Educational Resources & Tech Tools 05/10/2021

• 8 Workflow: projects | R for Data Science

  • There is a great pair of keyboard shortcuts that will work together to make sure you’ve captured the important parts of your code in the editor:

     
     
    1. Press Cmd/Ctrl + Shift + F10 to restart RStudio.
     
    2. Press Cmd/Ctrl + Shift + S to rerun the current script.
     
     

    I use this pattern hundreds of times a week.

  • You should never use absolute paths in your scripts, because they hinder sharing: no one else will have exactly the same directory configuration as you.

• 4 Workflow: basics | R for Data Science

  • All R statements where you create objects, assignment statements, have the same form:

     

    object_name <- value
    

     

    When reading that code say “object name gets value” in your head.

     

  • We recommend snake_case where you separate lowercase words with _.

• 1 Introduction | R for Data Science

  • Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation.
  • A good visualisation will show you things that you did not expect, or raise new questions about the data.
  • Visualisations can surprise you, but don’t scale particularly well because they require a human to interpret them.
  • Models are complementary tools to visualisation. Once you have made your questions sufficiently precise, you can use a model to answer them.
  • by its very nature a model cannot question its own assumptions. That means a model cannot fundamentally surprise you.
  • There’s a rough 80-20 rule at play; you can tackle about 80% of every project using the tools that you’ll learn in this book, but you’ll need other tools to tackle the remaining 20%.

  • The complement of hypothesis generation is hypothesis confirmation. Hypothesis confirmation is hard for two reasons:

     
     

    1. You need a precise mathematical model in order to generate falsifiable predictions. This often requires considerable statistical sophistication.

     

    2. You can only use an observation once to confirm a hypothesis.

    • Hypothesis generation and confirmation
  • models are often used for exploration, and with a little care you can use visualisation for confirmation. The key difference is how often do you look at each observation: if you look only once, it’s confirmation; if you look more than once, it’s exploration.
    • Models for exploration, visualizations for confirmation

• 2 Introduction | R for Data Science

  • The goal of data exploration is to generate many promising leads that you can later explore in more depth.

Posted from Diigo. The rest of my favorite links are here.