Category Archives: QlikView

Parsing Non-Standard Signs

Summary: I demonstrate using Num#() and Alt() functions to read numbers with non-standard signs.  Download link at bottom of post.

When reading from text files, the default Qlik interpretation of numeric sign syntax is as follows:

100:  No prefix, positive number
+65: “+” prefix, positive number
-110: “-” prefix, negative number

In the default interpretation a “-” suffix or “()” are not recognized as valid numbers and are loaded as text values.

120-
(200)

Sign indicators like “CREDIT” or “DEBIT” are by default unknown to Qlik and the value will be loaded as text.

300 CREDIT
400 DEBIT

In a Table Box, Chart Dimension or Listbox, numeric values are by default right aligned and text values are left aligned by default. This is a simple way to check what is text and what is numeric.

Aggregation functions, such as Sum(), treat text values as zero.  So a chart using the example numbers above would look like this:

 

 

 

 

 

I can utilize the num#() script function to tell Qlik how to read numbers using other than  default signs. For example, to indicate that a trailing minus is used:

Num#(Sample, '0;0-') as Amount2

That takes care of “120-“.  But what about the other odd signs?  I can nest multiple num#() functions inside Alt() to test various patterns:

 Alt(
   Num#(Sample, '0;0-')
   ,Num#(Sample, '0;(0)')
   ,Num#(Sample, '0 CREDIT;0 DEBIT')
 ) as Amount3

The chart demonstrates that all values are correctly recognized as numbers.  They do retain their input values as the display format.

 

 

 

 

 

If I want to harmonize the display formats, I can add an outer Num() function to indicate the display format for all.

 Num(
   Alt(
     Num#(Sample, '0;0-')
     ,Num#(Sample, '0;(0)')
     ,Num#(Sample, '0 CREDIT;0 DEBIT')
   )
 ,'#,##0.00;(#,##0.00)') as Amount4

Downloadable QV & QS examples to accompany this post can be found here.

-Rob

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Making it Touchy

I like my apps to work like dog toys. When I poke them, they should “squeak”.  I find myself poking at screens with my mouse or finger wondering “What’s this?”.

When I build a QlikView app I try to make it “touchy”.  That is, if a curious user pokes at something on the screen I  provide  more information about that object in the form of a popup/tooltip.  For example, explain the meaning of a conditional warning icon:

 

 

 

 

In this post I’ll look at some options available in QlikView to provide touchy feedback to users of your app.  A qvw example to accompany this post is available for download here.

The Help Text property is available on every object Caption pane.  The value  can be fixed text or an expression.

 

 

 

 

When Help Text is present in a chart, the  ?  icon will appear in the chart caption.  Touching the ? will display the Help Text in a tooltip.

 

Object Help Text is a good place to document the details of the chart and highlight anything special about this chart.  Try to anticipate questions  like “does this include…” or “where does the x number  come from..” and provide those answers in advance.

You can provide as much text as necessary and use newlines and indentation for formatting.  You cannot change the font face or color.

When Help Text is used in a Text Object or a Button,  a caption is not required.  If  caption is off for those objects the tooltip will appear when hovering anywhere in the object.  This is useful as seen in the icon  example at the beginning of this post.

The Chart Dimension or Expression “Comment” property can provide a tooltip for individual columns in a text chart.  For Dimensions, any data model comment for that field will be used if the Dimension Comment is empty.

 

 

 

 

Using multiline Dimensions or Expressions in a text table provides  cell level popups:

 

 

 

(More about cell popups here)

Scatter charts can provide focus to points of interest. As a user I typically want to know more about specific points.  Using additional expressions with ‘Text as Popup”  can provide rich detail, even new aggregations using different dimensions.

 

Check out the downloadable example “Make it Touchy”  for more tips including using transparent text objects to create tooltips anywhere.

-Rob

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qcb-qlik-sse, A General Purpose SSE

In Qlikview we have the ability to add function to the scripting language by writing VbScript in the document module (sometime called the “macro module”).  Typical  additions included regular expression matching & parsing,

Qlik Sense does not have the module feature, but both Sense and QlikView share a similar feature,  Server Side Extension (SSE).  SSE is typically positioned as a method to leverage an external calculation engine such as R or Python  from within Qlik script or charts.   The Qlik OSS team has produced a number of SSE examples in various languages.

SSE seems to be a good fit for building the “extra” functions (such as regex) that I am missing in Sense.  The same SSE can serve both Sense and QlikView.

Installing and managing a SSE takes some effort  so I’m  clear I don’t want to create a new SSE for every new small function addition.  What I want is a general purpose SSE where I can easily add new function, similar to the way QlikView Components does for scripting.

Miralem Drek has created a package, qlik-sse,  that makes for easy work of implementing an SSE using nodejs.  What I’ve done is use qlik-sse to create qcb-qlik-sse,  a general purpose SSE that allows functions to be written in javascript and added in a “plugin” fashion.

My motivating  principles for qcb-qlik-sse:

  • Customers set up the infrastructure — Qlik config & SSE task — once.
  • Allow function authors to focus on creating function and not SSE details.
  • Leverage community through a shared function repository.

I’ve implemented a number of functions already.  You can see the current list here. Most of the functions thus far are string functions like RegexTest and HtmlExtract that I frequently have implemented in  QlikView module or I’ve missed from other languages.

One of the more interesting functions I’ve implemented is CreateMeasure(), which allows you to create Master Measures from load script.  This is a problem I’ve been thinking about for some time and qcb-qlik-sse seemed to be a natural place to implement.

If you want to give qcb-qlik-sse a try, download or clone the project. Nodejs 8+ is required.  Some people report problems trying to install grpc using node 12, so if you are new to all this I recommend you install nodejs v10 instead of the latest v12.

If you are familiar with github and npm, you will hopefully find enough information in the readme(s) to get going. If not, here’s a quickstart.

  1. Install nodejs if not already present.  To check the version of nodejs on your machine, type at a command prompt:
    node --version
  2. Download and extract qcb-qlik-sse on the same machine as your Qlik desktop or server.
  3. From a command prompt in the qcb-qlik-sse-master directory install the dependent packages:
    npm install
  4. Configure the SSE plugin in Qlik.  Recommend prefix is QCB. If configuring in QlikView or Qlik Sense Desktop the ini statement will be:
     SSEPlugin=QCB,localhost:50051
  5. Start the SSE:
     ./runserver.cmd

The “apps” folder in the distribution contains a sample qvf/qvw that exercises the functions.

I’d love to get your feedback and suggestions on usage or installation.

-Rob

 

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A Common SSE Plugin Project

Summary: I introduce qcb-qlik-sse, a community Server Side Extension to share custom Qlik functions. 

At the Masters Summit for Qlik, I dive into several different methods of creating reusable script and custom functions.  In QlikView we have the ability to write custom functions using VbScript/Jscript in the qvw Module.

Custom functions have been useful for things like regular expressions, geo calculations, url encoding, encryption and others.  I’ll call them “edge functions” — some of us need them, some of us don’t.

Qlik Sense does not have the module facility. How can we satisfy the requirement for custom function in Qlik Sense?  The Server Side Extension (SSE) facility can fill the need and is available to both Qlik Sense and QlikView.

An SSE Plugin runs as a separate task and provides communication with a Qlik Script or chart Expression via a TCP port. The same SSE Plugin can serve both QS and QV.

Anyone can write an SSE. The Qlik team provides the SSE base and you write a plugin that wires your new functions to Qlik. The new functions can be used in both Script and Charts.  A number of plugins have already been produced.

SSE seems to be the ideal place to provide a collection of edge functions.  Rather than a bunch of one-offs, I’m thinking a good idea would be to pool resources into a single effort that could be shared, much like QlikView Components did for Script.

I’ve implemented this idea as qcb-qlik-sse.  This server uses as it’s base the qlik-sse package created by Miralem Drek.

qcb-qlik-sse is written in javascript and runs in node.js.  At startup, the server scans it’s “/functions” directory and discovers what functions are available.  The general idea is that you can add new function by creating a new js file.  You can remove function you don’t want available by deleting the corresponding js file.

See what functions I’ve already implemented in the doc here. I’ve also provided an example qvf and qvw that exercise the functions.

If you want to try it out,  download the project and define the plugin to QS or QV as documented here.  You will also need node.js 8 or later installed.

Defined functions will show up in the suggestion list in both the script and expression editors.

 

If you want to add functions, some javascript skills are required.  Follow the directions in the readme and submit a PR.

I’ve labeled the project as “experimental” at this stage because I anticipate there could be some significant restructuring as I get feedback.

Let me know your ideas and if you find this useful!

-Rob

Want to kick the tires on reusable code and make your Qlik team more efficient? Come to the Masters Summit for Qlik, a three day advanced training event for Qlik Developers. 

 

 

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Script Interpretation and Evaluation

Summary: Qlik Script is both interpreted and evaluated. Understanding the meaning of these terms is useful in writing advanced script and understanding why some script “tricks” work and others don’t.

In my Advanced Scripting session at the Masters Summit for Qlik, I sometimes begin with the question “What does $() do in script?”.  A common answer I hear is “it causes the variable contents to be treated as an expression and evaluated”.  While in some cases this is the practical result, the answer is incorrect.

$() “Dollar Sign Expansion or DSE”, which takes place during interpretation, replaces the variable name with the value of the variable.  This process is called “expansion” or “substitution”. The new value may subsequently get evaluated as an expression during the evaluation phase, but only if an expression is expected in that specific script statement.  Let’s take a look.

SET vVal = 1 + 1; // expecting "1 + 1"
LET v1 = $(vVal); // expecting "2"
SET v2 = $(vVal); // expecting "1 + 1"

$(vVal) will substitute the contents of the vVal variable which is the string “1 + 1”.   After execution, we expect v1 to be “2” because the behavior of a LET statement is to evaluate what is to the right of the equal sign as an expression.  We expect v2 will be “1 + 1” because the documented behavior of the SET statement is to treat the value to the right of equals as a literal string, not an expression.

The individual pieces of a script statement are carved up by the script parser into “symbols” or tokens. The script language definition defines what types — Expressions, Literals, Numbers, etc. — are valid for each symbol.  Only symbols defined as Expressions will be evaluated as such.

The TRACE statement expects a String as it’s symbol.  We can’t calculate a dynamic value within the TRACE statement itself.  We must calculate the value into a variable and then reference the variable like this:

LET vRows = NoOfRows('Orders');
Trace Orders has $(vRows) rows;

It would be useful if script supported the “$(=)” DSE form, like Set Analysis. Then we could do something like this.

Trace Orders has $(=NoOfRows('Orders')) rows;

Simple enough so far.  Let’s look at some more subtle examples.  A user on Qlik Community posted  a requirement to dynamically form several fieldnames in a load statement based off the current date.  She wanted to load a specific field as ‘Month_MMM’ where MMM is the current month-1, another field as current month-2 and so on.  She created a reusable (good idea) variable-with-parameter to create the names:

SET vMonthFieldname = 'Month_' & Date(AddMonths(today(),$1),'MMM');

If called in July as $(vMonthFieldname(-1)) the expected return value would be “Month_Jun”.  The LOAD statement would look something like:

LOAD
  Key,
  field1 as [$(vMonthField(-1))],
  field2 as [$(vMonthField(-2))]

This script executed without error, but to her surprise the final table had unexpected field names:

The variable expansion took place during interpretation, returning the expression string that was intended to form the fieldname.  However, the “as aliasname” clause only expects a literal so no evaluation took place and the string was used as-is for the fieldname.  Like the TRACE example, the workaround would be to first build the values  using LET:

LET vMonth1=$(vMonthField(-1));
LET vMonth2=$(vMonthField(-2));
Data:
LOAD
  Key,
  field1 as [$(vMonth1)],
  field2 as [$(vMonth2)]

How about if we used vMonthField  as the fieldref parameter (to the left of the “as” keyword)?

LOAD
  Key,
  $(vMonthField(-1)) as Month1,
  $(vMonthField(-2)) as Month2

The symbol to the left of “as” may be an Expression, so this would generate expected values. (Admittedly, this is not what the poster asked for, I include it only for illustration).

Let’s visit another illustration of interpretation and evaluation.  How many times will this loop execute?

SET vCounter = 1;
Do While $(vCounter) <= 3
  TRACE Counter is $(vCounter);
  LET vCounter = $(vCounter)+1;
Loop

If your answer is “forever”, you are correct.  Looking at the progress window, we can see  the counter increments beyond 3 but the script continues running.

The script execution log is where we can see what script looks like after interpretation and DSE.

2019-07-25 00:48:17 0021 Do While 1 <= 3
2019-07-25 00:48:17 0022 TRACE Counter is 1
2019-07-25 00:48:17 0022 Counter is 1
2019-07-25 00:48:17 0023 LET vCounter = 1+1
2019-07-25 00:48:18 0025 Loop
2019-07-25 00:48:18 0022 TRACE Counter is 2
2019-07-25 00:48:18 0022 Counter is 2
2019-07-25 00:48:18 0023 LET vCounter = 2+1
2019-07-25 00:48:19 0025 Loop
2019-07-25 00:48:19 0022 TRACE Counter is 3
2019-07-25 00:48:19 0022 Counter is 3
2019-07-25 00:48:19 0023 LET vCounter = 3+1

The Do While condition is fixed at “1 <= 3” which will always be true!  According to the documentation for Do While:

The while or until conditional clause must only appear once in any do..loop statement, i.e. either after do or after loop. Each condition is interpreted only the first time it is encountered but is evaluated for every time it is encountered in the loop.

The doc tells us our $() expansion — which is interpretation — will happen only once.  Evaluation, on the other hand, will be done repeatedly.  The correction to the loop will be to remove $() so our statement looks like this:

Do While vCounter <= 3

This is a valid expression and the loop will execute only 3 times.

So where do we need $() in this loop? We need it in the TRACE because we do not have evaluation, only interpretation.  The While condition and the LET symbol both allow Expressions, therefore we can count on evaluation to get the current variable value.

Do While vCounter <= 3
  TRACE Counter is $(vCounter);
  LET vCounter = vCounter + 1;
Loop

I hope this discussion and examples help you to understand script  interpretation and evaluation, especially in the context of $().

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Form Input and Commenting in Qlik

A common desire from Qlik customers over the years has been the ability to interactively edit data on a QV sheet and persist the changes in Qlik or maybe write back to another system. As Martin Mahler of VizLib describes it “turning a one-way data street into a two-way street”.

Sometimes the term “writeback” is used to describe the idea of inputting data in a Qlik app and persisting that data — writing back  the data — to some other system.  The new data is saved and shared with other users of the app. Importantly,  in a form/writeback application, the added data is associated with some row of the data model. For example, in a warranty claims app we may have a dropdown that allows the user to categorize each claim.  The assigned category is used in further analysis.

I think of “commenting” as making annotations on a chart, or chart data point, and a given set of selections.  Ideally, comments may turn into a discussion and use some sort of notification mechanism.

These are not pure terms. There are overlaps to be sure.

I’ve seen some interesting bespoke implementations by partners.  There is also a growing list of off the shelf products that enable writeback and or commentary within Qlik Sense or QlikView.

Some products are Qlik Sense only, some work with both QlikView and Qlik Sense.

Most products allow you to create table sheet objects that mix Qlik DImensions and Expressions with additional input fields such as freeform text, dropdowns or checkboxes.  They all persist the data to some type of backend store such as a database.

When evaluating a product for your requirements, here are some items to consider:

  • Do you have requirements for read-only and update users?
  • Are you looking to add additional data in a single chart or do you need to reference the new data from multiple charts?
  • Are you looking to add one to one new data or build complex workflow apps?
  • Does your business objective require structured form data,  free-form commentary or both?

There are an interesting range of products and capabilities out there.  Klikins and Emark Forms for example let you add new fields to a straight table.  One of the more interesting approaches is K4 Analytics, which embeds Excel into Qlik. This provides the full range of Excel formula and formatting functions. You can build some pretty powerful aps this way.

There are products that focus on finance reports. TrueChart creates a set of functional and beautiful IBCS compliant reports along with a nice navigation interface that can be reused throughout the Qlik app.

Both TrueChart and Climber Finance Report support the type of commenting and annotations user require in finance reporting.  I’m excited that the Climber commenting is being expanded and released as a generic commenting & collaboration product by VizLib. Qommentary is another global commenting solution.

Here in no particular order are some Writeback / Commenting products I’m aware of. The headings are links to the vendor site.

I’m sure I’ve missed some products.  Feel free to leave me a comment if you have something to share.  It would also be good to hear about use cases where you have found value in implementing an input/writeback solution.

Inform Write 

QlikView and Qlik Sense

K4 Analytics

QlikView and Qlik Sense

TrueChart

Qlik Sense

Emark Forms

Qlik Sense

Klikins

QlikView and Qlik Sense

Pomerol Writeback

Qlik Sense

Qommentary

Qlik Sense

VizLib 

Qlik Sense

 

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Understanding and Using Subset Ratio

Summary: If you are familiar with subset ratios in Qlik, you may not find much new in this post. But if you are new to Qlik or are unfamiliar with subset ratios in your data model, please read on. 

When loading data into Qlik and building a new data model, inspecting the Subset Ratio of key fields is an important exercise to ensure data quality.

Subset Ratio is displayed in the Preview Pane (Qlik Sense Data model viewer) or Table Viewer (QlikView) when a key field (field linking two or more tables) is selected.

After clicking a key field, in the Preview pane you will see three important numbers:

Total distinct values:  The count of all distinct values for this field (CustomerId) from all tables (Orders” and “Customers”) in the model.

Present distinct values:  The count of distinct values for this field (CustomerId) in the currently selected table (Customers).

Subset ratio: “Present distinct values” divided by  “Total Distinct Values”.  What percentage of total field values are represented in this table?  In this case, 100% of all CustomerId values in the data model are represented in the Customers table.  This is good. We will typically expect to see 100% Subset ratio in a dimension table.

Let’s take a look at the Subset ratio for the same field in our fact table — the Orders table,

It’s less than 100%.  Our Customer table is our “customer master” and represents all of our potential customers.  Our Orders table represents a limited time period, perhaps 12 months.  Only 44 distinct customers, or 22%,  are represented in the set of Orders we have loaded.

Less than %100 Subset ratio is a normal condition for a Fact table.  If we don’t want to include Customer data for those customers who have no orders, we can filter the Customer load with a “Where Exists(CustomerId)” clause.

So far we’ve seen “normal” subset ratios.  Let’s look at some exceptions.  What does it mean when the dimension table (Customers) has less than 100% subset ratio?

It means we have an order(s) that has no link to a Customer row.  That’s a data quality problem. In the example above we can see that we have 1 missing CustomerId (201 – 200).

Why do we have a missing Customer?  We would have to dig into the data to find out why.  It could be that we have loaded historical orders and “inactive” customers are archived from the Customers table.  It could be that we have some bad data due to a bug.  We have to analyze the data and decide on the best path to remediate.

By the way, what is the specific value(s) of the missing CustomerId?  A simple way to make this determination is to create a table chart with two columns — The key field and a field that has 100% density (every row has a value) from the Customers table.  I’ll use the CustomerName field. Sort the table by CustomerName and the key value in question will show at the bottom of the table with a null value for CustomerName.

What does it mean when the sum of the subset ratios for two tables equals 100%? It means there are no matching values between the two tables.   This can happen for instance when the keys come from two different systems that use slightly different nomenclature.  Perhaps in your ERP all ProductId values start with “P” but in the spreadsheet that someone provided for additional part info the “P” is excluded because none of the humans use the “P” when identifying parts.

Examining Subset ratio as you build up your data model is an important quality step.  Validating the quality of your data model will make the process of creating visualizations go much smoother.

-Rob

 

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Loading Varying Column Names

Summary:  A script pattern to wildcard load from multiple files when the column names vary and you want to harmonize the final fieldnames.  Download example file here.

I’m sometimes wondering “what’s the use case for the script ALIAS statement?”.  Here’s a useful example.

Imagine you have a number of text files to load; for example extract files from different regions.  The files are similar but have slight differences in field name spelling.   For example the US-English files use “Address” for a field name, the German file “Adresse” to represent the same field and the Spanish file “Dirección”.

We want to harmonize these different spellings so we have a single field in our final loaded table.  While we could code up individual load statements with “as xxx” clause to handle the rename, that approach could be difficult to maintain with many variations.  Ideally we want to load all files in a single load statement and describe any differences in a clear structure.  That’s where ALIAS is useful.  Before we load the files, use a set of ALIAS statements only for the fields we need to rename.

ALIAS Adresse as Address;
ALIAS Dirección as Address;
ALIAS Estado as Status;

The ALIAS will apply the equivalent “as” clause to those fields if found in a Load.

We can now load the files using wildcard “*” for both the fieldlist and the filename:

Clients:
LOAD *
FROM addr*.csv (ansi, txt, delimiter is ',', embedded labels, msq)
;

It’s magic I tell you!

What if the files have some extra fields picked up by “LOAD *” that we don’t want?  It’s also possible that the files have different numbers of fields in which case automatic concatenation won’t work.  We would get some number of “Client-n” tables which is incorrect.

First we will add the Concatenate keyword to force all files to be loaded into a single table.   As the table doesn’t exist, the script will error with “table not found” unless we are clever.  Here is my workaround for that problem.

Clients:
LOAD 0 as DummyField AutoGenerate 0;
Concatenate (Clients)
LOAD *
FROM addr*.csv (ansi, txt, delimiter is ',', embedded labels, msq)
;
DROP Field DummyField;

Now let’s get rid of those extra fields we don’t want.  First build a mapping list of the fields we want to keep.

MapFieldsToKeep:
 Mapping
 LOAD *, 1 Inline [
 Fieldname
 Address
 Status
 Client
 ]
 ;

I’ll use a loop to DROP fields that are not in our “keep list”.

For idx = NoOfFields('Clients') to 1 step -1
  let vFieldName = FieldName($(idx), 'Clients');
  if not ApplyMap('MapFieldsToKeep', '$(vFieldName)',   0) THEN
    Drop Field [$(vFieldName)] From Clients;
EndIf
Next idx

The final “Clients” table contains only the fields we want, with consistent fieldnames.

Working examples of this code for both  Qlik Sense and QlikView  can be downloaded here.

I hope you find some useful tips in this post. Happy scripting.

-Rob

 

 

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Loading Variables From Another QVW

I just read a good post by Kamal Kumar Sanguri on QlikCommunity.  Kamal’s post reminded me that managing variables in QlikView has always presented some challenges and over the years various techniques and code snippets have been shared to address those challenges.

Most folks quickly find that maintaining variables in external files  loaded with a script loop is a good approach and resolves common concerns regarding shareability, dollar sign escaping and so on.

Sometimes you encounter a need for an adhoc import or export of variables. Kamal’s post offers some useful code snippets for that.  Several years ago my colleague Barry Harmsen  wrote a post on QlikFix.com that shows some useful macros to manipulate Variables.  Barry and I  subsequently collaborated on a desktop utility that handles both import and export from a menu.  While the download link on the blog is dead, I’ve reposted the utility on the QlikViewCookbook Tools section for download.

Kamal said that there is no way to directly load variables from one QlikView document to another.  That got me to thinking.  It is possible, but gee, no one has ever asked for it.

What’s the use case?  When I want to do an adhoc copy, I use the desktop utility referenced above.  I have seen a number of customers who generate complex calendars into QVDs followed  by  generation of variable for use with the calendar.  Calendar QVD consumers incorporate the variable generation logic with an include file. It works.

Would it be any better if calendar consumers loaded the variables directly from the calendar generating QVW?  I think not because there is possibility of a mismatch between the QVW source and the Calendar QVD.

All that said, maybe you have a use case for loading variables from a QVW?  No one asked, but for the record here is the script to load variables directly from another QVW.

VariableDescription:
LOAD 
 Name,
 RawValue
FROM [..\..\data\StudentFile.qvw] 
(XmlSimple, Table is [DocumentSummary/VariableDescription])
Where IsConfig = 'false' and IsReserved = 'false' // Exclude system vars
// Any addtional filtering here
;

FOR idx=0 to NoOfRows('VariableDescription')-1
 LET vVarname = Peek('Name',$(idx),'VariableDescription');
 LET [$(vVarname)] = Peek('RawValue',$(idx),'VariableDescription');
NEXT idx

SET idx=;
SET vVarname=;
DROP Table VariableDescription;

Happy Scripting!

-Rob

 

 

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Distribution Plot in QlikView

Qlik Sense added a Distribution Plot visualization in the June 2017 release.   QlikView does not have a specific chart type for distribution plot, but you can achieve the same with a scatter plot.

The trick is to set the Y value (Expression #2) to a constant value such as “1”.  Here’s a distribution of Life Expectancy by Country (source: WHO 2017).

Dimension: Country
 X-Axis: =[Life Expectancy]
 Y-axis: =1

It works, but it’s difficult to understand how many points overlap.  You can switch the Style to the outlined ball similar to Qlik Sense and that helps.

I find a more effective technique is to add some transparency into the color.  Overlapped points will result in a darker color.

You can also highlight points using set analysis or alternate states.

 

Adding reference lines such as  Quartiles can provide additional understanding.

To add a second dimension e.g.  “Sex” (values: Male,  Female, Both)  replace the fixed Y-axis expression with an expression that generates an index number for the values.

=Dual(Sex, FieldIndex('Sex', Sex))

That will assign Y-values 1,2,3 to the Sex values.  The Dual() will ensure the text value will show in the popup. The Y-axis  will still display a numeric value so I’ve hidden the axis.  That leaves us without labels for the three lines.  We can either create labels using text-in-chart or use a color coding scheme.

Distribution plots can be oriented vertically by using a fixed X-axis. If you’ve used my ScriptRepository tool, you may recognize that the search results scroll-guide (the yellow dots) are a narrow scatter plot.

-Rob

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