Setting up a dataset with R

Overview

The following is a resource guide for Data Processing departments.

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)

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

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

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

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

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

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