Introduction to Data Types in Pivot Table: JSON

We understand your JSON data may contain different types of fields: numbers, dates, strings, time, days of the week and many others!

Setting proper data types at the very beginning is important for correct aggregation in the pivot table and successful data analysis.

Let WebDataRocks take care of handling data types in your JSON dataset. (more…)

There are no insignificant details when it comes to the design of your application. That’s why our team pays attention to every little detail and provides an opportunity to define how your reports will look and feel.

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As we appreciate the importance of personalization for you, we cannot allow the component to stand out from the general color scheme of your application. (more…)

Bring the brightness to your data

Do you want to make your web report look more interactive and appealing?

To emphasize on the visual representation of your data you are able to use any of the most popular charting libraries – Google Charts, Highcharts, FusionCharts or any third-party charting library you prefer.

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This article explains how to quickly start using WebDataRocks pivot grid in your AngularJS applications. AngularJS is an open-source front-end application framework written in JavaScript. Declarative templates with data-binding, MVC, dependency injection – these are only the most obvious reasons why we love AngularJS. Moreover, the framework is maintained by Google, which is always a quality guarantee. You can see how to get started with AngularJS on their official website.
WebDataRocks officially supports AngularJS framework.

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Good news, our JavaScript pivot grid is free and available to everyone. All you need is to download it into your project.

I have decided to come up with the new portion of useful tips. In the previous post, I shared the insights on how to prepare data from Excel and connect it to our demo. Today I am gonna describe which kind of JSON data is the best for our Free Web Reporting Tool.

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Hello and welcome from our WebDataRocks community!

We offer you to see your own data on the grid so you can play around with our live demo and try the functionality it provides. In our today’s blog post I am going to guide you through the process of preparing your Excel data and explain how to connect it to our demo. The process is simple and consists of a few steps:

Step 1. Clean up your data using a set of tools from Excel

Preparation of the dataset is an essential process that should precede any data analysis. Unprepared dataset often contains excessive or incomplete information which interferes with the analysis process. Before any manipulations we advise to create a backup copy of data, just to be able to revert the changes if the result is not satisfying. Follow our tips to clean up your data:

Use Sort & Filter to locate duplicates, apply naming conventions, find blank or incomplete records
Try one of the lookup and reference functions to add the columns with necessary details to your dataset
Format cells with phone numbers, zip codes, etc. to make sure they look the same way
Use Find and Replace feature to remove the unnecessary characters such as dashes of empty spaces

Step 2. Export the data from Excel

In this step, we are going to save the data we were preparing in the previous step. CSV format is considered to be really convenient for data analysis. Save data in this format by choosing Save As -> CSV UTF-8. To display your data correctly WebDataRocks requires the content to be encoded as UTF-8.

Step 3. Connect your dataset to WebDataRocks

Now it is time to display your data on the grid. Please open our demo, choose Connect -> To local CSV and select the CSV file generated in the previous step. Hooray! Your data from Excel is displayed in our demo. You do not have to worry about the security of your data as WebDataRocks is a client-side tool and we never store your data on our side.

Thank you for your attention. Want to add WebDataRocks to your website? Just go to our integration guide.

Stay tuned, in the upcoming posts, we will share some hints about JSON data preparation and the best ways to use our demo.

Correlation is a powerful statistical concept that refers to a linear relationship between variables. It lies in the center of regression analysis techniques. And when it comes to visualizing relationships between variables, you cannot avoid using charts. They are a great assistance in assessing the quality of predictive regression models.

Charts that show correlation are used at the first step toward detection of cause-effect relationships (but one should remember that correlation doesn’t always imply causation).

Here, we’ll cover the purpose and the structure of three basic charts – a scatter plot, a bubble chart, and a heatmap.

Correlation charts become even more powerful when combined with interactive analytical tools like WebDataRocks, allowing users to explore relationships in aggregated business data.

Scatter plot (scattergram)

A classical chart for any statistician when it comes to correlation and distribution analysis. It’s perfect for searching distribution trends in data.

Structure

The variable on the y-axis is a dependent variable while the x-axis variable – independent.

Purpose

Use it to check whether there is any relationship between two variables. The presence of a certain kind of relationship simply means that changes in the independent variable lead to changes in values of the dependent variable.

With this chart, you can also notice anomalies or clusters in data.

Recommendations

The more data, the better – include as much data points as you can.
To measure how strong the linear relationship is, a single chart is not enough – you need to calculate a correlation coefficient. The sign of the correlation coefficient can be defined by the direction of the line on the plot.
Data points of each variable should be depicted with different colors so as to be able to distinguish them easily.
You can transform the horizontal axis into a logarithmic scale – this way you’ll see the relationships between more widely distributed points.

Example

Check the relationship between the spent amount of hours studied and final grades results

Scatterplot to Show Correlation — Source: TutorialsPoint

If data points are scattered in a random pattern or form a curve, that means that there is no correlation. However, it’s possible that there is a non-linear relationship between variables.

Bubble chart

A bubble chart is simply a variation of a scatter chart.

Purpose

Use it to identify the relationship between data points.
The bubble chart is essential for visualizing the 3- or 4-dimensional data on the plane.

Do you want to be the first to test the grid with your data?

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Structure

The x-axis corresponds to an independent variable, the y-axis – to a dependent. The third and fourth variables can be represented by the size of a data point and its color. The size should be proportional to the value of the dependent variable and the color should correspond to a certain category.

Recommendations

If you can want to show time, you can add animation to present how the values of the variables change over time.
Limit the number of bubbles – don’t use too many of them. Otherwise, a plot may become hard to read.
Rather than labeling each value, add tooltips that appear once you hover over the bubbles and show hidden information. Such an interactive approach can help keep your chart not overcluttered and laconic.

Examples

Identify the correlation between life expectancy, fertility rate and the population of countries

Bubble Chart Example — Source: Charticulator

The brightest example of using this kind of chart is for project assessment: the projects can be evaluated by cost, risks, and value. The higher the value, the farther this project is to the right part of the chart. And the higher the risks, the closer the project is to the top of the chart. The size depicts its expected ROI. Such an approach helps companies to choose projects to invest in.

Heatmap

A heatmap is a commonly used and visually intuitive chart that relies on color gradients to represent the magnitude of values across a two-dimensional space.

Purpose

Use it to reveal patterns, correlations, and concentrations across a dataset at a glance.

A heatmap is especially effective when comparing multiple variables simultaneously or spotting clusters that would be difficult to detect in a standard scatter plot.

Structure

The x- and y-axes represent two categorical or continuous variables. Each cell at their intersection is filled with a color corresponding to a numerical value — typically ranging from cool tones (low values) to warm tones (high values). A color scale legend is always included to make values interpretable.

Recommendations

Choose your color palette carefully — a sequential palette works best for showing magnitude, while a diverging palette suits data with a meaningful midpoint (such as positive and negative correlation values).
Keep the number of rows and columns manageable; overly dense heatmaps become hard to read.
Add tooltips on hover to display exact values without cluttering the chart surface.
If your goal is to show a correlation matrix of multiple variables, a heatmap is often the clearest option.

Example

Visualize the correlation matrix between a set of financial indicators — such as revenue, profit margin, customer acquisition cost, and churn rate — across different time periods or product lines.

A strong positive correlation between two variables will appear as a deep, warm cell, while a strong negative correlation stands out in a contrasting cool tone, making relationships immediately visible without reading a single number.

Turn your pivot table into a heatmap

Great news, if you’re already working with a pivot table, you don’t need to build a heatmap from scratch. With Flexmonster, you can transform your pivot table data directly into a heatmap visualization. Check out this tutorial to see how it’s done step by step.

Moreover, the same capability is available in WebDataRocks. As you can see in our example below, revenue data broken down by country and product category instantly becomes a readable, color-coded heatmap, making it easy to spot top performers like Japan’s Automotive and Electronics figures at a glance.

Summary

Today we’ve discussed the charts which are widely used in predictive analytics.

We aim to share with you the most important information related to data visualization.

What’s next?

To deepen your knowledge about charts, check out other parts of the data visualization project:

Tutorials

If you want to visualize aggregated data in charts, you can integrate WebDataRocks Pivot Table with Google Charts or Highcharts:

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