Project Work

This tutorial will introduce you to the Details panel. The Details panel presents your data in a table and allows you to sort, filter and export samples of your data. To dive into the Details panel, we need a dataset and we need to build a project using it. We will be working with the same Fictional-Employees.csv dataset we used in our Setting Up tutorials. We'll use this to create a project focused on clustering employees based on their characteristic scores.

After we've built the project, we'll head over to the Details panel and start inspecting our dataset. We will use a combination of filtering and sorting to find key employees and then create a segmentation containing these employees. Then, we'll export the dataset again so that we have access to the variables that Graphext added when transforming your data to build the project.

"Analysis is the critical starting point of strategic thinking."

- Kenichi Ohmae

Inside of your project's Details panel, every value inside your data appears in a table. When you build a project, often Graphext will transform or enrich your data. The dataset presented in the Details panel includes the variables or transformations added to your data during the execution of your project. Additionally, any segmentation you create whilst working inside of your project will be added as a new variable to the data presented in your Details panel. You can use the sidebar variable charts to filter the data presented in the table and create segmentations just as you would in the Graph or Trends panels of your project.

Step 1. Quick Project Setup

If you followed our Setting Up introductions and have already setup a project clustering the Fictional Employees dataset using their characteristics as factors, move onto step 2.

Otherwise, make sure you have the Fictional-Employees.csv file downloaded to your computer. Upload this to the workspace of your Personal team in Graphext and select it to bring up the project setup wizard on the right-hand side of the screen. For a more detailed tutorial on datasets, have a look at our Intro to Datasets tutorial.

Choose Employees as your analysis type and then Clustering. Then, inside of the Clusters and Network Creation menu, set the Performance level variable as your target and set the 10 characteristic variables as your factors.

Finally, inside the Network Visualisation menu, set Name as the variable to identify your nodes by and add Gender to the list of pinned variables. That's it. Continue to name your project something like Fictional Employees: Clustering and execute it. Head back to the Projects panel of your Graphext workspace and hold on for a couple of minutes whilst Graphext builds your project.

For a more detailed tutorial on setting up a project, follow our tutorial Intro to Projects.

Step 2. Inspecting the Table

Before you open the project, navigate to the original Fictional Employees dataset from inside of the Datasets panel of your Personal team. Select the dataset to bring up a table similar to the one we will be inspecting inside of your project's Details panel. Notice that there are 14 variables in the dataset; Name, Gender, Description, Performance Level and 10 employee characteristics.

Now, from your Projects dashboard, find the Fictional Employees: Clustering project and open it. Then, head straight over to the Details panel of the project. Here, you will see all of your employees listed as rows in a table. Each variable in your dataset is a column.

As you can see the variables that were in the original dataset are still here but there is also 2 new variables in the dataset; umap_cluster and grp_title. These variables have been added to the dataset as you built the project. Umap_cluster is a categorical variable **representing the cluster that employees belong to, whilst grp_title is the name of employees and has been created by Graphext because you set Name as the variable to label your nodes.

Step 3. Sorting

The Details panel is a good way to discover the most extreme values in your data. This is often a useful sanity check when exploring your data visually or when investigating which data points represent outliers for specific variables.

Using the three dots next to each variable name you can sort the order of rows in the Details table according to their value for that variable. Let's find out which employees are the most competitive. Find the Competitiveness variable and select the three dots. Now, click Sort Desc from the menu list.

Your table has reordered to present the most competitive employees at the top of the table and the least competitive at the bottom. Notice that there are 13 employees with a Competitiveness score of 10.

Now find the Name variable and sort the table in ascending order. When you sort variables with text values as opposed to quantitative values, alphabetical order is used. Albert, Alejandro and Alex will be at the top of your table.

Step 4. Filtering and Segmenting

Sorting is useful as an exploratory tool but the real power of your Details panel lies in filtering and segmenting. The variable charts you have in your sidebars are there to be interacted with.

Filtering and Sorting

Let's use a combination of filtering and sorting to find the female employee with the highest Competitiveness score. First, sort your table in descending order for Competitiveness. Now, find Gender from the left sidebar and select the bar representing females. Inspect your table and notice that Donna is the employee at the top. With your filter applied your table will only have entries for female employees. Scrolling to the bottom confirms this as the number of rows has reduced from 100 to 45.

Multiple Filters

Now we will do something a little more advanced. Clear your previous filter and let's use the variable sidebar charts to find the employees with a high score for both Work Ethic and Intelligence. Find Work Ethic in your right sidebar and drag your cursor over the two highest bars representing values for scores of 9 and 10. Notice that your filter is already controlling the employees displayed in the table. Now, find Intelligence and perform the same filtering operation. That's it. You should see that only one employee is left after applying this filter. It seems that Phillip is the only employee with a high value for both Work Ethic and Intelligence.


When you create a segmentation, data points are grouped together to form a new categorical variable. This variable is added to your dataset and will be visible inside your Details panel.

We can use segmentations to divide our data into categories based on their value for a significant variable. Let's focus on the Prior Success variable. Find this variable from your right sidebar and notice that the charts are relatively evenly spread across the quantitative range. We will divide this range into High, Medium or Low and add employees to our segmentation based on their Prior Success value.

Create a segmentation by selecting New Segmentation from the top of your left sidebar. Select Manual from the menu list and name your segmentation Prior Success Categories. To add a segment we must have an active filter. Drag your cursor over the bottom 3 bars inside of the Prior Success variable chart. 31% of your data will have been selected. Click to plus icon inside of your new Prior Success Categories segmentation and name the segment Low. The employees in your filter have now been added to the Low group within your new segmentation.

Now add Medium and High segments by repeating the process using a different filter range inside of the Prior Success variable chart. Medium should represent employees with a Prior Success rating of either 4, 5, 6 or 7 and High should represent employees with a Prior Success rating of 8, 9 or 10. Add these segments to your new segmentation so that Prior Success Categories has values for Low, Medium and High.

When you have done this, save your new segmentation using the save icon from the top right of the segmentation card. Clear all of your filters and take a moment to inspect the table inside of your project's Details panel. Notice that the first column now represents Prior Success Categories. Your segmentation has been added as a variable to your data!

Step 5. Exporting

Now that we've added a segmentation to our data and it is visible inside of the Details panel, it would be useful to export the data. Exporting data from your Details panel means that you have local access to all of the transformations and enrichments made by Graphext or yourself throughout the process of building and analyzing your data.

To export the transformed dataset with the 3 new variables (Prior Success Categories, umap_cluster & grp_title), use the export icon sitting on the top right of your table. However, it doesn't really make sense to export the grp_title variable as this simply repeats the values that we already have inside the Name variable.

Hiding Variables

You can hide this variable so that it isn't displayed within your project's Details panel and isn't exported. Find grp_title from inside of your Details table, click the 3 dots next to the variable name and select Manage Variable from the menu list. This will bring up the Variables Manager tab of your Project Settings window. Toggle the eye icon representing the visibility of the grp_title variable inside of the Details panel. This will hide the variable. Now, save these settings and return to the Details table.

Browse through the columns in your table and notice that grp_title has been removed.


The data in your table is now in perfect shape for you to export it. Select the export icon and take a look at the download options available to you. Exporting as either an XLSX or CSV file will save the dataset to your local computer, whereas exporting as a Dataset will export the transformed dataset to the Datasets panel of the Graphext team where you build this project. For more information on file types, take a look at our article here.

Let's export it as an XLSX file. Choose this option from the menu and save the dataset to your local computer. That's it. You now have local access to all of the transformations you made throughout the course of building the project.

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