Data Source: Numbersense, Kaiser Fung, McGraw Hill, 2013

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## Loading required package: ggplot2
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Assignment


  1. Create a .CSV file (or optionally, a MySQL database!) that includes all of the information above.

Load Data

1.1 Load CSV file Load CSV file from desktop and validate it using Head.

uD <-  read.csv(paste0("C:/Users/Kleber/Documents/MSDS2019/DATA607/Week5/arrivalDelays.csv"), header=T)
kable(uD) %>%
  kable_styling() %>%
  scroll_box()
AirLine Status Los.Angeles Phoenix San.Diego San.Francisco Seattle
ALASKA onTime 497 221 212 503 1841
ALASKA delayed 62 12 20 102 305
AM WEST onTime 694 4840 383 320 201
AM WEST delayed 117 415 65 129 61



  1. Read CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data.

Tidy Data

2.1 Tidy Data - Untidy dataset by gathering the destination Column

uD <- uD %>% gather(Destination, n, Los.Angeles:Seattle)
kable(uD) %>%
  kable_styling() %>%
  scroll_box(height = "195px")
AirLine Status Destination n
ALASKA onTime Los.Angeles 497
ALASKA delayed Los.Angeles 62
AM WEST onTime Los.Angeles 694
AM WEST delayed Los.Angeles 117
ALASKA onTime Phoenix 221
ALASKA delayed Phoenix 12
AM WEST onTime Phoenix 4840
AM WEST delayed Phoenix 415
ALASKA onTime San.Diego 212
ALASKA delayed San.Diego 20
AM WEST onTime San.Diego 383
AM WEST delayed San.Diego 65
ALASKA onTime San.Francisco 503
ALASKA delayed San.Francisco 102
AM WEST onTime San.Francisco 320
AM WEST delayed San.Francisco 129
ALASKA onTime Seattle 1841
ALASKA delayed Seattle 305
AM WEST onTime Seattle 201
AM WEST delayed Seattle 61


2.2 Tidy Data - Create column names from Categorical Status to make the untidy dataset to tidy dataset variales.

tD <- uD %>% spread(Status, n)
kable(tD) %>%
  kable_styling() %>%
  scroll_box(height = "195px")
AirLine Destination delayed onTime
ALASKA Los.Angeles 62 497
ALASKA Phoenix 12 221
ALASKA San.Diego 20 212
ALASKA San.Francisco 102 503
ALASKA Seattle 305 1841
AM WEST Los.Angeles 117 694
AM WEST Phoenix 415 4840
AM WEST San.Diego 65 383
AM WEST San.Francisco 129 320
AM WEST Seattle 61 201



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

Analysis

3.1 Probability - Add onTimeProbability and DelayedProbability columns to tidy dataset

tD$onTimeProbability <- round((tD$onTime / (tD$delayed + tD$onTime)), digits = 3)
tD$DelayedProbability <- round((tD$delayed / (tD$delayed + tD$onTime)), digits = 3)
kable(tD) %>%
  kable_styling() %>%
  scroll_box(height = "195px")
AirLine Destination delayed onTime onTimeProbability DelayedProbability
ALASKA Los.Angeles 62 497 0.889 0.111
ALASKA Phoenix 12 221 0.948 0.052
ALASKA San.Diego 20 212 0.914 0.086
ALASKA San.Francisco 102 503 0.831 0.169
ALASKA Seattle 305 1841 0.858 0.142
AM WEST Los.Angeles 117 694 0.856 0.144
AM WEST Phoenix 415 4840 0.921 0.079
AM WEST San.Diego 65 383 0.855 0.145
AM WEST San.Francisco 129 320 0.713 0.287
AM WEST Seattle 61 201 0.767 0.233


3.2 Summarize Data - Below we have summarized data on the basis of Probability.

summary(tD)
##     AirLine  Destination           delayed           onTime      
##  ALASKA :5   Length:10          Min.   : 12.00   Min.   : 201.0  
##  AM WEST:5   Class :character   1st Qu.: 61.25   1st Qu.: 245.8  
##              Mode  :character   Median : 83.50   Median : 440.0  
##                                 Mean   :128.80   Mean   : 971.2  
##                                 3rd Qu.:126.00   3rd Qu.: 646.2  
##                                 Max.   :415.00   Max.   :4840.0  
##  onTimeProbability DelayedProbability
##  Min.   :0.7130    Min.   :0.05200   
##  1st Qu.:0.8370    1st Qu.:0.09225   
##  Median :0.8570    Median :0.14300   
##  Mean   :0.8552    Mean   :0.14480   
##  3rd Qu.:0.9077    3rd Qu.:0.16300   
##  Max.   :0.9480    Max.   :0.28700


Plots

Charts - The charts below show that Phoenix has best on time probability for both AirLines and San Francisco has least on time probability for both Airlines.

Point chart

sc <- ggplot(tD, aes(x = Destination , y = onTimeProbability))  + geom_point(aes(color = onTimeProbability, size = onTimeProbability, shape = factor(AirLine))) +  scale_colour_gradient(low = "#F0F8FF")
ggplotly(sc)

Line chart

ggplot(tD, aes(x = Destination, y = onTimeProbability, color = DelayedProbability)) +
  geom_line() +  scale_colour_gradient(low = "#db9833") +
  expand_limits(x = 0)

Bar chart

ggplot(tD, aes(x = Destination, y = onTimeProbability)) +
  geom_col(aes(colour = DelayedProbability)) +
  scale_colour_gradientn(colours = terrain.colors(10))



  1. R Markdown file, posted to rpubs.com, and github.



Please email to: kleber.perez@live.com for any suggestion.

    

    DATA607 Assignment - MSDS 2019 Program.