How to change the Border width of a chart in QlikView

This recipe helps you change the Border width of a chart in QlikView

Recipe Objective:-How to change the Border width of a chart in QlikView?

Step 1:-

Open QlikView 12 software. Here a Start page will, by default available; if we do not want we can also untick the check box given below and avoid the start page while launching QlikView every time.

Step 2:-

On the start page, we can see the Examples, Recent, and Favorites tab. The saved files will appear under the Recent tab.

Step 3:-

When the QlikView 12 Software gets Open, A blank window appears. Go to menu bar->click on File menu->click on New-> The Main sheet appears, Again go to menu bar->click on File menu->click on Edit script or we can also type Ctrl+E->Go to Table Files->Load the data source->here an excel file named as "Office City Sales" is being loaded and Select "Office City Sales" table->Now click on Reload button from the menu bar and save the file, So that data will also get loaded in sheet.

Access YOLO Real-Time Fruit Detection Computer Vision Project with Source Code

Step 4:-

Now from Main Sheet->Right click->New sheet object->Chart->Line chart->Select Dimension as "Market and State" also select "Office city sales" table->under Expression tab->Select "Sum" Aggregation->Select Table as "Office City Sales" table->Select Field as "Office Sales"->Click on Paste->Ok->then click on Next->Next->Next->Finish.The Line chart will be then available in the Main sheet/QlikView Document.

Step 5:-

Now go to Line chart in the Main sheet->Right click->Properties->Layout tab->Go to the Border width option->Select "5pt" border-width->Click on Apply and Ok->We can change the border width as per our requirement.

Step 6:-

Therefore in this way, the Line chart with changed border-width will be available in the Main sheet/QlikView document.

What Users are saying..

profile image

Savvy Sahai

Data Science Intern, Capgemini
linkedin profile url

As a student looking to break into the field of data engineering and data science, one can get really confused as to which path to take. Very few ways to do it are Google, YouTube, etc. I was one of... Read More

Relevant Projects

Mastering A/B Testing: A Practical Guide for Production
In this A/B Testing for Machine Learning Project, you will gain hands-on experience in conducting A/B tests, analyzing statistical significance, and understanding the challenges of building a solution for A/B testing in a production environment.

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Digit Recognition using CNN for MNIST Dataset in Python
In this deep learning project, you will build a convolutional neural network using MNIST dataset for handwritten digit recognition.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

NLP Project for Multi Class Text Classification using BERT Model
In this NLP Project, you will learn how to build a multi-class text classification model using using the pre-trained BERT model.

Deep Learning Project for Time Series Forecasting in Python
Deep Learning for Time Series Forecasting in Python -A Hands-On Approach to Build Deep Learning Models (MLP, CNN, LSTM, and a Hybrid Model CNN-LSTM) on Time Series Data.

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.