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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
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Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
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Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

2 Simple Hacks to improve information density in your QlikView dashboards

Sunil Ray
Sunil Ray Last Updated : 25 Sep, 2015
5 min read

Foreword

Few months back, I got an opportunity to work on a QlikView project with an insurance company. The objective was to create a sales dashboard to bring out business insights, which could help improve sales of the company. Needless to say, the dashboard required use of multiple metrics and dimensions.

One of the common ways to include multiple dimensions in QlikView is to arrange them using list boxes. While this simple approach works, it ends up taking a lot of space in the dashboard. This leads to lesser area being available to represent your information. In this situation, how do you accomplish the task? Some challenges were associated with this project as discussed below:

  • How can we keep all the dimensions as list boxes on the sheet without overpopulating the sheet?
  • How do we represent the important business information prudently on the same sheet?

There are multiple ways to overcome these challenges. I’ll discuss two such methods in this article:

  1. By conditionally enabling objects like list boxes and chart objects using text object or command button.
  2. By creating container that would utilize utmost sheet space to represent various chart objects.

Let’s look at both the options in detail.

Method 1:  Using Conditional Enable Option

Let’s look at the pre and post effect of this method on sheet space in QlikView (refer to the snapshot below):

SUB_Tabs_1Did you notice the difference in the two snapshots? Snapshot at the right has more space available for effective data representation. How did we manage to get this extra space? How did we reduce the number of list boxes?

Well…this isn’t rocket science! If you observe closely, you will notice three new sub tabs appearing on top left corner: “Policy“, “FPA” and “SM“. We have simply categorized the dimensions in three categories and enabled them conditionally.

Let’s look at the steps to perform this:

Step-1: Create a variable “Var_Active” and assign it the value 0 (zero). Go to Settings Menu –> Variable Overview. Then, click on ADD button to create new variable.

Variable

Step-2: Categorize the dimensions, identify the number of unique categories and create text boxes for each one. Here we have three different categories as Policy, FPA and SM represented by three text boxes.

Step-3: Set ‘action’ for all three text boxes to assign the unique value to variable “Var_Active“. This is how you can do:

Select Text box –> Go to properties –> Actions Tab –> Click on ADD  –> Select “External” action type and “Set Variable” as an action parameter. After that put the “Var_Active” in variable box and assign unique value for Value text box as 0.

Variable_Actions

Similar steps can be performed for FPA and SM text box also. Simply, assign unique value 1 for FPA and 2 for SM.

Step-4: Select list boxes individually and apply condition enable option using unique value of their respective category. Below are the steps:

Select List box (Branch City) –> Go to Properties –> Layout Tab –> Option button on for “Conditional Show” –> Put expression “Var_Active=0“. Here, we have assigned value ‘0’ to ‘Var_Active’ as it belongs to category ‘Policy'(value for Var_Active is set at ‘0’).

Conditional_Enable

Similarly, we can do it for other list boxes and assign respective variable value based on their category.

Step-5: This is an optional step. It enhances the visual effect. When we select a text box, it’s color changes, which indicates it as an active category. Below are the steps to do it:

Select text box (Policy) –> Go to properties window –> General tab –> Click on color button –>Select calculated option under base color bucket. Then, write the expression as: “=If(Var_Active=0,rgb(0,128,0),rgb(214,231,248))”. 

This expression can be read as: If variable value is ‘0’ then color should go as green else default color. Similar expression can be written for other text boxes also. Only conditional argument will change as “Var_Active=1″ for FPA and “Var_Active=2″ for SM.

Now, we have sub tabs for list boxes seen as Policy, FPA and SM. It will show list boxes related to the active category. Similarly, we can use command button to perform this conditional enable or disable list boxes. This feature can also be used with chart objects.

QlikView has another object that can be used to show multiple chart objects at the same place where charts objects can be accessed using tabs known as CONTAINER.

Method 2: Creating Container in QlikView

Container is a QlikView object which holds multiple charts in the same box as seen below. All charts will appear in the same window but only one chart will appear active at a given time and navigation to different charts can be done using the bottom tabs. Position of the tabs can be changed to top, left and right as well. Look at the snapshot below, here we have four different charts namely Region wise sales, Region wise Profit, Manpower Distribution, YoY Growth available in one container. Users can navigate through the charts by clicking on their respective tabs.

Container

Method to create container is as easy as drag and drop objects. It is just a two steps process. Let’s take a look:

Select Container from ‘New Sheet Object List’ –> Select objects from list of available objects on ‘General’ tab. Now, we have Container with selected list of objects. We can change the position of tab or container type by selecting different option under ‘Presentation’ tab.

End Note

In this article, we have looked at two methods for effective use of sheet space:

  • Conditionally enable or disable of objects using variables in QlikView.
  • Use of container to hold multiple objects at the same place.

These two techniques are quite effective in utilizing your sheet space. Have you used these techniques ever while working on QlikView? Do yo know of any other hacks to utilize sheet space effectively? Share your valuable experience with our readers through comments below.

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Sunil Ray
Sunil Ray

Sunil Ray is Chief Content Officer at Analytics Vidhya, India's largest Analytics community. I am deeply passionate about understanding and explaining concepts from first principles. In my current role, I am responsible for creating top notch content for Analytics Vidhya including its courses, conferences, blogs and Competitions.

I thrive in fast paced environment and love building and scaling products which unleash huge value for customers using data and technology. Over the last 6 years, I have built the content team and created multiple data products at Analytics Vidhya.

Prior to Analytics Vidhya, I have 7+ years of experience working with several insurance companies like Max Life, Max Bupa, Birla Sun Life & Aviva Life Insurance in different data roles.

Industry exposure: Insurance, and EdTech

Major capabilities: Content Development, Product Management, Analytics, Growth Strategy.

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Responses From Readers

Clear

Shashi
Shashi

Good tricks! w.r.t first trick we need to overlap the list boxes else it would look weird, when it comes to container i would keep same container next to each other(duplicating)..so that two charts comparison is possible. In the above example if we duplicate the container, comparison of region wise sales with that of profit would be easy.

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alexander boyko
alexander boyko

you have a good article on QlikView but where practical examples 20-30 lines?

123

Bhaskar
Bhaskar

Good tips indeed. Apart from conditional listboxes I also use multibox at times to save some real estate. However, I would go easy on containers if the charts have lot of calculations. Unlike other objects, containers force all the child objects do calculations when the container is activated (even though only one object is active at a time) - which is a pity. Also, if u put a container within another, your objects in IE Plugin access point may behave weird.

123

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