Chart Tool Basics

Chartio puts a ton of power in your hands very quickly. The best part is that it's very easy to learn how to use our tool. Below, we'll discuss some of its major features. Enjoy the tour...

Datasources

The image below gives you an example of what datasources look like in Chartio. These datasource tabs provide a convenient way to organize the diverse data your business collects for analysis in Chartio. If you need help adding your first datasource or want to add additional ones, please email datasources@chart.io.

To refresh a datasource, simply click on the arrow.

Tables

Expand a datasource by clicking it to reveal its tables. To edit the table name, double click on the table text, enter the new name, and press enter.

*Please note that your renamed table aliases apply only locally in Chartio and have no effect on your database.

Columns

Drill into a table to reveal its columns. These columns map to the columns contained inside your datasource. Broadly, columns are grouped into either measures or dimensions (click here for more about Chartio data types) Dimensions refer to categorical data such as time (day, week, month, etc.), department or state while measures refer to quantities (number of employees, revenue, units shipped and so on). To switch a column from a dimension to a measure, simply drag and drop it into your preferred category.

Data Types

Besides switching columns from measures to dimensions (and vice versa), you can change the data type of each column. Chartio supports three types of data that are commonly used in databases:

1. Number - This represents either type int or float (e.g., 50 or 3.2). It is represented by the "graph" icon.
2. Text -This represents text in your database and is represented by a chat bubble.
3. Date - This refers to datetime (or Unix time) in your database and is represented by a clock.

To change your data type, simply click on the icon.

Note to those who use Unixtime: Chartio doesn't yet recognize non Datetime columns as type datetime. To work around this, please switch the icon associated with your column from the Number type (graph icon) to the Date type (clock icon).

The Dataset Window

The dataset window lets you create visualizations of your datasource columns and rows.

This window is where you drop the columns (or tables) you want to visualize. Some of its principle parts include:

1. There are three main fields for your data, including: The X axis, the Y axis and the "Split By" field.
2. Three links on the right hand side, which allow you to enter raw SQL mode, add a filter and finally, delete your dataset
3. Above the window itself, a drop down that allows you to select your "Chart Type" and a button to create a new chart.

Note: when you have multiple layers/datasets for any given chart, deleting the dataset will only delete that particular dataset. Your remaining layers will stay intact.

It's important to note a couple additional considerations.

1. The "New" button at the top allows you to create an entirely new chart. It is not the same as adding an additional dataset to the current chart.
2. The greyed out "Save" button indicates that your chart has been automatically saved. Should you exit the program or simply return to your dashboard, you're work will have been saved.

Your First Chart

This is a preview of an example chart. Once you've dragged and dropped the columns (dimensions and measures) you want into the dataset pane, click "Chart it" to create the visualization. The resulting chart is automatically saved (and added to your dashboard), which always updates with the latest data.

Once your chart is in place, you now have the option to give it a title as well as label the axes. To change an axis label, simply click on it and enter your text.