## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

got <- read.csv("got.csv", header = TRUE, stringsAsFactors = FALSE)
kable(got)

lastname	firstname	major	year	gpa
Snow	John	Nordic Studies	Junior	3.23
Lannister	Tyrion	Communications	Sophomore	3.83
Targaryen	Daenerys	Zoology	Freshman	3.36
Bolton	Ramsay	Phys Ed	Freshman	2.24
Stark	Eddard	History	Senior	2.78
Clegane	Gregor	Phys Ed	Sophomore	3.23
Baelish	Peter	Communications	Freshman	2.84
Baratheon	Joffrey	History	Freshman	1.87
Drogo	Khal	Zoology	Senior	3.38
Tarly	Samwise	Nordic Studies	Freshman	2.39

Practice dplyr verbs

select()

Select columns
Change names

kable(got %>% select(surname = lastname, 3:5))

surname	major	year	gpa
Snow	Nordic Studies	Junior	3.23
Lannister	Communications	Sophomore	3.83
Targaryen	Zoology	Freshman	3.36
Bolton	Phys Ed	Freshman	2.24
Stark	History	Senior	2.78
Clegane	Phys Ed	Sophomore	3.23
Baelish	Communications	Freshman	2.84
Baratheon	History	Freshman	1.87
Drogo	Zoology	Senior	3.38
Tarly	Nordic Studies	Freshman	2.39

Can also use rename() to change names

kable(got %>% rename(surname = lastname))

surname	firstname	major	year	gpa
Snow	John	Nordic Studies	Junior	3.23
Lannister	Tyrion	Communications	Sophomore	3.83
Targaryen	Daenerys	Zoology	Freshman	3.36
Bolton	Ramsay	Phys Ed	Freshman	2.24
Stark	Eddard	History	Senior	2.78
Clegane	Gregor	Phys Ed	Sophomore	3.23
Baelish	Peter	Communications	Freshman	2.84
Baratheon	Joffrey	History	Freshman	1.87
Drogo	Khal	Zoology	Senior	3.38
Tarly	Samwise	Nordic Studies	Freshman	2.39

If want to see only those with a gpa >= 3.5, use filter()

Also used rename() to change the lastname variable

kable(got %>% filter(gpa >= 3.5) %>% rename(surname = lastname))

surname	firstname	major	year	gpa
Lannister	Tyrion	Communications	Sophomore	3.83

Sort from highest to lowest, use arrange()

kable(got %>% arrange(desc(gpa)))

lastname	firstname	major	year	gpa
Lannister	Tyrion	Communications	Sophomore	3.83
Drogo	Khal	Zoology	Senior	3.38
Targaryen	Daenerys	Zoology	Freshman	3.36
Snow	John	Nordic Studies	Junior	3.23
Clegane	Gregor	Phys Ed	Sophomore	3.23
Baelish	Peter	Communications	Freshman	2.84
Stark	Eddard	History	Senior	2.78
Tarly	Samwise	Nordic Studies	Freshman	2.39
Bolton	Ramsay	Phys Ed	Freshman	2.24
Baratheon	Joffrey	History	Freshman	1.87

Create new columns with transformed data, typically, use mutate()

kable(got %>% arrange(desc(gpa)) %>% mutate(dlist = gpa >=3.5))

lastname	firstname	major	year	gpa	dlist
Lannister	Tyrion	Communications	Sophomore	3.83	TRUE
Drogo	Khal	Zoology	Senior	3.38	FALSE
Targaryen	Daenerys	Zoology	Freshman	3.36	FALSE
Snow	John	Nordic Studies	Junior	3.23	FALSE
Clegane	Gregor	Phys Ed	Sophomore	3.23	FALSE
Baelish	Peter	Communications	Freshman	2.84	FALSE
Stark	Eddard	History	Senior	2.78	FALSE
Tarly	Samwise	Nordic Studies	Freshman	2.39	FALSE
Bolton	Ramsay	Phys Ed	Freshman	2.24	FALSE
Baratheon	Joffrey	History	Freshman	1.87	FALSE

To transform the data, but only show that newly created column, use transmute()

kable(got %>% transmute(name = paste(firstname, lastname), dlist = gpa >= 3.5))

name	dlist
John Snow	FALSE
Tyrion Lannister	TRUE
Daenerys Targaryen	FALSE
Ramsay Bolton	FALSE
Eddard Stark	FALSE
Gregor Clegane	FALSE
Peter Baelish	FALSE
Joffrey Baratheon	FALSE
Khal Drogo	FALSE
Samwise Tarly	FALSE

To look at distinct outputs, like the majors in this data frame, use distinct()

Also, can use arrange() to put the majors in alphabetical order

pander(got %>% distinct(major) %>% arrange(major))

major
Communications
History
Nordic Studies
Phys Ed
Zoology

To do aggregating, use group_by() and often summarise()

Here, to aggreagte by major, and then summarize by the average gpa…

kable(got %>% group_by(major) %>% summarise(average_gpa = mean(gpa)))

major	average_gpa
Communications	3.335
History	2.325
Nordic Studies	2.810
Phys Ed	2.735
Zoology	3.370

To see a particular segment of a group, use slice() which is a window function

Find the best student in each major

kable(got %>% group_by(major) %>% arrange(desc(gpa)) %>% slice(1))

lastname	firstname	major	year	gpa
Lannister	Tyrion	Communications	Sophomore	3.83
Stark	Eddard	History	Senior	2.78
Snow	John	Nordic Studies	Junior	3.23
Clegane	Gregor	Phys Ed	Sophomore	3.23
Drogo	Khal	Zoology	Senior	3.38

To do find the top , use top_n()

kable(got %>% group_by(major) %>% arrange(desc(gpa)) %>% top_n(1))

## Selecting by gpa

lastname	firstname	major	year	gpa
Lannister	Tyrion	Communications	Sophomore	3.83
Drogo	Khal	Zoology	Senior	3.38
Snow	John	Nordic Studies	Junior	3.23
Clegane	Gregor	Phys Ed	Sophomore	3.23
Stark	Eddard	History	Senior	2.78

To find the bottom of a group, use min_rank()

kable(got %>% group_by(major) %>% filter(min_rank(desc(gpa))== 1))

lastname	firstname	major	year	gpa
Snow	John	Nordic Studies	Junior	3.23
Lannister	Tyrion	Communications	Sophomore	3.83
Stark	Eddard	History	Senior	2.78
Clegane	Gregor	Phys Ed	Sophomore	3.23
Drogo	Khal	Zoology	Senior	3.38

Window functions can be broken into 2 categories

offset functions
ranking and ordering functions

Who is the lowest ranked gpa? Can use row_number()

kable(got %>% filter(row_number(desc(gpa)) == 7))

lastname	firstname	major	year	gpa
Stark	Eddard	History	Senior	2.78

Or can use filter(min_rank())

kable(got %>% filter(min_rank(desc(gpa)) == 7))

lastname	firstname	major	year	gpa
Stark	Eddard	History	Senior	2.78

It functions a litle differently than row number, i.e., if there’s a tie.

kable(got %>% filter(min_rank(desc(gpa)) == 4))

lastname	firstname	major	year	gpa
Snow	John	Nordic Studies	Junior	3.23
Clegane	Gregor	Phys Ed	Sophomore	3.23

There’s dense_rank()

To find the sixth ranked person, use 5

kable(got %>% filter(dense_rank(desc(gpa)) == 5))

lastname	firstname	major	year	gpa
Baelish	Peter	Communications	Freshman	2.84

Should use slice()

kable(got %>% arrange(desc(gpa)) %>% slice(6))

lastname	firstname	major	year	gpa
Baelish	Peter	Communications	Freshman	2.84

To find where a variable falls percentage wise, can use percent_rank()

kable(got %>% arrange(desc(gpa))%>% mutate(p_rank = percent_rank(gpa)), digits = 2)

lastname	firstname	major	year	gpa	p_rank
Lannister	Tyrion	Communications	Sophomore	3.83	1.00
Drogo	Khal	Zoology	Senior	3.38	0.89
Targaryen	Daenerys	Zoology	Freshman	3.36	0.78
Snow	John	Nordic Studies	Junior	3.23	0.56
Clegane	Gregor	Phys Ed	Sophomore	3.23	0.56
Baelish	Peter	Communications	Freshman	2.84	0.44
Stark	Eddard	History	Senior	2.78	0.33
Tarly	Samwise	Nordic Studies	Freshman	2.39	0.22
Bolton	Ramsay	Phys Ed	Freshman	2.24	0.11
Baratheon	Joffrey	History	Freshman	1.87	0.00

To find the proportion of all values less than or equal to the current rank, use cume_rank()

kable(got %>% arrange(desc(gpa))%>% mutate(cum_dist = cume_dist(gpa)), digits = 2)

lastname	firstname	major	year	gpa	cum_dist
Lannister	Tyrion	Communications	Sophomore	3.83	1.0
Drogo	Khal	Zoology	Senior	3.38	0.9
Targaryen	Daenerys	Zoology	Freshman	3.36	0.8
Snow	John	Nordic Studies	Junior	3.23	0.7
Clegane	Gregor	Phys Ed	Sophomore	3.23	0.7
Baelish	Peter	Communications	Freshman	2.84	0.5
Stark	Eddard	History	Senior	2.78	0.4
Tarly	Samwise	Nordic Studies	Freshman	2.39	0.3
Bolton	Ramsay	Phys Ed	Freshman	2.24	0.2
Baratheon	Joffrey	History	Freshman	1.87	0.1

To find a rough rank, use ntile() which breaks the vector into n buckets

kable(got %>% arrange(desc(gpa)) %>% mutate(ntile = ntile(gpa, 4)))

lastname	firstname	major	year	gpa	ntile
Lannister	Tyrion	Communications	Sophomore	3.83	4
Drogo	Khal	Zoology	Senior	3.38	4
Targaryen	Daenerys	Zoology	Freshman	3.36	3
Snow	John	Nordic Studies	Junior	3.23	3
Clegane	Gregor	Phys Ed	Sophomore	3.23	3
Baelish	Peter	Communications	Freshman	2.84	2
Stark	Eddard	History	Senior	2.78	2
Tarly	Samwise	Nordic Studies	Freshman	2.39	1
Bolton	Ramsay	Phys Ed	Freshman	2.24	1
Baratheon	Joffrey	History	Freshman	1.87	1

Can put these altoghther to get three variables

kable(got %>% arrange(desc(gpa)) %>% mutate(prank = percent_rank(gpa), crank = cume_dist(gpa), nrank = ntile(gpa, 4)), digits = 2)

lastname	firstname	major	year	gpa	prank	crank	nrank
Lannister	Tyrion	Communications	Sophomore	3.83	1.00	1.0	4
Drogo	Khal	Zoology	Senior	3.38	0.89	0.9	4
Targaryen	Daenerys	Zoology	Freshman	3.36	0.78	0.8	3
Snow	John	Nordic Studies	Junior	3.23	0.56	0.7	3
Clegane	Gregor	Phys Ed	Sophomore	3.23	0.56	0.7	3
Baelish	Peter	Communications	Freshman	2.84	0.44	0.5	2
Stark	Eddard	History	Senior	2.78	0.33	0.4	2
Tarly	Samwise	Nordic Studies	Freshman	2.39	0.22	0.3	1
Bolton	Ramsay	Phys Ed	Freshman	2.24	0.11	0.2	1
Baratheon	Joffrey	History	Freshman	1.87	0.00	0.1	1

To find the previous value in a vector, use lag()

kable(got %>% arrange(desc(gpa)) %>% mutate(OneBetterGot = lag(gpa )))

lastname	firstname	major	year	gpa	OneBetterGot
Lannister	Tyrion	Communications	Sophomore	3.83	NA
Drogo	Khal	Zoology	Senior	3.38	3.83
Targaryen	Daenerys	Zoology	Freshman	3.36	3.38
Snow	John	Nordic Studies	Junior	3.23	3.36
Clegane	Gregor	Phys Ed	Sophomore	3.23	3.23
Baelish	Peter	Communications	Freshman	2.84	3.23
Stark	Eddard	History	Senior	2.78	2.84
Tarly	Samwise	Nordic Studies	Freshman	2.39	2.78
Bolton	Ramsay	Phys Ed	Freshman	2.24	2.39
Baratheon	Joffrey	History	Freshman	1.87	2.24

To see the value of the next below, use lead()

kable(got %>% arrange(desc(gpa)) %>% mutate(OneWorseGot = lead(gpa)))

lastname	firstname	major	year	gpa	OneWorseGot
Lannister	Tyrion	Communications	Sophomore	3.83	3.38
Drogo	Khal	Zoology	Senior	3.38	3.36
Targaryen	Daenerys	Zoology	Freshman	3.36	3.23
Snow	John	Nordic Studies	Junior	3.23	3.23
Clegane	Gregor	Phys Ed	Sophomore	3.23	2.84
Baelish	Peter	Communications	Freshman	2.84	2.78
Stark	Eddard	History	Senior	2.78	2.39
Tarly	Samwise	Nordic Studies	Freshman	2.39	2.24
Bolton	Ramsay	Phys Ed	Freshman	2.24	1.87
Baratheon	Joffrey	History	Freshman	1.87	NA

And can combine them

kable(got %>% mutate(Name = paste(lastname, firstname)) %>%  arrange(desc(gpa)) %>% mutate(OneBetter = lag(gpa), OneWorse = lead(gpa))%>% select(Name, year, major, gpa,  OneBetter, OneWorse))

Name	year	major	gpa	OneBetter	OneWorse
Lannister Tyrion	Sophomore	Communications	3.83	NA	3.38
Drogo Khal	Senior	Zoology	3.38	3.83	3.36
Targaryen Daenerys	Freshman	Zoology	3.36	3.38	3.23
Snow John	Junior	Nordic Studies	3.23	3.36	3.23
Clegane Gregor	Sophomore	Phys Ed	3.23	3.23	2.84
Baelish Peter	Freshman	Communications	2.84	3.23	2.78
Stark Eddard	Senior	History	2.78	2.84	2.39
Tarly Samwise	Freshman	Nordic Studies	2.39	2.78	2.24
Bolton Ramsay	Freshman	Phys Ed	2.24	2.39	1.87
Baratheon Joffrey	Freshman	History	1.87	2.24	NA

Create a variable of the major with the worst mean gpa

worstmajor <- got %>% group_by(major) %>% summarise(meangpa = mean(gpa)) %>% filter(min_rank(meangpa)== 1) %>% .$major
worstmajor

## [1] "History"

Show all the information on all of the students in the worst major

worst <- got %>% filter(major == worstmajor)
worst

##    lastname firstname   major     year  gpa
## 1     Stark    Eddard History   Senior 2.78
## 2 Baratheon   Joffrey History Freshman 1.87

Find the top 25%, they make up the dean’s list

deans <- got %>% arrange(desc(gpa)) %>% filter(cume_dist(gpa)>= .75)
deans

##    lastname firstname          major      year  gpa
## 1 Lannister    Tyrion Communications Sophomore 3.83
## 2     Drogo      Khal        Zoology    Senior 3.38
## 3 Targaryen  Daenerys        Zoology  Freshman 3.36

How many gpa points separate the worst student from the best not on the dean’s list?

got %>% arrange(desc(gpa)) %>% mutate(diff = lag(gpa,1)-gpa) %>% filter(cume_dist(gpa)< .75) %>% slice(1) %>% .$diff

## [1] 0.13

How many gpa points separate the best and worst freshmen?

got %>% group_by(year) %>% filter(year == "Freshman") %>% 
summarise(diff = max(gpa)- min(gpa))

## # A tibble: 1 × 2
##       year  diff
##      <chr> <dbl>
## 1 Freshman  1.49

Make a list that shows the full_name, major, and year of students not in Phys Ed or Zoology

GList <-got %>% filter(!major %in% c('Phys Ed', 'Zoology')) %>% transmute(full_name = paste(firstname, lastname), major = major, year = year) %>% arrange(full_name)
kable(GList)

full_name	major	year
Eddard Stark	History	Senior
Joffrey Baratheon	History	Freshman
John Snow	Nordic Studies	Junior
Peter Baelish	Communications	Freshman
Samwise Tarly	Nordic Studies	Freshman
Tyrion Lannister	Communications	Sophomore

Create a tally of students by year

tally <- got %>% group_by(year) %>% summarise(count = n())
pander(tally)

year	count
Freshman	5
Junior	1
Senior	2
Sophomore	2

Fotmat the table to wide

library(tidyr)
set.caption("Student Count by Year")
pander(tally %>% spread(year, count))

Student Count by Year
Freshman	Junior	Senior	Sophomore
5	1	2	2

Create a scatter plot with gpa on the x-axis and top_n_percentile on the y.

got %>% arrange(gpa) %>% mutate(top_n_percentile = 1- percent_rank(gpa)) %>% ggvis(~gpa, ~top_n_percentile) %>% layer_points(fill := "red", size := 150)

Practice Questions Unit 6

FlyMe <- read.csv("me_flights1.csv", header = TRUE, stringsAsFactors = FALSE)
kable(head(FlyMe) %>% select(1:5))

FlightDate	Carrier	TailNum	FlightNum	Origin
1/5/2016	AA	N745VJ	1815	PWM
1/1/2016	AA	N748UW	1815	PWM
1/6/2016	AA	N829AW	1815	PWM
1/4/2016	AA	N755US	1815	PWM
1/7/2016	AA	N818AW	1815	PWM
1/8/2016	AA	N765US	1815	PWM

kable(names(FlyMe))

FlightDate

Carrier

TailNum

FlightNum

Origin

Dest

DestCityName

DestState

DestStateName

CRSDepTime

DepTime

WheelsOff

WheelsOn

CRSArrTime

ArrTime

Cancelled

Diverted

CRSElapsedTime

ActualElapsedTime

Distance

kable(head(FlyMe))

FlightDate	Carrier	TailNum	FlightNum	Origin	Dest	DestCityName	DestState	DestStateName	CRSDepTime	DepTime	WheelsOff	WheelsOn	CRSArrTime	ArrTime	CRSElapsedTime	ActualElapsedTime	Distance
1/5/2016	AA	N745VJ	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	906	923	1111	1047	1127	167	141	813
1/1/2016	AA	N748UW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	805	802	814	1034	1050	1040	165	158	813
1/6/2016	AA	N829AW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	755	815	1022	1047	1027	167	152	813
1/4/2016	AA	N755US	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	805	803	817	1037	1050	1044	165	161	813
1/7/2016	AA	N818AW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	758	821	1034	1047	1039	167	161	813
1/8/2016	AA	N765US	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	801	842	1048	1047	1053	167	172	813

str(FlyMe)

## 'data.frame':    383 obs. of  20 variables:
##  $ FlightDate       : chr  "1/5/2016" "1/1/2016" "1/6/2016" "1/4/2016" ...
##  $ Carrier          : chr  "AA" "AA" "AA" "AA" ...
##  $ TailNum          : chr  "N745VJ" "N748UW" "N829AW" "N755US" ...
##  $ FlightNum        : int  1815 1815 1815 1815 1815 1815 850 653 1815 1815 ...
##  $ Origin           : chr  "PWM" "PWM" "PWM" "PWM" ...
##  $ Dest             : chr  "CLT" "CLT" "CLT" "CLT" ...
##  $ DestCityName     : chr  "Charlotte, NC" "Charlotte, NC" "Charlotte, NC" "Charlotte, NC" ...
##  $ DestState        : chr  "NC" "NC" "NC" "NC" ...
##  $ DestStateName    : chr  "North Carolina" "North Carolina" "North Carolina" "North Carolina" ...
##  $ CRSDepTime       : int  800 805 800 805 800 800 800 910 800 800 ...
##  $ DepTime          : int  906 802 755 803 758 801 755 903 817 757 ...
##  $ WheelsOff        : int  923 814 815 817 821 842 815 919 828 850 ...
##  $ WheelsOn         : int  1111 1034 1022 1037 1034 1048 1026 1130 1039 1057 ...
##  $ CRSArrTime       : int  1047 1050 1047 1050 1047 1047 1047 1201 1047 1047 ...
##  $ ArrTime          : int  1127 1040 1027 1044 1039 1053 1030 1138 1045 1102 ...
##  $ Cancelled        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Diverted         : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ CRSElapsedTime   : int  167 165 167 165 167 167 167 171 167 167 ...
##  $ ActualElapsedTime: int  141 158 152 161 161 172 155 155 148 185 ...
##  $ Distance         : int  813 813 813 813 813 813 813 813 813 813 ...

Convert FlightDate from 01/01/2016 to the format used in R.

FlyMe$FlightDate <- as.Date(FlyMe$FlightDate, format ="%m/%d/%Y")

FlyMe <- FlyMe %>% mutate(new_CRSDepTime = paste(FlightDate, sprintf("%04d", CRSDepTime)),
                          
                          new_DepTime = paste(FlightDate, sprintf("%04d", DepTime)),
                          
                          new_CRSArrTime = paste(FlightDate, sprintf("%04d", CRSArrTime)),
                          
                          new_ArrTime = paste(FlightDate, sprintf("%04d", ArrTime)),
                          
                          new_WheelsOff = paste(FlightDate, sprintf("%04d", WheelsOff)),
                          
                          new_WheelsOn = paste(FlightDate, sprintf("%04d", WheelsOn)))

FlyMe$new_CRSDepTime <- as.POSIXct(FlyMe$new_CRSDepTime, format = "%Y-%m-%d %H%M")

FlyMe$new_DepTime <- as.POSIXct(FlyMe$new_DepTime, format = "%Y-%m-%d %H%M")

FlyMe$new_CRSArrTime <- as.POSIXct(FlyMe$new_CRSArrTime, format = "%Y-%m-%d %H%M")

FlyMe$new_ArrTime <- as.POSIXct(FlyMe$new_ArrTime, format = "%Y-%m-%d %H%M")

FlyMe$new_WheelsOff <- as.POSIXct(FlyMe$new_WheelsOff, format = "%Y-%m-%d %H%M")

FlyMe$new_WheelsOn <- as.POSIXct(FlyMe$new_WheelsOn, format = "%Y-%m-%d %H%M")

kable(head(FlyMe))

FlightDate	Carrier	TailNum	FlightNum	Origin	Dest	DestCityName	DestState	DestStateName	CRSDepTime	DepTime	WheelsOff	WheelsOn	CRSArrTime	ArrTime	CRSElapsedTime	ActualElapsedTime	Distance	new_CRSDepTime	new_DepTime	new_CRSArrTime	new_ArrTime	new_WheelsOff	new_WheelsOn
2016-01-05	AA	N745VJ	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	906	923	1111	1047	1127	167	141	813	2016-01-05 08:00:00	2016-01-05 09:06:00	2016-01-05 10:47:00	2016-01-05 11:27:00	2016-01-05 09:23:00	2016-01-05 11:11:00
2016-01-01	AA	N748UW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	805	802	814	1034	1050	1040	165	158	813	2016-01-01 08:05:00	2016-01-01 08:02:00	2016-01-01 10:50:00	2016-01-01 10:40:00	2016-01-01 08:14:00	2016-01-01 10:34:00
2016-01-06	AA	N829AW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	755	815	1022	1047	1027	167	152	813	2016-01-06 08:00:00	2016-01-06 07:55:00	2016-01-06 10:47:00	2016-01-06 10:27:00	2016-01-06 08:15:00	2016-01-06 10:22:00
2016-01-04	AA	N755US	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	805	803	817	1037	1050	1044	165	161	813	2016-01-04 08:05:00	2016-01-04 08:03:00	2016-01-04 10:50:00	2016-01-04 10:44:00	2016-01-04 08:17:00	2016-01-04 10:37:00
2016-01-07	AA	N818AW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	758	821	1034	1047	1039	167	161	813	2016-01-07 08:00:00	2016-01-07 07:58:00	2016-01-07 10:47:00	2016-01-07 10:39:00	2016-01-07 08:21:00	2016-01-07 10:34:00
2016-01-08	AA	N765US	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	801	842	1048	1047	1053	167	172	813	2016-01-08 08:00:00	2016-01-08 08:01:00	2016-01-08 10:47:00	2016-01-08 10:53:00	2016-01-08 08:42:00	2016-01-08 10:48:00

str(FlyMe)

## 'data.frame':    383 obs. of  26 variables:
##  $ FlightDate       : Date, format: "2016-01-05" "2016-01-01" ...
##  $ Carrier          : chr  "AA" "AA" "AA" "AA" ...
##  $ TailNum          : chr  "N745VJ" "N748UW" "N829AW" "N755US" ...
##  $ FlightNum        : int  1815 1815 1815 1815 1815 1815 850 653 1815 1815 ...
##  $ Origin           : chr  "PWM" "PWM" "PWM" "PWM" ...
##  $ Dest             : chr  "CLT" "CLT" "CLT" "CLT" ...
##  $ DestCityName     : chr  "Charlotte, NC" "Charlotte, NC" "Charlotte, NC" "Charlotte, NC" ...
##  $ DestState        : chr  "NC" "NC" "NC" "NC" ...
##  $ DestStateName    : chr  "North Carolina" "North Carolina" "North Carolina" "North Carolina" ...
##  $ CRSDepTime       : int  800 805 800 805 800 800 800 910 800 800 ...
##  $ DepTime          : int  906 802 755 803 758 801 755 903 817 757 ...
##  $ WheelsOff        : int  923 814 815 817 821 842 815 919 828 850 ...
##  $ WheelsOn         : int  1111 1034 1022 1037 1034 1048 1026 1130 1039 1057 ...
##  $ CRSArrTime       : int  1047 1050 1047 1050 1047 1047 1047 1201 1047 1047 ...
##  $ ArrTime          : int  1127 1040 1027 1044 1039 1053 1030 1138 1045 1102 ...
##  $ Cancelled        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Diverted         : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ CRSElapsedTime   : int  167 165 167 165 167 167 167 171 167 167 ...
##  $ ActualElapsedTime: int  141 158 152 161 161 172 155 155 148 185 ...
##  $ Distance         : int  813 813 813 813 813 813 813 813 813 813 ...
##  $ new_CRSDepTime   : POSIXct, format: "2016-01-05 08:00:00" "2016-01-01 08:05:00" ...
##  $ new_DepTime      : POSIXct, format: "2016-01-05 09:06:00" "2016-01-01 08:02:00" ...
##  $ new_CRSArrTime   : POSIXct, format: "2016-01-05 10:47:00" "2016-01-01 10:50:00" ...
##  $ new_ArrTime      : POSIXct, format: "2016-01-05 11:27:00" "2016-01-01 10:40:00" ...
##  $ new_WheelsOff    : POSIXct, format: "2016-01-05 09:23:00" "2016-01-01 08:14:00" ...
##  $ new_WheelsOn     : POSIXct, format: "2016-01-05 11:11:00" "2016-01-01 10:34:00" ...

FlyMe <- FlyMe %>% 
  mutate(DepDelay = as.integer(difftime(new_DepTime, new_CRSDepTime, units = "mins")), 
         
         ArrDelay = as.integer(difftime(new_ArrTime, new_CRSArrTime, units = "mins")),
         
         TaxiOut = as.integer(difftime(new_WheelsOff, new_DepTime, units = "mins")), 
         
         TaxiIn = as.integer(difftime(new_ArrTime, new_WheelsOn, units = "mins")), 
         
         AirDelayMinutes = ifelse(ArrDelay < 0, 0, ArrDelay),
         
         ArrDelay15 = ifelse(ArrDelay >= 15,1,0), 
         
         FlightTimeBuffer = CRSElapsedTime - ActualElapsedTime, AirTime = ActualElapsedTime - TaxiOut - TaxiIn, 
AirSpeed = Distance/(AirTime/60))

kable(head(FlyMe))

FlightDate	Carrier	TailNum	FlightNum	Origin	Dest	DestCityName	DestState	DestStateName	CRSDepTime	DepTime	WheelsOff	WheelsOn	CRSArrTime	ArrTime	CRSElapsedTime	ActualElapsedTime	Distance	new_CRSDepTime	new_DepTime	new_CRSArrTime	new_ArrTime	new_WheelsOff	new_WheelsOn	DepDelay	ArrDelay	TaxiOut	TaxiIn	AirDelayMinutes	ArrDelay15	FlightTimeBuffer	AirTime	AirSpeed
2016-01-05	AA	N745VJ	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	906	923	1111	1047	1127	167	141	813	2016-01-05 08:00:00	2016-01-05 09:06:00	2016-01-05 10:47:00	2016-01-05 11:27:00	2016-01-05 09:23:00	2016-01-05 11:11:00	66	40	17	16	40	1	26	108	451.6667
2016-01-01	AA	N748UW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	805	802	814	1034	1050	1040	165	158	813	2016-01-01 08:05:00	2016-01-01 08:02:00	2016-01-01 10:50:00	2016-01-01 10:40:00	2016-01-01 08:14:00	2016-01-01 10:34:00	-3	-10	12	6	0	0	7	140	348.4286
2016-01-06	AA	N829AW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	755	815	1022	1047	1027	167	152	813	2016-01-06 08:00:00	2016-01-06 07:55:00	2016-01-06 10:47:00	2016-01-06 10:27:00	2016-01-06 08:15:00	2016-01-06 10:22:00	-5	-20	20	5	0	0	15	127	384.0945
2016-01-04	AA	N755US	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	805	803	817	1037	1050	1044	165	161	813	2016-01-04 08:05:00	2016-01-04 08:03:00	2016-01-04 10:50:00	2016-01-04 10:44:00	2016-01-04 08:17:00	2016-01-04 10:37:00	-2	-6	14	7	0	0	4	140	348.4286
2016-01-07	AA	N818AW	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	758	821	1034	1047	1039	167	161	813	2016-01-07 08:00:00	2016-01-07 07:58:00	2016-01-07 10:47:00	2016-01-07 10:39:00	2016-01-07 08:21:00	2016-01-07 10:34:00	-2	-8	23	5	0	0	6	133	366.7669
2016-01-08	AA	N765US	1815	PWM	CLT	Charlotte, NC	NC	North Carolina	800	801	842	1048	1047	1053	167	172	813	2016-01-08 08:00:00	2016-01-08 08:01:00	2016-01-08 10:47:00	2016-01-08 10:53:00	2016-01-08 08:42:00	2016-01-08 10:48:00	1	6	41	5	6	0	-5	126	387.1429

Is a pilot more likely to fly faster if there is a departure delay?

FlyMe %>% filter(Cancelled == 0) %>% mutate(delayed = DepDelay > 0) %>% group_by(delayed) %>% summarise(avg_AirSpeed = mean(AirSpeed))

## # A tibble: 2 × 2
##   delayed avg_AirSpeed
##     <lgl>        <dbl>
## 1   FALSE     335.0505
## 2    TRUE     350.7744

Is there a day of the week when pilots fly faster?

FlyMe2 <- FlyMe %>% filter(Cancelled == 0)

Using library(lubridate) for days of the week functions

Q3 <- FlyMe2 %>% group_by(day = wday(FlightDate, label = TRUE)) %>% summarise(Av_AirSpeed = mean(AirSpeed))
Q3 %>% ggvis(~day, ~Av_AirSpeed)

## Guessing layer_bars()

Do pilots fly faster on the weekend? Need to use library(chron)

library(chron)

## 
## Attaching package: 'chron'

## The following objects are masked from 'package:lubridate':
## 
##     days, hours, minutes, seconds, years

FlyMe2 %>% group_by((is.weekend(FlightDate))) %>% summarise(Av_AirSpeed = mean(AirSpeed))

## # A tibble: 2 × 2
##   `(is.weekend(FlightDate))` Av_AirSpeed
##                        <lgl>       <dbl>
## 1                      FALSE    337.0506
## 2                       TRUE    343.4683

Does it look like any of the arrivals were actually on the next day and they are incorrectly coded? Look for difference between departure and arrival times.

Q5 <- FlyMe2 %>% mutate(Time = difftime(new_ArrTime, new_DepTime), LongthanDay = ifelse(Time >=24, 1,0)) %>% group_by(LongthanDay) %>% summarise(count = n())
Q5

## # A tibble: 1 × 2
##   LongthanDay count
##         <dbl> <int>
## 1           0   361

GoT

Christine Iyer

October 11, 2016