,element_blank)
 importFrom(ggplot2,element_text)
 importFrom(ggplot2,geom_boxplot)
+importFrom(ggplot2,geom_col)
 importFrom(ggplot2,geom_line)
 importFrom(ggplot2,geom_point)
-importFrom(ggplot2,geom_tile)
 importFrom(ggplot2,ggplot)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,scale_color_manual)
@@ -118,7 +110,6 @@ importFrom(plotly,layout)
 importFrom(plotly,plot_ly)
 importFrom(progress,progress_bar)
 importFrom(quadprog,solve.QP)
-importFrom(rlang,.data)
 importFrom(stats,dpois)
 importFrom(stats,formula)
 importFrom(stats,glm)
@@ -134,7 +125,6 @@ importFrom(stats,rnorm)
 importFrom(stats,rpois)
 importFrom(stats,runif)
 importFrom(stats,summary.glm)
-importFrom(tidyr,crossing)
 importFrom(utils,combn)
 importFrom(utils,modifyList)
 importFrom(utils,read.table)

R/fit_poisson_nmf.R

@@ -455,7 +455,7 @@ fit_poisson_nmf <- function (X, k, fit0, numiter = 100,
     cat(sprintf("Running at most %d %s updates, %s extrapolation ",
                 numiter,method.text,
                 ifelse(control$extrapolate,"with","without")))
-    cat("(fastTopics 0.7-4).\n")
+    cat("(fastTopics 0.7-3).\n")
   }
 
   # INITIALIZE ESTIMATES

R/structure_plot.R

@@ -79,9 +79,6 @@
 #'   \code{structure_plot_ggplot_call} with your own function to
 #'   customize the appearance of the plot.
 #'
-#' @param res The resolution of the tiling along the y-axis
-#'   (in units of topic probabilities).
-#'
 #' @param \dots Additional arguments passed to \code{structure_plot}
 #'   (for the \code{plot} method) or \code{embed_method} (for
 #'   function \code{structure_plot}).
@@ -170,10 +167,9 @@
 #'
 structure_plot <-
   function (fit, topics, grouping, loadings_order = "embed", n = 2000,
-            colors, gap = 1, 
+            colors, gap = 1,
             embed_method = structure_plot_default_embed_method,
-            ggplot_call = structure_plot_ggplot_call, res = 0.01,
-            ...) {
+            ggplot_call = structure_plot_ggplot_call, ...) {
 
   # Check and process input argument "fit".
   if (is.matrix(fit)) {
@@ -270,7 +266,7 @@ structure_plot <-
     return(ggplot_call(dat,colors))
   } else {
     out <- compile_grouped_structure_plot_data(fit$L,topics,grouping,gap)
-    return(ggplot_call(out$dat,colors,out$ticks,res = res))
+    return(ggplot_call(out$dat,colors,out$ticks))
   }
 }
 
@@ -332,21 +328,12 @@ plot.multinom_topic_model_fit <- function (x, ...)
 #'   \code{ticks = NULL}.
 #'
 #' @param font.size Font size used in plot.
-#'
-#' @importFrom rlang .data
-#' @importFrom dplyr group_by
-#' @importFrom dplyr ungroup
-#' @importFrom dplyr arrange
-#' @importFrom dplyr mutate
-#' @importFrom dplyr coalesce
-#' @importFrom dplyr lag
-#' @importFrom dplyr left_join
-#' @importFrom dplyr join_by
-#' @importFrom tidyr crossing
+#' 
 #' @importFrom ggplot2 ggplot
 #' @importFrom ggplot2 aes_string
-#' @importFrom ggplot2 geom_tile
+#' @importFrom ggplot2 geom_col
 #' @importFrom ggplot2 scale_x_continuous
+#' @importFrom ggplot2 scale_color_manual
 #' @importFrom ggplot2 scale_fill_manual
 #' @importFrom ggplot2 labs
 #' @importFrom ggplot2 theme
@@ -357,32 +344,19 @@ plot.multinom_topic_model_fit <- function (x, ...)
 #' @export
 #'
 structure_plot_ggplot_call <- function (dat, colors, ticks = NULL,
-                                        font.size = 9, res = 0.01) {
-  dat <- group_by(dat,.data$sample)
-  dat <- arrange(dat,.data$topic)
-  dat <- mutate(dat,"top" = cumsum(.data$prop))
-  dat <- mutate(dat,"bot" = coalesce(lag(.data$top),0))
-  dat <- ungroup(dat)
-  dat <- dat[,c("sample","topic","top","bot")]
-  upper <- max(dat$top)
-  res   <- res/upper
-  rasterdat <- dat["sample"]
-  rasterdat <- crossing(rasterdat,
-                        data.frame("y" = seq(0 + res/2,upper - res/2,
-                                             by = res)))
-  rasterdat <- left_join(rasterdat,dat,
-                         by = join_by("sample",x$y > y$bot,x$y < y$top))
-  return(ggplot(rasterdat,aes_string(x = "sample",y = "y",fill = "topic")) +
-         geom_tile() +
-         scale_fill_manual(values = colors) +
-         scale_x_continuous(limits = c(0,max(dat$sample) + 1),breaks = ticks,
-                            labels = names(ticks)) +
-         labs(x = "",y = "topic proportion") +
-         theme_cowplot(font.size) +
-         theme(axis.line   = element_blank(),
-               axis.ticks  = element_blank(),
-               axis.text.x = element_text(angle = 45,hjust = 1)))
-}
+                                        font.size = 9)
+  ggplot(dat,aes_string(x = "sample",y = "prop",color = "topic",
+                        fill = "topic")) +
+    geom_col() +
+    scale_x_continuous(limits = c(0,max(dat$sample) + 1),breaks = ticks,
+                       labels = names(ticks)) +
+    scale_color_manual(values = colors) +
+    scale_fill_manual(values = colors) +
+    labs(x = "",y = "topic proportion") +
+    theme_cowplot(font.size) +
+    theme(axis.line   = element_blank(),
+          axis.ticks  = element_blank(),
+          axis.text.x = element_text(angle = 45,hjust = 1))
 
 # This is used by structure_plot to create a data frame suitable for
 # plotting with 'ggplot'. Input argument L is the topic proportions