inst/qf_wordcloud_app_single_file/app.R

In giocomai/edjnetquotefinder: EDJNet's QuoteFinder

library("dplyr")
library("tidyr")
library("purrr")
library("scales")
library("ggplot2")
library("shiny")
library("tidytext")
library("wordcloud")
library("wordcloud2")
library("stringr")
library("reshape2")
library("stopwords")
library("syuzhet")
library("colourpicker")
library("shinyWidgets")
library("RColorBrewer")
library("shinydashboard")
library("shinycustomloader")
library("DT")
library("webshot")
# install phantomjs at first run to enable downloading png wordclouds
# webshot::install_phantomjs()

dataset <- readRDS(file = file.path("qf_data", "tweets_processed", "dataset.rds"))
hashtagsList <- readRDS(file = file.path("qf_data", "tweets_processed", "tweets_hashtags_list.rds"))
trendingHashtags <- readRDS(file = file.path("qf_data", "tweets_processed", "tweets_trending_hashtags_list.rds"))
lang <- readRDS(file = file.path("qf_data", "tweets_processed", "tweets_lang_list.rds"))
EPGroupShort <- readRDS(file = file.path("qf_data", "tweets_processed", "EPGroupShort.rds"))
countries <- readRDS(file = file.path("qf_data", "tweets_processed", "countries.rds"))
palettes <- readRDS(file = file.path("qf_data", "tweets_processed", "palettes.rds"))

langTable <- left_join(x = tibble::tibble(lang = unlist(lang)),
                       y = readRDS(file.path("qf_data", "tweets_processed", "langCode.rds")) %>%
                         rename(lang = alpha2), by = "lang") %>% 
  mutate(English = stringr::str_extract(string = English, pattern = regex("[[:alnum:]]+")))



embed_tweet_js <- function(id, i) {
  HTML(paste0('<div id="tweetcontainer', i, '"></div>',
              "<script>twttr.widgets.createTweet('", id, "',
  document.getElementById('tweetcontainer", i, "'),
  {
    theme: 'light'
  }
); </script>"))
}

embed_profile <- function(screen_name) {
  HTML(paste0('<a class="twitter-timeline" data-height="600" href="https://twitter.com/', screen_name, '">Tweets by ', screen_name, '</a> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>'))
}



pal <- brewer.pal(9,"Blues")
pal <- pal[-(1:5)]


# European formatting of large numbers
point <- scales::format_format(big.mark = ".", decimal.mark = ",", scientific = FALSE)

## function to give wordcloud2 click interactivity
## from https://github.com/Lchiffon/wordcloud2/issues/25
wc2ClickedWord = function(cloudOutputId, inputId) {
  shiny::tags$script(shiny::HTML(
    sprintf("$(document).on('click', '#%s', function() {", cloudOutputId),
    'word = document.getElementById("wcSpan").innerHTML;',
    sprintf("Shiny.onInputChange('%s', word);", inputId),
    "});"
  ))
}

# wordcloud2 html output fix from https://github.com/Lchiffon/wordcloud2/issues/20
simpleFixWc2 <- function(inputFile, outputFile){
  a = readLines(inputFile)
  output = paste(a, collapse = "\n")
  output = gsub(">\n\n</div>","></div>",output)
  writeLines(output, outputFile)
  invisible(NULL)
}


# Enable bookmarking
enableBookmarking(store = "server")

ui = shinydashboard::dashboardPage(
  shinydashboard::dashboardHeader(title = "EdjNet QuoteFinder"),
  shinydashboard::dashboardSidebar(sidebarMenu(
    menuItem("Twitter", tabName = "TwitterMEP", icon = icon("twitter")),
    shiny::HTML("<hr><div class='col-sm-12'><p><b>The QuoteFinder lets you explore</b></p></div>"),
    infoBox(title = "tweets", value = point(nrow(dataset)), icon = icon("twitter"), width = 12, color = "blue", fill = TRUE),
    infoBox(title = "by", value = paste(length(unique(dataset$screen_name)), "MEPs"), icon = icon("users"), width = 12, color = "blue", fill = TRUE)),
    infoBox(title = "posted since", value = min(dataset$date), icon = icon("calendar"), width = 12, color = "blue", fill = TRUE),
    disable = TRUE
  ),
  dashboardBody(
    tags$head(HTML('<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>')),
    #  wc2ClickedWord(cloudOutputId = "wordcloud2", inputId = "selected_word"),
    tabItems(
      
      ##### TwitterMEP tab ####
      tabItem(tabName = "TwitterMEP",
              
              #### Box 1: Wordcloud ####
              tabBox(id = "wordcloud_plot",
                     tabPanel("Wordcloud",
                              uiOutput("warning"), 
                              wordcloud2Output("wordcloud2"),
                              splitLayout(
                                shiny::sliderInput(inputId = "sizeVarWC2",
                                                   label = "Wordcloud size",
                                                   min = 0.1, 
                                                   max = 2,
                                                   value = 0.5,
                                                   sep = "."
                                ),
                                shiny::sliderInput(inputId = "MaxWords",
                                                   label = "Max number of words",
                                                   min = 0L, 
                                                   max = 1000L,
                                                   value = 200L,
                                                   sep = ".", width = "95%"
                                )
                              ),
                              splitLayout(shiny::uiOutput(outputId = "colourMost_UI"),
                                          shiny::uiOutput(outputId = "colourLeast_UI"),
                                          cellWidths = "50%",
                                          tags$head(tags$style(HTML("
                              .shiny-split-layout > div {
                                overflow: visible;
                              }
                              ")))),
                              splitLayout(downloadButton("downloadHtml", "Download Html"),
                                          downloadButton("downloadPng", "Download PNG"))
                              
                              # , 
                              # HTML("<b>Tip</b>: by clicking on a term in the wordcloud, only tweets including it are shown in the table below.")
                     ),
                     
                     #### Box1, Tab2: Barcharts #####
                     tabPanel("Barcharts",
                              plotOutput('barchartGG'),
                              shiny::sliderInput(inputId = "MaxWordsInBarchart",
                                                 label = "Max number of words",
                                                 min = 0L, 
                                                 max = 30L,
                                                 value = 20L,
                                                 sep = ".",
                                                 width = "95%",
                                                 round = TRUE
                              ),
                              downloadButton("downloadTidyWordCount", "Download as spreadsheet")),
                     tabPanel("EP group comparison",
                              plotOutput("barchartComparisonGG"),
                              radioButtons(inputId = "wcOrBarchartComparison",
                                           label = "How would you like to compare?",
                                           choices = as.list(c("Comparison wordcloud",
                                                               "Commonality wordcloud",
                                                               "Comparison barchart")))
                     )
                     # ,
                     # tabPanel("Classic wordcloud",
                     #          plotOutput(outputId = "wordcloud"))
              ),
              
              #### Box 2: Date and language ####
              box(
                shiny::radioButtons(inputId = "dateRangeRadio",
                                    label = "Select date range",
                                    choices = as.list(c("Last week",
                                                        "Last month",
                                                        #"Last three months",
                                                        "Custom range")),
                                    selected = "Last month", inline = TRUE),
                conditionalPanel(
                  condition = "input.dateRangeRadio == 'Custom range'",
                  shiny::dateRangeInput(inputId = 'dateRange',
                                        weekstart = 1,
                                        label = "Select date range",
                                        start = min(dataset$date), end = max(dataset$date)
                  )
                )
                ,
                HTML("<b>Filter tweets by language</b>"),
                shiny::checkboxInput(inputId = "anyLanguage",
                                     label = "Any language",
                                     value = FALSE),
                conditionalPanel("input.anyLanguage == false",
                                 shiny::selectInput(inputId = "language",
                                                    label = NULL,
                                                    choices = lang,
                                                    selected = "en")),
                conditionalPanel("input.anyLanguage == true",
                                 shiny::checkboxInput(inputId = "colourLanguage",
                                                      label = "A different colour for each language?",
                                                      value = TRUE))
                ,
                uiOutput(outputId = "hashtags_UI"),
                uiOutput(outputId = "trendingHashtagsTitle"),
                uiOutput(outputId = "trendingHashtags"),
                conditionalPanel("input.anyLanguage == false",
                                 shiny::radioButtons(inputId = "sentimentL",
                                                     label = "Type of wordcloud",
                                                     choices = c("Unified",
                                                                 "Sentiment by tweet",
                                                                 "Sentiment by word"),
                                                     inline = TRUE)
                )
              ),
              
              #### Box 3: Wordcloud filters ####
              
              tabBox(title = "Search and filter",
                     id = "wordcloud_filters",
                     tabPanel("By string",
                              textInput(inputId = 'string', label = NULL)
                              # splitLayout(textInput(inputId = 'string', label = NULL),
                              #             #actionButton("filter", "Filter", icon = icon("filter", class = "font-awesome")),
                              #             cellWidths = c("75%", "25%"))
                     )
                     ,
                     tabPanel("By EP group",
                              shiny::checkboxGroupInput(inputId = "EPgroup",
                                                        label = "Choose group",
                                                        choices = EPGroupShort,
                                                        inline = TRUE)
                              #,actionButton("filterByGroup", "Filter", icon = icon("filter", class = "font-awesome"))
                     ),
                     tabPanel("By MEP", shiny::uiOutput(outputId = "MEPfilter_UI")),
                     tabPanel("By country", shiny::uiOutput(outputId = "country_filter_UI"))
                     #  ,
                     # tabPanel("By sentiment", shiny::checkboxGroupInput(inputId = "sentimentFilter",
                     #                                                      label = "Choose sentiment",
                     #                                                      choices = as.list(c("Positive", "Negative", "Neutral")),
                     #                                                      inline = TRUE))
              ), 
              box(actionButton(inputId = "reset",
                               label = "Reset filters",
                               icon = icon(name = "recycle", lib = "font-awesome")),
                  bookmarkButton(label = "Get direct link with current settings")
              ),
              
              #### Box 4: infobox ####
              box(title = NULL,
                  shiny::htmlOutput(outputId = "HeaderInfoBox"),
                  infoBoxOutput(outputId = "TweetsNr"),
                  infoBoxOutput(outputId = "MEPsNr"),
                  infoBoxOutput(outputId = "DaysNr"),width = 12
              )
              ,
              #### Box 5: table ####
              fluidRow(DT::dataTableOutput(outputId = "table")),
              #### Tweet wall ####
              fluidRow(uiOutput(outputId = "selected_tweets_wall"))
      )
    )
  )
)

server = function(input, output, session) {
  
  randomString <- stringi::stri_rand_strings(n=1, length=16)
  
  #### Reset ####
  
  observeEvent(input$reset, {
    updateTextInput(session = session, inputId = "string", value = "")
    updateCheckboxGroupInput(session = session, inputId = "EPgroup", selected = character(0))
    updateSelectizeInput(session = session, inputId = "MEPfilter", selected = character(0))
    updateSelectizeInput(session = session, inputId = "selectedHashtag", selected = "All tweets")
    updateSelectizeInput(session = session, inputId = "countryFilter", selected = character(0))
  })
  
  
  
  observeEvent(input$wordcloud_plot, {
    if (input$wordcloud_plot=="EP group comparison") {
      updateTabsetPanel(session = session,
                        inputId = "wordcloud_filters",
                        selected = "By EP group")
      shiny::updateCheckboxGroupInput(session = session,
                                      inputId = "EPgroup",
                                      selected = c("S&D", "EPP"))
    }
  })
  
  #### Reactive ####
  
  currentDataset <- reactive({
    # filter date
    if (input$dateRangeRadio=="Last week") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-7)
    } else if (input$dateRangeRadio=="Last month") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-31)
    } else if (input$dateRangeRadio=="Last three months") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-91)
    } else {
      dataset <- dataset %>%
        filter(date>=min(as.Date(input$dateRange))&date<=max(as.Date(input$dateRange))) 
    }
    # filter by language
    if (input$anyLanguage==FALSE) {
      dataset <- dataset %>% 
        filter(lang==input$language)
    }
    
    # filter by country
    if (is.null(input$countryFilter)==FALSE) {
      dataset <- dataset %>% 
        filter(stringr::str_detect(string = country, pattern = paste(input$countryFilter, collapse = "|")))
    }
    
    #filter hashtag
    if(is.null(input$selectedHashtag)){
      
    } else if(input$selectedHashtag=="All tweets") {
      
    } else {
      dataset <- dataset %>% 
        filter(purrr::map_lgl(.x = hashtags, .f = function (x) is.element(el = tolower(input$selectedHashtag), set = tolower(x))))
    }
    
    if (input$string!="") {
      dataset <- dataset %>%
        filter(stringr::str_detect(string = text, pattern = stringr::regex(pattern = input$string, ignore_case = TRUE)))
    }
    
    if (is.null(input$EPgroup)==FALSE) {
      dataset <- dataset %>%
        filter(stringr::str_detect(string = GroupShort, pattern = paste(input$EPgroup, collapse = "|")))
    }
    
    if (is.null(input$MEPfilter)==FALSE) {
      dataset <- dataset %>%
        filter(stringr::str_detect(string = screen_name, pattern = paste(input$MEPfilter, collapse = "|")))
    }
    dataset
  })
  
  currentHashtags <- reactive({
    
    if (input$anyLanguage==FALSE) {
      dataset <- dataset %>% 
        filter(lang==input$language)
    }
    
    if (input$dateRangeRadio=="Last week") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-7)
    } else if (input$dateRangeRadio=="Last month") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-31)
    } else if (input$dateRangeRadio=="Last three months") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-91)
    } else {
      dataset <- dataset %>%
        filter(date>=min(as.Date(input$dateRange))&date<=max(as.Date(input$dateRange))) 
    }
    if (is.null(input$EPgroup)==FALSE) {
      dataset <- dataset %>%
        filter(stringr::str_detect(string = GroupShort, pattern = paste(input$EPgroup, collapse = "|")))
    }
    
    currentHashtagsDF <- dataset %>%
      select(screen_name, hashtags) %>%
      unnest(cols = c(hashtags)) %>%
      na.omit() %>% 
      group_by(hashtags) %>%
      add_count(sort = TRUE) %>% 
      rename(nTotalOrig = n) %>% 
      mutate(hashtagsLower = tolower(hashtags)) %>% # ignore case, but keep the case of the most frequently found case combination
      group_by(hashtagsLower) %>%
      add_tally() %>%
      ungroup() %>% 
      rename(nTotal = n) %>% 
      group_by(hashtags, nTotal) %>% 
      distinct(screen_name, .keep_all = TRUE) %>% 
      add_count() %>% 
      rename(nMepPerHashtag = n) %>% 
      select(-screen_name) %>% 
      arrange(desc(nMepPerHashtag), desc(nTotal)) %>% 
      ungroup() %>% 
      distinct(hashtagsLower, .keep_all = TRUE) %>% 
      mutate(hashtagString = paste0("#", hashtags, " (", nMepPerHashtag, " MEPs, ", nTotal, " tweets)"))
    currentHashtagsList <- as.list(currentHashtagsDF$hashtags)
    names(currentHashtagsList) <- currentHashtagsDF$hashtagString
    
    currentHashtagsList
  })
  
  currentTrendingHashtags <- reactive({
    if (input$anyLanguage==TRUE)  {
      as.character(trendingHashtags$AnyLanguage)
    } else {
      as.character(unlist(trendingHashtags[names(trendingHashtags)==input$language]))
    }
  })
  
  currentMEPs <- reactive({
    
    if (input$dateRangeRadio=="Last week") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-7)
    } else if (input$dateRangeRadio=="Last month") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-31)
    } else if (input$dateRangeRadio=="Last three months") {
      dataset <-  dataset %>% 
        filter(date>Sys.Date()-91)
    } else {
      dataset <- dataset %>%
        filter(date>=min(as.Date(input$dateRange))&date<=max(as.Date(input$dateRange))) 
    }
    
    if (is.null(input$EPgroup)==FALSE) {
      dataset <- dataset %>%
        filter(stringr::str_detect(string = GroupShort, pattern = paste(input$EPgroup, collapse = "|")))
    }
    
    # filter language
    if (input$anyLanguage==FALSE) {
      dataset <- dataset %>% 
        filter(lang==input$language)
    }
    
    #filter hashtag
    if(is.null(input$selectedHashtag)){
      
    } else if(input$selectedHashtag=="All tweets") {
      
    } else {
      dataset <- dataset %>% 
        filter(purrr::map_lgl(.x = hashtags, .f = function (x) is.element(el = tolower(input$selectedHashtag), set = tolower(x))))
    }
    
    if (input$string!="") {
      dataset <- dataset %>%
        filter(stringr::str_detect(string = text, pattern = stringr::regex(pattern = input$string, ignore_case = TRUE)))
    }
    
    if (is.null(input$EPgroup)==FALSE) {
      dataset <- dataset %>%
        filter(stringr::str_detect(string = GroupShort, pattern = paste(input$EPgroup, collapse = "|")))
    }
    
    temp <- dataset %>% distinct(fullName, screen_name) %>% filter(is.na(fullName)==FALSE) %>% arrange(fullName)
    currentMEPsList <- structure(as.list(temp$screen_name), names = as.character(temp$fullName))
    currentMEPsList
    
  })
  
  
  #### UI ####
  
  output$warning <- renderUI({
    if (input$sentimentL=="Sentiment by tweet") {
      HTML(text = "<b>Warning</b>: sentiment by tweet currently works with English tweets only")
    }
  })
  
  output$hashtags_UI <- renderUI({
    shiny::selectizeInput(inputId = "selectedHashtag",
                          label = "Select hashtag",
                          choices = c(list("All tweets"),
                                      currentHashtags()))
  })
  
  output$MEPfilter_UI <- renderUI({
    shiny::selectizeInput(inputId = "MEPfilter", label = "Filter by MEP",
                          choices = currentMEPs(),
                          multiple = TRUE)
  })
  
  output$country_filter_UI <- renderUI({
    shiny::selectizeInput(inputId = "countryFilter", label = "Filter by country",
                          choices = as.list(unique(dataset$country) %>% sort()),
                          multiple = TRUE)
  })
  
  output$MaxWords_UI <- renderUI({
    shiny::sliderInput(inputId = "MaxWords",
                       label = "Maximum number of words in the wordcloud",
                       min = 1, 
                       max = 1000
    )
  })
  
  output$colourMost_UI <- renderUI({
    if (input$sentimentL=="Sentiment by word") {
      colourInput(inputId = "colourPositive", label = "Colour for positive terms", value = "#00BFC4", showColour = "both")
    } else if (input$anyLanguage==TRUE&input$colourLanguage==TRUE) {
      pickerInput(
        inputId = "multilingual_palette", label = "Choose a palette:",
        choices = palettes$colors_pal, selected = "Set1", width = "200%",
        choicesOpt = list(
          content = sprintf(
            "<div style='width:100%%;padding:5px;border-radius:4px;background:%s;color:%s'>%s</div>",
            unname(palettes$background_pals), palettes$colortext_pals, names(palettes$background_pals)
          )
        )
      )
    } else {
      colourInput(inputId = "colourMost", label = "Colour for most frequent terms", value = "#08306B", showColour = "both")
    } 
  })
  
  output$colourLeast_UI <- renderUI({
    if (input$sentimentL=="Sentiment by word") {
      colourInput(inputId = "colourNegative", label = "Colour for negative terms", value = "#F8766D", showColour = "both")
    } else if (input$anyLanguage==TRUE&input$colourLanguage==TRUE) {
      # leave empty
    }
    else {
      colourInput(inputId = "colourLeast", label = "Colour for least frequent terms", value = "#4292C6", showColour = "both")  
    }
  })
  
  observe({
    updateActionButton(session = session, inputId = "trendingHashtag_1", label = currentTrendingHashtags()[1])
    updateActionButton(session = session, inputId = "trendingHashtag_2", label = currentTrendingHashtags()[2])
    updateActionButton(session = session, inputId = "trendingHashtag_3", label = currentTrendingHashtags()[3])
    updateActionButton(session = session, inputId = "trendingHashtag_4", label = currentTrendingHashtags()[4])
    updateActionButton(session = session, inputId = "trendingHashtag_5", label = currentTrendingHashtags()[5])
    updateActionButton(session = session, inputId = "trendingHashtag_6", label = currentTrendingHashtags()[6])
    updateActionButton(session = session, inputId = "trendingHashtag_7", label = currentTrendingHashtags()[7])
    updateActionButton(session = session, inputId = "trendingHashtag_8", label = currentTrendingHashtags()[8])
  })
  output$trendingHashtagsTitle <- renderUI({
    if (length(currentTrendingHashtags())>0) {
      HTML("<b>Trending hashtags</b><br />")
    }
  })
  
  output$trendingHashtags <- renderUI({
    lapply(seq_along(currentTrendingHashtags()[1:8]), function(x){
      do.call(actionButton, list(inputId = paste("trendingHashtag", x, sep = "_"), label = currentTrendingHashtags()[x]))
    }
    )
  })
  
  observeEvent(eventExpr = input$trendingHashtag_1, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[1], pattern = "#"))])
  })
  observeEvent(eventExpr = input$trendingHashtag_2, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[2], pattern = "#"))])
  })
  observeEvent(eventExpr = input$trendingHashtag_3, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[3], pattern = "#"))])
  })
  observeEvent(eventExpr = input$trendingHashtag_4, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[4], pattern = "#"))])
  })
  observeEvent(eventExpr = input$trendingHashtag_5, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[5], pattern = "#"))])
  })
  observeEvent(eventExpr = input$trendingHashtag_6, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[6], pattern = "#"))])
  })
  observeEvent(eventExpr = input$trendingHashtag_7, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[7], pattern = "#"))])
  })
  observeEvent(eventExpr = input$trendingHashtag_8, {
    updateSelectizeInput(session = session,
                         inputId = "selectedHashtag",
                         selected = currentHashtags()[tolower(as.character(unlist(currentHashtags())))==tolower(stringr::str_remove(string = currentTrendingHashtags()[8], pattern = "#"))])
  })
  #### Subset date range ####
  
  observe({
    if (input$dateRangeRadio=="Last week") {
      startDate <- Sys.Date()-7
    } else if (input$dateRangeRadio=="Last month") {
      startDate <- Sys.Date()-31
    } else if (input$dateRangeRadio=="Last three months") {
      startDate <- Sys.Date()-91
    }
    
    if (input$dateRangeRadio=="Custom range") {
      updateDateRangeInput(session, "dateRange",
                           start = min(dataset$date),
                           end = Sys.Date())
    }
  })
  
  
  #### Wordcloud ####
  
  output$wordcloud <- renderPlot(expr = {
    # reload if dateRange or hashtag is changed
    input$dateRange
    input$dateRangeRadio
    input$selectedHashtag
    
    # If tab is "By hashtag"
    if (input$wordcloud_filters=="By hashtag") {
      if (is.null(input$selectedHashtag)==FALSE) {
        par(mar = rep(0, 4))
        if (input$selectedHashtag=="All tweets") {
          temp <- dataset %>% 
            filter(lang==input$language) %>% 
            select(clean_text) %>% 
            unnest_tokens(input = clean_text, output = word) %>% 
            # remove stopwords, if list for the relevant language is available, otherwise do nothing
            when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
                   anti_join(., tibble::tibble(word = stopwords::stopwords(language = input$language, source = "stopwords-iso")), by = "word"),
                 ~ .)
        } else {
          temp <- dataset %>% 
            filter(lang==input$language) %>% 
            filter(purrr::map_lgl(.x = hashtags, .f = function (x) is.element(el = tolower(input$selectedHashtag), set = tolower(x)))) %>%
            select(clean_text) %>% 
            unnest_tokens(input = clean_text, output = word) %>% 
            # remove stopwords, if list for the relevant language is available, otherwise do nothing
            when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
                   anti_join(., tibble::tibble(word = stopwords::stopwords(language = input$language, source = "stopwords-iso")), by = "word"),
                 ~ .)
        }
        if (input$sentimentL=="Sentiment by word") {
          # for log
          message(paste(Sys.time(), "WordcloudSentimentCreated", input$language, sep = "-"))
          par(mar = rep(0, 4))
          temp %>% 
            # nrc sentiment, removing words that are both positive and negative
            inner_join(y = syuzhet::get_sentiment_dictionary(dictionary = "nrc",
                                                             lang = langTable %>% filter(lang=="en") %>% pull(English) %>% tolower()) %>%
                         dplyr::filter(sentiment=="negative"|sentiment=="positive") %>% add_count(word) %>% filter(n==1) %>% select(-n), by = "word") %>%
            count(word, sentiment, sort = TRUE) %>%
            acast(word ~ sentiment, value.var = "n", fill = 0) %>%
            comparison.cloud(colors = c("#F8766D", "#00BFC4"),
                             max.words = 100, scale = c(3, 1), family = "Carlito", font = 1)
        } else {
          # for log
          message(paste(Sys.time(), "WordcloudUnifiedCreated", input$language, sep = "-"))
          par(mar = rep(0, 4))
          temp %>% 
            count(word) %>% 
            with(wordcloud(word, n, scale = c(3, 1), max.words = 100, min.freq = 1,
                           random.order = FALSE, family = "Carlito", font = 1, colors = pal))
          
        }
      }
      ##### By EP Group #####
    } else if (input$wordcloud_filters=="By EP group") {
      par(mar = rep(0, 4))
      temp <- dataset %>% 
        filter(lang==input$language) %>% 
        filter(stringr::str_detect(string = GroupShort, pattern = paste(input$EPgroup, collapse = "|"))) %>%
        select(clean_text, country, GroupShort) %>% 
        unnest_tokens(input = clean_text, output = word) %>% 
        # remove stopwords, if list for the relevant language is available, otherwise do nothing
        when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
               anti_join(., tibble::tibble(word = stopwords::stopwords(language = input$language, source = "stopwords-iso")), by = "word"),
             ~ .)
      if (length(input$EPgroup)==1) {
        
      } else if (length(input$EPgroup)>1) {
        par(mar = rep(0, 4))
        temp %>% 
          count(word, GroupShort, sort = TRUE) %>% 
          acast(word ~ GroupShort, value.var = "n", fill = 0) %>%
          comparison.cloud(
            #colors = c("#F8766D", "#00BFC4"),
            max.words = 100, scale = c(3, 1), family = "Carlito", font = 1)
      }
    }
  }, execOnResize = TRUE)
  
  #### Wordcloud 2 ####
  
  wc2 <- reactive({
    
    if (is.null(input$colourMost)==FALSE) {
      createPalette <- colorRampPalette(colors = c(input$colourMost, input$colourLeast))
    } else if(input$anyLanguage==TRUE&input$colourLanguage==TRUE) {
      ### 
    } else {
      createPalette <- colorRampPalette(colors = c("#08306B", "#4292C6"))
    }
    
    if (is.null(input$selectedHashtag)==FALSE) {
      
      ##### Wordcloud2 sentiment or unified #####
      if (input$sentimentL=="Sentiment by word") {
        if (input$language=="en") {
          sentimentDictionary <- tidytext::get_sentiments("bing")
        } else {
          sentimentDictionary <-
            syuzhet::get_sentiment_dictionary(dictionary = "nrc",
                                              lang = langTable %>%
                                                filter(lang==input$language) %>%
                                                pull(English) %>%
                                                tolower()) %>%
            filter(sentiment=="negative"|sentiment=="positive") %>%
            add_count(word) %>%
            filter(n==1) %>%
            select(word, sentiment)
        }
        
        dataset <- currentDataset() %>% 
          select(clean_text) %>% 
          unnest_tokens(input = clean_text, output = word) %>% 
          # remove stopwords, if list for the relevant language is available, otherwise do nothing
          when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "snowball")) ~
                 anti_join(., tibble::tibble(word = stopwords::stopwords(language = input$language, source = "snowball")), by = "word"),
               is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
                 anti_join(., tibble::tibble(word = stopwords::stopwords(language = input$language, source = "stopwords-iso")), by = "word"),
               ~ .) %>% 
          # nrc sentiment, removing words that are both positive and negative
          inner_join(y = sentimentDictionary, by = "word") %>%
          count(word, sentiment, sort = TRUE) %>% 
          slice(1:input$MaxWords) %>% 
          mutate(colour = if_else(condition = sentiment=="negative",
                                  true = if (is.null(input$colourNegative)) "#F8766D" else input$colourNegative,
                                  false = if (is.null(input$colourPositive)) "#00BFC4" else input$colourPositive)) %>%
          select(-sentiment)
        
      } else if (input$sentimentL=="Sentiment by tweet") {
        
        dataset <- currentDataset() %>% 
          select(clean_text) %>%
          mutate(sentiment = syuzhet::get_sentiment(char_v = clean_text, language = input$language)) %>% 
          # remove neutral
          filter(sentiment!=0) %>% 
          mutate(sentiment = if_else(condition = sentiment>0, true = "positive", false = "negative")) %>% 
          tidytext::unnest_tokens(input = clean_text, output = word) %>% 
          # remove stopwords, if list for the relevant language is available, otherwise do nothing
          when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "snowball")) ~
                 anti_join(., tibble::tibble(word = stopwords::stopwords(language = input$language, source = "snowball")), by = "word"),
               is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
                 anti_join(., tibble::tibble(word = stopwords::stopwords(language = input$language, source = "stopwords-iso")), by = "word"),
               ~ .) %>% 
          count(word, sentiment, sort = TRUE) %>% 
          slice(1:input$MaxWords) %>% 
          mutate(colour = if_else(condition = sentiment=="negative",
                                  true = if (is.null(input$colourNegative)) "#F8766D" else input$colourNegative,
                                  false = if (is.null(input$colourPositive)) "#00BFC4" else input$colourPositive)) %>%
          select(-sentiment)
        
      } else {
        
        if (input$anyLanguage==TRUE) {
          dataset <- currentDataset() %>% 
            select(clean_text, lang) %>% 
            unnest_tokens(input = clean_text, output = word) %>% 
            mutate(stopword = FALSE)
          
          for (i in stopwords::stopwords_getlanguages(source = "stopwords-iso")) {
            if (length(dataset$stopword[dataset$lang==i])!=0) {
              dataset$stopword[dataset$lang==i] <- is.element(el = dataset$word[dataset$lang==i], set = c("via", "retweeted", stopwords::stopwords(language = i, source = "stopwords-iso")))
            }
          }
          if (input$colourLanguage==TRUE) {
            dataset <- dataset %>%
              filter(stopword==FALSE) %>% 
              group_by(lang) %>% 
              count(word, sort = TRUE) %>% 
              add_tally(wt = n) %>% 
              arrange(desc(n)) %>% 
              ungroup() %>% 
              mutate(lang = factor(x = lang, levels = unique(lang)))
            
            dataset$LangRank <- dataset %>% group_indices(., factor(lang, levels = unique(lang)))
            
            if (is.null(input$multilingual_palette)) {
              MaxLang <- 9
            } else {
              MaxLang <- brewer.pal.info$maxcolors[rownames(brewer.pal.info)==input$multilingual_palette]
            }
            
            dataset <- dataset %>% 
              filter(LangRank<(MaxLang+1)) %>% 
              ungroup() %>% 
              top_n(n = input$MaxWords, wt = n) %>% 
              arrange(LangRank) %>% 
              left_join(tibble::tibble(LangRank = if (is.null(input$multilingual_palette)) 1:9 else if(brewer.pal.info$category[rownames(brewer.pal.info)==input$multilingual_palette]=="seq") MaxLang:1 else 1:MaxLang, colour = brewer.pal(n = MaxLang, name = if (is.null(input$multilingual_palette)) "Set1" else input$multilingual_palette)), by = "LangRank") %>% 
              select(word, n, colour) %>% 
              arrange(desc(n))
            
            
          } else if (input$colourLanguage==FALSE) {
            dataset <- dataset %>%
              filter(stopword==FALSE) %>% 
              count(word, sort = TRUE) %>%
              slice(1:input$MaxWords) %>% 
              mutate(colour = createPalette(n()))
          }
        } else {
          dataset <- currentDataset() %>%
            select(clean_text) %>% 
            unnest_tokens(input = clean_text, output = word) %>% 
            # remove stopwords, if list for the relevant language is available, otherwise do nothing
            when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
                   anti_join(., tibble::tibble(word = c("via", stopwords::stopwords(language = input$language, source = "stopwords-iso"))), by = "word"),
                 ~ .) %>% 
            count(word, sort = TRUE) %>%
            slice(1:input$MaxWords) %>% 
            mutate(colour = createPalette(n()))
        }
      }
      
      # add for log
      
      if (input$sentimentL=="Sentiment by word") {
        message(paste(Sys.time(), "Wordcloud2SentimentCreated", input$language, sep = "-"))
      } else {
        message(paste(Sys.time(), "Wordcloud2UnifiedCreated", input$language, sep = "-"))
      }
      
      dataset 
    }
    
    
  })
  
  output$wordcloud2 <- renderWordcloud2(
    if (is.null(wc2())==FALSE) wc2() %>% wordcloud2(size = if (is.null(input$sizeVarWC2)) 0.5 else input$sizeVarWC2,
                                                    color = wc2()$colour))
  
  #### Barcharts ####
  
  output$barchartGG <- renderPlot({
    currentDataset() %>%
      select(clean_text) %>% 
      unnest_tokens(input = clean_text, output = word) %>% 
      # remove stopwords, if list for the relevant language is available, otherwise do nothing
      when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
             anti_join(., tibble::tibble(word = c("via", stopwords::stopwords(language = input$language, source = "stopwords-iso"))), by = "word"),
           ~ .) %>% 
      count(word, sort = TRUE) %>% 
      head(input$MaxWordsInBarchart) %>% 
      arrange(n) %>% 
      mutate(word = forcats::as_factor(word)) %>% 
      ggplot(mapping = aes(x = word, y = n, fill = "singlecolor")) +
      geom_col() +
      scale_x_discrete("") +
      scale_y_continuous("Number of occurrences") +
      scale_fill_manual(values = "#08306B") +
      coord_flip() +
      theme_minimal() +
      theme(axis.text=element_text(size=14),
            axis.title=element_text(size=14,face="bold")) +
      guides(fill=FALSE)
  })
  
  
  #### Comparisons ####
  
  output$barchartComparisonGG <- renderPlot({
    
    if (is.null(input$EPgroup)==FALSE&length(input$EPgroup)<5&length(input$EPgroup)>1) {
      tidy_tweets <- currentDataset() %>%
        select(clean_text, GroupShort) %>% 
        filter(stringr::str_detect(string = GroupShort, pattern = paste(input$EPgroup, collapse = "|"))) %>% 
        unnest_tokens(output = word, input = clean_text) %>% 
        # remove stopwords, if list for the relevant language is available, otherwise do nothing
        when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
               anti_join(., tibble::tibble(word = c("via", "retweeted", stopwords::stopwords(language = input$language, source = "stopwords-iso"))), by = "word"),
             ~ .)
      
      if (input$wcOrBarchartComparison == "Comparison barchart") {
        if (is.null(input$EPgroup)==FALSE&length(input$EPgroup)==2) {
          temp <- tidy_tweets %>% 
            count(word, GroupShort) %>%
            filter(sum(n) >= 5) %>%
            ungroup() %>%
            spread(GroupShort, n, fill = 0) %>%
            mutate_if(is.numeric, funs((. + 1) / sum(. + 1))) %>%
            mutate(logratio = log(.[[2]] / .[[3]])) %>%
            arrange(desc(logratio)) %>% 
            group_by(logratio < 0) %>%
            top_n(10, abs(logratio)) %>%
            slice(1:10) %>% 
            ungroup() %>%
            mutate(word = reorder(word, logratio))
          
          temp %>% 
            ggplot(aes(word, logratio, fill = logratio < 0)) +
            geom_col() +
            coord_flip() +
            ylab("Log odds ratio") +
            scale_x_discrete("") +
            scale_fill_discrete(name = "", labels = colnames(temp)[2:3]) +
            theme_minimal() +
            theme(axis.text=element_text(size=14),
                  axis.title=element_text(size=14)) 
        } else {
          ggplot() +
            labs(title = "Comparison barchart requires two EP groups") +
            theme_void() +
            theme(plot.title = element_text(colour = "#7F3D17"))
        }
        
      } else {
        temp <- tidy_tweets %>% 
          group_by(word, GroupShort) %>% 
          count() %>% 
          ungroup() %>%
          spread(GroupShort, n, fill = 0)
        
        rownames(temp) <- temp$word
        
        par(mar = rep(0, 4))
        
        temp <- temp %>% 
          select(-word)
        
        if (input$wcOrBarchartComparison ==  "Commonality wordcloud") {
          temp %>% 
            commonality.cloud(random.order=FALSE, max.words = 100, scale = c(5, 0.5), family = "Carlito", font = 1)
        } else if (input$wcOrBarchartComparison ==  "Comparison wordcloud") {
          temp %>% 
            comparison.cloud(random.order=FALSE, max.words = 100, scale = c(5, 0.5), family = "Carlito", font = 1)   
        }
      }
      
    }  else {
      ggplot() +
        labs(title = "Minimium 2 and maximum 4 EP groups are allowed") +
        theme_void() +
        theme(plot.title = element_text(colour = "#7F3D17"))
    }
    
    
    
  })
  
  #### Download ####
  
  output$downloadTidyWordCount <- downloadHandler(
    filename = function() {
      paste0("QuoteFinderWordCount", ".csv")
    },
    content = function(file) {
      write_csv(x = currentDataset() %>%
                  select(clean_text) %>% 
                  unnest_tokens(input = clean_text, output = word) %>% 
                  # remove stopwords, if list for the relevant language is available, otherwise do nothing
                  when(is.element(el = input$language, set = stopwords::stopwords_getlanguages(source = "stopwords-iso")) ~
                         anti_join(., tibble::tibble(word = c("via", "retweeted", stopwords::stopwords(language = input$language, source = "stopwords-iso"))), by = "word"),
                       ~ .) %>% 
                  count(word, sort = TRUE),
                file)
    }
  )
  
  
  output$downloadPng <- downloadHandler(
    filename = paste0(randomString, ".png"),
    content = function(file) {
      owd <- setwd(tempdir())
      on.exit(setwd(owd))
      saveWidget(widget = wc2() %>% wordcloud2(size = input$sizeVarWC2*2,
                                               color = wc2()$colour), file = paste0(randomString, ".html"), selfcontained = FALSE)
      webshot(url = paste0(randomString, ".html"), file = file, delay = if_else(condition = input$MaxWords>500, true = 5, false = 3), vwidth = 1280, vheight = 960)
    },
    contentType = "image/png")
  
  output$downloadHtml <- downloadHandler(
    filename = paste0(randomString, ".html"),
    content = function(file) {
      owd <- setwd(tempdir())
      on.exit(setwd(owd))
      saveWidget(widget = wc2() %>% wordcloud2(size = input$sizeVarWC2*2,
                                               color = wc2()$colour), file = file, selfcontained = TRUE)
      simpleFixWc2(file, file)
    })
  
  
  #### DataTable ####
  
  output$table <- DT::renderDataTable({
    DT::datatable(data = currentDataset() %>% 
                    arrange(desc(time))%>%
                    select(full_name, screen_name, date, text, Link, GroupShort) %>% 
                    #head(10000) %>% 
                    rename(`Full name` = full_name, `Twitter handle` = screen_name, Date = date, Tweet = text, `EP Group` = "GroupShort"),
                  escape = FALSE, options = list(pageLength = 4, lengthMenu = c(3, 4, 5, 10, 15, 20)), rownames=FALSE)
  })
  
  ### InfoBox ####
  
  output$HeaderInfoBox <- renderText({
    
    paste0(paste0("<div class='col-sm-12'><b>Enabled filters</b>: language: <i>", input$language, "</i>;"),
           if (is.null(input$selectedHashtag)==TRUE) (" hashtag: <i>All tweets</i>;") else if (input$selectedHashtag=="All tweets") (" hashtag: <i>All tweets</i>;") else paste0(" hashtag: <i>#", input$selectedHashtag, "</i>;"),
           if (input$string!="") paste0(" string: <i>", input$string, "</i>;"),
           if (is.null(input$EPgroup)==FALSE) paste0(" EP group: <i>", paste(input$EPgroup, collapse = ", "), "</i>;"),
           if (is.null(input$countryFilter)==FALSE) paste0(" Country: <i>", paste(input$countryFilter, collapse = ", "), "</i>;"),
           #" selected word: <i>", gsub(":.*","",input$selected_word), 
           "</div>")
  })
  
  output$TweetsNr <- renderInfoBox({
    infoBox(title = "Tweets",
            value =  point(nrow(currentDataset())),
            icon = icon("twitter"),
            color = "blue"
    )
  })
  
  output$MEPsNr <- renderInfoBox({
    
    infoBox(title = "by",
            value = paste(length(unique(currentDataset()$screen_name)), "MEPs"),
            icon = icon("users"), color = "blue", fill = FALSE)
    
  })
  
  output$DaysNr <- renderInfoBox({
    # reload if dateRange is changed
    input$dateRange
    input$dateRangeRadio
    
    if (input$dateRangeRadio=="Last week") {
      infoBox(title = "posted in", value = paste(7, "days"),
              icon = icon("calendar"), color = "blue", fill = FALSE)
    } else if (input$dateRangeRadio=="Last month") {
      infoBox(title = "posted in", value = paste(31, "days"),
              icon = icon("calendar"), color = "blue", fill = FALSE)
    } else if (input$dateRangeRadio=="Last three months") {
      infoBox(title = "posted in", value = paste(91, "days"),
              icon = icon("calendar"), color = "blue", fill = FALSE)
    } else {
      infoBox(title = "posted in", value = paste(as.Date(input$dateRange[2])-as.Date(input$dateRange[1]), "days"),
              icon = icon("calendar"), color = "blue", fill = FALSE)
    }
    
  })
  
  output$selected_tweets_wall <- renderUI({
    
    if(nrow(currentDataset()) == 0){
      return(NULL)
    }
    
    if (is.null(input$table_rows_current)) {
      return(NULL)
    }
    
    tempTweets <- currentDataset() %>% 
      slice(input$table_rows_current)
    
    wall <- tagList()
    
    tweet_distribute <- tibble::tibble(status_id = tempTweets$status_id,
                                       column_selector = rep_along(along = tempTweets$status_id, x = 1:4), 
                                       div_id = seq_along(along = tempTweets$status_id))
    
    
    tweet_distribute$tweet_embed <- purrr::map_chr(.x = seq_along(along = tempTweets$status_id),
                                                   .f = function(x) embed_tweet_js(id = tweet_distribute$status_id[x],
                                                                                   i = tweet_distribute$div_id[x]))
    
    
    for (i in 1:4) { #create four columns and distribute tweets
      wall[[i]] <- shiny::column(
        width = 3,
        tweet_distribute %>%
          dplyr::filter(column_selector==i) %>%
          dplyr::pull(tweet_embed) %>% 
          paste(collapse = " ") %>% 
          shiny::HTML()
      )
    }
    fluidRow(wall)
  })
  
  
}

shinyApp(ui = ui, server = server)