Test 3- CSV File

TORNADOES

Question 1

(A) How many tornadoes occurred in WA (Washington) and (Mississippi)?
20 Tornadoes occurred in Washington while in Mississippi, there were 510 Tornadoes. This makes a total of 530 Tornadoes occurrences in both Washington and Mississippi.

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/tornado.csv", 
                    header = TRUE)
head( csvData )

##     yr mo dy       date     time tz st stf stn    f
## 1 2007  1  4 2007-01-04 15:45:00  3 LA  22   1 EF-1
## 2 2007  1  4 2007-01-04 16:35:00  3 LA  22   2 EF-1
## 3 2007  1  5 2007-01-05 00:27:00  3 MS  28   1 EF-1
## 4 2007  1  5 2007-01-05 00:40:00  3 MS  28   2 EF-0
## 5 2007  1  5 2007-01-05 00:57:00  3 MS  28   3 EF-1
## 6 2007  1  5 2007-01-05 01:07:00  3 MS  28   4 EF-1

sum(csvData$st == "WA")

## [1] 20

sum(csvData$st == "MS")

## [1] 510

sum(csvData$st == "WA" | csvData$st =="MS")

## [1] 530

(B) How many Tornadoes occurred in Washington before 2012?
13 Tornadoes occurred in Washington before 2012.

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/tornado.csv", 
                    header = TRUE)
sum(csvData$st == "WA" & csvData$date <= "2012")

## [1] 13

(C) All Tornadoes that occurred in Washington in 2011, 2013 and 2014.
Nine Tornadoes occurred in Washington as at 2011, seven in 2013 and five in 2014. This makes a total of 21 Tornadoes that occurred in 2011, 2013 and 2014.

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/tornado.csv", 
                    header = TRUE)
sum(csvData$st == "WA" & csvData$date >= "2011")

## [1] 9

sum(csvData$st == "WA" & csvData$date >= "2013")

## [1] 7

sum(csvData$st == "WA" & csvData$date >= "2014")

## [1] 5

(D) Which ‘mo’(month) had the most tornadoes in HI(Hawaii) and how do you know?
The code below shows the frequent occurrence of tornadoes in Hawaii, in some specific months. In February(2), Hawaii experienced two tornadoes i.e., twice, once in September(9), once in December(12), once in march(3) and once in April(4). Based on this, it is evident that February had the most tornadoes in Hawaii.

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/tornado.csv", 
                    header = TRUE)
Hawaii <- (csvData$st == "HI")
unique(csvData[Hawaii, ])

##         yr mo dy       date     time tz st stf stn    f
## 2766  2008  9 23 2008-09-23 18:27:00  3 HI  15   0 EF-0
## 2842  2008 12 13 2008-12-13 16:50:00  3 HI  15   0 EF-0
## 2878  2009  2 11 2009-02-11 17:05:00  3 HI  15   0 EF-0
## 5371  2011  2 11 2011-02-11 18:15:00  3 HI  15   1 EF-0
## 7359  2012  3  9 2012-03-09 11:10:00  3 HI  15   0 EF-0
## 10093 2015  4 23 2015-04-23 18:56:00  3 HI  15   0 EF-0

csvData[Hawaii, c(2)]

## [1]  9 12  2  2  3  4

(E) Get the count of Tornadoes by year.
As shown below, there were tornado occurrences from 2007-2015, and within this 9-year period, 11,141 Tornadoes occurred.

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/tornado.csv", 
                    header = TRUE)
unique(csvData$yr)

## [1] 2007 2008 2009 2010 2011 2012 2013 2014 2015

sum(csvData$yr == 2007)

## [1] 1116

sum(csvData$yr == 2008)

## [1] 1738

sum(csvData$yr == 2009)

## [1] 1182

sum(csvData$yr == 2010)

## [1] 1315

sum(csvData$yr == 2011)

## [1] 1777

sum(csvData$yr == 2012)

## [1] 956

sum(csvData$yr == 2013)

## [1] 939

sum(csvData$yr == 2014)

## [1] 906

sum(csvData$yr == 2015)

## [1] 1212

sum(csvData$yr == 2007 | csvData$yr == 2008 | csvData$yr == 2009 | csvData$yr == 2010 | csvData$yr == 2011 | csvData$yr == 2012 | csvData$yr == 2013 | csvData$yr == 2014 | csvData$yr == 2015)

## [1] 11141

AIRLINES

Question 2

(A)

Carrier (UniqueCarrier), Flight Number (FlightNum) and Origin of Flights that departed (DepTime) after 9pm and flew to Nashville(‘BNA’)?

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
head( csvData )

##   Year Month DayofMonth DayOfWeek DepTime CRSDepTime ArrTime CRSArrTime
## 1 2004     3         25         4    1118       1125    1231       1236
## 2 2004     3         25         4     810        815    1016       1033
## 3 2004     3         25         4    1529       1530    2101       2104
## 4 2004     3         25         4    1119       1125    1423       1441
## 5 2004     3         25         4    1156       1155    1452       1444
## 6 2004     3         25         4     903        905    1047       1054
##   UniqueCarrier FlightNum TailNum ActualElapsedTime CRSElapsedTime AirTime
## 1            UA       425  N840UA                73             71      54
## 2            UA       425  N840UA               246            258     211
## 3            UA       426  N335UA               212            214     191
## 4            UA       426  N335UA               124            136     111
## 5            UA       427  N567UA               236            229     199
## 6            UA       427  N567UA               164            169     143
##   ArrDelay DepDelay Origin Dest Distance TaxiIn TaxiOut Cancelled
## 1       -5       -7    DEN  ABQ      349      3      16         0
## 2      -17       -5    PHL  DEN     1557      5      30         0
## 3       -3       -1    DEN  EWR     1605      7      14         0
## 4      -18       -6    SAN  DEN      853      4       9         0
## 5        8        1    ORD  PHX     1440      7      30         0
## 6       -7       -2    TPA  ORD     1012      5      16         0
##   CancellationCode Diverted CarrierDelay WeatherDelay NASDelay SecurityDelay
## 1                         0            0            0        0             0
## 2                         0            0            0        0             0
## 3                         0            0            0        0             0
## 4                         0            0            0        0             0
## 5                         0            0            0        0             0
## 6                         0            0            0        0             0
##   LateAircraftDelay
## 1                 0
## 2                 0
## 3                 0
## 4                 0
## 5                 0
## 6                 0

Departafter9pm <- (csvData$DepTime > 2100 & csvData$Dest == "BNA")
na.omit(unique(csvData[Departafter9pm, c(5, 9, 10, 17, 18) ]))

##       DepTime UniqueCarrier FlightNum Origin Dest
## 2588     2105            WN      2181    BWI  BNA
## 7021     2312            OH      5421    CVG  BNA
## 10692    2157            DL       938    ATL  BNA
## 17401    2217            AA      2435    ORD  BNA
## 19478    2317            DH      7332    ORD  BNA

Carrier (UniqueCarrier), Flight Number (FlightNum) and Origin of Flights that departed (DepTime) after 9pm but originated (origin) from Nashville(‘BNA’) or landed(Dest) in Memphis?

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
Departafter9pm2 <- (csvData$DepTime > 2100 & csvData$Origin == "BNA")
na.omit(unique(csvData[Departafter9pm2, c(5, 9, 10, 17, 18) ]))

##      DepTime UniqueCarrier FlightNum Origin Dest
## 2392    2218            WN      1447    BNA  AUS
## 2406    2150            WN      2658    BNA  BWI
## 2446    2150            WN      1511    BNA  MDW
## 2469    2125            WN      1732    BNA  RDU
## 2472    2110            WN      2769    BNA  SAT
## 2474    2125            WN       459    BNA  TPA

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
Departafter9pm2A <- (csvData$DepTime > 2100 & csvData$Dest == "MEM")
na.omit (unique(csvData[Departafter9pm2A, c(5, 9, 10, 17, 18)]))

##       DepTime UniqueCarrier FlightNum Origin Dest
## 8169     2201            OO      6838    ORD  MEM
## 10405    2217            DL       579    ATL  MEM
## 12744    2110            FL      1723    ATL  MEM
## 15214    2103            NW       279    DTW  MEM
## 19352    2313            DH      6270    CVG  MEM

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
Departafter9pm2B <- (csvData$DepTime > 2100 & csvData$Origin == "BNA" & csvData$Dest == "MEM")
unique(csvData[Departafter9pm2B, c(5, 9, 10, 17, 18)])

## [1] DepTime       UniqueCarrier FlightNum     Origin        Dest         
## <0 rows> (or 0-length row.names)

Carrier (UniqueCarrier), Flight Number (FlightNum) and Origin of Flights that were early (ArrDelay). Note that a negative delay is a flight that arrived early.

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
Early <- csvData$ArrDelay < 0 & csvData$ArrTime < 8
na.omit( csvData[ Early, c(5, 7, 9, 10, 17, 18) ] )

##       DepTime ArrTime UniqueCarrier FlightNum Origin Dest
## 2714     2150       1            WN      1033    BWI  TPA
## 6047     2253       1            OH      5235    CVG  CHO
## 6048     2304       5            OH      5736    CVG  SCE
## 6049     2340       6            OH      5932    ATL  TYS
## 6050     2258       7            OH      5589    CVG  MDT
## 9754     2126       2            UA       205    IAD  SFO
## 12664    2243       5            FL       218    ATL  CAK
## 16935    2118       3            AA      1742    SJU  JFK
## 17667    2140       6            AS       197    SEA  ANC
## 19167    2250       5            DH      6135    CVG  CAE
## 19275    2304       3            DH      6197    CVG  CHA

Carrier (UniqueCarrier), Flight Number (FlightNum) and Origin of Flights that arrived (ArrDelay) more than two hours late, but didn’t leave late (DepDelay).

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
Twohourslate <- (csvData$ArrDelay >= 120 & csvData$DepDelay <= 1)
na.omit(csvData[Twohourslate, c(5, 9, 10, 15, 16, 17, 18)])

##      DepTime UniqueCarrier FlightNum ArrDelay DepDelay Origin Dest
## 9495    1135            TZ       505      193       -8    PIE  MDW

(B)

Sort flights to find the 5 least delayed (DepDelay) flights

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
Fiveleastdelayed1 <- order(csvData$DepDelay)
head (csvData[ Fiveleastdelayed1, c(5, 9, 10, 15, 16, 17, 18), 5 ])

##       DepTime UniqueCarrier FlightNum ArrDelay DepDelay Origin Dest
## 14202    2047            MQ      3647       -2      -26    DFW  SGF
## 6293      911            OH      5260      -16      -24    RIC  CVG
## 5109     1053            NW       707      -30      -22    GTF  FCA
## 16874    2109            AA      1685      -19      -21    HOU  AUS
## 5983     1015            NW      1926      -17      -20    MDW  DTW
## 6628     1410            OH      5474      -22      -20    BGR  CVG

Sort flights to find the 5 least delay (ArrDelay) flights

csvData <- read.csv(file = "http://latul.be/mbaa_531/data/airline.csv",
                    header = TRUE)
Fiveleastdelayed2 <- order(csvData$ArrDelay)
head (csvData[ Fiveleastdelayed2, c(5, 9, 10, 15, 16, 17, 18), 5 ])

##       DepTime UniqueCarrier FlightNum ArrDelay DepDelay Origin Dest
## 6128      600            OH      5071      -52       -5    BUF  ATL
## 6099      557            OH      5710      -49       -3    BGM  CVG
## 6688     1530            OH      5082      -49      -10    BUF  ATL
## 8118     1355            OO      6704      -48        0    MFR  DEN
## 10935    1647            DL      1181      -48       -4    BOS  DFW
## 15908    1240            AA       577      -48       -5    BOS  DFW

BONUS Section

(A). Add a new column reporting departure delays minus the median departure delay to the new data frame

new_col=median(csvData$DepDelay)
new_col=sapply(csvData$DepDelay,median)
head(new_col)

## [1] -7 -5 -1 -6  1 -2

tail(new_col)

## [1] -8 -1 -6 -2 -7 -5

csvData$DepMed_newcolumn <- csvData$DepDelay - sapply(csvData$DepDelay,median)
head(csvData$DepMed_newcolumn)

## [1] 0 0 0 0 0 0

(B). Convert the departure and arrival delays columns from minutes into hours

minutes_to_hours <- csvData$DepDelay / 60
head(csvData[minutes_to_hours, c(1, 2, 15, 16), 5])

##     Year Month ArrDelay DepDelay
## 1   2004     3       -5       -7
## 1.1 2004     3       -5       -7
## 1.2 2004     3       -5       -7
## 2   2004     3      -17       -5
## 2.1 2004     3      -17       -5
## 2.2 2004     3      -17       -5

(C). Add a column with the average flight speed (in mph)

csvData$average_flight_speed <- (csvData$Distance/csvData$AirTime) * 1000
head(csvData$average_flight_speed)

## [1] 6462.963 7379.147 8403.141 7684.685 7236.181 7076.923

(D).

summary (csvData$DepMed_newcolumn)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0       0       0       0       0       0     183

summary (csvData$average_flight_speed)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   -1736    5571    6603    6336    7307  237500     207

dim(csvData)

## [1] 19928    31

Test 3- CSV File

Oluwaferanmi Soyege_658482823

13 May, 2022