R Bridge Final Project

I found different data sets to download but reading about packages I found a very interesting way to load a data set using a library hflights.

This dataset contains all flights departing from Houston airports IAH (George Bush Intercontinental) and HOU (Houston Hobby) in 2011. The data comes from the Research and Innovation Technology Administration at the Bureau of Transporation statistics.

Install:

From CRAN, with install.packages(“hflights”)

From gituhb, with devtools::install_github(“hadley/hflights”)

Loading Libraries

Note: “Some of the related packages were already mentioned in chapter 1, Hello, data!, where we benchmarked reading a relatively large amount of data from the hflights package into R.” Page 67. Mastering Data Analysis with R By Gergely Daroczi

library(hflights)
library(ggplot2)
library(dplyr)

## 
## 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

Transforming Data

Change Days of Week to Character Values

Days of the week were numerical, so instead of having to remember what day of the week corresponded to which number, I changed the DayOfWeek values to the appropriate character values

hflights$DayOfWeek <- gsub(1, 'Monday', hflights$DayOfWeek)
hflights$DayOfWeek <- gsub(2, 'Tuesday', hflights$DayOfWeek)
hflights$DayOfWeek <- gsub(3, 'Wednesday', hflights$DayOfWeek)
hflights$DayOfWeek <- gsub(4, 'Thursday', hflights$DayOfWeek)
hflights$DayOfWeek <- gsub(5, 'Friday', hflights$DayOfWeek)
hflights$DayOfWeek <- gsub(6, 'Saturday', hflights$DayOfWeek)
hflights$DayOfWeek <- gsub(7, 'Sunday', hflights$DayOfWeek)

Combine Date Columns.

flight_date <- paste(hflights$Month, hflights$DayofMonth, hflights$Year, sep="/")
hflights$Date <- flight_date
hflights$Date <- as.Date(flight_date, "%m/%d/%Y")

Combine the data into a new data frame.

myflights <- subset(hflights, select=c(Date, DayOfWeek, UniqueCarrier, FlightNum, TailNum, TaxiIn, TaxiOut, AirTime, ArrDelay, 
                                       DepDelay, ActualElapsedTime, Origin, Dest, Distance))

Add a Distance/Time.

I created a combined field by dividing the total distance of the flight by the “Actual Elapsed Time” field. This gave me a number I could use to convey the range of time it took to fly the same distance.

dis_per_min <- (myflights$Distance / myflights$ActualElapsedTime)
myflights$mi_per_min <- dis_per_min

Preview of my new data frame:

head(myflights)

##            Date DayOfWeek UniqueCarrier FlightNum TailNum TaxiIn TaxiOut
## 5424 2011-01-01  Saturday            AA       428  N576AA      7      13
## 5425 2011-01-02    Sunday            AA       428  N557AA      6       9
## 5426 2011-01-03    Monday            AA       428  N541AA      5      17
## 5427 2011-01-04   Tuesday            AA       428  N403AA      9      22
## 5428 2011-01-05 Wednesday            AA       428  N492AA      9       9
## 5429 2011-01-06  Thursday            AA       428  N262AA      6      13
##      AirTime ArrDelay DepDelay ActualElapsedTime Origin Dest Distance
## 5424      40      -10        0                60    IAH  DFW      224
## 5425      45       -9        1                60    IAH  DFW      224
## 5426      48       -8       -8                70    IAH  DFW      224
## 5427      39        3        3                70    IAH  DFW      224
## 5428      44       -3        5                62    IAH  DFW      224
## 5429      45       -7       -1                64    IAH  DFW      224
##      mi_per_min
## 5424   3.733333
## 5425   3.733333
## 5426   3.200000
## 5427   3.200000
## 5428   3.612903
## 5429   3.500000

Flight Times and Delays by Day of Week

Plotting Flight Time, Distance, and Day of Week

pattern to the distance traveled, the flight time, and the day of the week.

qplot(Distance, ActualElapsedTime, color=DayOfWeek, data=myflights, main='Total Flight Time by Distance')

## Warning: Removed 3622 rows containing missing values (geom_point).

Departure delays by day of the week.

qplot(DepDelay, data=myflights, geom='histogram', binwidth = 5, xlim =c(-25, 200), main='Departure Delays by Day of Week') + facet_wrap(~DayOfWeek)

## Warning: Removed 3681 rows containing non-finite values (stat_bin).

## Warning: Removed 14 rows containing missing values (geom_bar).

Airlines with the most flights, and the destinations with the most flights.

ggplot(data=myflights) + geom_bar(aes(x=UniqueCarrier), bin=5) + ggtitle('Flights by Airline')

## Warning: Ignoring unknown parameters: bin

Reduce the number to the destinations with the most flights.

dests <- ggplot(data=myflights) + geom_bar(aes(x=Dest))
dests + theme(axis.text.x=element_text(angle=-90)) + ggtitle('Flights by Destination')

Top Destinations.

Instead of using `dplyr` Here, I use `table` to select the Destination column, and turn that into a data frame. I can then subset destinations with 5000 flights or more.

top_dests <- data.frame(table(myflights$Dest))
top_dests <- subset(top_dests, Freq > 5000)
head(top_dests)

##    Var1 Freq
## 7   ATL 7886
## 8   AUS 5022
## 29  DAL 9820
## 32  DEN 5920
## 33  DFW 6653
## 60  LAX 6064

Dataframe with the flights information selecting cities:

most_dest <- filter(myflights, Dest == 'ATL' | Dest == 'AUS' | Dest == 'DAL' | Dest == 'DEN' | Dest == 'DFW' | Dest == 'LAX' | 
                      Dest == 'MSY' | Dest == 'ORD' | Dest == 'PHX')

Plotting Data

Destinations with highest frequency of flights, range of distance covered per minute (includes taxi time, flight, delays)

Destinations with miles per minute, by carrier - who is the best performer per destination?

qplot(mi_per_min, data=most_dest, geom='density', color=UniqueCarrier) + facet_grid(Dest ~.)

According to the data analyzed and the graph obtained, we can conclude that:

• Friday is the day with the greatest Departure delays

• The airline with the most flights is ExpressJet Airlines, followed by Continental Airlines.

• The destination with the most flights is Dallas

• Atlanta is the destination with the highest frequency of flights, range of distance covered per minute

These data and graphs allow us to analyze routes, flights, frequency and capacity trends, flight statistics by route and airport performance, which provides a better commercial and operational understanding of the air transport industry.

R bridge course final project

Gabriel Santos

2022-07-17

R Bridge Final Project

I found different data sets to download but reading about packages I found a very interesting way to load a data set using a library hflights.

This dataset contains all flights departing from Houston airports IAH (George Bush Intercontinental) and HOU (Houston Hobby) in 2011. The data comes from the Research and Innovation Technology Administration at the Bureau of Transporation statistics.

Install:

From CRAN, with install.packages(“hflights”)

From gituhb, with devtools::install_github(“hadley/hflights”)

Loading Libraries

Transforming Data

Change Days of Week to Character Values

Days of the week were numerical, so instead of having to remember what day of the week corresponded to which number, I changed the DayOfWeek values to the appropriate character values

Combine Date Columns.

Combine the data into a new data frame.

Add a Distance/Time.

Preview of my new data frame:

Flight Times and Delays by Day of Week

Plotting Flight Time, Distance, and Day of Week

pattern to the distance traveled, the flight time, and the day of the week.

Departure delays by day of the week.

Airlines with the most flights, and the destinations with the most flights.

Reduce the number to the destinations with the most flights.

Top Destinations.

Instead of using `dplyr` Here, I use `table` to select the Destination column, and turn that into a data frame. I can then subset destinations with 5000 flights or more.

Dataframe with the flights information selecting cities:

Plotting Data

Destinations with highest frequency of flights, range of distance covered per minute (includes taxi time, flight, delays)

Destinations with miles per minute, by carrier - who is the best performer per destination?

According to the data analyzed and the graph obtained, we can conclude that:

• Friday is the day with the greatest Departure delays

• The airline with the most flights is ExpressJet Airlines, followed by Continental Airlines.

• The destination with the most flights is Dallas

• Atlanta is the destination with the highest frequency of flights, range of distance covered per minute

These data and graphs allow us to analyze routes, flights, frequency and capacity trends, flight statistics by route and airport performance, which provides a better commercial and operational understanding of the air transport industry.

R bridge course final project

Gabriel Santos

2022-07-17

R Bridge Final Project

I found different data sets to download but reading about packages I found a very interesting way to load a data set using a library hflights.

This dataset contains all flights departing from Houston airports IAH (George Bush Intercontinental) and HOU (Houston Hobby) in 2011. The data comes from the Research and Innovation Technology Administration at the Bureau of Transporation statistics.

Install:

From CRAN, with install.packages(“hflights”)

From gituhb, with devtools::install_github(“hadley/hflights”)

Loading Libraries

Note: “Some of the related packages were already mentioned in chapter 1, Hello, data!, where we benchmarked reading a relatively large amount of data from the hflights package into R.” Page 67. Mastering Data Analysis with R By Gergely Daroczi

Transforming Data

Change Days of Week to Character Values

Days of the week were numerical, so instead of having to remember what day of the week corresponded to which number, I changed the DayOfWeek values to the appropriate character values

Combine Date Columns.

Combine the data into a new data frame.

Add a Distance/Time.

Preview of my new data frame:

Flight Times and Delays by Day of Week

Plotting Flight Time, Distance, and Day of Week

pattern to the distance traveled, the flight time, and the day of the week.

Departure delays by day of the week.

Airlines with the most flights, and the destinations with the most flights.

Reduce the number to the destinations with the most flights.

Top Destinations.

Instead of using dplyr Here, I use table to select the Destination column, and turn that into a data frame. I can then subset destinations with 5000 flights or more.

Dataframe with the flights information selecting cities:

Plotting Data

Destinations with highest frequency of flights, range of distance covered per minute (includes taxi time, flight, delays)

Destinations with miles per minute, by carrier - who is the best performer per destination?

According to the data analyzed and the graph obtained, we can conclude that:

• Friday is the day with the greatest Departure delays

• The airline with the most flights is ExpressJet Airlines, followed by Continental Airlines.

• The destination with the most flights is Dallas

• Atlanta is the destination with the highest frequency of flights, range of distance covered per minute

These data and graphs allow us to analyze routes, flights, frequency and capacity trends, flight statistics by route and airport performance, which provides a better commercial and operational understanding of the air transport industry.

Instead of using `dplyr` Here, I use `table` to select the Destination column, and turn that into a data frame. I can then subset destinations with 5000 flights or more.