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Setting up a dataset with R

Setting up a dataset with R

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Overview

The following is a resource guide for Data Processing departments.

Basic setup

  1. Install R Studio and Crunch packages.
  2. Load your dataset.
  3. File your dataset into the right project folder

Optional setup

Upfront cleaning

Checking and changing variable properties

  • Change the variables Names
    • A descriptive title that summaries what the variable is (e.g., 'Gender')
  • Give variable Descriptions (optional but recommended)
    • Typically, this includes the actual wording of the question (e.g.,  'Q1: Are you male or female?')
  • Change the Aliases (optional) 
  • Correct the Variable Type
    • Change any variables that are numeric to be categorical
    • Change any categorical variables to be numeric
    • Set any relevant variables as date/time
  • Make any variables that are supposed to be weights available as a weight
  • Make any variables that are supposed to be filters available as filters
  • Correct the values for any variables for which you want the mean shown, including:
    • NPS recoding, scale reverse for rating scale questions or other recodings
    • capping (if relevant)
    • setting outliers to missing values (if relevant)

Creating multi-variable sets

  • Define Multiple Response questions
    • Derive new variable with an option to hide the original contributing source variables
  • Define Categorical Arrays
    • Derive new variable with an option to hide the original contributing source variables
  • Split Categorical Array into component variables
  • Split Multiple Response questions into component variables

Creating more variables (optional)

  • Banded time variables (as an approach to time-series smoothing)
    • Improved smoothing techniques will become available soon in the Crunch web app
  • Any of the following:
    • filters
    • interaction variables
    • weights
    • banners (called Multitables in Crunch)
    • other variables you may need
      • Standardize variables

Setting the base

  • Defining Missing Values for each variable
    • For example, include or exclude 'Don’t Knows' from a scale
  • Rebasing questions based on other questions
    • Fixing survey skips
    • Basing to another question’s response

Changing variable summary information

  • Set subtotals (NETs)
  • Merging categories
    • Derive new variable with option to hide the original contributing source variables
  • Creating banded categories ("Buckets")
    • From a numeric variable
    • From a categorical variable
    • Derive a new variable with an option to hide the original contributing source variables

Housekeeping

  • Setting up the Variable Organizer ("Accordion")
    • Make folders and sub-folders in the variable organizer
    • Put variables into folders
    • Change or set the order of variables within each folder
  • Hiding variables that you no longer need to see and removing clutter, such as:
    • Variables that were used to define the categorical arrays and/or MR questions

Final cleaning

  • Removing cases you don’t want in the dataset (aka, "Exclusions"), such as:
    • Bad sample (e.g., not meet screener)
    • Outliers
    • Speedsters
    • Flat-liners/straight-liners

Analysis

Exporting

 

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