Submitted data file with at least some count analysis (+45)
Recreated File in Same Format as given, including missing data where there were empty cells in source data. (+5)
Placed file in Internet-accessible location, such as a publicly-accessible GitHub repo. Also OK (if less scaleable) to generated and populated dataframe from code. (+5)
Provided code to populate missing data. (+5)
Transformed data from wide to long format. (+10)
Compared percentage (not just counts) of either delays or arrival rates for two airlines overall. [Either with a chart, or a table; both would be exemplary]. Include text summarizes findings from this comparison. (+10)
Compared percentage (not just counts) of either delays or arrival rates for two airlines across five cities. [Either with a chart, or a table; both would be exemplary]. Include text summarizes findings from this comparison. (+10)
Describe discrepancy between comparing two airlines’ flight performances city-by-city and overall. (+5)
Assignment Tidying and Transforming Data
Explain discrepancy between comparing two airlines’ flight performances city-by-city and overall.(+5)
The chart above describes arrival delays for two airlines across five destinations. Your task is to: (1) Create a .CSV file (or optionally, a MySQL database!) that includes all of the information above. You’re encouraged to use a “wide” structure similar to how the information appears above, so that you can practice tidying and transformations as described below. (2) Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. (3) Perform analysis to compare the arrival delays for the two airlines.
Step 1: Create database and table to insert given two airlines delay data (wide structure data) in postgreSQL. Step 2: Connect R to PostgreSQL Step 3: Read a SQL table into R Step 4: Save the SQL table as CSV Step 5: Read wide structure data from github into R. Step 6: Transform wide data to long structure data. Step 7: Compare two airlines arrival delays across five cities using plot percentage
converting wide structure data to long structure. Making comparison between two airlines across all cities.