Getting Started

Row64 allows you to analyze, manipulate, and summarize data using a simple evaluation language with Excel-style syntax. The Row64 evaluation language consists of two components: Functions and Formulas. Functions can be used in Dataframes, while Formulas work in both Dataframes and Spreadsheets.

The Formula language component has extensive Excel formula compatibility and includes additional complex formulas (i.e., Regex) for advanced users. In addition, the formula references are not limited to just Spreadsheet tabs. Row64 allows sheet link references to both Spreadsheet and Dataframe tab types.

Dataframes are tables with typed columns, which allow you you to run evaluations on large number of records in an optimized way. Spreadsheets allow you to create formatted table summaries. We recommend doing the majority of your analytical work in Dataframes and then building summary tables in Spreadsheets.

Getting Data

Dataframes are the primary way of working with data in Row64. To work with Dataframes in Row64 Studio, you must be connected to a Row64 Server. For instructions on how to install and get started with a Row64 Server, please see the Connect To Server page. Once you have a running Server, you can download tables or upload your own data.

To access tables from your Server, navigate to the Data tab and click on Get Data. This will open a drop-down menu. Select the Server Data option.

This opens a pop-up window that lists all the available dataframes. Select the table you would like to retrieve. Click the OK button to continue. This will download a preview of the table and put it in a new Preview Tab.

Preview Nodes

A preview of the table opens in a new tab. You can tell from the icon on the bottom tab that you are in a Preview tab. The Preview tab type displays a preview of data from the server and allows you to scroll and browse through the records.

NOTE: The top editor tells you the table source and group where it came from. If you want to change the table that you are previewing, you can modify the fields within CONNECT and GETTABLE, and then click RUN.

The PREVIEW label on the top left displays that you are currently on a Preview tab.

The bottom tab icon also indicates that the sheet tab type is a Preview tab.

Data Nodes

Now that we have a data preview, we can connect it to a data node to start analyzing data. In the top bar, click on New Data Node under the Data tab.

This will pop up a window that displays all the columns from the preview. Select the columns you want to keep and click OK. If you don't select any columns, the new data node will keep all columns from the preview by default.

NOTE: You can also select the specific columns you want to keep, then click on New Data Node. This will only import those columns from the pop-up.

Once you create a new data node, you will see a new Dataframe tab, which was created from the subset of columns from the preview table. When data is on a Dataframe tab, you can perform a running analysis on the data. The newly created Dataframe tab sits on the data node in the project's Global Graph.

The DATA NODE label on the top left lets you know that you are currently on a Dataframe tab.

The bottom tab icon also indicates the sheet tab type is a Dataframe tab.

The Global Graph is an overview of the evaluation graph within the Row64 Studio project. This describes how data is related and transformed within the project. The Global Graph window pops up whenever you create a new data node. In our example, you can see we retrieved a Preview from the Atari table from the Row64/Examples data group, and then we derived a Dataframe from Columns B and E using the CROSSLINK function.

If you close the Global Graph window, you can always re-open it again by clicking on the Global Graph button on the top right.

Analyzing Data

To get started with analyzing Dataframes, click on the Data Functions button to get a list of preset function examples. This is a quick lookup for inserting function examples into your data node editor.

Click on the Transform category to see all available presets that transform the data.

From the list of presets, find the Group & Sum option below the Group & Collapse Rows category.

NOTE: If you click on the Run button in the highlighted row, this will insert the example code and run the evaluation. If you just want to insert the example code without running the evaluation, click on the gray highlight area of the row.

Notice, this inserted a line with the GROUPSUM function into your editor. This function will sum up all numeric columns by unique values found in the specified column.

The default example always inserts Column B. In this Atari example, our categorical values are in Column A. Change the GROUPSUM column to A:A, and then click on click on RUN. This will return a table with the total Sales for each Developer / Publisher.

When you are finished modifying your Dataframe, you can add it to a dashboard, use it for charting, or create a new data node from your current node.

Global Graph

The Global Graph is an overview of the evaluation graph within the Row64 Studio project. This describes how data is related and transformed within the project. It describes the relationship between parent and child datasets, and how their columns are related.

When you publish a Row64 dashboard, the Global Graph is at the core of the evaluation engine. Interactivity within a dashboard depends on the relationships described in the Global Graph.

When a filter operation is performed on a dashboard, an evaluation of the Global Graph with the applied filter is performed in real time. Any datasets, charts, or summaries that depend on that filtered field will reflect the updated values. We call this crossfiltering since the filter operation flows across the Global Graph data nodes. This allows the dashboard users to do advanced data investigations without having to write advanced SQL queries and custom code.

The Seasonality Analysis example dashboard showcases the dashboard crossfiltering capabilities. If you click on the Q1 bar of the bottom right bar chart, then all datasets and charts linked to that Q1 column will be filtered to only Q1 records.

In this case, when you filter by Q1, the evaluation engine traces the Global Graph and filters the originating dataset column by Q1. The Global Graph is then re-evaluated with the filtered dataset in place, so all dependencies are updated to reflect the Q1 filter.

For more details on the Seasonality Analysis example, visit the Dashboard Training page.