Assignment_5

Data Wrangling

For this Assignment I use dplyr, tidyr, ggplot2 and Datatable. First of all, I create the csv file and then read the file into RStudio. I use method such gather and spread to get a wide structure of the data and also use mutate method in order to add column to the data frame.

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.5.2

## 
## 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

library(tidyr)
library(ggplot2)
library(DT)

#Reading the file .CSV
df <- data.frame(read.csv2(file = "https://raw.githubusercontent.com/Anth350z/DATA-607/master/Assignment_5/flight.csv", sep= ",",header = TRUE)) %>% na.omit()
names(df)[1:2] <- c("Airlines", "Status")
df[c(2,4),1] <- c("ALASKA", "AM WEST")

#using gather and spread in order to organize the data
tidy.data <- gather(df,City, flight_num , 3:7 ) 
tidy.data.wide <- tidy.data  %>% spread(Status, flight_num) %>% mutate(Total_flight = `on time` + delayed) %>% mutate(time_ratio = `on time` / Total_flight) %>% mutate(delayed_ratio = 1- time_ratio)

Datatable

datatable(tidy.data) 

Graphs

#counting the on time and delayed by Airlines
Airlines.count <- tidy.data %>% group_by(Airlines,Status) %>% summarise(Total = sum(flight_num)) 

ggplot(Airlines.count, aes(fill=Airlines, y=Total, x=Status)) + 
    geom_bar( position="dodge", 
              stat="identity") + scale_fill_manual(values=c("#999999", "#E69F00"))

#on time count
ggplot(tidy.data.wide, aes(fill=Airlines, y=`on time`, x=City)) + 
    geom_bar( position="dodge", 
              stat="identity") + scale_fill_manual(values=c("#999999", "#E69F00"))

#delayed count
ggplot(tidy.data.wide, aes(fill=Airlines, y=delayed, x=City)) + 
    geom_bar( position="dodge", 
              stat="identity") + scale_fill_manual(values=c("#999999", "#E69F00"))

#on time ratio
ggplot(tidy.data.wide, aes(fill=Airlines, y=time_ratio, x=City)) + 
    geom_bar( position="dodge", 
              stat="identity") + scale_fill_manual(values=c("#999999", "#E69F00"))

#delayed time Ratio
ggplot(tidy.data.wide, aes(fill=Airlines, y=delayed_ratio, x=City)) + 
    geom_bar( position="dodge", 
              stat="identity") + scale_fill_manual(values=c("#999999", "#E69F00"))

conclusion

Looking at the graphs airline AM WEST has more on time flights comparing to ALASKA. but when we compared the Ratio on time by city of both airlines we noticed that ALASKA airline has better performing result on time ratio flights.