Week 4 / Assignment – Tidying and Transforming Data

Introduction

This report focuses on tidying and transforming data in R. Using tidyr and dplyr, we’ll reshape data between wide and long formats, making it more suitable for analysis.

Loading Data

flight_data <- read_csv("/Users/alina_vikhnevich/Desktop/Spring 2025/DATA 607/DATA607/flight_data.csv")
print(flight_data)

## # A tibble: 4 × 7
##   Airline Status  `Los Angeles` Phoenix `San Diego` `San Francisco` Seattle
##   <chr>   <chr>           <dbl>   <dbl>       <dbl>           <dbl>   <dbl>
## 1 ALASKA  on time           497     221         212             503    1841
## 2 ALASKA  delayed            62      12          20             102     305
## 3 AM WEST on time           694    4840         383             320     201
## 4 AM WEST delayed           117     415          65             129      61

Tidying the Data

Reshaping Wide to Long Format

tidy_flight_data <- flight_data %>% 
  pivot_longer(cols = -c(Airline, Status), 
               names_to = "Destination", 
               values_to = "Count")
print(tidy_flight_data)

## # A tibble: 20 × 4
##    Airline Status  Destination   Count
##    <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 Francisco   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 Francisco   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 Francisco   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 Francisco   129
## 20 AM WEST delayed Seattle          61

Summarizing the Data

Total Flights by Airline and Status

summarized_data <- tidy_flight_data %>% 
  group_by(Airline, Status) %>% 
  summarise(Total_Flights = sum(Count), .groups = 'drop')
print(summarized_data)

## # A tibble: 4 × 3
##   Airline Status  Total_Flights
##   <chr>   <chr>           <dbl>
## 1 ALASKA  delayed           501
## 2 ALASKA  on time          3274
## 3 AM WEST delayed           787
## 4 AM WEST on time          6438

Visualizing the Data

Bar Plot: On-Time vs Delayed Flights

Conclusion

This assignment demonstrated how to reshape and analyze flight data in R. Using tidyr and dplyr, we converted wide-format data into a tidy structure and summarized key insights. This process makes data easier to work with for visualization and analysis.