Homework5
LeTicia Cancel
9/25/2020
#Libraries needed
library(RCurl)
library(dplyr)
library(tidyverse)
library(tidyfast)
library(tidyr)
library(ggplot2)Import Flights Data
Import Flights data from GitHub
site <- getURL("https://raw.githubusercontent.com/ltcancel/Homework-5/master/Airline%20Data.csv")
df <- read.csv(text = site, sep = ",", header = TRUE)
head(df)## X X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1841
## 2 delayed 62 12 20 102 305
## 3 NA NA NA NA NA
## 4 AM WEST on time 694 4840 383 320 201
## 5 delayed 117 415 65 129 61Tidy the data
Some of the columns need to be renamed. The data also imported with null values where a row will need to be removed or observations will need to be filled with the appropriate data.
#Fix column names of the first two columns
df <- rename(df, Airline = X, Status = X.1)
df## Airline Status Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1841
## 2 delayed 62 12 20 102 305
## 3 NA NA NA NA NA
## 4 AM WEST on time 694 4840 383 320 201
## 5 delayed 117 415 65 129 61#Remove the null row
df <- df %>% drop_na()
df## Airline Status Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1841
## 2 delayed 62 12 20 102 305
## 4 AM WEST on time 694 4840 383 320 201
## 5 delayed 117 415 65 129 61I tried to use the fill function to fill in the Airline data in the observations that are blank but it did not work at first. R did not read these observations as NULL or NA so fill did not work. I made these observations NA so fill would work. Even though I could have just filled in the observations with the exact values, I chose to go this route to practice using some of these functions.
#Make blank Airline NULL so it can be filled
df[2,1] <- NA
df[4,1] <- NA
df## Airline Status Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1841
## 2 <NA> delayed 62 12 20 102 305
## 4 AM WEST on time 694 4840 383 320 201
## 5 <NA> delayed 117 415 65 129 61#Fill the new NA columns with the values from the row above
df <- fill(df, Airline)
df## Airline Status Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1841
## 2 ALASKA delayed 62 12 20 102 305
## 4 AM WEST on time 694 4840 383 320 201
## 5 AM WEST delayed 117 415 65 129 61#Create a new data frame making some of the columns as rows instead
long_df <- df %>% dt_pivot_longer(
cols = -c(Airline,Status),
names_to = "City",
values_to = "Flights"
)
head(long_df)## Airline Status City Flights
## 1: ALASKA on time Los.Angeles 497
## 2: ALASKA delayed Los.Angeles 62
## 3: AM WEST on time Los.Angeles 694
## 4: AM WEST delayed Los.Angeles 117
## 5: ALASKA on time Phoenix 221
## 6: ALASKA delayed Phoenix 12Flights Analysis
Analysis to compare arrival delays for both airlines.
#split data by flight status
onTime <- long_df %>%
filter(Status == "on time")
delayed <- long_df %>%
filter(Status == "delayed")
onTime## Airline Status City Flights
## 1 ALASKA on time Los.Angeles 497
## 2 AM WEST on time Los.Angeles 694
## 3 ALASKA on time Phoenix 221
## 4 AM WEST on time Phoenix 4840
## 5 ALASKA on time San.Diego 212
## 6 AM WEST on time San.Diego 383
## 7 ALASKA on time San.Francisco 503
## 8 AM WEST on time San.Francisco 320
## 9 ALASKA on time Seattle 1841
## 10 AM WEST on time Seattle 201delayed## Airline Status City Flights
## 1 ALASKA delayed Los.Angeles 62
## 2 AM WEST delayed Los.Angeles 117
## 3 ALASKA delayed Phoenix 12
## 4 AM WEST delayed Phoenix 415
## 5 ALASKA delayed San.Diego 20
## 6 AM WEST delayed San.Diego 65
## 7 ALASKA delayed San.Francisco 102
## 8 AM WEST delayed San.Francisco 129
## 9 ALASKA delayed Seattle 305
## 10 AM WEST delayed Seattle 61#combine subsets so each flight status is in their own column
final_flights <- cbind(onTime, delayed[,4])
final_flights## Airline Status City Flights delayed[, 4]
## 1 ALASKA on time Los.Angeles 497 62
## 2 AM WEST on time Los.Angeles 694 117
## 3 ALASKA on time Phoenix 221 12
## 4 AM WEST on time Phoenix 4840 415
## 5 ALASKA on time San.Diego 212 20
## 6 AM WEST on time San.Diego 383 65
## 7 ALASKA on time San.Francisco 503 102
## 8 AM WEST on time San.Francisco 320 129
## 9 ALASKA on time Seattle 1841 305
## 10 AM WEST on time Seattle 201 61#Tidy final data frame to keep only the columns needed and rename columns
final_flights <- final_flights %>%
rename(
onTime = Flights,
Delayed = `delayed[, 4]`
) %>%
select(Airline,City,onTime,Delayed)
final_flights## Airline City onTime Delayed
## 1 ALASKA Los.Angeles 497 62
## 2 AM WEST Los.Angeles 694 117
## 3 ALASKA Phoenix 221 12
## 4 AM WEST Phoenix 4840 415
## 5 ALASKA San.Diego 212 20
## 6 AM WEST San.Diego 383 65
## 7 ALASKA San.Francisco 503 102
## 8 AM WEST San.Francisco 320 129
## 9 ALASKA Seattle 1841 305
## 10 AM WEST Seattle 201 61#Comparison of on time flights by Airline for each city
ggplot(final_flights, aes(x = Airline, y = onTime, fill = Airline)) +
geom_bar(stat = "identity") +
facet_wrap(~City)
#Comparison of delayed flights by Airline for each city
ggplot(final_flights, aes(x = Airline, y = Delayed, fill = Airline)) +
geom_bar(stat = "identity") +
facet_wrap(~City)
#Comparison of cumulative status for each airline
ggplot(long_df, aes(x = Status, y = Flights, fill = Airline)) +
geom_bar(stat = "identity") +
facet_wrap(~Airline)