Load the required packages
suppressPackageStartupMessages({
library(dplyr, quietly = T)
library(lubridate, quietly = T)
})Let’s read the data set into R studio environment
setwd("F:\\E-learn\\Jigsaw\\Intro to R\\Data Manipulation")
fd=read.csv("FlightDelays.csv")
class(fd)## [1] "data.frame"dim(fd)## [1] 2201 13str(fd)## 'data.frame': 2201 obs. of 13 variables:
## $ schedtime : int 1455 1640 1245 1715 1039 840 1240 1645 1715 2120 ...
## $ carrier : Factor w/ 8 levels "CO","DH","DL",..: 5 2 2 2 2 2 2 2 2 2 ...
## $ deptime : int 1455 1640 1245 1709 1035 839 1243 1644 1710 2129 ...
## $ dest : Factor w/ 3 levels "EWR","JFK","LGA": 2 2 3 3 3 2 2 2 2 2 ...
## $ distance : int 184 213 229 229 229 228 228 228 228 228 ...
## $ date : Factor w/ 31 levels "01-01-2004","01-02-2004",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ flightnumber: int 5935 6155 7208 7215 7792 7800 7806 7810 7812 7814 ...
## $ origin : Factor w/ 3 levels "BWI","DCA","IAD": 1 2 3 3 3 3 3 3 3 3 ...
## $ weather : int 0 0 0 0 0 0 0 0 0 0 ...
## $ dayweek : int 4 4 4 4 4 4 4 4 4 4 ...
## $ daymonth : int 1 1 1 1 1 1 1 1 1 1 ...
## $ tailnu : Factor w/ 549 levels "N10323","N10575",..: 526 263 382 350 385 374 241 227 246 372 ...
## $ delay : Factor w/ 2 levels "delayed","ontime": 2 2 2 2 2 2 2 2 2 2 ...summary(fd)## schedtime carrier deptime dest distance
## Min. : 600 DH :551 Min. : 10 EWR: 665 Min. :169.0
## 1st Qu.:1000 RU :408 1st Qu.:1004 JFK: 386 1st Qu.:213.0
## Median :1455 US :404 Median :1450 LGA:1150 Median :214.0
## Mean :1372 DL :388 Mean :1369 Mean :211.9
## 3rd Qu.:1710 MQ :295 3rd Qu.:1709 3rd Qu.:214.0
## Max. :2130 CO : 94 Max. :2330 Max. :229.0
## (Other): 61
## date flightnumber origin weather dayweek
## 1/22/2004 : 86 Min. : 746 BWI: 145 Min. :0.00000 Min. :1.000
## 01-06-2004: 85 1st Qu.:2156 DCA:1370 1st Qu.:0.00000 1st Qu.:2.000
## 01-08-2004: 85 Median :2385 IAD: 686 Median :0.00000 Median :4.000
## 1/13/2004 : 85 Mean :3815 Mean :0.01454 Mean :3.905
## 1/20/2004 : 85 3rd Qu.:6155 3rd Qu.:0.00000 3rd Qu.:5.000
## 1/21/2004 : 85 Max. :7924 Max. :1.00000 Max. :7.000
## (Other) :1690
## daymonth tailnu delay
## Min. : 1.00 N225DL : 65 delayed: 428
## 1st Qu.: 8.00 N242DL : 56 ontime :1773
## Median :16.00 N223DZ : 50
## Mean :16.02 N221DL : 45
## 3rd Qu.:23.00 N241DL : 36
## Max. :31.00 N722UW : 36
## (Other):1913Variables weather, dayweek and daymonth are stored as numrical variables. These are supposed to be categorical variables. Let’s convert them into categorical variables
fd$weather=as.factor(fd$weather)
fd$dayweek=as.factor(fd$dayweek)
fd$daymonth=as.factor(fd$daymonth)
str(fd)## 'data.frame': 2201 obs. of 13 variables:
## $ schedtime : int 1455 1640 1245 1715 1039 840 1240 1645 1715 2120 ...
## $ carrier : Factor w/ 8 levels "CO","DH","DL",..: 5 2 2 2 2 2 2 2 2 2 ...
## $ deptime : int 1455 1640 1245 1709 1035 839 1243 1644 1710 2129 ...
## $ dest : Factor w/ 3 levels "EWR","JFK","LGA": 2 2 3 3 3 2 2 2 2 2 ...
## $ distance : int 184 213 229 229 229 228 228 228 228 228 ...
## $ date : Factor w/ 31 levels "01-01-2004","01-02-2004",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ flightnumber: int 5935 6155 7208 7215 7792 7800 7806 7810 7812 7814 ...
## $ origin : Factor w/ 3 levels "BWI","DCA","IAD": 1 2 3 3 3 3 3 3 3 3 ...
## $ weather : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ dayweek : Factor w/ 7 levels "1","2","3","4",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ daymonth : Factor w/ 31 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ tailnu : Factor w/ 549 levels "N10323","N10575",..: 526 263 382 350 385 374 241 227 246 372 ...
## $ delay : Factor w/ 2 levels "delayed","ontime": 2 2 2 2 2 2 2 2 2 2 ...head(fd$date)## [1] 01-01-2004 01-01-2004 01-01-2004 01-01-2004 01-01-2004 01-01-2004
## 31 Levels: 01-01-2004 01-02-2004 01-03-2004 01-04-2004 ... 1/31/2004str(fd$date)## Factor w/ 31 levels "01-01-2004","01-02-2004",..: 1 1 1 1 1 1 1 1 1 1 ...Converting the variable date into Date format. Looking at the first 6 and last 6 observations in the date column it is clear that the date is of the format mdy Date column is converted into date format using the lubridate() library.
fd$date=mdy(fd$date)
head(fd$date)## [1] "2004-01-01" "2004-01-01" "2004-01-01" "2004-01-01" "2004-01-01"
## [6] "2004-01-01"str(fd$date)## Date[1:2201], format: "2004-01-01" "2004-01-01" "2004-01-01" "2004-01-01" "2004-01-01" ...Q1.Find out the number of delayed flights for all weekdays
fd%>%filter(delay=="delayed"&weekdays(date)!="Saturday"&weekdays(date)!="Sunday")%>%nrow()## [1] 336Q2 Find the average distance, total distance, and count for all delayed flights on Friday
fd%>%filter(weekdays(date)=="Friday" & delay=="delayed")%>%summarize(mean(distance),sum(distance),length(distance))Q3 Find out how many flights were on time on Week days and Weekends (Consider Saturday and Sunday as weekends)
fd%>%filter(delay=="ontime",weekdays(date)!="Saturday"&weekdays(date)!="Sunday")%>%nrow()# number of delayed flights on weekdays## [1] 1362fd%>%filter(delay=="ontime",weekdays(date)=="Saturday"|weekdays(date)=="Sunday")%>%nrow()# number of delayed flights on weekends## [1] 411Q4 Find out the number of flights for each destination across all weekdays
table(weekdays(fd$date),fd$dest)##
## EWR JFK LGA
## Friday 122 64 205
## Monday 90 47 171
## Saturday 86 59 105
## Sunday 72 55 126
## Thursday 113 63 196
## Tuesday 86 48 173
## Wednesday 96 50 174Q5 Find out the number of times weather was bad across all weekdays. (1 indicates bad weather)
fd%>%filter(weather==1)%>%group_by(dayweek)%>%summarise(n=n())