DataThink Development
  • Modules
  • R Help
  • Resources
    • Course Textbook
    • R for Data Science
    • Git/GitHub and R
    • R Markdown: The Definitive Guide
    • Geocomputation with R

    • Supplemental Material
    • Happy Git and Github for the useR
    • plotly for R
    • Computing in R for Social Sciences
    • Statistical Concepts in Presenting Data:
    • Advanced R
    • R Packages
    • Tidy evaluation
    • Fundamentals of Data Visualization
    • Geocomputation with R
    • Crosstalk: htmlwidgets add-on

On this page

  • Case Study 3: Becoming a databender
    • Background
    • Reading
    • Tasks

cs-03

Case Study 3: Becoming a databender

Background

You just started your internship at a big firm in New York, and your manager gave you an extensive file of flights that departed JFK, LGA, or EWR in 2013. From this data (nycflights13::flights), which you can obtain in R (install.packages("nycflights13"); library(nycflights13)), your manager wants you to answer the following questions;

  1. If I am leaving before noon, which two airlines do you recommend at each airport (JFK, LGA, EWR) that will have the lowest delay time at the 75th percentile?
  2. Which origin airport is best to minimize my chances of a late arrival when I am using Delta Airlines?
  3. Which destination airport is the worst (you decide on the metric for worst) airport for arrival time?
  • Course Website

Reading

This reading will help you complete the tasks below.

  • o Example of GitHub Issue conversation
  • o Posting Issues on GitHub
  • o reprex R Package

Tasks


I made up databending. It does not mean that we make up data or that we alter it. Like airbenders we control our data to answer the questions we need answered. The key to databending is flexibility and finding and following the path of least resistence.