The creative market researcher always seeks to go beyond the limitations of the source data and add value.   One way of doing this is to create new variables or data points from existing ones.

Consider a scenario where there is a desire to plot:

• market penetration, an existing variable (“Ever”) …
• against adoption rate, a new dimension created by dividing the result for “Monthly” by the result for “Ever”.

A typical workflow might be …

There are however potential problems with this approach:

1. If the output needs to be updated, for example when there is new data added, many if not all the steps need to be repeated.
2. If the output needs to be replicated for say different filters or scenarios, this too can involve repetition.
3. If the updates or replications are to be done by someone other than the original author, the need to find the right Excel Workbook and the right location within it can take up valuable time.

The Power of Having Everything Connected

These problems are instantly resolved by having your data, outputs, and workings housed and connected in one document, as you can do in Displayr.   Zooming out in this example we can see the following set up.

• A) Hidden tables for the two source questions, “Ever” and “Last Month”.
• B) A hidden calculation, dividing the results for “Last Month” by “Ever”, our adoption ratio.
• C) A Calculation Grid, extracting brands from A and B and linking data via cell references.
• D) A scatter-plot visualization linked to C.

There are also some filter controls top left and a dynamic date filter applied to all inputs so the analysis will always show the most recent quarter as the data is updated.

Hidden items (indicated by grey cross-hatch shading) means that the document editor can see the output but it will not be shown when published to a dashboard or exported to PowerPoint.

If someone comes to edit the document later the relationships are easily identified via Displayr’s dependency graph (right click on an item to find it).  The scatter plot is created from the summary table, the summary table is a combination of Q3 and the adoption calculation, and so on.  This addresses problem 3 noted earlier (difficulty in tracing workings when they are in a different place).

But the real benefits kick in when it comes to updating or replicating this output (problems 1 and 2).   It should be self-evident but watch in this video how seamless it is to create new versions of the framework via filtering and updating it with new data … even the footer descripting the sample date and size changes.

If you want to take a closer look and study the workings in detail, you can get a copy of this document here: Embedded Workings

Once you’ve tried embedding your workings in your report and have both linked directly to the source data, it’s very hard to go back to using external workbooks or sheets.

A quick Google search on "BCG Matrix" demonstrates it's one of the most popular strategic tools around.   Courtesy of the Boston Consulting Group, it plots relative market share against growth rate, to help determine marketing or resource allocation effort:

• Invest in "Stars"
• Milk (maintanance-level investment) "Cash Cows"
• Reduce investment in "Dogs"
• Strategize high-growth/low share "Question Marks"

It can be used to study either your own portolio or an entire market, to help qualify the strength of competitive threats or opportunities.   Here is an example for a fictituous fast food market:

... made even more powerful!

With the required data at hand, it's not that hard to create a BCG matrix in Excel and dress it up in PowerPoint.  But what if you wanted to replicate the matrix for different geographic markets or revisit it when you have new data?   That's potentially a lot of time and effort.  Displayr makes it easy to create a dynamic BCG matrix.  You can easily switch between markets (or any other filters) and have it update automatically overtime, when new data is added.

This is enabled by your data, you workings, and the output all being connected and in the one place.   Changes in to any item automatically impact other items "downstream".

If our brand is "Burger Chef" the strategic position is vastly different in North and South America, requiring different marketing solutions.

You can access this dashboard, explore its workings, and see how the dynamic matrix was built in Displayr here.   The key steps in the process are also set out below ...

_______________________________________________________________________________

1 Source some market share data

Here we've used occasion counts to calculate share, and shown this overtime.   With the table selected, we've renamed it "market.share" in the Object Inspector (so it's easy to find in drop down lists, etc).

2 Calculate the x-axis (relative market share)

1. In our case we want to matrix to update annually so it's ready for each new phase of strategic planning.   We created a table using Table > Select > Input "market.share", then Select columns by > Last columns (1), so it's always showing the most recent market share.
2. With "current.share" selected, Calculation > Maximum > Maximum Each Column, name this calculation "maximum.share"
3. Select "current.share" table first, then holding down Shift, select "maximum.share" output, Calculation > Divide.  The selection order makes things more efficient when using mathematical functions.  Name the new output "relative.share"

3 Calculate the y-axis (year-on-year growth rate)

1. Find the "market.share" table in your document (Step 1) and duplicate it.   It will automatically be named "market.share.2".  Rename it "absolute.change".  Move it to a new page or working space.
2. With the table selected, Rules > Table Computations > Different Between Pair of Columns.  In the Rule settings, select "Last Column" as Column 1 input and "2nd Last Column"  as Column 2 input.  Again, in this way the data will always be up to date when it changes.   Label the new column "Change YOY".
3. An easy way to calculate relative change, our target metric, is to insert Calculation > Custom Code, then click on items to reference them or simply start typing their name.   This is the end result of the custom code needed:
   absolute.change[, 4]/(current.share-absolute.change[, 4])*100.

   Here's how easy it is to do ....

4 'Rough-in' the matrix

1. Visualization > Bubble > Labelled Bubble, then click and drag the cursor to locate the chart
2. Inputs > DATA SOURCE > X coordinates > relative.share, Y cordinates > growth.rate, Sizes > current.share
3. We opted to exclude a couple of outliers - Arnold's, as it's the known dominant leader and Bread Basket as it's new and has an artificually high growth rate.   In Inputs > ROW MANIPULATIONS > Rows to ignore add Arnold's, Bread Basket
4. In Chart, tidy up as required - add data and axes lables, remove grid lines, etc.

5 And finally, add a control

1. Anything > Filter > Control > Combo Box (Drop Down) on an Output, select the variable "Market" and allow the user to select more than one category when prompted.   A new variable will be created called "Combo Box Filter Market" which you can rename if you wish.
2. Find the the two source tables market.share and absolute.change, select them and apply the combo box filter via Inputs > Filter(s)

After Steps 4 and 5 you will have a working framework as per below.  We then handed the document to our designers to create the final version (as shown above), ready for publishing.    Before publishing you would Hide all the working steps above (hiding either the pages they are on or the individual items if they are below the main dashboard).

Calculation grids allow you to add a new type of table onto a page, where the table can be a mix of text,  values, or calculations, including links to other tables.

Calculation grids allow you to do things in Displayr that you previously would have had to do in Excel or PowerPoint.

How to use

Calculation grids are added from the Calculation menu in the toolbar. You can add data into a calculation grid by:

1. Typing directly into it. If you want to type text, surround it by quotation marks (e.g., "my text")
2. Copying either data from another table, or part of a table, and right-clicking and choosing from the paste options.

Example use case

It's often useful to create tables like the one below, which draw in data from multiple other tables.

These are now easy to do using calculation grids. The 2 minute demo video below runs through this use case.

Key differences from Excel

Feature roadmap

• Custom formatting of individual cells (font, color, number format, borders)
• Custom row and column heights/widths
• Speed and performance of calculations
• Excel like updating of cell references when copying formulas across cells
• Custom Displayr R functions to simplify formula creation e.g. vlookups

We hope you find calculation grids to be useful and if you have any feedback or questions please reach out to us via the help menu in app

Many dashboards and infographics are collages of images and visualizations. For example, the dashboard below consists of 32 visualizations on top of a background image. Each visualization below shows a single number, either as a bar or a circle. When a visualization contains a single number, we call it a Number Visualization. (Most traditional visualizations show multiple numbers; e.g., a pie chart has a number for each pie segment.)

They're now faster

The underlying technology we initially used to create the single number visualizations was relatively slow. The more you had on a page, the slower things got. They have now been rebuilt with faster technology.

The user interface has changed

The old user interface was a bit clunky. The new interface is more straightforward and more flexible. For more information see How to Create a Number in a Shape Visualization.

Migrating existing visualizations

Any dashboards you've already created will still work, and there is no need to do anything. However, they will be using older technology. If you want to switch to the newer visualizations:

1. Select each visualization
2. Press the Visualization button in the object inspector.
3. Choose your desired visualization in the Number group.

Displayr's text coding functionality is designed with needs of the survey researcher front and centre.   For many years the text categorization functions in Displayr have already supported what we might call a manual workflow.  We make it easy to view, sort, and filter text responses, create and structure categories, and assign or code responses to those categories.    More recently we've added semi-automated functions to the interface and extensively upgraded the algorithms that drive them.  We believe our tools in this space are state-of-the-art ...

• In selecting "Semi-Automatic" Text Categorization, users are presented immediately with a draft set of categories with the bulk of the data already coded.  So in a matter of minutes you are off to a great start
• The algorithms that create this output are based on analysing context and meaning (not word similarity, like many other tools).   Your draft code frames are intuitive from the get-go
• We've made this work effectively for multiple response categorizations, (where responses can be assigned to more than one code), which are historically more challenging to automate
• For tracking and related research, we have specific algorithms that recognize and categorize unaided brand awareness questions
• Once you have your draft categories, the user interface makes it easy to edit, with tools to combine, rename, and split categories

So the workflow now becomes:

1. Let Displayr do the hard work and get you most of the way there (via a draft categorization), but in a fraction of the time it would take manually
2. You then fine tune and edit the categories via the intuitive user-interface.

Accessing the automated functions

The quickest way to do this is to select a text variable in the Data Set tree, hover above or below it to '+' insert a new variable, and follow the prompts via the Semi-Automatic menu path:

We know some users might want to start the process manually.   This could involve reading through some responses and create some pre-planned categories.    Even if you follow the Manual menu path, you can access the Automatic categorization function.  At any time you can speed up the coding of remaing uncategorized data.    In the categoriztion interface, set "Sort by:" to Fuzzy match, (as matching is a key building block of the algorithm), and the "Auto" button appears:

The functionality and workflow in action

Take a look at the process in action in this short video.  It uses an open ended question on how people feel about 'Tom Cruise' as input*

You can get a broader overview of text analysis methods and solutions in this webinar recording. How to quickly analyze text data

Streamline your text data analysis.

The process of turning open text responses into usable data is traditionally time consuming and expensive (being often outsourced).   Displayr's text categorization tools are state of the art.  You can create a draft categoriztion in minutes automatically and then quickly fine tune it into a polished codeframe.   If you use a lot of text data and want to know more, book a demo or take a free trial.

* Discretion is advised - the data used in the video is from a real survey containing unvarnished attitudes to Tom Cruise.   Some respondents have written unkind, distasteful and potentially offensive things.   Displayr does not condone or endorse any of the comments that have been made.

Use any Source and Output language.

You are then prompted to select the Source language:

• Automatically detect language
• Specify with variable (use this option if the source language is identified by a variable in your data set. This option is particularly useful if your file contains multiple languages)
• A specific language - the default language is English.

You can set the Output language here as well.

Text Translation in Action

Consider a simple scenario where data has been collected on hotel reviews and it includes a "comment" option - guests of course need to complete the survey in a language they are comfortable with:

• The data file will have multiple languages, and (typically) an additional variable classifying the language selected.
• The person responsible for categorizing (coding) the data will want to do so in their language
• The outputs, being the categories (or code-frame), will also need to be in their language.

Displayr makes all this very easy, including dealing with multiple language inputs simultaneously.    And once translated, you can create an intial catergorization (code frame) automatically.

Take a look at the process in action in this short video, covering both single and multiple language translation ...

Streamline your text data translation and analysis.

The process of translating text data into the analyst's language is traditionally time consuming and expensive (being typically outsourced).   Displayr's translation tools are now available directly in the text categorization interface - you can even create a draft code frame in your preferred language automatically.   If you use non-native language text data and want to know more, book a demo or take a free trial.

One of the dilemmas a researcher faces in building a report is to work out just how much detail to show.  Consider a typical cross-tab (in this case using a banner) that has lots of interesting significant differences, as indicated by the blue and red number formatting

Of course the key finding here is the Total or Net result, so we have to show it.   Rather than asking the audience to study the cross-tab in detail a common way of visualizing this type of a result is a bar chart with call-outs or labels to highlight the key findings:

This takes a while to set up in PowerPoint.  And in the case of a typical tracking study, where the results will change wave-to-wave, it can be a very tedious to update.   Not anymore - Displayr's visualization suite now contains an option to automate this type of chart, in seconds!.   See it in action in this short video:

Try Bar Charts with Skews now

Existing customers will quickly see how much time they can save using this new visualization. Anyone else can book a demo or take a free trial.

Displayr has a large and growing library of automated variable creation options.   This library now includes the ability to automate combining the data contained within certain types of variables into new and interesting categories:

• Transform numeric variables into useful categorical variables
• Combine or collapse categorical variables into meaningful groups based on patterns in the data
• Convert information like Zip or Postal Codes into logical geographic areas

This automation can save you a lot of time. Most importantly, these functions make relationships in data clearer, to help sharpen your analysis and story building.

Displayr users can find and explore these features in Ready-Made New Variables > Automatically Combine Categories. Here are a few examples to bring these features to life...

Create Categories Based on Data Values

Select a numeric variable as input and have a high degree of flexibility in how you transform it - you can automatically create data-driven tidy categories, percentiles, equally spaced categories (where you can control the increments), or custom categories.  Custom categorization allows you to enter pre-defined 'cut-points' - the variable is created without having to use code or make manual edits. Displayr can also automate the Label style, with a range of options dependent on the categorization method selected.

In this video we create a complex numeric variable and transform it using a range of these methods:

Combine or Collapse Categories based on Patterns in Related Data

This option will automatically collapse categories in the input variable based on the statistical relationships to another variable. It does this using CHAID (Chi-Squared Automatic Interaction Detection). Simplifying the variable of interest gives the analysis a lot more focus, providing insight and promoting actionability.

In this example, looking at the impact of household structure on category consumption frequency, we started with 9 categories that indicated some relationships in the data and automatically transformed that to 3 categories where the relationships are abundantly clear. 'Young Singles' is the primary demographic segment of interest and 'Younger Groups' is the secondary segment of interest.

In Displayr, select Ready-Made New Variables > Automatically Combine Categories > By Pattern (CHAID). When working with ordinal variables you can force the function to only collapse adjacent categories.

Convert one type of Geographic Data into Another

The proliferation of online research and the integration of survey research with CRM systems has led to much larger sample sizes. This brings into play geographic variables like ZIP or postal codes.   Displayr has functionality to transform these and other types of geographical data for North America, Europe, Australia and New Zealand. This geographic heat map was created from scratch, combining ZIP code and category frequency data, in less than 2 minutes.

You can see the steps in this video:

Explore Automatic Combinations now!

Existing customers will quickly see how much time they can save and how much power they can add to their analysis using the automated functions to combine numeric, categorical, and geographic data. Anyone else can book a demo or take a free trial.[/vc_column_text][/vc_column][/vc_row]

However you can make the process of creating inputs for visualizations, charting, and tables even easier by adding calculations or values directly to the data input drop-down.

Example 1 – Custom Selection from a complex table

Here is a typical table that has been created from some consumption data for fictitious fast-food brands.

Let’s say we are only interested in charting the data for Burger Chef and a direct competitor, Burger Shack.    One way to do this would be to:

1. Duplicate the question (to keep the original data and table intact)
2. Create the table
3. Hide all but the two columns in question, or, use the Tables > Selection function to create a subset of the table
4. Insert a visualization, say for a column chart
5. Hook up the table to the visualization.

This would work fine but it also adds some complexity – Step 1 adds over 100 new variables to the Data Set (some of the rows have been merged) and there are several interdependent elements in the workflow described above.

With the ability to ' Enter a calculation or value' directly in the drop-down, in combination with Displayr’s point-and-click formula creation, the process is a whole lot easier.   You can see this in action here.

We took that slowly so you can see the steps, but in less than 30 seconds we’ve achieved the required result, and with fewer steps.

Example 2 – Modifying a Variable before visualization

The same Data Set contains information on the estimated number of visits each brand attracted in a month.

Let’s say we wanted to express this as a weekly average, to conform with other market metrics (e.g. weekly sales).   Again, one way to do with would be to:

1. Select the variable set
2. Calculate > Divide, which creates a new copy of the variable set
3. Enter ‘4’ as the single number to divide by, as a proxy for weekly data
4. Insert a visualization, say for a bar chart
5. Hook up the table to the visualization

Using the ‘Enter a calculation or value’ function this can be done without the need to create any new variables, as shown in this video

Again, all the required steps have been condensed to a few simple ones, and in just a few seconds

Learn more about custom selections and formula creation in Displayr: Calculate Anything! or Boost your analysis with in-built Calculations

The possibilities for Displayr’s custom selection and formula creation functions are vast – now you can easily add such calculations to visualization drop-downs, the only limit is your imagination!

You can view the dependency graph of an output by right-clicking on it and selecting Dependency Graph from the context menu.

As an example, the dependency graph above is for the word cloud shown below. We can see from this dependency graph that the word cloud has been created using the variables Reasons for being a detractor: Bakery and Detractors: Bakery.

More information is in our help center article Viewing Dependency Graphs to Understand Calculations and Performance.

In addition to creating tables and common advanced analysis functions (like clustering, correspondence mapping), Displayr has built-in operations to create simple calculations from variables or tables.

You can read more about these functions here.

Built In Calculations

Create your own custom code

You can however go a lot further with custom code.  Point-and-click functionality allows you to select the specific inputs you need.  This can refer to:

• variables
• specific table cells,
• entire rows or columns and ranges.

See how easy it is to create your own custom code here.

Calculate Anything

Bespoke analysis functions

Displays allows you to perform the types of functions that people ordinarily do in Excel (e.g. Sum, Average, Count, etc.)

There are a range of examples here

Bespoke Analyses

Calculate Anything In Displayr!

Next time you find yourself in problem-solving mode with your data, find the solution with Displayr's suite of calculation functions.

In the era of global, online survey search, the amount of data captured and stored for a large-scale tracking study can be substantial.  Data files larger than 1 Gigabyte are no longer uncommon.   In this situation loading and processing times can become noticeably slower, regardless of the software used.

Sometimes the task involves having to convert SQL, Excel or CSV (Comma Separated Values) data.  This requires more processing steps, and performance-speed issues can be compounded.

Introducing *.QDat

You can now compress large or complex data files in Displayr by converting them to our proprietary *.QDat format.  QDat will significantly improve performance and speed (and in many cases, dramatically so).

Once your data is imported, cleaned, and set up the way you want,  all you need to do is select this option:

There is a setting to update the QDat file whenever your document is published or just once.   You can then add the compressed, faster, QDat formatted data to any project in Displayr.  Just "Add Data" and select "Displayr Cloud Drive" as the location (which is where QDat files are stored).

You can read more about the steps here: Publishing Data to the Displayr Cloud Drive

Recommended Workflow

A good practice is to use the QDat file as a master and share it with end users as required.

• Someone on the team is tasked with importing the original data and setting it up in Displayr
• They publish the data to the Displayr Cloud Drive in QDat format
• Whenever the data is updated (e.g. a new wave of tracking data), the QDat file is also updated.
• Multiple end users or projects then access the compressed, faster file, for analysis, reporting and dashboard purposes

So, next time you find yourself waiting for a file to load or calculations to process, convert the data to *.QDat and see the difference!

1:  Have a document with your PowerPoint template ready.

This will typically be in Standard (4:3 or A4) or Widescreen (16:9) format.   Decide what format your new Displayr template is to be in.

2:  Open a blank Displayr document

From the Document page, select +New Document, check that the tab at the top is set to your desired format, then select ’Blank’

3:  Go to the Page Master and edit the Displayr defaults

In addition to a “Blank” page (which requires no editing), there are default layouts for Title Only, Title Page, and Title and various Content Layouts.

This is the key step – you basically need to change the titles, text box formats and default chart colors to reflect your PowerPoint template and paste in any graphical elements from PowerPoint.

You can duplicate and edit any of these pages if you want to add more master pages.

4:  Save your template using the required file naming conversion and download a QPack.

Publish > Export Data > Download Document (*.QPack)

Name the *.QPack as per these conventions (this is required for it to be read in when uploaded, in Step 6).

• Widescreen templates: [Template Name].widescreen.template
• Standard templates: [Template Name].template

Be case specific for the "widescreen" and "template" terms

5: Create thumbnail images of some of the key pages (optional)

This step enables you to see a preview of your template in the gallery when creating a new document, so it’s worth doing. These are also used in Step 6.

The file-naming convention for the first image to show in the gallery is:

• Widescreen templates: [Template Name] 1.widescreen.template.png
• Standard templates: [Template Name] 1.template.png

For second and subsequent images, replace the “1”, with 2, 3, etc.  Note, apart tom the name text before the number, these terms are case sensitive and there must be a space between the template name and the number.

 6: Upload the template *.QPack and related *.png files to your Displayr Cloud Drive

The Displayr Cloud Drive is accessed via the Icon at the top right of screen:

Shortly after this step you will see your own template available from the gallery of options when you create a new document (as per the “Burger Chef” example here).

The steps in action

This video works through the key steps.

Anyone with basic PowerPoint skills can easily set their own corporate or client templates up in Displayr.  Once done, your analysis and report build can always be “on-brand”, saving you time and producing better looking reports

Some try and do this via writing code, but this can be a struggle.  For most, Excel is the app of choice, it's very flexible and easy to use and therefore an efficient way of creating custom analysis.

But how much more efficient would it be if your bespoke analysis was embedded in your report and accessed the data directly?

Displayr’s Calculations menu makes this possible.

Accessing Calculations

The Calculations functions are accessed from the tool bar using the button with the same name.   There is already and extensive range of functions and we regularly add more.

For those comfortable writing formulas using code, you can also press the “=” part of the button and drag a placeholder for a calculation on to the page. The code editor opens and you can type in a formula.

Calculations in Action

The possibilities for using calculations are endless.   Here are four examples of how calculations can be used, which should get the creative juices flowing.

Calculating a New Variable

This creates a custom variable from a subset of brands in a numerical survey question, using the “Sum” function.  Once created, variables like this can be linked to tables, charts and other visualizations. They can be duplicated or edited to create more variables of interest.

Simple Custom Calculations

This begins by making some simple custom calculations using basic code then applying these to a table of data.   We then show how to perform the same operation with out code, using Displayr’s built in functionality.

Calculations from a Table

Here we take a cross tab and selectively calculate the sum for each row.   The calculations can be sorted in rank order and hold.  If we make changes to the source table, these are also reflected in the calculations.

There are setting in Displayr to give you more control over your calculations.   Using this example, you can be even more selective, specifying specific columns to leave out of the calculation.  Just select the calculation and find “Columns to exclude” in the object inspector.

Calculations from Multiple Tables

This uses a range of Displayr’s calculations to create all the inputs required for a classic marketing framework, the BCG Matrix.   One the matrix is set up, it is easy to repurpose – brand inputs can be changed and the calculations and the matrix visualization all update instantly.   Filter can be set up and dynamically linked to the source data – calculations change dynamically with each filter change.

Turbo-charge your analysis with Calculations

Displayr's Calculations allow you to customize your analysis, making it easy to go beyond the observed data without having your workings somewhere else.   You can easily change and manipulate calculations once they are set up, everything is connected.  This saves you time, which you can then use to apply your thinking to even more bespoke calculations!

Some use cases for instance:

• Identifying and removing rogue respondents (speeders, flat-liners*, inconsistent responses, etc.).
• Fixing up an error that was missed in the survey scripting process
• Pasting in matching variables from another source (typically Excel), to allow them to be included in the analysis.
• Publishing filtered versions of the data file.
• Publishing clean versions of the data file (i.e. without ‘created’ variables, hidden programming variables) for external client use.

Displayr’s raw data editing and publishing features have a lot of built in functionality. As a result the deliver to these needs, with ease.

* Tip: Displayr can automatically identify flat-lining or straight-lining (respondents giving the same answer for all questions in a set). It gives you a report on questions effected.  From the main menu select Anything > Reports

Accessing the Raw Data Editor

To open the Data Editor, select one or more variables (or combined variable sets) in the Data Tree, then right-click and select "View in Data Editor". Alternatively, there is a button in the Object Inspector with the same name if you have a variable/variable set selected. You can also use the button that appears on the preview tooltip when hovering over a variable or question. The Editor will appear at the bottom of the screen showing the raw variables that you have selected.

You can also drag more variables to it once open and drag-and-drop columns inside the Data Editor. As a result you can reorder them as you like.

Using the Raw Data Editor

Manipulating the table

The column headers contain the 'names' (not labels) of the variables. The row headers contain the Raw Case Numbers. The rounded bars above each column show which Variables are grouped into Variable Sets. To toggle between showing raw values or labels, use the control at the right of the header.

You can sort your data using the sorting icons in the column headers. The first time you click, that column will be sorted in Ascending order. The second time you click it will toggle to Descending and a third click will reset it to not be sorted. You can sort by multiple columns in the order you activate their sorting.

Editing

Values can be edited directly by double-clicking on a cell or pressing “Enter” *. Edited cells have a “flag” added so you know which have been modified.

In the Data Editor you can right-click on edited values and select “Revert” to restore the data to it's original state. You can also do this in the Data Sets tree for entire variables.

You can also copy and paste values or ranges of values within the Data Editor or copy to or paste from Excel. If you have one or more full columns of values in Excel, you can copy them, right-click on a column header in the Data Editor and select "Insert New Variable(s)". These are added to your Data Source in Displayr. You can then use the new variable(s) as you would any other in Displayr.

In this example, we temporarily copied “Postcode” from the Data Editor, pasted into Excel, used a LOOKUP function to assign a Geographic Zone code, and “Inserted” this in Displayr.  as a result it becomes available in the Data Sets tree.

* Calculated variables (e.g. JavaScript or R variables) cannot be edited.

Deleting

Firstly, you can delete individual rows (cases) by clicking on the row header. Use Ctrl-clicking to add more rows tor Shift-clicking to select a range of rows. Just right-click in a selections and select "Delete Row(s)".

Secondly, you can delete rows that match a filter using the control near the right of the header of the Raw Data Editor. Select an existing filter (or create one in the Data Editor) by selecting "New") and the matching rows will be highlighted in Green. Right-click one of the matching rows, then select "Delete Row(s) Matching Filter".

This example is from a large scale “Usage and Attitude” study, where we would expect the interview length to be an absolute minimum of 15 minutes. Survey length here is measured in seconds. We created a filter to select respondents with the equivalent of 15 minutes in seconds and these respondents are highlighted. We can easily now remove them from the data.

To restore deleted rows, select the data source in the Data Tree, then in the Object Inspector click "Undelete observations...".

Publishing (or exporting) edited data.

You can publish a data file that reflects the edits made - from the "Publish" menu (top of screen) select "Export Data" and then choose your format. Currently there are options for SPSS (*.sav) or Excel (*.csv) formats.

Use cases include for instance:

• If the original file required a lot of cleaning, you may want to export and use an updated version, for the purposes of project efficiency.
  You may want to (for example) break a multi-country study into country specific files.
• You may want a version of the file for your external client that has your workings and “programmed” variables hidden.

Read more about this here: Publishing Data Files

The complete package.

Displayr already features state-of-the-art advanced analysis tools, excellent table and visualization functions, and editable and updatable PowerPoint exporting. With the Data Editing feature, it is even more so the complete package, for all your survey analysis and reporting needs.

Data file: Holiday Destinations and Attitudes.sav
Image file: Island.png

Dashboard 2: Campsite Performance

Data file: Camping Initial.sav
Image file: Campground_Background.png

Dashboard 3: Bubble Gum Reach Calculator

Data file: Bubble Gum Flavors.sav
Image file: BubbleGum-Girl.png

Dashboard 4: Burger Tracker

Initial data file: Restaurant Chains Oct 2017.sav
Data file with 3 more months of data: Restaurant Chains Dec 2017.sav
Title page background image file: Burger-Chef-Title-Page-Background.png
Title and content background image file: Burger-Chef-Title-and-Content-Background.png