In addition to some of the more basic checking and cleaning processes, you can make using your data even more efficient in Displayr by organizing it a bit. This will help when you are collaborating with other teammates as well as when you are under extreme time pressure. There are a number of strategies you can use to tidy your data:
Method - Which questions go where
Method - Creating a report "shell"
Method - Which questions go where
Overall structure
Where the users are very familiar with the questionnaire and its structure, it is usually best to have the data file reflect the order of the questionnaire. In other situations, the following structures can be better:
- Ordering data according to how often it will be used, with the most regularly used data at the top (e.g., demographics and segments).
- General-to-specific. For instance, category questions could be listed first, then brand questions. And, in the case of trackers, you may choose to have questions that are current and consistently asked at the top and historic or ad-hoc questions at the bottom.
- WHO, WHAT, WHERE, WHEN, and WHY.
- SITUATION (when, who with, and other aspects of context), BEHAVIOR (i.e., action), and PERSON (personality, values, demographics, etc.).
- INFORMATION SEARCH, AWARENESS, CONSIDERATION, TRIAL, USAGE FREQUENCY and SATISFACTION.
- DEMOGRAPHICS, MEDIA, ATTITUDES, CATEGORY BEHAVIOUR, BRAND BEHAVIOUR.
Data can be re-ordered on by dragging and dropping, automations in the Anything > Data > Move Data menu, and through right click > Sort in the Data Sets pane.
Standard analysis variables
Any standard analysis variables should be included in the data file or created. These will typically vary by client and industry. For example:
- In packaged goods and financial services studies "family lifestage" is usually relevant.
- In media studies, Age-by-Gender is often valuable.
- Medical studies typically create variables on patient attributes.
Hiding uninteresting data
You can click on the variable in the Data Sets tree and Hide and/or see How to Hide Uninteresting Data.
Creating sections headings in the data
Although Displayr does not allow you to create folders of variables, you can get a similar effect by inserting variables as section headings. Sections can be created by inserting a new Filter variable and giving it a distinctive design to separate blocks of questions in your data. For instance, you may make it indented and in capitals such as #### COMPLETE FUNNEL #### and show the proportion of people to complete the section, with the label showing a description of the sample.
Method - Tidying questions
Names and labels
Often the names shown for the Variables and Variable Sets in the Data Sets tree are messy, containing strange programming characters and truncated question wordings. It is generally a good idea to:
- Tidy them.
- Abbreviate them, so that when they appear in menus and exports they are easy to read.
- For questionnaires that are ordered by question number, include the question number in the name of the question (e.g., Q1. Age). Note that you can include the full wording of the question in the footer (see below).
The following can be useful ways of quickly tidying up names and labels:
- Ensuring that they are created in a neat and organized way in the original data file (e.g., see How to Set Up Your SPSS File for Importing into Displayr).
- Modifications can be made to label using Find/Replace, which supports wildcards (see Tips for Working Efficiently in Displayr).
- How to Suggest Better Variable Names from Source Labels
- How to Remove Truncated Text from Variable Labels
Sorting categories within a variable set
How to sort can either be done by dragging and dropping, but there are also several options for automatic sorting. Most options are present in the Sort menu at the top of the toolbar and are available based on the item selected:
All sorting options, other than Rows in Table (Automatically Updates When Data Changes), will govern how the variable set is shown in all tables and analyses.
Merging together small categories
Merge together categories with small counts (e.g., collapsing age categories and brands with less than 2% market share). The general process is to:
- Follow How to Create Tables for Data Checking to create tables that highlight low counts and outliers.
- Then review those tables and merging together categories by dragging and dropping.
Removing irrelevant SUM and NET categories
Sometimes the SUM and NET categories are unhelpful. For example, if using a Numeric - Multi variable set to represent rating scales. They can be removed by right-clicking on them and selecting Hide.
Method - Creating a report "shell"
It is sometimes useful when setting up a project to create the "shell" of a report, which can be modified as per requirements by users.
Creating tables
The Pages pane in Displayr is a useful way of setting out the most important findings in the data, or for providing an overview against key groups such as segments, countries, or targets. Depending on the user, this will either be:
- A set of summary tables or charts, that can provide a starting point for the user to use in exploring the project.
- A set of crosstabs with all the tables crossed by a few standard questions
Although there are lots of tools for quickly creating a number of overview tables, the most straightforward approach may be to use one of the following automations see:
- How to Create Summary Tables
- How to Create Summary Plots
- How to Create Lots of Crosstabs
- Anything > Report > Banner Tables
It is often useful to create Folders of Pages to make it easier for users to navigate. Pages can be grouped into folders by dragging one Page onto another in the Pages pane.
Filters
There are lots of ways to create filters from the data depending on how it is formatted. You can create lots of filters at once from categorical data using the Create Filter from Selected Data automation. There is a list of all the different methods of creating filters in the How to Tag a Variable as a Filter article.
Footers, also known as Captions, can be customized. In most cases, this is best done by editing the CAPTION settings on the table directly, see How to Show / Hide / Edit Captions for Tables and Charts. However, if adding footnotes containing longer wordings, this is best done using the process in How to Add a Description of Selected Data (e.g., Question Name, Skips, Filtering) to a Table's Footer. You can also add and edit footers to R-based items, see How to Add Custom Captions to R-based Tables, Visualizations, and Outputs.
Changing the Appearance of Charts and Tables
See How to Customize the Formatting of Tables in Displayr and How to Create a Template Document.
Sample size warnings and automated data hiding
There are a variety of automations and Rules that can be used to give appropriate warnings and to hide data. To apply these:
- Type the words sample size into the Search box at the top.
- Follow the yellow highlights to select the desired option. Popular methods are:
Transposing tables
Sometimes tables of Nominal-Multi and -Grid variable sets are easier to read if transposed (i.e., right-click on a column or row and select Swap Rows and Columns).
Statistics
Statistics can be placed on multiple tables at one time by either:
- Multi-selecting lots of tables or pages in the Pages pane and using whichever is appropriate of STATISTICS > Cells, Right, or Below in the object inspector.
- Rules (adding a custom rule using the code in Modifying The Whole Table or Plot > Always Show Sample Size).
With tables involving Nominal-Multi or Nominal variable sets, it is often a good idea to use Statistics > Right and Statistics > Below when setting up the report, for things like How to Calculate an Average Value from Categorical Data in Displayr.
Customizing the names of statistics
Statistics can be renamed (e.g., changing Average to Mean or Net Promoter Score), by either:
- Following How to Modify Date Labels, Statistic Names, and Other Default Labels.
- Using Rules (e.g., Modify Headers > Automatically Rename Row Labels).
However, in general, it is often a bad idea to rename statistics, as it can make it hard for users to understand how Displayr works, as it will cause the version they are using to appear different from the version that appears in all of Displayr's documentation.