@@ -853,28 +853,29 @@ selectize_frequent_ngrams<- function(e, TN, CF, minN, maxN, threshold){
 frequent_ngrams <- function(e, TN, CF, minN, maxN, threshold){
 
   # initialize the output
-  f = count_ngrams(e,TN, CF,minN)
+  ng = count_ngrams(e,TN, CF,minN)
 
   if (maxN > minN){
       for (i in seq(minN+1,maxN,1)){
-        f = rbind(f,count_ngrams(e,TN, CF,i)) }
+        ng = rbind(ng,count_ngrams(e,TN, CF,i)) }
   }
   # remove the rows that happen once and only keep the columns we want
-  f=f[f$freq>threshold,c('ngrams','freq')]
+  ng=ng[ng$freq>threshold,c('ngrams','freq')]
 
   # just take the maximal ones
-   f=maximal_ngrams(f)
+   ng=maximal_ngrams(ng)
 
   # return the set sorted by most frequent
-  return(f[order(-f$freq),])
+  return(ng[order(-ng$freq),])
 }
 
 # this filters out ngrams that are contained within others ('2 2' is part of '2 2 2')
-maximal_ngrams <- function(f){
+
+maximal_ngrams <- function(ng){
 
   # find out if each ngram is contained in all the others
-  w = lapply(1:nrow(f), function(i){
-         grep(f$ngrams[i],f$ngrams)}
+  w = lapply(1:nrow(ng), function(i){
+         grep(ng$ngrams[i],ng$ngrams)}
          )
 
   # get howMany times each one appears
@@ -883,14 +884,22 @@ maximal_ngrams <- function(f){
   )
 
   # return the ones that are unique
-  return(f[which(howMany==1),])
+  return(ng[which(howMany==1),])
 }
 
 # compute support level for each ngram
 # tv = text vectors for the threads
 # ng = frequent ngrams data frame
 support_level <- function(tv, ng) {
 
+  # create a support matrix where rows are threads and columns are the ngrams
+  s = matrix(nrow=length(tv), ncol=nrow(ng),0)
+  print(s)
+
+  # find out how many times each ngram is contained in each TV
+  w = lapply(1:nrow(ng), function(i){
+    grep(ng$ngrams[i],tv)}
 
+  )
 
 }
@@ -261,6 +261,18 @@ ng_bar_chart <- function(o,TN, CF, n, mincount){
   return(ngp)
 }
 
+# this version we pass in the ngram data frame already sorted
+ng_bar_chart_freq <- function(ngdf){
+
+  # put them in descending order -- tricky (http://stackoverflow.com/questions/40224892/r-plotly-barplot-sort-by-value)
+    ngdf$ngrams = factor(ngdf$ngrams, levels =unique(ngdf$ngrams)[order(ngdf$freq, decreasing = FALSE)])
+
+  ngp <- plot_ly( ngdf, y = ~ngrams, x = ~freq, type = "bar",showlegend=FALSE) %>%
+    layout(xaxis= list(showticklabels = TRUE, title='Frequency'))  %>%
+    layout(yaxis= list(showticklabels = FALSE, title=''))
+
+  return(ngp)
+}
 
 
 #############################################################################
@@ -546,25 +558,25 @@ Comparison_Plots <- function(e, CF, CF_levels, nTimePeriods=1, ng_size , zoom_le
 #'
 #' @return standard R plot
 #' @export
-traminer_threadMap <- function(df,TN, CF){
-
-  # setting color palettes
-  # first find the number of distinct colors
-  nColors = length(unique(df[,CF]))
-  pal <- diverge_hcl(nColors)
-
-  #  reformat the data for traminerR
-  df = convert_TN_to_TramineR(df, TN, CF)
-
-
-  #plot sequence - also try seqfplot?
-  # add grouping variable?
-  return(seqiplot( seqdef(df, cpal=pal) , withlegend = T, main = "ThreadMap", border = NA,idxs=1:nrow(df)) )
-
-  #  return(seqfplot( seqdef(df, cpal=pal) , withlegend = T, main = "ThreadMap", border = NA,idxs=1:nrow(df)) )
-
-
-}
+# traminer_threadMap <- function(df,TN, CF){
+#
+#   # setting color palettes
+#   # first find the number of distinct colors
+#   nColors = length(unique(df[,CF]))
+#   pal <- diverge_hcl(nColors)
+#
+#   #  reformat the data for traminerR
+#   df = convert_TN_to_TramineR(df, TN, CF)
+#
+#
+#   #plot sequence - also try seqfplot?
+#   # add grouping variable?
+#   return(seqiplot( seqdef(df, cpal=pal) , withlegend = T, main = "ThreadMap", border = NA,idxs=1:nrow(df)) )
+#
+#   #  return(seqfplot( seqdef(df, cpal=pal) , withlegend = T, main = "ThreadMap", border = NA,idxs=1:nrow(df)) )
+#
+#
+# }
 
 # This one is not currently used.
 threadLengthBarchart <- function(o, TN){
 
@@ -150,8 +150,10 @@ ui <- fluidPage(
 
                           tabPanel("Frequent ngrams",
                                    uiOutput("freqnGramControls"),
-                                   tableOutput("freqnGramTable"),
-                                   plotlyOutput("freqnGramBarchart")
+                                   fluidRow(
+                                     column(2, tableOutput("freqnGramTable")),
+                                     column(10,plotlyOutput("freqnGramBarchart"))
+                                   )
                                   ),
 
                           tabPanel("Whole Sequences",
 
@@ -580,16 +580,18 @@ server <- shinyServer(function(input, output, session) {
 
 
   #  frequent NGRAM  displays #
+  # just compute this data once -- not sure of the best way to display it... Table?  Bar chart?
 
-  output$freqnGramTable <- renderTable(frequent_ngrams(threadedEventsViz() , 'threadNum',
-                                                       get_Zoom_VIZ(),
-                                                       input$freqnGramLengthID[1],
-                                                       input$freqnGramLengthID[2],
-                                                       input$freqnGramDisplayThresholdID ) )
+  fng <-reactive(frequent_ngrams(threadedEventsViz() , 'threadNum',
+                                 get_Zoom_VIZ(),
+                                 input$freqnGramLengthID[1],
+                                 input$freqnGramLengthID[2],
+                                 input$freqnGramDisplayThresholdID ))
+
+  output$freqnGramTable <- renderTable( fng() )
+
+  output$freqnGramBarchart = renderPlotly({ ng_bar_chart_freq(fng() )})
 
-  output$freqnGramBarchart = renderPlotly({
-    ng_bar_chart(threadedEventsViz(), "threadNum", get_Zoom_VIZ(), input$freqnGramLengthID[2], input$freqnGramDisplayThresholdID)
-  })
 
   ######## Whole sequence tab  ###############