442 lines
17 KiB
R
442 lines
17 KiB
R
# dataset: https://www.kaggle.com/aiaiaidavid/airplane-crash-fatalities-since-1908-dv-03032020
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# setup srodowiska ####
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setwd('/Users/patrycjalazna/Desktop/wizualizacja-danych/projekt_3/')
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# importy ####
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library(shiny)
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library(shinyTime)
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library(shinyjs)
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library(highcharter)
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library(splusTimeDate)
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library(tidyverse)
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library(lubridate)
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library(gridExtra)
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library(usmap)
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library(tidytext)
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library(tm)
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library(SnowballC)
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library(wordcloud)
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library(dplyr)
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library(streamgraph)
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library(plotly)
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library(DT)
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# zaladowanie datasetu ####
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airplane_crashes <- read.csv('airplane_crashes.csv')
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# podsumowanie ####
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summary <- summary(airplane_crashes)
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colnames(airplane_crashes)
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# preprocessing ####
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# zmiana nazwy kolumn
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colnames(airplane_crashes) <- c('Date', 'Time', 'Location', 'Operator', 'Flight', 'Route', 'Type',
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'Registration', 'cn/ln', 'Total_Onboard', 'Passengers_Onboard',
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'Crew_Onboard', 'Total_Fatalities', 'Passengers_Fatalities',
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'Crew_Fatalities', 'Ground', 'Summary')
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# konwersja na prawidlowy format Date
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airplane_crashes$Date <- mdy(airplane_crashes$Date)
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airplane_crashes$Time <- hm(airplane_crashes$Time)
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# ekstrakcja informacji z datasetu, zapis w poprawnym formacie
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# ekstrakcja informacji z kolumny Date
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airplane_crashes$Year <- year(airplane_crashes$Date)
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airplane_crashes$Month <- as.factor(month(airplane_crashes$Date))
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airplane_crashes$Day <- as.factor(day(airplane_crashes$Date))
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airplane_crashes$Weekday <- as.factor(wday(airplane_crashes$Date))
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airplane_crashes$Is_Leap_Year <- leap_year(airplane_crashes$Date)
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airplane_crashes$Decade <- year(floor_date(airplane_crashes$Date, years(10)))
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airplane_crashes$Date <- format(as.Date(airplane_crashes$Date, '%m/%d/%Y'), '%d/%m/%Y')
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# ekstrakcja godziny, minuty i AM/PM z kolumny Time
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airplane_crashes$Hour <- as.integer(hour(airplane_crashes$Time))
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airplane_crashes$Minute <- as.factor(minute(airplane_crashes$Time))
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airplane_crashes$AM_PM <- if_else(am(airplane_crashes$Time), 'AM', 'PM')
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airplane_crashes$btwn_6PM_6AM <- if_else
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# ShinyApp ####
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ui <- fluidPage(
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id = "main_content",
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navbarPage("Airplane crashes from 1908 to 2020",
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# tabPanel("General overview",
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# sidebarLayout(
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# sidebarPanel(
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# # wybór daty
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# dateRangeInput('dates',
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# 'Date range:',
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# min(airplane_crashes$Date),
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# max(airplane_crashes$Date)),
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# # wybor godziny
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# timeInput("time", "Time (local):", seconds = FALSE)
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# ),
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# mainPanel(
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# # wykresiki
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# plotlyOutput("weekdayCrashes"),
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# fixedRow(
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# column(3, plotlyOutput("hourCrashes")),
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# column(3, plotlyOutput("monthCrashes"))
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# )
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# )
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# )
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# ),
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tabPanel("General overview",
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fixedRow(
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column(3,
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dateRangeInput('dates',
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'Date range:',
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min(airplane_crashes$Date),
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max(airplane_crashes$Date)),
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# wybor godziny
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timeInput("time", "Time (local):", seconds = FALSE)
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),
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column(6, plotOutput("crashes_each_year"))
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),
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fixedRow(
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column(3, plotlyOutput("hourCrashes")),
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column(3, plotlyOutput("weekdayCrashes")),
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column(3, plotlyOutput("monthCrashes"))
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)),
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tabPanel("Streamgraph",
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mainPanel(
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streamgraphOutput("operatorCrashes")
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)
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),
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tabPanel("Map",
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mainPanel(
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# mapy
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plotlyOutput("mapPlot")
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#plotOutput("mapPlot")
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)
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),
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tabPanel("Data",
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sidebarPanel(
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dateRangeInput('dataDates',
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'Date range:',
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min(airplane_crashes$Date),
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max(airplane_crashes$Date)),
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downloadButton('downloadData', 'Download')
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),
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mainPanel(
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DT::dataTableOutput('allData')
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))
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)
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)
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server <- function(input, output, session) {
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# godzina
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observe(print(strftime(input$time, "%R")))
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output$crashes_each_year <- renderPlot({
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year_wise <- airplane_crashes %>% count(Year)
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year_wise <- year_wise %>%
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filter(Year > year(input$dates[1])) %>%
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filter(Year < year(input$dates[2]))
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ggplot(year_wise, aes(x = Year, y = n)) +
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geom_col(fill = '#0f4c75', col = 'white') +
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labs(title = 'No. of Airplane crashes since 1908 each year', x = '', y = '') +
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scale_x_continuous(breaks = seq(1908, 2020, 4))
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})
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output$weekdayCrashes <- renderPlotly({
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week_day <- airplane_crashes %>% count(Weekday)
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week_day$Weekday <- as.character(week_day$Weekday)
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week_day$Weekday[week_day$Weekday == 1] <- "Monday"
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week_day$Weekday[week_day$Weekday == 2] <- "Tuesday"
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week_day$Weekday[week_day$Weekday == 3] <- "Wednesday"
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week_day$Weekday[week_day$Weekday == 4] <- "Thursday"
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week_day$Weekday[week_day$Weekday == 5] <- "Friday"
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week_day$Weekday[week_day$Weekday == 6] <- "Saturday"
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week_day$Weekday[week_day$Weekday == 7] <- "Sunday"
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# zmiana kolejnosci dni; dni nie powinny sie wyswietlac alfabetycznie
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week_day$Weekday <- factor(week_day$Weekday, levels= c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'))
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# plot_ly(
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# x = week_day$Weekday, y = week_day$n,
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# name = 'No. of crashes per week day', type = "bar"
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# ) %>%
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# layout(title = 'Week Day Crashes',
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# xaxis = list(title = "Weekdays",
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# zeroline = FALSE),
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# yaxis = list(title = "No. of crashes",
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# zeroline = FALSE))
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df <- week_day
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df <- df %>% arrange(Weekday)
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fig <- df %>% plot_ly(labels = ~Weekday, values = ~n, sort=FALSE)
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fig <- fig %>% add_pie(hole = 0.4)
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fig <- fig %>% layout(title = "No. of crashes per weekday", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
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yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
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fig
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#
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# df <- df %>% arrange(Hour)
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# df$Hour <- as.character(df$Hour)
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# df$Hour <- paste(df$Hour, ":00", sep = "")
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# fig <- df %>% plot_ly(labels = ~Hour, values = ~n, sort=FALSE)
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# fig <- fig %>% add_pie(hole = 0.4)
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# fig <- fig %>% layout(title = "No. of crashes per hour", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
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# yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
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#
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# fig
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})
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output$hourCrashes <- renderPlotly({
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crashes_per_hours <- airplane_crashes %>% count(Hour)
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crashes_per_hours <- crashes_per_hours[-c(1,26,27,28), ]
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#dodanie pozycji labeli
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# crashes_per_hours <- crashes_per_hours %>%
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# arrange(Hour) %>%
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# mutate(lab.ypos = cumsum(n) - 0.5*n)
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#
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# ggplot(crashes_per_hours, aes(x = "", y = n, fill = Hour)) +
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# geom_bar(stat="identity", width=1, color="white") +
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# coord_polar("y", start=0) +
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# geom_text(aes(y = lab.ypos, label = Hour), color = "white")+
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# theme_void() +
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# theme(legend.position="none")
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df <- crashes_per_hours
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crashes_per_months$Month[crashes_per_months$Month == 1] <- "January"
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df <- df %>% arrange(Hour)
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df$Hour <- as.character(df$Hour)
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df$Hour <- paste(df$Hour, ":00", sep = "")
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fig <- df %>% plot_ly(labels = ~Hour, values = ~n, sort=FALSE)
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fig <- fig %>% add_pie(hole = 0.4)
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fig <- fig %>% layout(title = "No. of crashes per hour", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
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yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
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fig
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})
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output$monthCrashes <- renderPlotly({
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crashes_per_months <- airplane_crashes %>% count(Month)
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crashes_per_months$Month <- as.character(crashes_per_months$Month)
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crashes_per_months$Month[crashes_per_months$Month == 1] <- "January"
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crashes_per_months$Month[crashes_per_months$Month == 2] <- "February"
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crashes_per_months$Month[crashes_per_months$Month == 3] <- "March"
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crashes_per_months$Month[crashes_per_months$Month == 4] <- "April"
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crashes_per_months$Month[crashes_per_months$Month == 5] <- "May"
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crashes_per_months$Month[crashes_per_months$Month == 6] <- "June"
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crashes_per_months$Month[crashes_per_months$Month == 7] <- "July"
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crashes_per_months$Month[crashes_per_months$Month == 8] <- "August"
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crashes_per_months$Month[crashes_per_months$Month == 9] <- "September"
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crashes_per_months$Month[crashes_per_months$Month == 10] <- "October"
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crashes_per_months$Month[crashes_per_months$Month == 11] <- "November"
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crashes_per_months$Month[crashes_per_months$Month == 12] <- "December"
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df <- crashes_per_months
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df <- df %>% arrange(match(Month, c("January", "February","March", "April", "May", "June", "July", "August", "September", "October", "November", "December")))
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fig <- df %>% plot_ly(labels = ~Month, values = ~n, sort=FALSE)
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fig <- fig %>% add_pie(hole = 0.4)
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fig <- fig %>% layout(title = "No. of crashes per month", xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
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yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))
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fig
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})
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output$operatorCrashes <- renderStreamgraph({
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main_type_wise_year <- airplane_crashes %>%
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select(Year, Type) %>%
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# replace model number by empty strings
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mutate(main_type = str_replace_all(Type, "[A-Za-z]*-?\\d+-?[A-Za-z]*.*", "")) %>%
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# skip empty strings row
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filter(main_type > 'A') %>%
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group_by(Year, main_type) %>%
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summarize(n = n()) %>%
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group_by(Year) %>%
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top_n(5, n)
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# i teraz z tego streamgraph
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main_type_wise_year <- main_type_wise_year %>% arrange(-n)
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streamgraph <- main_type_wise_year %>%
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group_by(Year, main_type) %>%
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tally(wt=n) %>%
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streamgraph("main_type", "n", "Year", interpolate="cardinal") %>%
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sg_fill_brewer("PuOr") %>%
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sg_legend(TRUE)
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streamgraph
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})
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output$mapPlot <- renderPlotly({
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states_list <- c('Alabama','Alaska','Alaksa','Arizona','Arkansas',"California",
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"Colorado", "Connecticut","Delaware","Florida","Georgia",
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"Hawaii","Idaho","Illinois", "Indiana","Iowa","Kansas",
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"Kentucky","Louisiana","Maine","Maryland", "Massachusetts",
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"Massachusett", "Michigan","Minnesota","Mississippi","Missouri",
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"Montana", "Nebraska","Nevada","New Hampshire","New Jersey",
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"New Mexico","New York", "North Carolina","North Dakota","Ohio",
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"Oklahoma", "Oklohoma", "Oregon","Pennsylvania", "Rhode Island",
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"South Carolina",
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"South Dakota",'Tennesee',"Tennessee","Texas","Utah", "Vermont",
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'Virginia',"Washington D.C.", "Washington, D.C.", "Washington",
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"West Virginia","Wisconsin","Wyoming",
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"AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DC", "DE", "FL", "GA",
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"HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD",
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"MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ",
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"NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
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"SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY")
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location <- airplane_crashes %>%
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select(Location)
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for(state in states_list) {
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location <- location %>%
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mutate(Location = str_replace_all(Location, state, paste(state, ', USA', sep = ''))) %>%
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mutate(Location = str_replace_all(Location, 'USA.*, ', '')) %>%
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mutate(Location = str_replace(Location, 'West Virginia, USA,', '')) %>%
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mutate(Location = str_replace(Location, 'Afghanstan', 'Afghanistan')) %>%
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mutate(Location = str_replace(Location, 'Airzona|Arazona', 'Arizona')) %>%
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mutate(Location = str_replace(Location, 'Alakska', 'Alaska')) %>%
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mutate(Location = str_replace(Location, 'Cailifornia|Calilfornia', 'California')) %>%
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mutate(Location = str_replace(Location, 'D.*Congo', 'DR Congo')) %>%
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mutate(Location = str_replace(Location, 'Domincan Republic', 'Dominican Republic')) %>%
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mutate(Location = str_replace(Location, 'Hati', 'Haiti')) %>%
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mutate(Location = str_replace(Location, ' International Airport', '')) %>%
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mutate(Location = str_replace(Location, 'Morrocco|Morroco', 'Morocco')) %>%
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mutate(Location = str_replace(Location, 'Phillipines', 'Phillipines')) %>%
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mutate(Location = str_replace(Location, 'Burma', 'Myanmar')) %>%
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mutate(Location = str_replace(Location, '([Ss]outhern|[Nn]orthern|[Ww]estern|[Ee]astern) ', ''))}
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country_state <- location %>%
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select(Location) %>%
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filter(!str_detect(Location, '[Oo]cean|[Ss]ea|[Cc]hannel|Gulf of')) %>%
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mutate(Location = str_replace(Location, '(Near|Off|Over) ', '')) %>%
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mutate(Location = str_replace(Location, 'USA, Australia', 'Australia')) %>%
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mutate(State_Province = str_replace(Location, '(.*, )?(.*), (.*)', '\\2')) %>%
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mutate(Country = str_replace(Location, '.*,\\s*', ''))
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cntry <- country_state %>%
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group_by(Country) %>%
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summarize(n = n()) %>%
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arrange(desc(n))
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# podzial na kategorie wg liczby wypadkow w danym kraju
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cntry <- cntry %>%
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mutate(m = case_when(
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n > 200 ~ "200 +",
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n < 200 & n >= 100 ~ "199 - 100",
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n < 100 & n >= 50 ~ "99 - 50",
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n < 50 & n >= 10 ~ "49 - 10",
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n < 10 ~ "< 10")) %>%
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mutate(m = factor(m, levels = c("< 10", "49 - 10", "99 - 50", "199 - 100", "200 +")))
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world_map <- map_data("world")
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map_data <- cntry %>%
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full_join(world_map, by = c('Country' = 'region'))
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map_pal = c('#E6CE39', '#F09B3C', '#D94B41', '#CD3CF0', '#555CEB')
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ggplot_obj <-
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ggplot(map_data, aes(x = long, y = lat, group = group, fill = m)) +
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geom_polygon(colour = "white") +
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labs(title = 'No. of crashes in each country', x = '', y = '', fill = '') +
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scale_fill_manual(values = map_pal, na.value = 'whitesmoke') +
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theme(legend.position='right', legend.justification = "top") +
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guides(fill = guide_legend(reverse = TRUE))
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ggplotly(
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p = ggplot_obj,
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tooltip = "all",
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dynamicTicks = FALSE,
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layerData = 1,
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originalData = TRUE
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)
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})
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output$downloadData <- downloadHandler(
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filename = function() {
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paste('airplane_crashes', '.csv', sep='')
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},
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content = function(file) {
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write.csv(airplane_crashes, file)
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}
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)
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output$allData <- DT::renderDataTable({
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airplane_crashes %>%
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# filter(Year == input$dataDates) %>%
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DT::datatable()
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})
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}
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shinyApp(ui = ui, server = server)
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# tutaj sb rysuje wykresiki demo, zeby nie odpalac shiny za kazdym razem ####
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# number of fatal accidents by operator - top 5
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##
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# extract and group by manufacturer
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# main_type_wise_year <- airplane_crashes %>%
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# select(Year, Type) %>%
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# # replace model number by empty strings
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# mutate(main_type = str_replace_all(Type, "[A-Za-z]*-?\\d+-?[A-Za-z]*.*", "")) %>%
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# # skip empty strings row
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# filter(main_type > 'A') %>%
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# group_by(Year, main_type) %>%
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# summarize(n = n()) %>%
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# group_by(Year) %>%
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# top_n(5, n)
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# #%>% mutate(main_type = reorder_within(-n, main_type, Year))
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#
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# # i teraz z tego streamgraph
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# main_type_wise_year <- main_type_wise_year %>% arrange(-n)
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# main_type_wise_year %>%
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# group_by(Year, main_type) %>%
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# tally(wt=n) %>%
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# streamgraph("main_type", "n", "Year", interpolate="cardinal") %>%
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# sg_fill_brewer("PuOr") %>%
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# sg_legend(TRUE)
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####
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# airplane_operators <- airplane_crashes %>% count(Operator, sort = TRUE)
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# airplane_operators <- head(airplane_operators, 5)
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# ggplot(airplane_operators, aes(x = Operator, y = n)) +
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# geom_point(color = "#765432", alpha = 0.8) +
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# labs(title = "No. of fatal accidents by operator", x = '', y = '')
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#
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# airplane_operators %>%
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# streamgraph(key="Operator", value=, date="Year", offset="zero") %>%
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# sg_fill_brewer("BuPu")
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# wykres ile wypadkow kazdego operatora w kazdym roku
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