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flat_code.R
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flat_code.R
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library(rgdal)
library(plyr)
library(leaflet)
library(dplyr)
library(data.table)
library(DT)
library(car)
library(tidyverse)
library(sqldf)
# reading csv's
airport<-read.csv("airports.csv")
airline<-read.csv("airlines.csv")
flights<-read.csv("flights.csv") # takes 2-3 minutes
red_flight <- subset(flight, ORIGIN_AIRPORT == 'ORD' | ORIGIN_AIRPORT == 'PHL' ,
select=c(AIRLINE, ARRIVAL_DELAY, ORIGIN_AIRPORT))
flights<-subset(flights, !(is.na(flights$SCHEDULED_TIME)))
net_flights <- subset(flights, flights$CANCELLED==0)
net_flights <- subset(net_flights, !(is.na(net_flights$AIR_TIME)))
na_percentages <- sapply(net_flights, function(x) sum(is.na(x))) / nrow(net_flights) * 100
sort(format(round(na_percentages, 4), nsmall = 4))
imp_flight_data <- subset(flights, select = c(MONTH, DAY_OF_WEEK, AIRLINE, ORIGIN_AIRPORT,
DESTINATION_AIRPORT, DEPARTURE_DELAY,SCHEDULED_TIME,
ARRIVAL_DELAY, AIR_SYSTEM_DELAY, SECURITY_DELAY,
AIRLINE_DELAY, LATE_AIRCRAFT_DELAY, WEATHER_DELAY))
red_flight
# Manual filling codes for 3 airports
#######################
# manually pull missing coordinate values
# Northwest Florida Beaches International Airport, Plattsburgh International Airport, Northeast Florida Regional Airport
# LAT, LONG - (30.3548871,-85.8016595), (44.6520635,-73.470109), (29.9543946,-81.3450803)
airport[97, "LATITUDE"] <- 30.3548871
airport[97, "LONGITUDE"] <- -85.8016595
airport[235, "LATITUDE"] <- 44.6520635
airport[235, "LONGITUDE"] <- -73.470109
airport[314, "LATITUDE"] <- 29.9543946
airport[314, "LONGITUDE"] <- -81.3450803
# Airport Map USA front page
# US map of all Airports
## Plot
#########################
library(leaflet.extras)
leaflet() %>%
addProviderTiles("CartoDB.Positron") %>%
addCircleMarkers(~LONGITUDE, ~LATITUDE,
data = airport,
color = "purple",
radius = 2,
weight = 1,
popup = paste("Airport:",
airport$AIRPORT,
"<br>",
"IATA_CODE:",
airport$IATA_CODE,
"<br>",
"CITY:",
airport$CITY))
net_flights
new_obj <-net_flights %>% group_by(ORIGIN_AIRPORT_NAME, status) %>% tally()
new_obj
new_df_haha<-subset(net_flights,!is.na(df_use_2$status)) %>%
group_by(ORIGIN_AIRPORT) %>%
dplyr::summarise(mean_taxiIn=mean(TAXI_OUT), count_of_fly=n()) %>%
arrange(ORIGIN_AIRPORT)
##########################
read.table("flights.csv", sep=",", header = TRUE, fill = TRUE, nrows = 5)
###########################################################
####### Making SQL Databases/Tables for flights csv######## # bad option
###########################################################
{
conemp <- dbConnect(SQLite(), dbname = "flights.db")
if(dbExistsTable(conemp, "flights")) dbRemoveTable(conemp, "flights")
dbWriteTable(conemp, "flights",
"flights.csv", sep = ",", header = TRUE,
row.names = FALSE) #" RSQLite doesn't handle commas in quotes
dbDisconnect(conemp)
}
conemp <- dbConnect(SQLite(), dbname = "flights.db")
res <- dbSendQuery(conemp, "
SELECT *
FROM flights
WHERE ARRIVAL_DELAY > 15
")
fetch(res, n = 50)
# after fetching result, clear it
dbClearResult(res)
# order of IATA code according to number of datapoints of corr airline
##################
unique(flight$AIRLINE)
sort(airline$IATA_CODE) == sort(unique(flight$AIRLINE))
airline
# ----------------------------------------------------------
# flights_count vs Mean.Arrival.delay
#################
list(table(red_flight$AIRLINE))
new_col_df <- as.data.frame(table(red_flight$AIRLINE))
new_col_df
airline.avg.delay <- 0
airline.avg.delay <- aggregate(red_flight$ARRIVAL_DELAY, by=list(red_flight$AIRLINE), mean, na.rm=T)
names(airline.avg.delay) <- c("AirlineCode", "Mean.Arrival.Delay")
airline.avg.delay <- merge(airline.avg.delay, airline, by.x="AirlineCode", by.y="IATA_CODE", all.x=TRUE)
airline.avg.delay <- airline.avg.delay[order(airline.avg.delay$Mean.Arrival.Delay), ]
airline.avg.delay <- merge(airline.avg.delay, new_col_df, by.x="AirlineCode", by.y="Var1", all.x=TRUE,
all.y = TRUE)
airline.avg.delay
ggplot(data=airline.avg.delay) +
geom_point(mapping = aes(x = Mean.Arrival.Delay, y = Freq, color=AIRLINE))
# add text to this
# airpot vs Mean.Arrival Delay for ORD - Chicago
###########################
list(table(flight$ORIGIN_AIRPORT))
# States with most airports
###############
table(airport$STATE)
top10 <- as.data.frame(table(airport$STATE))
top10 <- top10[order(top10$Freq, decreasing=T), ][1:10, ]
barplot(top10$Freq,
names.arg = top10$Var1,
col = "dark blue",
main = "Number of Airports by State - Top 10",
xlab = "Frequency",
ylab = "State",
horiz=T)
# Delay analysis by time(Month wise)
################################
flight$status<-'ON TIME'
flight$status[flight$DEPARTURE_DELAY>0] <-'DEPARTURE DELAY'
flight$status[flight$ARRIVAL_DELAY>0 & (flight$ARRIVAL_DELAY-flight$DEPARTURE_DELAY)>0 & flight$DEPARTURE_DELAY>0] <-'DEPR & ARVL_DELAY'
flight$status[flight$ARRIVAL_DELAY>0 & flight$DEPARTURE_DELAY<=0] <-'ARRIVAL_DELAY'
flight$status<-factor(flight$status)
ggplot(flight, aes(fill=status, x=DAY_OF_WEEK),binwidth=12) +
geom_bar(position="stack", stat="count") +
scale_x_discrete("Day of the Week", breaks = c("1","2","3","4","5","6","7"), labels=c("Mon","Tues","Wed","Th","Fri","Sat","Sun"))
### Taxi time relation with num flights frm airport
###############################################
taxiInAirport<-subset(net_flights,!is.na(net_flights$TAXI_IN)) %>%
group_by(DESTINATION_AIRPORT) %>%
dplyr::summarise(mean_taxiIn=mean(TAXI_IN), count_of_fly=n()) %>%
arrange(DESTINATION_AIRPORT)
taxiInAirport<-subset(net_flights,!is.na(net_flights$TAXI_IN)) %>%
group_by(DESTINATION_AIRPORT,DESTINATION_CITY,DESTINATION_STATE) %>%
dplyr::summarise(mean_taxiIn=mean(TAXI_IN), count_of_fly=n()) %>%
arrange(DESTINATION_AIRPORT)
cor.test(taxiInAirport$mean_taxiIn,taxiInAirport$count_of_fly)
# install.packages("shinycustomloader")
# install.packages('shinydashboard')
library(shinycustomloader)
library(shinydashboard)
shinyExample()
withLoader(plotOutput("distPlot"), type="image", loader="buffer.gif")
marketShare <- flights %>%
group_by(AIRLINE) %>%
dplyr::summarise(Count = n(),
mean_ARRIVAL_DELAY = mean(ARRIVAL_DELAY),
median_ARRIVAL_DELAY = median(ARRIVAL_DELAY),
min_ARRIVAL_DELAY = min(ARRIVAL_DELAY),
max_ARRIVAL_DELAY = max(ARRIVAL_DELAY),
mean_DEPARTURE_DELAY = mean(DEPARTURE_DELAY),
median_DEPARTURE_DELAY = median(DEPARTURE_DELAY),
min_DEPARTURE_DELAY = min(DEPARTURE_DELAY),
max_DEPARTURE_DELAY = max(DEPARTURE_DELAY)
) %>% arrange(desc(Count))
plot_ly(marketShare, labels = ~AIRLINE, values = ~Count, type = 'pie',
textposition = 'inside',
textinfo = 'label+percent',
insidetextfont = list(color = '#FFFFFF'),
hoverinfo = 'text',
marker = list(
line = list(color = '#FFFFFF', width = 1)),
showlegend = FALSE)
net_flights
net_flights$status<-'ON TIME'
net_flights$status[net_flights$DEPARTURE_DELAY>0] <-'DEPARTURE DELAY'
net_flights$status[net_flights$CANCELLED==1] <-'CANCELLED FLIGHTS'
net_flights$status[net_flights$ARRIVAL_DELAY>0 & (net_flights$ARRIVAL_DELAY-net_flights$DEPARTURE_DELAY)>0 & net_flights$DEPARTURE_DELAY>0] <-'DEPR & ARVL_DELAY'
net_flights$status[net_flights$ARRIVAL_DELAY>0 & net_flights$DEPARTURE_DELAY<=0] <-'ARRIVAL_DELAY'
net_flights$status<-factor(net_flights$status)
ggplotly(ggplot(net_flights,aes(x=AIRLINE,fill=status),binwidth=12) +
geom_bar(position = "fill") +
ggtitle("Flights by Months According to Flights Status"))
net_flights$DEP_HOUR <- NA
net_flights$DEP_HOUR <- as.integer(net_flights$DEPARTURE_TIME / 100)
net_flights$modified_delay <- NA
net_flights$modified_delay <- case_when(
net_flights$ARRIVAL_DELAY <= 5 ~"On Time (0-5 mins)",
net_flights$ARRIVAL_DELAY > 5 & net_flights$ARRIVAL_DELAY <= 15 ~"Small Delay (5-15 mins)",
net_flights$ARRIVAL_DELAY > 15 ~ "Large Delay (> 15 mins)" )
ggplot(data=net_flights, aes(x=factor(net_flights$DAY_OF_WEEK),fill=factor(net_flights$modified_delay))) +
geom_histogram(stat = "Count",position = "dodge", col="red") +
labs(title="Histogram for Airline") +
labs(x="Departure Hours", y="Number of Flights") +
scale_x_discrete() +
labs(x = c("Monday", "Tuesday",
"Wednesday", "Thursday",
"Friday", "Saturday",
"Sunday"))