Week5_Assignment
Victor Torres
2024-02-25
I create a .CSV file named “airflight_chart” in excel and uploaded into my Github Account, from there, I downloaded the file into R studio using the read.csv file command to get the information into R studio.
airflight_chart<-read.csv('https://raw.githubusercontent.com/vitugo23/DATA607/main/airflight_chart.csv', stringsAsFactors = FALSE)###loading the libraries required for the assignment. ###
library(tidyr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)I’m going to give a name of the first two columns(“Name of airline” and “arrival status”), as well as to collect the names of the cities or destinations.
colnames(airflight_chart)[1] = "Airline_name"
colnames(airflight_chart)[2] = "Arrival_Status"
destination_names <- colnames(airflight_chart[3:length(airflight_chart)])
destination_names## [1] "Los.Angeles" "Phoenix" "San.Diego" "San.Fransisco"
## [5] "Seattle"I’m removing all NA rows with the “na.omit” command, to make the data look more organized.
airflight_chart <- airflight_chart %>% na.omit()
print(airflight_chart)## Airline_name Arrival_Status Los.Angeles Phoenix San.Diego San.Fransisco
## 1 ALASKA on time 497 221 212 503
## 2 delayed 62 12 20 102
## 4 AM WEST on time 694 4,840 383 320
## 5 delayed 117 415 65 129
## Seattle
## 1 1,841
## 2 305
## 4 201
## 5 61I am going to remove the commas from large numbers and covert string into numeric values. I am going to use “FUN = function(x) as.numeric” command to apply this into my dataset.
airflight_chart[destination_names] <- airflight_chart[destination_names] %>% apply(MARGIN = 2, FUN = function(x) as.numeric(str_remove(x,",")))
print(airflight_chart)## Airline_name Arrival_Status Los.Angeles Phoenix San.Diego San.Fransisco
## 1 ALASKA on time 497 221 212 503
## 2 delayed 62 12 20 102
## 4 AM WEST on time 694 4840 383 320
## 5 delayed 117 415 65 129
## Seattle
## 1 1841
## 2 305
## 4 201
## 5 61I’m going to change data type from wide to long, to make the dataset more organized, also, I’m going to name two more columns(Name of destination and Number of flights) to display the data in a long form.
l_data <- airflight_chart %>% pivot_longer(cols=destination_names,names_to = "Destination_name", values_to = "Num_of_flights")## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(destination_names)
##
## # Now:
## data %>% select(all_of(destination_names))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.print(l_data)## # A tibble: 20 × 4
## Airline_name Arrival_Status Destination_name Num_of_flights
## <chr> <chr> <chr> <dbl>
## 1 "ALASKA" on time Los.Angeles 497
## 2 "ALASKA" on time Phoenix 221
## 3 "ALASKA" on time San.Diego 212
## 4 "ALASKA" on time San.Fransisco 503
## 5 "ALASKA" on time Seattle 1841
## 6 "" delayed Los.Angeles 62
## 7 "" delayed Phoenix 12
## 8 "" delayed San.Diego 20
## 9 "" delayed San.Fransisco 102
## 10 "" delayed Seattle 305
## 11 "AM WEST" on time Los.Angeles 694
## 12 "AM WEST" on time Phoenix 4840
## 13 "AM WEST" on time San.Diego 383
## 14 "AM WEST" on time San.Fransisco 320
## 15 "AM WEST" on time Seattle 201
## 16 "" delayed Los.Angeles 117
## 17 "" delayed Phoenix 415
## 18 "" delayed San.Diego 65
## 19 "" delayed San.Fransisco 129
## 20 "" delayed Seattle 61Replace the empty spaces with a “NA” value to make the table more professional.
l_data <- l_data %>% mutate(Airline_name = ifelse(Airline_name == "", NA, Airline_name))
print(l_data)## # A tibble: 20 × 4
## Airline_name Arrival_Status Destination_name Num_of_flights
## <chr> <chr> <chr> <dbl>
## 1 ALASKA on time Los.Angeles 497
## 2 ALASKA on time Phoenix 221
## 3 ALASKA on time San.Diego 212
## 4 ALASKA on time San.Fransisco 503
## 5 ALASKA on time Seattle 1841
## 6 <NA> delayed Los.Angeles 62
## 7 <NA> delayed Phoenix 12
## 8 <NA> delayed San.Diego 20
## 9 <NA> delayed San.Fransisco 102
## 10 <NA> delayed Seattle 305
## 11 AM WEST on time Los.Angeles 694
## 12 AM WEST on time Phoenix 4840
## 13 AM WEST on time San.Diego 383
## 14 AM WEST on time San.Fransisco 320
## 15 AM WEST on time Seattle 201
## 16 <NA> delayed Los.Angeles 117
## 17 <NA> delayed Phoenix 415
## 18 <NA> delayed San.Diego 65
## 19 <NA> delayed San.Fransisco 129
## 20 <NA> delayed Seattle 61l_data <- l_data %>% fill(Airline_name)
print(l_data)## # A tibble: 20 × 4
## Airline_name Arrival_Status Destination_name Num_of_flights
## <chr> <chr> <chr> <dbl>
## 1 ALASKA on time Los.Angeles 497
## 2 ALASKA on time Phoenix 221
## 3 ALASKA on time San.Diego 212
## 4 ALASKA on time San.Fransisco 503
## 5 ALASKA on time Seattle 1841
## 6 ALASKA delayed Los.Angeles 62
## 7 ALASKA delayed Phoenix 12
## 8 ALASKA delayed San.Diego 20
## 9 ALASKA delayed San.Fransisco 102
## 10 ALASKA delayed Seattle 305
## 11 AM WEST on time Los.Angeles 694
## 12 AM WEST on time Phoenix 4840
## 13 AM WEST on time San.Diego 383
## 14 AM WEST on time San.Fransisco 320
## 15 AM WEST on time Seattle 201
## 16 AM WEST delayed Los.Angeles 117
## 17 AM WEST delayed Phoenix 415
## 18 AM WEST delayed San.Diego 65
## 19 AM WEST delayed San.Fransisco 129
## 20 AM WEST delayed Seattle 61Use janitor function to clean the dirty data.
l_data <- l_data %>% janitor::clean_names()
print(l_data)## # A tibble: 20 × 4
## airline_name arrival_status destination_name num_of_flights
## <chr> <chr> <chr> <dbl>
## 1 ALASKA on time Los.Angeles 497
## 2 ALASKA on time Phoenix 221
## 3 ALASKA on time San.Diego 212
## 4 ALASKA on time San.Fransisco 503
## 5 ALASKA on time Seattle 1841
## 6 ALASKA delayed Los.Angeles 62
## 7 ALASKA delayed Phoenix 12
## 8 ALASKA delayed San.Diego 20
## 9 ALASKA delayed San.Fransisco 102
## 10 ALASKA delayed Seattle 305
## 11 AM WEST on time Los.Angeles 694
## 12 AM WEST on time Phoenix 4840
## 13 AM WEST on time San.Diego 383
## 14 AM WEST on time San.Fransisco 320
## 15 AM WEST on time Seattle 201
## 16 AM WEST delayed Los.Angeles 117
## 17 AM WEST delayed Phoenix 415
## 18 AM WEST delayed San.Diego 65
## 19 AM WEST delayed San.Fransisco 129
## 20 AM WEST delayed Seattle 61t_data <- l_data %>% spread(key=arrival_status, value=num_of_flights)
print(t_data)## # A tibble: 10 × 4
## airline_name destination_name delayed `on time`
## <chr> <chr> <dbl> <dbl>
## 1 ALASKA Los.Angeles 62 497
## 2 ALASKA Phoenix 12 221
## 3 ALASKA San.Diego 20 212
## 4 ALASKA San.Fransisco 102 503
## 5 ALASKA Seattle 305 1841
## 6 AM WEST Los.Angeles 117 694
## 7 AM WEST Phoenix 415 4840
## 8 AM WEST San.Diego 65 383
## 9 AM WEST San.Fransisco 129 320
## 10 AM WEST Seattle 61 201t_data <- t_data %>% janitor::clean_names()
print(t_data)## # A tibble: 10 × 4
## airline_name destination_name delayed on_time
## <chr> <chr> <dbl> <dbl>
## 1 ALASKA Los.Angeles 62 497
## 2 ALASKA Phoenix 12 221
## 3 ALASKA San.Diego 20 212
## 4 ALASKA San.Fransisco 102 503
## 5 ALASKA Seattle 305 1841
## 6 AM WEST Los.Angeles 117 694
## 7 AM WEST Phoenix 415 4840
## 8 AM WEST San.Diego 65 383
## 9 AM WEST San.Fransisco 129 320
## 10 AM WEST Seattle 61 201Remove dots “.” from city name with str_replace function.
t_data <- t_data %>% mutate(destination_name = str_replace(destination_name, "\\.", " "))
print(t_data)## # A tibble: 10 × 4
## airline_name destination_name delayed on_time
## <chr> <chr> <dbl> <dbl>
## 1 ALASKA Los Angeles 62 497
## 2 ALASKA Phoenix 12 221
## 3 ALASKA San Diego 20 212
## 4 ALASKA San Fransisco 102 503
## 5 ALASKA Seattle 305 1841
## 6 AM WEST Los Angeles 117 694
## 7 AM WEST Phoenix 415 4840
## 8 AM WEST San Diego 65 383
## 9 AM WEST San Fransisco 129 320
## 10 AM WEST Seattle 61 201#Analisys# ### I am adding a number of flights, delayed, and on time fields to analized data between the two airlines.###
airflight_data <- t_data
airflight_data <- airflight_data %>% mutate(num_of_flights = delayed+on_time,
pct_delayed = delayed/num_of_flights,
pct_ontime = on_time/num_of_flights)
print(airflight_data)## # A tibble: 10 × 7
## airline_name destination_name delayed on_time num_of_flights pct_delayed
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ALASKA Los Angeles 62 497 559 0.111
## 2 ALASKA Phoenix 12 221 233 0.0515
## 3 ALASKA San Diego 20 212 232 0.0862
## 4 ALASKA San Fransisco 102 503 605 0.169
## 5 ALASKA Seattle 305 1841 2146 0.142
## 6 AM WEST Los Angeles 117 694 811 0.144
## 7 AM WEST Phoenix 415 4840 5255 0.0790
## 8 AM WEST San Diego 65 383 448 0.145
## 9 AM WEST San Fransisco 129 320 449 0.287
## 10 AM WEST Seattle 61 201 262 0.233
## # ℹ 1 more variable: pct_ontime <dbl>compare between two airlines total flights, delayed, on time, and the percentage of each one to analisys.
airline_summary <- airflight_data %>% group_by(airline_name) %>% summarize(total_delayed = sum(delayed),
total_ontime = sum(on_time),
total_flights = sum(num_of_flights),
pct_delayed = (total_delayed/total_flights),
pct_ontime = (total_ontime/total_flights))
print(airline_summary)## # A tibble: 2 × 6
## airline_name total_delayed total_ontime total_flights pct_delayed pct_ontime
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ALASKA 501 3274 3775 0.133 0.867
## 2 AM WEST 787 6438 7225 0.109 0.891library(ggplot2)
ggplot(data=airline_summary, aes(x=airline_name, y=total_delayed, fill=airline_name)) +
geom_bar(stat='identity') +
labs(title = "\n Number of Delayed Flights by Airline", y="Number of Delayed Flights", fill="Airline Name") +
theme(axis.title.x = element_blank()) +
geom_text(aes(label=total_delayed), vjust = 1.2,position = position_dodge(.8), size = 4)