Week5_Assignment
Heleine Fouda
2023-10-07
This assignment will consist in three main tasks. First, we will create a .CSV file from the chart provided. Then, we will read the information from our .CSV file into R, and perform some data cleaning and data transformation as needed. Finally, we will use the cleaned data to analyze and compare the arrival delays of the two airline carriers listed in the data set.
Setting up our Environment
Let’s first set up our work environment.
The Data Set
Let’s create a .CSV file from the provided pdf document.
# Creating a .CSV file
flights_data<- data.frame(
Cities= c("Los Angeles", "Phoenix", "San Diego", "San Francisco", "Seattle"),
Alaska_on_time = c(497, 221, 212, 503, 1841),
Alaska_delayed = c(62, 12, 20, 102, 305),
Am_west_on_time = c(694, 4840, 383, 320, 201),
Am_west_delayed = c(117,415, 65, 129, 61)
)
file_path <- "flights_data.csv"
write.csv(flights_data, file = file_path, row.names = FALSE)
cat("Data has been saved to", file_path, "\n")## Data has been saved to flights_data.csvLet’s read the .CSV file information into R
# Reading the .CSV file info into R
flights_data <- read.csv("flights_data.csv", sep = ",")
flights_data## Cities Alaska_on_time Alaska_delayed Am_west_on_time Am_west_delayed
## 1 Los Angeles 497 62 694 117
## 2 Phoenix 221 12 4840 415
## 3 San Diego 212 20 383 65
## 4 San Francisco 503 102 320 129
## 5 Seattle 1841 305 201 61Data exploration
Let’s take a peek at the data set
# Glimpse of the data set
glimpse(flights_data)## Rows: 5
## Columns: 5
## $ Cities <chr> "Los Angeles", "Phoenix", "San Diego", "San Francisco"…
## $ Alaska_on_time <int> 497, 221, 212, 503, 1841
## $ Alaska_delayed <int> 62, 12, 20, 102, 305
## $ Am_west_on_time <int> 694, 4840, 383, 320, 201
## $ Am_west_delayed <int> 117, 415, 65, 129, 61# Data Head
head(flights_data)## Cities Alaska_on_time Alaska_delayed Am_west_on_time Am_west_delayed
## 1 Los Angeles 497 62 694 117
## 2 Phoenix 221 12 4840 415
## 3 San Diego 212 20 383 65
## 4 San Francisco 503 102 320 129
## 5 Seattle 1841 305 201 61# | Data Structure
str(flights_data)## 'data.frame': 5 obs. of 5 variables:
## $ Cities : chr "Los Angeles" "Phoenix" "San Diego" "San Francisco" ...
## $ Alaska_on_time : int 497 221 212 503 1841
## $ Alaska_delayed : int 62 12 20 102 305
## $ Am_west_on_time: int 694 4840 383 320 201
## $ Am_west_delayed: int 117 415 65 129 61Below is the summary statistics
# Data summary
summary(flights_data)## Cities Alaska_on_time Alaska_delayed Am_west_on_time
## Length:5 Min. : 212.0 Min. : 12.0 Min. : 201
## Class :character 1st Qu.: 221.0 1st Qu.: 20.0 1st Qu.: 320
## Mode :character Median : 497.0 Median : 62.0 Median : 383
## Mean : 654.8 Mean :100.2 Mean :1288
## 3rd Qu.: 503.0 3rd Qu.:102.0 3rd Qu.: 694
## Max. :1841.0 Max. :305.0 Max. :4840
## Am_west_delayed
## Min. : 61.0
## 1st Qu.: 65.0
## Median :117.0
## Mean :157.4
## 3rd Qu.:129.0
## Max. :415.0Data Transformation and Data Cleaning
Let’s new columns or variables to our data frame
# Adding On-time column
New_flights<-flights_data|>
select(Cities, Alaska_on_time, Am_west_on_time, Alaska_delayed,Am_west_delayed)|>
mutate(On_time = (Am_west_on_time) + (Alaska_on_time))
New_flights## Cities Alaska_on_time Am_west_on_time Alaska_delayed Am_west_delayed
## 1 Los Angeles 497 694 62 117
## 2 Phoenix 221 4840 12 415
## 3 San Diego 212 383 20 65
## 4 San Francisco 503 320 102 129
## 5 Seattle 1841 201 305 61
## On_time
## 1 1191
## 2 5061
## 3 595
## 4 823
## 5 2042# Adding the Delayed column
new_flights <-New_flights|>
mutate(Delayed = ( Alaska_delayed) + (Am_west_delayed))
new_flights## Cities Alaska_on_time Am_west_on_time Alaska_delayed Am_west_delayed
## 1 Los Angeles 497 694 62 117
## 2 Phoenix 221 4840 12 415
## 3 San Diego 212 383 20 65
## 4 San Francisco 503 320 102 129
## 5 Seattle 1841 201 305 61
## On_time Delayed
## 1 1191 179
## 2 5061 427
## 3 595 85
## 4 823 231
## 5 2042 366# Adding the Airlines column
new_flights <-New_flights|>
mutate(Airlines = ( Alaska_delayed) + (Am_west_delayed) + (Alaska_on_time) + (Am_west_on_time))
new_flights## Cities Alaska_on_time Am_west_on_time Alaska_delayed Am_west_delayed
## 1 Los Angeles 497 694 62 117
## 2 Phoenix 221 4840 12 415
## 3 San Diego 212 383 20 65
## 4 San Francisco 503 320 102 129
## 5 Seattle 1841 201 305 61
## On_time Airlines
## 1 1191 1370
## 2 5061 5488
## 3 595 680
## 4 823 1054
## 5 2042 2408# Delayed Flights
flights_delay <- new_flights|>
group_by(Cities) |>
reframe(Total_delayed = flights_data$Alaska_delayed + flights_data$Am_west_delayed)
flights_delay ## # A tibble: 25 × 2
## Cities Total_delayed
## <chr> <int>
## 1 Los Angeles 179
## 2 Los Angeles 427
## 3 Los Angeles 85
## 4 Los Angeles 231
## 5 Los Angeles 366
## 6 Phoenix 179
## 7 Phoenix 427
## 8 Phoenix 85
## 9 Phoenix 231
## 10 Phoenix 366
## # ℹ 15 more rows# Delayed flights arranged in descending order
flights_delay <- new_flights|>
group_by(Cities) |>
reframe(Total_delayed = flights_data$Alaska_delayed + flights_data$Am_west_delayed)|>
arrange(desc(Total_delayed))
flights_delay ## # A tibble: 25 × 2
## Cities Total_delayed
## <chr> <int>
## 1 Los Angeles 427
## 2 Phoenix 427
## 3 San Diego 427
## 4 San Francisco 427
## 5 Seattle 427
## 6 Los Angeles 366
## 7 Phoenix 366
## 8 San Diego 366
## 9 San Francisco 366
## 10 Seattle 366
## # ℹ 15 more rows# On-time flights
flights_ont <- new_flights|>
group_by(Cities) |>
reframe(Total_ontime = flights_data$Alaska_on_time+ flights_data$Am_west_on_time)
flights_ont## # A tibble: 25 × 2
## Cities Total_ontime
## <chr> <int>
## 1 Los Angeles 1191
## 2 Los Angeles 5061
## 3 Los Angeles 595
## 4 Los Angeles 823
## 5 Los Angeles 2042
## 6 Phoenix 1191
## 7 Phoenix 5061
## 8 Phoenix 595
## 9 Phoenix 823
## 10 Phoenix 2042
## # ℹ 15 more rowsLet’s find the number of on-time flights and delayed flight per carrier or airline
# Delayed and on- time flights - per airline
flights_data <-flights_data|>
mutate(Delayed = Am_west_delayed + Alaska_delayed
)
flights_data## Cities Alaska_on_time Alaska_delayed Am_west_on_time Am_west_delayed
## 1 Los Angeles 497 62 694 117
## 2 Phoenix 221 12 4840 415
## 3 San Diego 212 20 383 65
## 4 San Francisco 503 102 320 129
## 5 Seattle 1841 305 201 61
## Delayed
## 1 179
## 2 427
## 3 85
## 4 231
## 5 366Let’s now put the data into a format that makes the analysis easier
# Pivot-longer
pivot_longer(flights_data, cols = 2:5, names_to = "Airline", values_to = "Count")## # A tibble: 20 × 4
## Cities Delayed Airline Count
## <chr> <int> <chr> <int>
## 1 Los Angeles 179 Alaska_on_time 497
## 2 Los Angeles 179 Alaska_delayed 62
## 3 Los Angeles 179 Am_west_on_time 694
## 4 Los Angeles 179 Am_west_delayed 117
## 5 Phoenix 427 Alaska_on_time 221
## 6 Phoenix 427 Alaska_delayed 12
## 7 Phoenix 427 Am_west_on_time 4840
## 8 Phoenix 427 Am_west_delayed 415
## 9 San Diego 85 Alaska_on_time 212
## 10 San Diego 85 Alaska_delayed 20
## 11 San Diego 85 Am_west_on_time 383
## 12 San Diego 85 Am_west_delayed 65
## 13 San Francisco 231 Alaska_on_time 503
## 14 San Francisco 231 Alaska_delayed 102
## 15 San Francisco 231 Am_west_on_time 320
## 16 San Francisco 231 Am_west_delayed 129
## 17 Seattle 366 Alaska_on_time 1841
## 18 Seattle 366 Alaska_delayed 305
## 19 Seattle 366 Am_west_on_time 201
## 20 Seattle 366 Am_west_delayed 61flights_data <- c("Cities", "Airline", "Delayed", "Count")
flights_data## [1] "Cities" "Airline" "Delayed" "Count"Data Visualization
# AM_West delays- by -Cities
df <- data.frame(new_flights)
ggplot(data = df, aes(x = Am_west_delayed, y = Cities))+
geom_bar(stat="identity")
# Alaska delays by cities
ggplot(data = new_flights, aes(x = Alaska_delayed, y = Cities))+
geom_bar(stat="identity")
Findings & Conclusion
From the charts above, one sees that San Francisco is the Destination where both Alaska and AM West seems to experience an almost similar level of delays.