R Markdown
library(tidyverse)
## -- Attaching packages ------------------------------------------------------------------------------------ tidyverse 1.3.0 --
## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts --------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(dplyr)
library(tidyr)
Flight_Information<-read.csv("https://raw.githubusercontent.com/maliat-hossain/Air-lane-Data-607/main/Flight%20Information1.csv",sep = ",")
Flight_Information
## X X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 Alaska On Time 497 221 212 503 1841
## 2 Delayed 62 12 20 102 305
## 3 NA NA NA NA NA
## 4 AmWest On Time 694 4840 383 320 201
## 5 Delayed 117 415 65 129 61
colnames(Flight_Information)[1:2] <- c("airline","status")
Flight_Information[[1]][2] <- Flight_Information[[1]][1]
Flight_Information[[1]][5] <- Flight_Information[[1]][4]
Flight_Information
## airline status Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 Alaska On Time 497 221 212 503 1841
## 2 Alaska Delayed 62 12 20 102 305
## 3 NA NA NA NA NA
## 4 AmWest On Time 694 4840 383 320 201
## 5 AmWest Delayed 117 415 65 129 61
Flight_Information <- gather(Flight_Information,city,number.airlines,Los.Angeles:Seattle)
Flight_Information <- filter(Flight_Information,status!="")
i <- 1
while(i <= length(Flight_Information$city)) {
Flight_Information $city[i] <- gsub("\\."," ",Flight_Information $city[i])
Flight_Information $number.airlines[i] <- gsub(",","",Flight_Information$number.airlines[i])
i <- i + 1
}
Flight_Information $number.airlines <- as.numeric(Flight_Information$number.airlines)
Flight_Information
## airline status city number.airlines
## 1 Alaska On Time Los Angeles 497
## 2 Alaska Delayed Los Angeles 62
## 3 AmWest On Time Los Angeles 694
## 4 AmWest Delayed Los Angeles 117
## 5 Alaska On Time Phoenix 221
## 6 Alaska Delayed Phoenix 12
## 7 AmWest On Time Phoenix 4840
## 8 AmWest Delayed Phoenix 415
## 9 Alaska On Time San Diego 212
## 10 Alaska Delayed San Diego 20
## 11 AmWest On Time San Diego 383
## 12 AmWest Delayed San Diego 65
## 13 Alaska On Time San Francisco 503
## 14 Alaska Delayed San Francisco 102
## 15 AmWest On Time San Francisco 320
## 16 AmWest Delayed San Francisco 129
## 17 Alaska On Time Seattle 1841
## 18 Alaska Delayed Seattle 305
## 19 AmWest On Time Seattle 201
## 20 AmWest Delayed Seattle 61
Flight_Information_Analysis <- Flight_Information %>% group_by(airline,city) %>% summarise(total.airlines = sum(number.airlines))
## `summarise()` regrouping output by 'airline' (override with `.groups` argument)
i <- 1
rate.status <- vector()
while(i <= length(Flight_Information$number.airlines)){
ap <- Flight_Information$airline[i]
ct <- Flight_Information$city[i]
rate.status[i] <- round(Flight_Information$number.airlines[i]/Flight_Information_Analysis$total.airlines[Flight_Information_Analysis$airline==ap & Flight_Information_Analysis$city == ct],3)
i <- i + 1
}
Flight_Information <- cbind(Flight_Information,rate.status)
Flight_Information
## airline status city number.airlines rate.status
## 1 Alaska On Time Los Angeles 497 0.889
## 2 Alaska Delayed Los Angeles 62 0.111
## 3 AmWest On Time Los Angeles 694 0.856
## 4 AmWest Delayed Los Angeles 117 0.144
## 5 Alaska On Time Phoenix 221 0.948
## 6 Alaska Delayed Phoenix 12 0.052
## 7 AmWest On Time Phoenix 4840 0.921
## 8 AmWest Delayed Phoenix 415 0.079
## 9 Alaska On Time San Diego 212 0.914
## 10 Alaska Delayed San Diego 20 0.086
## 11 AmWest On Time San Diego 383 0.855
## 12 AmWest Delayed San Diego 65 0.145
## 13 Alaska On Time San Francisco 503 0.831
## 14 Alaska Delayed San Francisco 102 0.169
## 15 AmWest On Time San Francisco 320 0.713
## 16 AmWest Delayed San Francisco 129 0.287
## 17 Alaska On Time Seattle 1841 0.858
## 18 Alaska Delayed Seattle 305 0.142
## 19 AmWest On Time Seattle 201 0.767
## 20 AmWest Delayed Seattle 61 0.233
Delayed_Flights<-filter(Flight_Information,status=="Delayed")
Delayed_Flights
## airline status city number.airlines rate.status
## 1 Alaska Delayed Los Angeles 62 0.111
## 2 AmWest Delayed Los Angeles 117 0.144
## 3 Alaska Delayed Phoenix 12 0.052
## 4 AmWest Delayed Phoenix 415 0.079
## 5 Alaska Delayed San Diego 20 0.086
## 6 AmWest Delayed San Diego 65 0.145
## 7 Alaska Delayed San Francisco 102 0.169
## 8 AmWest Delayed San Francisco 129 0.287
## 9 Alaska Delayed Seattle 305 0.142
## 10 AmWest Delayed Seattle 61 0.233