wfkwan

library(reticulate)

use_python("<PYTHONPATH>")

Connect Amazon Elasticsearch Service
py_awsauth <- import("requests_aws4auth")
credentials <- data.frame(list('<key>', '<secret>', '<region>', '<service>'))
authr <- py_awsauth$AWS4Auth(credentials$key, credentials$secret, credentials$region, credentials$service)
colnames(credentials) <- c("key", "secret", "region", "service")
hosts = c("<domain>:<port>")

wfkwan

box(
    title = "Airline Flight Destination Weather",
    sliderInput("wordcloud_size", "Size of the Wordcloud:", 
                min=0.1,
                max=0.6,
                value=0.6
    ),
    wordcloud2Output("dest_weather", height = 200),
  )

wfkwan

  output$dest_weather <- renderWordcloud2({
    data <- setNames(annotate_agg1_es_result((es$search("kibana_sample_data_flights", dest_weather_query))),
                     c("weather", "ndest"))
    wordcloud2(data, size = input$wordcloud_size)
  })

wfkwan

  output$delay_time <- renderPlot({
    data <- setNames(annotate_agg2_es_result((es$search("kibana_sample_data_flights", delay_type_query))),
                     c("time", "airline", "ndelays"))
    data$time <- as.POSIXct(data$time, format="%Y-%m-%dT%H:%M:%S")
    data <- data[(data$time >= input$delay_time_range[1]) & (data$time <= input$delay_time_range[2]), ]
    ggplot(data, aes(x=time, y=ndelays, fill=airline)) + geom_bar(stat="identity") + theme_minimal()
    
  })

wfkwan

  output$airline_flights <- renderPlotly({
    data <- setNames(annotate_agg1_es_result((es$search("kibana_sample_data_flights", airline_carrier_query))),
                     c("airline", "nflights"))
    colors <- c('rgb(211,94,96)', 'rgb(128,133,133)', 'rgb(144,103,167)', 'rgb(171,104,87)', 'rgb(114,147,203)')
    plot_ly(data, labels = ~airline, values = ~nflights, type = 'pie',
                   textposition = 'inside',
                   textinfo = 'label+percent',
                   insidetextfont = list(color = '#FFFFFF'),
                   marker = list(colors = colors,
                                 line = list(color = '#FFFFFF', width = 1)),

wfkwan

annotate_agg1_es_result <- function(result, include_others=FALSE) {
  name <- c()
  count <- c()
  aggregation_result <- result$aggregations[[1]]
   if (include_others){
    if ("key_as_string" %in% names(aggregation_result)) name <- c(name, aggregation_result$key_as_string)
    else name <- c(name, aggregation_result$key)
    name <- c(name, "Others")
    count <- c(count, aggregation_result$sum_other_doc_count)
   }

wfkwan

library(DBI)
library(reticulate)

use_python("<PYTHONPATH>")

# Connect Amazon Elasticsearch Service
# py_awsauth <- import("requests_aws4auth")
# credentials <- data.frame(list('<key>', '<secret>', '<region>', '<service>'))
# authr <- py_awsauth$AWS4Auth(credentials$key, credentials$secret, credentials$region, credentials$service)
# colnames(credentials) <- c("key", "secret", "region", "service")