IS607_HW5

Practicing data clean-up and transformations using tidyr and dplyr. CRAN documentation available for tidyr is available here and dplr documentation can be found here.

library(tidyr)
library(dplyr)

1. Create a CSV file that includes all of the information above. You’re encouraged to use a “wide” structure similar to how the information appears above, so that you can practice tidying and transformations:

# Create a dataframe that contains the data pictured above:
hw5data <- data.frame(
  airline = rep(c('Alaska', 'AMWest'), each = 2),
  status = c('on-time', 'delayed', 'on-time', 'delayed'),
  LosAngeles = c(497,62,694,117),
  Phoenix = c(221,12,4840,415),
  SanDiego = c(212,20,383,65),
  SanFrancisco = c(503,102,320,129),
  Seattle = c(1841,305,201,61)
)

# Write the data to a CSV and save it to current working directory
write.csv(hw5data, "IS607_HW5_data.csv")

2. Read the information from your CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data.

data <- read.csv("IS607_HW5_data.csv") # Reads in the CSV file

str(data) # check out the structure

## 'data.frame':    4 obs. of  8 variables:
##  $ X           : int  1 2 3 4
##  $ airline     : Factor w/ 2 levels "Alaska","AMWest": 1 1 2 2
##  $ status      : Factor w/ 2 levels "delayed","on-time": 2 1 2 1
##  $ LosAngeles  : int  497 62 694 117
##  $ Phoenix     : int  221 12 4840 415
##  $ SanDiego    : int  212 20 383 65
##  $ SanFrancisco: int  503 102 320 129
##  $ Seattle     : int  1841 305 201 61

data <- data[,2:7] # remove ID column (X)

data 

##   airline  status LosAngeles Phoenix SanDiego SanFrancisco
## 1  Alaska on-time        497     221      212          503
## 2  Alaska delayed         62      12       20          102
## 3  AMWest on-time        694    4840      383          320
## 4  AMWest delayed        117     415       65          129

3. Restructure the data into a more useful format:

tidy_data <- data %>%
  gather(city, count, -airline, -status) # Convert the data into a long format using gather() from tidyr package

tidy_data 

##    airline  status         city count
## 1   Alaska on-time   LosAngeles   497
## 2   Alaska delayed   LosAngeles    62
## 3   AMWest on-time   LosAngeles   694
## 4   AMWest delayed   LosAngeles   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     SanDiego   212
## 10  Alaska delayed     SanDiego    20
## 11  AMWest on-time     SanDiego   383
## 12  AMWest delayed     SanDiego    65
## 13  Alaska on-time SanFrancisco   503
## 14  Alaska delayed SanFrancisco   102
## 15  AMWest on-time SanFrancisco   320
## 16  AMWest delayed SanFrancisco   129

3. Perform analysis to compare the arrival delays for the two airlines.

# Calculate total number of flights for each airline using aggregate():
total_flights <- aggregate(tidy_data$count, by=list(airline=tidy_data$airline), FUN=sum)
colnames(total_flights) <- c("airline", "count") # name columns in resulting df
total_flights 

##   airline count
## 1  Alaska  1629
## 2  AMWest  6963

# Sum the total number of delayed flights for each airline using dplyr filter()
# Alaska delayed flights:
d_alaska <- filter(tidy_data, airline == "Alaska", status == "delayed")
d_alaska <- sum(d_alaska$count)
d_alaska

## [1] 196

# AMWest delayed flights:
d_AMWest <- filter(tidy_data, airline == "AMWest", status == "delayed")
d_AMWest <- sum(d_AMWest$count)
d_AMWest

## [1] 726

# Calculate the percentage of delayed flights for each airline:
p_d_alaska <- d_alaska/total_flights$count[1] # percentage of delays with Alaska
p_d_alaska

## [1] 0.1203192

p_d_AMWest <- d_AMWest/total_flights$count[2] # percentage of delays with AMWest
p_d_AMWest

## [1] 0.1042654

# Find difference between percentage of delays among airlines using absolute value:
abs(p_d_AMWest - p_d_alaska)

## [1] 0.01605381