First, I imported the necessary packages to use for this assignment. I had created and saved an ‘airlines.csv’ in the current working directory with the data from the homework. Then, I imported the data set of airlines data. For sake of reproducibility, the csv was uploaded to github and this rmd will get the csv from there.
After importing the data, I took a look at it. I wanted to separate the data by airlines then by status (delayed vs. on time). To do this, first I replaced empty strings with NA. Then, I piped my airlines data through the fill function, which is from tidyr. The fill value filled in the missing values in the X column (airline name). I specified the direction down so that the values are filled going down. Afterwards, I passed that through rename(). rename from dplyr changed the name of column X to Airline and column X.1 to Status. Finally, I passed my data through filter function of dplyr to take out rows where status is empty.
library(readr)
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(tidyr)
airlines_dat=read.csv(url('https://raw.githubusercontent.com/sleepysloth12/data607_wk5/main/airlines.csv'))
airlines_datairlines_dat$X[airlines_dat$X==""]=NA
clean_data= airlines_dat %>%
fill(X, .direction='down') %>%
rename(Airline = X, Status = X.1) %>%
filter(Status != "")
clean_dataThere are too many cities. I just made one city column where I placed all of the city’s name. To do this, I piped my clean data above through the gather function from tidyr. The gather function converts from wide to long format. It takes a new key name and makes that into a column. In this case, it creates a city column and a count column (to consolidate the counts in one column). By writing -Airlines and -Status, those two columns are left as is.
clean_data = clean_data %>%
gather(key = "City", value = "Count", -Airline, -Status)
clean_dataFinally, the last step I took in cleaning this data was removing empty columns. I did this by using the filter function again.
clean_data = clean_data %>%
filter(!is.na(Count))
clean_dataFor the assignment, we were then asked to perform an analysis on our clean data set and compare the arrival delays between the two airlines.
To do this, used dplyr to get the summary statistics of the data we care about. First, I piped my clean data through filter() to only get the points where Status == delayed. Then, I passed it through group_by which grouped my data by Airline first, then City.
summary_stats = clean_data %>%
filter(Status == "delayed") %>%
group_by(Airline, City)
summary_statsAfter returning the count of each and comparing the cities and airlines, ALASKA airlines experience less delays than AM WEST. Specifically, ALASKA experienced less delays than AM WEST in 4 out of the 5 airports included in this data set.
ALASKA experiences more delays in Seattle when compared to AM West. If you want to go to Seattle and not experience delays, go with AM WEST.
The most noticeable difference in delays occurred at Phoenix Airport, where AM WEST experienced 415 delays while ALASKA only experienced 12. If you are traveling to Phoenix and don’t want to be delayed, use ALASKA.
The least noticeable difference in delays occurred at San Francisco Airport. AM WEST had 129 delays and ALASKA had 102. It seems like San Francisco is just a busy airport.
In conclusion, the Tidyr and dplyr packages are really useful when it comes to cleaning and tidying data that is in a complex/weird format. These packages make the job easier because instead of hardcoding some of the functions already included in the package you can just focus and spend more time on analyzing the data. In this homework assignment, I made a wide CSV with airlines data. Then, I cleaned up that wide CSV into a format that is usable by R. Finally, I used my clean data set to compare the delays between the two airlines.