inst/app/R/6orig_server.R
In jcai849/iNZightTA: iNZight Text Analytics
#####################################################
#################### Original Server ####################
#####################################################
# This file containts mostly the server code from the original inzightta shiny app.
# Added a button to download the data behind the visualization. Ongoing addition:
# data table below vis plot
prepped <- eventReactive(input$prep_button, {
from_sw_file <- reactive({
if (is.null(input$sw_file)) {""}
else {readLines(input$sw_file$datapath)}
})
addl_stopwords <- reactive({
input_sw <- unlist(strsplit(input$addl_stopwords, split = ","))
c(input_sw, from_sw_file())
})
imported() %>%
inzightta::format_data(input$lemmatise, input$stopwords, input$sw_lexicon, addl_stopwords())
})
sectioned <- reactive({
data <- prepped()
if (isTruthy(input$section_by)){
data <- data %>%
section(input$section_by)}
data})
filtered <- reactive({
data <- sectioned()
if (isTruthy(input$filter_var) &
isTruthy(input$filter_pred)){
# data <- data %>%
# dplyr::filter(!! dplyr::sym(input$filter_var) == input$filter_pred)}
###########################
data <- data %>%
dplyr::filter(stringr::str_detect(!! dplyr::sym(input$filter_var), input$filter_pred))
###########################
}
data
})
# grouped <- reactive({
# data <- filtered()
# if (isTruthy(input$group_var)){
# data <- data %>%
# dplyr::group_by(!! dplyr::sym(input$group_var))}
# data
# })
# # ############################################
# # Attempt at conditioning on features
# # ############################################
#
observe({
if (input$subset_data == input$restore_data) {
shinyjs::disable("restore_data")
shinyjs::enable("subset_data")
} else {
shinyjs::enable("restore_data")
shinyjs::disable("subset_data")
}
})
# observe({
# if (input$restore_data == 1) {
# shinyjs::disable("restore_data")
# }
# })
# observe({
# if (input$subset_data == 0) {
# shinyjs::disable("restore_data")
# } else {
# shinyjs::enable("restore_data")
# shinyjs::disable("subset_data")
# }
# })
#
# observe({
# if (input$restore_data == 1) {
# shinyjs::disable("restore_data")
# }
# })
values <- reactiveValues(data=NULL)
observeEvent(input$subset_data, {
filtered_rows <- input$insighted_table_rows_all
values$data <- filtered_rows
})
insighted_filtered <- eventReactive(input$subset_data, {
full_data <- filtered()
full_data[values$data, ]
})
grouped <- reactive({
if (input$restore_data >= input$subset_data){
data <- filtered()
if (isTruthy(input$group_var)){
data <- data %>%
dplyr::group_by(!! dplyr::sym(input$group_var))}
data
}
else{
insighted_filtered()
}
})
# ############################################
###########################################
output$table <- renderTable({
filtered() %>% utils::head(300)})
output$sw_lexicon <- renderUI(selectInput("sw_lexicon", "Select the Stopword Lexicon",
stopwords::stopwords_getsources()))
output$vars_to_filter <- renderUI(selectInput("filter_var",
"select which column to apply filtering to",
c("", names(sectioned())) %||% c("")))
output$group_by <- renderUI({
input$what_vis
if (input$what_vis == "Term Frequency-Inverse Document Frequency"){
selectInput("group_var",
"select which columns to group on (grouping is combined with faceting)",
c("", names(filtered())) %||% c(""))
}
else {
selectInput("group_var",
"select which columns to group on",
c("", names(filtered())) %||% c(""))
}
})
output$insight_options <- renderUI({
switch(input$what_vis,
"Term Frequency" = selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
"Term Frequency-Inverse Document Frequency" = selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
"n-gram Frequency" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
sliderInput("n_gram",
"n-gram count",
2, 8, 2)),
"Key Words" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
selectInput("summ_method",
"Method of summary generation",
list("TextRank")
#list("TextRank", "LexRank")
)),
##########################
"Word Tree" = tagList(
textInput(
inputId = "firstword",
label = "Input the root word."),
selectizeInput("tree_type", "Tree Type",
c("suffix", "prefix", "double"),
selected = "suffix")
),
##########################
"Term Sentiment" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Page View",
"Word Cloud",
"Time Series",
"Bar",
"Density",
"Histogram"))#,
# selectInput("sent_lex",
# "Lexicon for Sentiment Dictionary",
# list("afinn", "bing",
# "loughran", "nrc",
# "nrc emotions", "loughran - all sentiments"))
),
"Moving Average Term Sentiment" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Time Series",
"Word Cloud",
"Page View",
"Bar",
"Density",
"Histogram")),
sliderInput("term_sent_lag",
"Lag Length for Calculation of Moving Average",
3,10000,250) #,
# selectInput("sent_lex",
# "Lexicon for Sentiment Dictionary",
# list("afinn", "bing",
# "loughran", "nrc"))
),
"Aggregated Term Count" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
selectInput("agg_var",
"Select which variable to aggregate on",
c("", names(grouped())) %||% c(""))),
"Key Sections" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
selectInput("summ_method",
"Method of summary generation",
list("LexRank")
#list("TextRank", "LexRank")
),
selectInput("agg_var",
"Select which variable to aggregate on",
c("", names(grouped())) %||% c(""))),
"Aggregated Sentiment" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Page View",
"Word Cloud",
"Time Series",
"Bar",
"Density",
"Histogram")),
# selectInput("sent_lex",
# "Lexicon for Sentiment Dictionary",
# list("afinn", "bing",
# "loughran", "nrc")),
selectInput("agg_var",
"Select which variable to aggregate on",
c("", names(grouped())) %||% c(""))))})
################
output$senti_choices <- renderUI({
if (input$what_vis == "Term Sentiment" && input$vis_type %in% c("Page View", "Bar")){
selectInput("sent_lex",
"Lexicon for Sentiment Dictionary",
list("afinn", "bing",
"loughran", "nrc",
"nrc - emotions", "loughran - emotions"))
}
else {
if(input$what_vis %in% c("Term Sentiment", "Moving Average Term Sentiment", "Aggregated Sentiment"))
selectInput("sent_lex",
"Lexicon for Sentiment Dictionary",
list("afinn", "bing",
"loughran", "nrc"))
}
})
output$senti_choices2 <- renderUI({
if (input$what_vis == "Term Sentiment" && input$vis_type %in% c("Page View", "Bar")){
if (input$sent_lex == "nrc - emotions"){
selectInput("spec_senti",
"Highlight words with a sentiment of",
list("anger", "anticipation", "disgust", "fear",
"joy", "sadness", "surprise", "trust"))
}
else if (input$sent_lex == "loughran - emotions"){
selectInput("spec_senti",
"Highlight words with a sentiment of",
list("litigious", "uncertainty",
"constraining","superfluous"))
}
}
})
insighted <- reactive({
switch(input$what_vis,
"Term Frequency" = get_term_insight(grouped(),
input$what_vis),
"n-gram Frequency" = get_term_insight(grouped(),
c("n-grams", "n-gram Frequency"),
input$n_gram),
"Key Words" = get_term_insight(grouped(),
input$what_vis,
input$summ_method),
########################################
"Term Frequency-Inverse Document Frequency" = get_tf_idf(grouped(), input$group_var),
########################################
"Term Sentiment" = get_term_insight(grouped(),
input$what_vis,
input$sent_lex,
##############
input$spec_senti
),
"Moving Average Term Sentiment" = get_term_insight(grouped(),
input$what_vis,
input$sent_lex,
input$term_sent_lag))})
insighted_agg <- reactive({
shiny::validate(
need(input$agg_var != "", "Please select which variable to aggregate on")
)
switch(input$what_vis,
"Aggregated Term Count" = get_aggregate_insight(grouped(),
c("Bound Aggregates", input$what_vis),
input$agg_var),
"Key Sections" = get_aggregate_insight(grouped(),
c("Bound Aggregates", input$what_vis),
input$agg_var,
input$summ_method),
"Aggregated Sentiment" = get_aggregate_insight(grouped(),
c("Bound Aggregates", input$what_vis),
input$agg_var,
input$sent_lex,
to_scale = input$scale_senti))
})
output$vis_options <- renderUI({
switch(input$vis_type,
"Word Cloud" = tagList(sliderInput("num_terms",
"Select the number of terms to visualise",
3, 50, 5),
selectInput("wordcloud_shape",
"Select the shape of the wordcloud",
list("circle",
"cardioid",
"diamond",
"square",
"triangle-forward",
"triangle-upright",
"pentagon",
"star"))),
"Page View" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = tagList(sliderInput("num_terms",
"Select the number of terms to visualise",
3, 400, 100),
sliderInput("term_index",
"Select the point to begin visualisation from",
1, nrow(insighted_agg()), 1),
selectInput("palette",
"Select the colour palette type",
list("Sequential", "Diverging"))),
tagList(sliderInput("num_terms",
"Select the number of terms to visualise",
3, 400, 100),
sliderInput("term_index",
"Select the point to begin visualisation from",
1, nrow(insighted()), 1),
selectInput("palette",
"Select the colour palette type",
list("Sequential", "Diverging")))),
"Bar" = tagList(sliderInput("num_terms", "Select the number of terms to visualise",
2,50,5),
checkboxInput("desc", "Sort descending")))})
output$vis_facet_by <- renderUI({input$what_vis
if (input$what_vis == "Term Frequency-Inverse Document Frequency"){
tagList(selectInput("vis_facet",
"select which variable to facet on (Faceting plot by more than 50 levels may take a few minutes.)",
c("", names(grouped())) %||% c(""), selected = input$group_var),
checkboxInput("scale_fixed", "Scale Fixed", value=FALSE))
}
else{
tagList(selectInput("vis_facet",
"select which variable to facet on (Faceting plot by more than 50 levels may take a few minutes.)",
c("", names(grouped())) %||% c(""), selected = NULL),
checkboxInput("scale_fixed", "Scale Fixed", value=FALSE))
}
})
visualisation <- reactive({
switch(input$vis_type,
"Word Cloud" = switch(input$what_vis,
"n-gram Frequency" = get_vis(
insighted(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `n-grams`,
shape = input$wordcloud_shape,
ncol = input$n_col_facet),
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(
insighted_agg(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `Bound Aggregates`,
shape = input$wordcloud_shape,
ncol = input$n_col_facet),
get_vis(insighted(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
shape = input$wordcloud_shape,
ncol = input$n_col_facet)),
"Page View" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
input$term_index,
palette = input$palette,
ncol = input$n_col_facet,
word_size = input$page_text_size),
get_vis(insighted(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
input$term_index,
palette = input$palette,
ncol = input$n_col_facet,
word_size = input$page_text_size)),
"Time Series" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet)),
"Bar" = switch(input$what_vis,
"n-gram Frequency" = get_vis(insighted(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `n-grams`,
desc = input$desc,
ncol = input$n_col_facet),
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `Bound Aggregates`,
desc = input$desc,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis,
facet_by = input$vis_facet,
input$scale_fixed,
input$num_terms,
desc = input$desc,
ncol = input$n_col_facet)),
"Density" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis, input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet)),
"Histogram" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet))
)})
output$plot <- renderPlot({
visualisation() + ggplot2::theme(axis.text = element_text(size = input$text_size))
})
# to allow users to dynamically alter the plot
output$plot.ui <- renderUI({
plotOutput("plot", height = input$plot_height)
})
output$insighted_table <- DT::renderDT({
if (input$what_vis %in% c("Aggregated Term Count","Key Sections","Aggregated Sentiment")){
insighted_agg()
}
else{
insighted()
}
},
filter = "bottom")
output$downloadData <- downloadHandler(
filename = function() {
paste(input$what_vis, ".csv", sep = "")
},
content = function(file) {
if (input$what_vis %in% c("Aggregated Term Count","Key Sections","Aggregated Sentiment")){
utils::write.csv(insighted_agg(), file, row.names = FALSE)
}
else{
utils::write.csv(insighted(), file, row.names = FALSE)
}
}
)
output$downloadprocessed <- downloadHandler(
filename = function() {
paste("processed", "RDS", sep = ".")
},
content = function(file) {
saveRDS(filtered(), file)
}
)
jcai849/iNZightTA documentation built on Dec. 2, 2020, 6:59 a.m.
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#####################################################
#################### Original Server ####################
#####################################################
# This file containts mostly the server code from the original inzightta shiny app.
# Added a button to download the data behind the visualization. Ongoing addition:
# data table below vis plot
prepped <- eventReactive(input$prep_button, {
from_sw_file <- reactive({
if (is.null(input$sw_file)) {""}
else {readLines(input$sw_file$datapath)}
})
addl_stopwords <- reactive({
input_sw <- unlist(strsplit(input$addl_stopwords, split = ","))
c(input_sw, from_sw_file())
})
imported() %>%
inzightta::format_data(input$lemmatise, input$stopwords, input$sw_lexicon, addl_stopwords())
})
sectioned <- reactive({
data <- prepped()
if (isTruthy(input$section_by)){
data <- data %>%
section(input$section_by)}
data})
filtered <- reactive({
data <- sectioned()
if (isTruthy(input$filter_var) &
isTruthy(input$filter_pred)){
# data <- data %>%
# dplyr::filter(!! dplyr::sym(input$filter_var) == input$filter_pred)}
###########################
data <- data %>%
dplyr::filter(stringr::str_detect(!! dplyr::sym(input$filter_var), input$filter_pred))
###########################
}
data
})
# grouped <- reactive({
# data <- filtered()
# if (isTruthy(input$group_var)){
# data <- data %>%
# dplyr::group_by(!! dplyr::sym(input$group_var))}
# data
# })
# # ############################################
# # Attempt at conditioning on features
# # ############################################
#
observe({
if (input$subset_data == input$restore_data) {
shinyjs::disable("restore_data")
shinyjs::enable("subset_data")
} else {
shinyjs::enable("restore_data")
shinyjs::disable("subset_data")
}
})
# observe({
# if (input$restore_data == 1) {
# shinyjs::disable("restore_data")
# }
# })
# observe({
# if (input$subset_data == 0) {
# shinyjs::disable("restore_data")
# } else {
# shinyjs::enable("restore_data")
# shinyjs::disable("subset_data")
# }
# })
#
# observe({
# if (input$restore_data == 1) {
# shinyjs::disable("restore_data")
# }
# })
values <- reactiveValues(data=NULL)
observeEvent(input$subset_data, {
filtered_rows <- input$insighted_table_rows_all
values$data <- filtered_rows
})
insighted_filtered <- eventReactive(input$subset_data, {
full_data <- filtered()
full_data[values$data, ]
})
grouped <- reactive({
if (input$restore_data >= input$subset_data){
data <- filtered()
if (isTruthy(input$group_var)){
data <- data %>%
dplyr::group_by(!! dplyr::sym(input$group_var))}
data
}
else{
insighted_filtered()
}
})
# ############################################
###########################################
output$table <- renderTable({
filtered() %>% utils::head(300)})
output$sw_lexicon <- renderUI(selectInput("sw_lexicon", "Select the Stopword Lexicon",
stopwords::stopwords_getsources()))
output$vars_to_filter <- renderUI(selectInput("filter_var",
"select which column to apply filtering to",
c("", names(sectioned())) %||% c("")))
output$group_by <- renderUI({
input$what_vis
if (input$what_vis == "Term Frequency-Inverse Document Frequency"){
selectInput("group_var",
"select which columns to group on (grouping is combined with faceting)",
c("", names(filtered())) %||% c(""))
}
else {
selectInput("group_var",
"select which columns to group on",
c("", names(filtered())) %||% c(""))
}
})
output$insight_options <- renderUI({
switch(input$what_vis,
"Term Frequency" = selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
"Term Frequency-Inverse Document Frequency" = selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
"n-gram Frequency" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
sliderInput("n_gram",
"n-gram count",
2, 8, 2)),
"Key Words" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
selectInput("summ_method",
"Method of summary generation",
list("TextRank")
#list("TextRank", "LexRank")
)),
##########################
"Word Tree" = tagList(
textInput(
inputId = "firstword",
label = "Input the root word."),
selectizeInput("tree_type", "Tree Type",
c("suffix", "prefix", "double"),
selected = "suffix")
),
##########################
"Term Sentiment" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Page View",
"Word Cloud",
"Time Series",
"Bar",
"Density",
"Histogram"))#,
# selectInput("sent_lex",
# "Lexicon for Sentiment Dictionary",
# list("afinn", "bing",
# "loughran", "nrc",
# "nrc emotions", "loughran - all sentiments"))
),
"Moving Average Term Sentiment" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Time Series",
"Word Cloud",
"Page View",
"Bar",
"Density",
"Histogram")),
sliderInput("term_sent_lag",
"Lag Length for Calculation of Moving Average",
3,10000,250) #,
# selectInput("sent_lex",
# "Lexicon for Sentiment Dictionary",
# list("afinn", "bing",
# "loughran", "nrc"))
),
"Aggregated Term Count" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
selectInput("agg_var",
"Select which variable to aggregate on",
c("", names(grouped())) %||% c(""))),
"Key Sections" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Bar",
"Word Cloud",
"Page View",
"Time Series",
"Density",
"Histogram")),
selectInput("summ_method",
"Method of summary generation",
list("LexRank")
#list("TextRank", "LexRank")
),
selectInput("agg_var",
"Select which variable to aggregate on",
c("", names(grouped())) %||% c(""))),
"Aggregated Sentiment" = tagList(selectInput("vis_type",
"Select how to Visualise it",
list("Page View",
"Word Cloud",
"Time Series",
"Bar",
"Density",
"Histogram")),
# selectInput("sent_lex",
# "Lexicon for Sentiment Dictionary",
# list("afinn", "bing",
# "loughran", "nrc")),
selectInput("agg_var",
"Select which variable to aggregate on",
c("", names(grouped())) %||% c(""))))})
################
output$senti_choices <- renderUI({
if (input$what_vis == "Term Sentiment" && input$vis_type %in% c("Page View", "Bar")){
selectInput("sent_lex",
"Lexicon for Sentiment Dictionary",
list("afinn", "bing",
"loughran", "nrc",
"nrc - emotions", "loughran - emotions"))
}
else {
if(input$what_vis %in% c("Term Sentiment", "Moving Average Term Sentiment", "Aggregated Sentiment"))
selectInput("sent_lex",
"Lexicon for Sentiment Dictionary",
list("afinn", "bing",
"loughran", "nrc"))
}
})
output$senti_choices2 <- renderUI({
if (input$what_vis == "Term Sentiment" && input$vis_type %in% c("Page View", "Bar")){
if (input$sent_lex == "nrc - emotions"){
selectInput("spec_senti",
"Highlight words with a sentiment of",
list("anger", "anticipation", "disgust", "fear",
"joy", "sadness", "surprise", "trust"))
}
else if (input$sent_lex == "loughran - emotions"){
selectInput("spec_senti",
"Highlight words with a sentiment of",
list("litigious", "uncertainty",
"constraining","superfluous"))
}
}
})
insighted <- reactive({
switch(input$what_vis,
"Term Frequency" = get_term_insight(grouped(),
input$what_vis),
"n-gram Frequency" = get_term_insight(grouped(),
c("n-grams", "n-gram Frequency"),
input$n_gram),
"Key Words" = get_term_insight(grouped(),
input$what_vis,
input$summ_method),
########################################
"Term Frequency-Inverse Document Frequency" = get_tf_idf(grouped(), input$group_var),
########################################
"Term Sentiment" = get_term_insight(grouped(),
input$what_vis,
input$sent_lex,
##############
input$spec_senti
),
"Moving Average Term Sentiment" = get_term_insight(grouped(),
input$what_vis,
input$sent_lex,
input$term_sent_lag))})
insighted_agg <- reactive({
shiny::validate(
need(input$agg_var != "", "Please select which variable to aggregate on")
)
switch(input$what_vis,
"Aggregated Term Count" = get_aggregate_insight(grouped(),
c("Bound Aggregates", input$what_vis),
input$agg_var),
"Key Sections" = get_aggregate_insight(grouped(),
c("Bound Aggregates", input$what_vis),
input$agg_var,
input$summ_method),
"Aggregated Sentiment" = get_aggregate_insight(grouped(),
c("Bound Aggregates", input$what_vis),
input$agg_var,
input$sent_lex,
to_scale = input$scale_senti))
})
output$vis_options <- renderUI({
switch(input$vis_type,
"Word Cloud" = tagList(sliderInput("num_terms",
"Select the number of terms to visualise",
3, 50, 5),
selectInput("wordcloud_shape",
"Select the shape of the wordcloud",
list("circle",
"cardioid",
"diamond",
"square",
"triangle-forward",
"triangle-upright",
"pentagon",
"star"))),
"Page View" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = tagList(sliderInput("num_terms",
"Select the number of terms to visualise",
3, 400, 100),
sliderInput("term_index",
"Select the point to begin visualisation from",
1, nrow(insighted_agg()), 1),
selectInput("palette",
"Select the colour palette type",
list("Sequential", "Diverging"))),
tagList(sliderInput("num_terms",
"Select the number of terms to visualise",
3, 400, 100),
sliderInput("term_index",
"Select the point to begin visualisation from",
1, nrow(insighted()), 1),
selectInput("palette",
"Select the colour palette type",
list("Sequential", "Diverging")))),
"Bar" = tagList(sliderInput("num_terms", "Select the number of terms to visualise",
2,50,5),
checkboxInput("desc", "Sort descending")))})
output$vis_facet_by <- renderUI({input$what_vis
if (input$what_vis == "Term Frequency-Inverse Document Frequency"){
tagList(selectInput("vis_facet",
"select which variable to facet on (Faceting plot by more than 50 levels may take a few minutes.)",
c("", names(grouped())) %||% c(""), selected = input$group_var),
checkboxInput("scale_fixed", "Scale Fixed", value=FALSE))
}
else{
tagList(selectInput("vis_facet",
"select which variable to facet on (Faceting plot by more than 50 levels may take a few minutes.)",
c("", names(grouped())) %||% c(""), selected = NULL),
checkboxInput("scale_fixed", "Scale Fixed", value=FALSE))
}
})
visualisation <- reactive({
switch(input$vis_type,
"Word Cloud" = switch(input$what_vis,
"n-gram Frequency" = get_vis(
insighted(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `n-grams`,
shape = input$wordcloud_shape,
ncol = input$n_col_facet),
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(
insighted_agg(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `Bound Aggregates`,
shape = input$wordcloud_shape,
ncol = input$n_col_facet),
get_vis(insighted(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
shape = input$wordcloud_shape,
ncol = input$n_col_facet)),
"Page View" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
input$term_index,
palette = input$palette,
ncol = input$n_col_facet,
word_size = input$page_text_size),
get_vis(insighted(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
input$term_index,
palette = input$palette,
ncol = input$n_col_facet,
word_size = input$page_text_size)),
"Time Series" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet)),
"Bar" = switch(input$what_vis,
"n-gram Frequency" = get_vis(insighted(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `n-grams`,
desc = input$desc,
ncol = input$n_col_facet),
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(),
input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
input$num_terms,
x = `Bound Aggregates`,
desc = input$desc,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis,
facet_by = input$vis_facet,
input$scale_fixed,
input$num_terms,
desc = input$desc,
ncol = input$n_col_facet)),
"Density" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis, input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet)),
"Histogram" = switch(input$what_vis,
"Aggregated Term Count" =,
"Key Sections" =,
"Aggregated Sentiment" = get_vis(insighted_agg(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet),
get_vis(insighted(), input$vis_type,
input$what_vis,
input$vis_facet,
input$scale_fixed,
ncol = input$n_col_facet))
)})
output$plot <- renderPlot({
visualisation() + ggplot2::theme(axis.text = element_text(size = input$text_size))
})
# to allow users to dynamically alter the plot
output$plot.ui <- renderUI({
plotOutput("plot", height = input$plot_height)
})
output$insighted_table <- DT::renderDT({
if (input$what_vis %in% c("Aggregated Term Count","Key Sections","Aggregated Sentiment")){
insighted_agg()
}
else{
insighted()
}
},
filter = "bottom")
output$downloadData <- downloadHandler(
filename = function() {
paste(input$what_vis, ".csv", sep = "")
},
content = function(file) {
if (input$what_vis %in% c("Aggregated Term Count","Key Sections","Aggregated Sentiment")){
utils::write.csv(insighted_agg(), file, row.names = FALSE)
}
else{
utils::write.csv(insighted(), file, row.names = FALSE)
}
}
)
output$downloadprocessed <- downloadHandler(
filename = function() {
paste("processed", "RDS", sep = ".")
},
content = function(file) {
saveRDS(filtered(), file)
}
)
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