wizualizacja-danych/proj3/projekt.R

# dataset: https://www.kaggle.com/aiaiaidavid/airplane-crash-fatalities-since-1908-dv-03032020
# setup srodowiska ####
setwd('/Users/patrycjalazna/Desktop/wizualizacja-danych/projekt_3/')

# importy ####
library(shiny)
library(shinyTime)
library(shinyjs)
library(highcharter)
library(splusTimeDate)
library(tidyverse)
library(lubridate)
library(gridExtra)
library(usmap)
library(tidytext)
library(tm)
library(SnowballC)
library(wordcloud)
library(dplyr)
library(streamgraph)
library(plotly)
library(DT)


# zaladowanie datasetu ####
airplane_crashes <- read.csv('airplane_crashes.csv')

# podsumowanie ####
summary <- summary(airplane_crashes)
colnames(airplane_crashes)

# preprocessing ####
# zmiana nazwy kolumn
colnames(airplane_crashes) <- c('Date', 'Time', 'Location', 'Operator', 'Flight', 'Route', 'Type', 
                                'Registration', 'cn/ln', 'Total_Onboard', 'Passengers_Onboard', 
                                'Crew_Onboard', 'Total_Fatalities', 'Passengers_Fatalities', 
                                'Crew_Fatalities', 'Ground', 'Summary')

# konwersja na prawidlowy format Date
airplane_crashes$Date <- mdy(airplane_crashes$Date)
airplane_crashes$Time <- hm(airplane_crashes$Time)

# ekstrakcja informacji z datasetu, zapis w poprawnym formacie
# ekstrakcja informacji z kolumny Date
airplane_crashes$Year <- year(airplane_crashes$Date)
airplane_crashes$Month <- as.factor(month(airplane_crashes$Date))
airplane_crashes$Day <- as.factor(day(airplane_crashes$Date))
airplane_crashes$Weekday <- as.factor(wday(airplane_crashes$Date))
airplane_crashes$Is_Leap_Year <- leap_year(airplane_crashes$Date)
airplane_crashes$Decade <- year(floor_date(airplane_crashes$Date, years(10)))
airplane_crashes$Date <- format(as.Date(airplane_crashes$Date, '%m/%d/%Y'), '%d/%m/%Y')

# ekstrakcja godziny, minuty i AM/PM z kolumny Time
airplane_crashes$Hour <- as.integer(hour(airplane_crashes$Time))
airplane_crashes$Minute <- as.factor(minute(airplane_crashes$Time))
airplane_crashes$AM_PM <- if_else(am(airplane_crashes$Time), 'AM', 'PM')
airplane_crashes$btwn_6PM_6AM <- if_else


# ShinyApp ####


ui <- fluidPage(
  id = "main_content",
  navbarPage("Airplane crashes from 1908 to 2020",
             # tabPanel("General overview",
             #          sidebarLayout(
             #            sidebarPanel(
             #              # wybór daty
             #              dateRangeInput('dates',
             #                             'Date range:',
             #                             min(airplane_crashes$Date),
             #                             max(airplane_crashes$Date)),
             #              # wybor godziny
             #              timeInput("time", "Time (local):", seconds = FALSE)
             #            ),
             #            mainPanel(
             #              # wykresiki
             #              plotlyOutput("weekdayCrashes"),
             #              fixedRow(
             #                column(3, plotlyOutput("hourCrashes")),
             #                column(3, plotlyOutput("monthCrashes"))
             #              )
             #            )
             #          )
             # ),
             tabPanel("General overview",
                      fixedRow(
                        column(3,
                               dateRangeInput('dates',
                                              'Date range:',
                                              min(airplane_crashes$Date),
                                              max(airplane_crashes$Date)),
                               # wybor godziny
                               timeInput("time", "Time (local):", seconds = FALSE)
                               ),
                        column(6, plotOutput("crashes_each_year"))
                      ),
                      
                      fixedRow(
                         column(3, plotlyOutput("hourCrashes")),
                         column(3, plotlyOutput("weekdayCrashes")),
                         column(3, plotlyOutput("monthCrashes"))
                      )),
             tabPanel("Streamgraph",
                      mainPanel(
                        streamgraphOutput("operatorCrashes")
                      )
             ),
             
             tabPanel("Map",
                      mainPanel(
                        # mapy
                        plotlyOutput("mapPlot")
                        #plotOutput("mapPlot")
                      )
             ),
             tabPanel("Data",
                      sidebarPanel(
                        dateRangeInput('dataDates',
                                       'Date range:',
                                       min(airplane_crashes$Date),
                                       max(airplane_crashes$Date)),
                        downloadButton('downloadData', 'Download')
                      ),
                      mainPanel(
                        DT::dataTableOutput('allData')
                  
                      ))
  )
)


server <- function(input, output, session) {
  # godzina
  observe(print(strftime(input$time, "%R")))
  
  output$crashes_each_year <- renderPlot({
 
    year_wise <- airplane_crashes %>% count(Year)
    
    year_wise <- year_wise %>%
      filter(Year > year(input$dates[1])) %>%
      filter(Year < year(input$dates[2]))
    
    ggplot(year_wise, aes(x = Year, y = n)) +
      geom_col(fill = '#0f4c75', col = 'white') +
      labs(title = 'No. of Airplane crashes since 1908 each year', x = '', y = '') +
      scale_x_continuous(breaks = seq(1908, 2020, 4))
  })
  
  output$weekdayCrashes <- renderPlotly({
  
    week_day <- airplane_crashes %>% count(Weekday)
    week_day$Weekday <- as.character(week_day$Weekday) 
    
    week_day$Weekday[week_day$Weekday == 1] <- "Monday"
    week_day$Weekday[week_day$Weekday == 2] <- "Tuesday"
    week_day$Weekday[week_day$Weekday == 3] <- "Wednesday"
    week_day$Weekday[week_day$Weekday == 4] <- "Thursday"
    week_day$Weekday[week_day$Weekday == 5] <- "Friday"
    week_day$Weekday[week_day$Weekday == 6] <- "Saturday"
    week_day$Weekday[week_day$Weekday == 7] <- "Sunday"
    
    # zmiana kolejnosci dni; dni nie powinny sie wyswietlac alfabetycznie 
    week_day$Weekday <-  factor(week_day$Weekday, levels= c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'))
    
    
    # plot_ly(
    #   x = week_day$Weekday, y = week_day$n,
    #   name = 'No. of crashes per week day', type = "bar"
    # ) %>% 
    #   layout(title = 'Week Day Crashes',
    #          xaxis = list(title = "Weekdays",
    #                       zeroline = FALSE),
    #          yaxis = list(title = "No. of crashes",
    #                       zeroline = FALSE))
    
    df <- week_day
    df <- df %>% arrange(Weekday)
    fig <-  df %>% plot_ly(labels = ~Weekday, values = ~n, sort=FALSE)
    fig <- fig %>% add_pie(hole = 0.4)
    fig <- fig %>% layout(title = "No. of crashes per weekday", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                          yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

    fig
    # 
    # df <- df %>% arrange(Hour)
    # df$Hour <- as.character(df$Hour)
    # df$Hour <- paste(df$Hour, ":00", sep = "")
    # fig <-  df %>% plot_ly(labels = ~Hour, values = ~n, sort=FALSE)
    # fig <- fig %>% add_pie(hole = 0.4) 
    # fig <- fig %>% layout(title = "No. of crashes per hour", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
    #                       yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
    # 
    # fig
    
    
  })
  
  output$hourCrashes <- renderPlotly({
    
    crashes_per_hours <- airplane_crashes %>% count(Hour)
    crashes_per_hours <- crashes_per_hours[-c(1,26,27,28), ]
    
    #dodanie pozycji labeli
    # crashes_per_hours <- crashes_per_hours %>%
    #   arrange(Hour) %>%
    #   mutate(lab.ypos = cumsum(n) - 0.5*n)
    # 
    # ggplot(crashes_per_hours, aes(x = "", y = n, fill = Hour)) +
    #   geom_bar(stat="identity", width=1, color="white") +
    #   coord_polar("y", start=0) +
    #   geom_text(aes(y = lab.ypos, label = Hour), color = "white")+
    #   theme_void() + 
    #   theme(legend.position="none")
    
    df <- crashes_per_hours
    
    crashes_per_months$Month[crashes_per_months$Month == 1] <- "January"
    
    df <- df %>% arrange(Hour)
    df$Hour <- as.character(df$Hour)
    df$Hour <- paste(df$Hour, ":00", sep = "")
    fig <-  df %>% plot_ly(labels = ~Hour, values = ~n, sort=FALSE)
    fig <- fig %>% add_pie(hole = 0.4) 
    fig <- fig %>% layout(title = "No. of crashes per hour", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                          yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
    
    fig
  
    
  })
  
  output$monthCrashes <- renderPlotly({
    
    crashes_per_months <- airplane_crashes %>% count(Month)
    
    crashes_per_months$Month <- as.character(crashes_per_months$Month)
    
    crashes_per_months$Month[crashes_per_months$Month == 1] <- "January"
    crashes_per_months$Month[crashes_per_months$Month == 2] <- "February"
    crashes_per_months$Month[crashes_per_months$Month == 3] <- "March"
    crashes_per_months$Month[crashes_per_months$Month == 4] <- "April"
    crashes_per_months$Month[crashes_per_months$Month == 5] <- "May"
    crashes_per_months$Month[crashes_per_months$Month == 6] <- "June"
    crashes_per_months$Month[crashes_per_months$Month == 7] <- "July"
    crashes_per_months$Month[crashes_per_months$Month == 8] <- "August"
    crashes_per_months$Month[crashes_per_months$Month == 9] <- "September"
    crashes_per_months$Month[crashes_per_months$Month == 10] <- "October"
    crashes_per_months$Month[crashes_per_months$Month == 11] <- "November"
    crashes_per_months$Month[crashes_per_months$Month == 12] <- "December"
    
    
    df <- crashes_per_months
    df <- df %>% arrange(match(Month, c("January", "February","March", "April", "May", "June", "July", "August", "September", "October", "November", "December")))
    fig <-  df %>% plot_ly(labels = ~Month, values = ~n, sort=FALSE)
    fig <- fig %>% add_pie(hole = 0.4) 
    fig <- fig %>% layout(title = "No. of crashes per month", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                          yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
    
    fig
    
  })
  
  
  output$operatorCrashes <- renderStreamgraph({
    
    main_type_wise_year <- airplane_crashes %>%
      select(Year, Type) %>%
      # replace model number by empty strings
      mutate(main_type = str_replace_all(Type, "[A-Za-z]*-?\\d+-?[A-Za-z]*.*", "")) %>%
      # skip empty strings row
      filter(main_type > 'A') %>%
      group_by(Year, main_type) %>%
      summarize(n = n()) %>%
      group_by(Year) %>%
      top_n(5, n)
    
    # i teraz z tego streamgraph
    main_type_wise_year <-  main_type_wise_year %>% arrange(-n)
    streamgraph <-  main_type_wise_year %>%
      group_by(Year, main_type) %>%
      tally(wt=n) %>%
      streamgraph("main_type", "n", "Year", interpolate="cardinal") %>%
      sg_fill_brewer("PuOr") %>%
      sg_legend(TRUE)
    
    streamgraph
  })
  
  output$mapPlot <- renderPlotly({
    
    states_list <- c('Alabama','Alaska','Alaksa','Arizona','Arkansas',"California",
                     "Colorado", "Connecticut","Delaware","Florida","Georgia",
                     "Hawaii","Idaho","Illinois", "Indiana","Iowa","Kansas",
                     "Kentucky","Louisiana","Maine","Maryland", "Massachusetts",
                     "Massachusett", "Michigan","Minnesota","Mississippi","Missouri",
                     "Montana", "Nebraska","Nevada","New Hampshire","New Jersey",
                     "New Mexico","New York", "North Carolina","North Dakota","Ohio",
                     "Oklahoma", "Oklohoma", "Oregon","Pennsylvania", "Rhode Island",
                     "South Carolina",
                     "South Dakota",'Tennesee',"Tennessee","Texas","Utah", "Vermont",
                     'Virginia',"Washington D.C.", "Washington, D.C.", "Washington", 
                     "West Virginia","Wisconsin","Wyoming",
                     "AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DC", "DE", "FL", "GA",
                     "HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD",
                     "MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ",
                     "NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
                     "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY")
    
    location <- airplane_crashes %>%
      select(Location) 
    
    for(state in states_list) {
      location <- location %>%
        mutate(Location = str_replace_all(Location, state, paste(state, ', USA', sep = ''))) %>%
        mutate(Location = str_replace_all(Location, 'USA.*, ', '')) %>%
        mutate(Location = str_replace(Location, 'West Virginia, USA,', '')) %>%
        mutate(Location = str_replace(Location, 'Afghanstan', 'Afghanistan')) %>%
        mutate(Location = str_replace(Location, 'Airzona|Arazona', 'Arizona')) %>%
        mutate(Location = str_replace(Location, 'Alakska', 'Alaska')) %>%
        mutate(Location = str_replace(Location, 'Cailifornia|Calilfornia', 'California')) %>%
        mutate(Location = str_replace(Location, 'D.*Congo', 'DR Congo')) %>%
        mutate(Location = str_replace(Location, 'Domincan Republic', 'Dominican Republic')) %>%
        mutate(Location = str_replace(Location, 'Hati', 'Haiti')) %>%
        mutate(Location = str_replace(Location, ' International Airport', '')) %>%
        mutate(Location = str_replace(Location, 'Morrocco|Morroco', 'Morocco')) %>%
        mutate(Location = str_replace(Location, 'Phillipines', 'Phillipines')) %>%
        mutate(Location = str_replace(Location, 'Burma', 'Myanmar')) %>%
        mutate(Location = str_replace(Location, '([Ss]outhern|[Nn]orthern|[Ww]estern|[Ee]astern) ', ''))}
    
    country_state <- location %>%
      select(Location) %>%
      filter(!str_detect(Location, '[Oo]cean|[Ss]ea|[Cc]hannel|Gulf of')) %>%
      mutate(Location = str_replace(Location, '(Near|Off|Over) ', '')) %>%
      mutate(Location = str_replace(Location, 'USA, Australia', 'Australia')) %>%
      mutate(State_Province = str_replace(Location, '(.*, )?(.*), (.*)', '\\2')) %>%
      mutate(Country = str_replace(Location, '.*,\\s*', '')) 
    
    cntry <- country_state %>%
      group_by(Country) %>%
      summarize(n = n()) %>%
      arrange(desc(n))
    
    # podzial na kategorie wg liczby wypadkow w danym kraju
    cntry <- cntry %>%
      mutate(m = case_when(
        n > 200  ~ "200 +",
        n < 200 & n >= 100 ~ "199 - 100",
        n < 100 & n >= 50 ~ "99 - 50",
        n < 50 & n >= 10 ~ "49 - 10",
        n < 10  ~ "< 10")) %>%
      mutate(m = factor(m, levels = c("< 10", "49 - 10", "99 - 50", "199 - 100", "200 +")))
    
    
    world_map <- map_data("world")
    
    map_data <- cntry %>% 
      full_join(world_map, by = c('Country' = 'region')) 
    
    map_pal = c('#E6CE39', '#F09B3C', '#D94B41', '#CD3CF0', '#555CEB')
    
    ggplot_obj <- 
      ggplot(map_data, aes(x = long, y = lat, group = group, fill = m)) +
      geom_polygon(colour = "white") + 
      labs(title = 'No. of crashes in each country', x = '', y = '', fill = '') +
      scale_fill_manual(values = map_pal, na.value = 'whitesmoke') + 
      theme(legend.position='right', legend.justification = "top") + 
      guides(fill = guide_legend(reverse = TRUE))
    
    ggplotly(
      p = ggplot_obj,
      tooltip = "all",
      dynamicTicks = FALSE,
      layerData = 1,
      originalData = TRUE
    )
  })
 
  output$downloadData <- downloadHandler(
      filename = function() {
        paste('airplane_crashes', '.csv', sep='')
      },
      content = function(file) {
        write.csv(airplane_crashes, file)
      }
    )
    
  output$allData <- DT::renderDataTable({
    airplane_crashes %>%
      # filter(Year == input$dataDates) %>%
      DT::datatable()
  })
  
}

shinyApp(ui = ui, server = server)




# tutaj sb rysuje wykresiki demo, zeby nie odpalac shiny za kazdym razem ####

# number of fatal accidents by operator - top 5

##
# extract and group by manufacturer
# main_type_wise_year <- airplane_crashes %>%
#   select(Year, Type) %>%
#   # replace model number by empty strings
#   mutate(main_type = str_replace_all(Type, "[A-Za-z]*-?\\d+-?[A-Za-z]*.*", "")) %>% 
#   # skip empty strings row
#   filter(main_type > 'A') %>%
#   group_by(Year, main_type) %>%
#   summarize(n = n()) %>%
#   group_by(Year) %>%
#   top_n(5, n) 
# #%>% mutate(main_type = reorder_within(-n, main_type, Year))
# 
# # i teraz z tego streamgraph
# main_type_wise_year <-  main_type_wise_year %>% arrange(-n)
# main_type_wise_year %>%
#   group_by(Year, main_type) %>%
#   tally(wt=n) %>%
#   streamgraph("main_type", "n", "Year", interpolate="cardinal") %>%
#   sg_fill_brewer("PuOr") %>%
#   sg_legend(TRUE)

####
# airplane_operators <- airplane_crashes %>% count(Operator, sort = TRUE)
# airplane_operators <- head(airplane_operators, 5)
# ggplot(airplane_operators, aes(x = Operator, y = n)) + 
#   geom_point(color = "#765432", alpha = 0.8) +
#   labs(title = "No. of fatal accidents by operator", x = '', y = '')
# 
# airplane_operators %>% 
#   streamgraph(key="Operator", value=, date="Year", offset="zero") %>%
#   sg_fill_brewer("BuPu")

# wykres ile wypadkow kazdego operatora w kazdym roku