Analyze and visualize

Irelia analyze & visualize

Irelia offers several powerful ways to analyze and visualize data. In this tutorial, you'll learn how to:

  • Create summary tables

  • Create and configure charts

  • Links charts dynamically

To explain these features, we'll use the sample document "Investment Research" which includes companies and investments in them up to 2013 (the dataset comes from Kaggle.).

For more about the "Investment Research" data info, please refer to the tips of the data in the tail page.

Explore the data

Upload your document "Investment Research"; The first thing you'll see is "Overview".

  • The top data if head 10 items from companies.

  • The bottom left has a pie showing the distribution of investments by category. The table next to it has the same data in tabular form.

  • The right of the pie chart is a bar graph showing the total invesments raised by year. It is also accompanied by the same data in the table next to it in tabular form.

Get the data

Let's import the raw data. We'll import two CSV files, where each will become its own table. To follow along, save the files from crunchbase_companies_ny.csv and crunchbase_investments_ny.csv to your computer first. Then, create an Irelia document by importing the first file from the home page.

Next, import the second table using the "Add New" button and the "Import from file" option.

In the import dialog box, finish by clicking "Import" on the bottom left.

The tables you've imported will be named "crunchbase_companies_ny" and "crunchbase_investments_ny".

Make it relational

The power of Irelia comes from giving structure to the data.

Take a look at the "Investments" table. Sort by the first column and you'll notice how much repetition there is: each row contains the complete company info, which both duplicates the data in the "Companies" table, and is repeated multiple times when multiple investments apply to the same company.

The reality is that each investment applies to a single company. Each investment row only needs to contain a reference to a company, and the data specific to that investment.

To make it so, find a column that identifies a company uniquely. In this dataset, the first column, "company_permalink", does it best. Click on the arrow in the column header and click "Column Options". Click on the arrow beside "Text" under the "Column Type" in the dialogue box at the right of the screen and select "Reference" from the list.

Summarize

The powerful feature you’ve been waiting for is the one that summarizes the data. To utilize this, let’s add a table showing companies grouped by "category_code".

Dynamically charts

If you've read our other tutorials on linking data, this will come naturally. Charts are simply a different way to show data, and they can be linked in the same way as tables.

For our example, we'll add a new page with a summary table: select widget "Table", data "Investments", group by "funded_year", click "Add Page".

Let's rename this new page "Breakdowns".

Next, add a widget to this page, selecting widget "Chart", and data "Investments". For "Group By", we pick two columns: "Company_category_code" and "funded_year".

The "Select By" dropdown at the bottom left of the dialog box lists widgets already on the screen that can control the selection of data in the chart we are adding. In "Select By", choose "INVESTMENTS [by funded_year]", and click "Add to Page".

We want to be able to select a year, and then show a pie chart for that year that displays the total for each category code. The "Select By" option we chose ensures that only the selected year's data is used. All that's left is to change the chart type to "Pie Chart", set "Visible Series" to only "category_code" and "raised_amount_usd" and hide the other data series.

We can now click through the categories, and see the history of investment in each one.

About Dataset

Context

Decide upon investment and business opportunities using available data.

Content

What can be learned from the Crunchbase information? A small snapshot to explore while we wait to access the live data API Did these companies thrive? What patterns can be discovered regarding categories, quantity in a category, country, etc?

Download crunchbase_companies_ny.csv and crunchbase_investments_ny.csv.

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