You can refresh and recreate Graphext projects created with data from Google Sheets or database integrations. On top of this, it's now simple to change the color of values from anywhere in your projects and you can switch color palettes inside of your project settings!
We've added the ability to refresh and recreate projects built with integrated datasets from Google Sheets, SQL databases and more remotely hosted sources.
Find a project you've created with integrated data and choose to Refresh and Recreate the project. Graphext will then retrieve a new - up to date - dataset from your source and automatically create a new project using the data.
Your Compare and Trends charts now support the full spectrum of variable colors. Not only this, but you can change the color of any categorical value across the interface.
Recently, we extended the number of automatically generated variable colors but - up until now - these weren't available in Compare or Trends charts. Now, you can see the full range of colors across all interface panels as well as changing these colors directly in either Compare or Trends charts.
You can now switch the color palettes used to represent data in your projects. Color is crucial to grouping and spotting connections between data. Head over to the Appearance tab inside your project settings to change color palettes.
Choose Horus for the standard Graphext color palette. Choose Osiris for a more vivid color palette. We'll be adding more color palettes to this list very soon!
You can now expand charts in Compare & Correlations. Because expanded charts are BIGGER, they let you inspect more values at the same time.
To expand Compare or Correlations charts, click on the 3 dots from the top right of your chart card and choose Expand chart. Insights that you save from expanded charts will also be bigger and contain more values than standard-sized charts.
We've improved the way that data is presented inside Trends charts. You can now represent values in time-series charts using a Cumulative Sum - which works like a running total. Choosing Cumulative Sum - instead of a count or an average - means that the y-axis in your Trends charts can now represent the total sum of data as it grows over time.
- Added the ability for users to set up more than one SQL integration.
- Fixed an issue with Amazon S3 Data Integrations.
- Fixed a bug in the Text - Keywords analysis type.
- Fixed a bug with the Social Media - Analyze Author Bios analysis type.
- Fixed an issue with dataset names containing a long sequence of characters.
Market Segmentation means splitting your customer base into distinct communities based on the similarity of their features. This guide walks through the fundamental techniques, tools & types of market segmentation and shows you how to perform advanced market segmentation with Graphext. Read more.
This guide is intended to walk you through the process of creating a clustering model to group your data. We'll build a project using a dataset of 1000 supermarket transactions from stores in Myanmar and expose the supermarket's most valuable market segment. Read More.
How can we use text analysis of data from Twitter to improve our understanding of markets? This is the question prompting Paul, a strategist in our business team, to scrape tweets about Lloyds bank and conduct a Twitter topic analysis using advanced NLP and network creation. Read More.
Graphext is now more powerful at text analysis. We've added support for the incredible range of NLP models at Hugging Face including intent detection and sentiment analysis. On top of this, we've built a new enrichment to group location values that are spelt differently
We've integrated Hugging Face models with Graphext. You can now build, train and deploy state of the art models for common NLP tasks including intent detection, sentiment analysis and token classification. Hugging Face also has models for translation, image classification and speech recognition.
Check the Hugging Face model documentation to browse the models you can now use in Graphext, check how to use them and try them out! We're so convinced about the usefulness of these models that we've updated the default embeddings in Text - Topics projects to use the Hugging Face SBERT transformer.
We'll soon be adding an easier way to deploy Hugging Face models on your text but for now - open up the code editor and paste in a code snippet from our docs.
When working with URLs in your data, it is often useful to extract new variables containing the domain, path and schema of the URL. Using this enrichment you can parse the URL values in your data and use the components of a URL to filter your data.
After you built a project that extracts URL components - look for the new variables in your data; path, domain, query, schema and more ...
Variation in the way that people write and record location data can make for a messy analysis.
Similar to the way that our Group Similar Spellings enrichment works, standardizing location data means grouping variations that refer to the same place but are spelt differently.
For instance, without deploying this enrichment, 'Manchester' and 'Manchester, UK' would be considered as two separate places. Our enrichment has been designed to let you collect these two values and filter your data more accurately with locations.
- Fixed a bug preventing the saving of new team names.
- Fixed a bug causing quantitative filter ranges to jump unexpectedly.
- Fixed a bug allowing incorrectly formatted data sources to be referenced (not an URL).
Our team set out to build a type of analysis that could be used to measure the strength of association between variables in a dataset. Read more.
Our team set out to build a type of analysis that could be used to measure the strength of association between variables in a dataset. Read more.
It's been a colorful month ... we've added the ability to change the color of any categorical variable and extended the spectrum of colors automatically generated for your values. We've also added a new enrichment letting you fill missing values in your data!
We've increased the scale of our default color palette to include 30 colors!
On top of this - clicking to show more categorical values will add appropriate color to nodes in your Graph that would previously have been grey.
Color is a powerful analytical tool and lets you quickly identify the features of your data points inside visualisations. Up until now, we've used grey to color any categorical value beyond the 10 most frequently occurring.
We believe that more color means more clarity. Clicking to see more categorical values will extend the colors presented in your Graph.
You can now change the color of any categorical value!
Although every value in your categorical variable will be automatically assigned a color - you can change these by editing the variable and selecting a new one using the color picker.
We've built an enrichment to fill missing values in your data. Missing values can be annoying, misleading and disruptive. Replacing them with specific values can help to clean up and prepare your dataset for analysis.
Choose Fill Missing Values from the data enrichment tab inside of the project setup wizard to start replacing missing values. Then, select a variable with missing values and tell Graphext how you would like to fill these values. You can choose from options like using a constant value, using the most or least frequently occurring value and using the column's minimum or maximum value. Look for the replaced variable in your transformed dataset.
If you'd prefer, you can always use a different enrichment to predict missing values!
We've added space to describe your dataset and reference it's original source inside Graphext.
Context is always important but when dealing with data - it is essential. Referencing your data leaves behind a trail that other team members or researchers can trace to validate or continue your analysis.
To start describing and referencing a dataset, find it from inside of your team's Graphext workspace. Then open the dataset info menu using the 3 dots on the far right of your dataset card. Enter the source URL and write a description then click on the dataset to see this information listed above your data.
- Added the ability to change the name of a team.
- Fixed an issue with info cards not appearing after clicking on a node in the Graph.
- Fixed a bug causing the creation of a new project to fail after moving some data to the trash.
- Added a menu button to instantly open a variable in the Correlations panel.
- Added a legend to list variables. A white circle indicates that white-coloured nodes refer to data belonging to more than one list category.
Our team clustered 1000 supermarket sales in order to segment customers according to their buying habits.
'España' and 'Españha' are just spelling variations. We built a way of grouping words spelt differently but referring to the same concept and made it available alongside any type of analysis you perform with Graphext.
We've created a collection of Getting Started videos to help guide you in using Graphext's interface panels and core features.
Our new Correlations panel lets you study the relationships between variables. Find it inside of any new Graphext project you create and start discovering the associations in your data.
Correlation is a statistical concept referring to the relationship between two variables. We can use correlation to understand whether observing a change in variable A will also mean observing a change in variable B.
Positive correlations refer to a relationship between two variables in which both variables move in the same direction. Negative correlations refer to a relationship between two variables in which an increase in one variable is associated with a decrease in the other.
“Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing 'look over there'.”
- Randall Munroe
Inside your project's Correlations panel you'll find a series of charts as you would inside the Compare panel. Choose a variable to study using the search bar and Graphext will generate charts showing the correlation between this variable and other variables in your data.
Use correlation charts to understand how the values of one variable are associated with the values of another. You can export charts from the Correlations panel or save them as insights.
Charts in your Correlations panel reveal the number of data points where values from two variables meet. Your y-axis represents values from the variable in your search bar and the x-axis represents values from the correlated variable - labelled in the top right of each card.
The blue circles in your correlation charts represent the number of data points at each value intersection. Bigger and brighter circles represent a higher number of data points at an intersection whereas lower and duller circles represent fewer data points at an intersection.
A strong positive correlation would be signified by a trend of big & bright circles moving diagonally upwards from left to right 📈
A strong negative correlation would be signified by a trend of big & bright circles moving diagonally upwards from right to left 📉
Correlation is a powerful tool but its key concepts aren't always self-explanatory. Here are a couple of articles to help you use and understand Correlations.
Start here. This article walks you around the new Correlations panel - pointing out the different features and showing you how to use them.
Read about the concepts key to understanding correlation. In this article, we explain how correlation works, the different types of correlation alongside pointing out how to measure degrees of correlation in Graphext.
How can I start using it?
We're excited to say that we've added a new panel to your projects! Find Models after you've built a prediction model using the Models | Train & Predict analysis type.
Models is designed to help you understand more about the creation and performance of predictions models you build. You'll find three tabs - General - Training - Result - each containing distinct information about your model.
This information helps you unpick how your model was built, its strengths and weaknesses as well as how it performed in specific areas.
As the statistician, George E. P. Box wrote, "All models are wrong, but some models are useful." What he meant by that is that all models are simplifications of the universe, as they must necessarily be."
Nate Silver, The Signal and the Noise
Examining the mechanics behind a model is a crucial aspect of ensuring that it's application is appropriate. We've included references to the technology used to develop the model, descriptions of its primary use cases as well as notes from our team on its performance.
Evaluating a prediction model helps us to understand how good the model is. Using accuracy scores and other performance metrics, we get a sense of whether the model was able to make correct or inaccurate predictions. Not only this, but through evaluation of a model, we can understand how to improve it by changing its factors or parameters.
Prediction can be pretty complex at the best of times. We've written a few articles to help you get your head around the concepts key to Models.
Start here. This article walks you around the new Models panel - pointing out the different features and showing you how to use them.
Learn why and how to build models. Here, we explicate the process of building a prediction model in Graphext - considering the reasons for doing so alongside some of the key concepts involved.
Use as a dictionary to your Models panel. As well as explaining the usefulness of evaluating models, this article offers explanations for all of the technical terms used inside your project's Models panel.
How can I start using it?
Throughout May, we've been pouring our efforts into fixing bugs and making improvements in Graphext's UX. We've redesigned the project setup wizard, cleaning up the types of analysis you can conduct as well as improving key flows. We've also added a new enrichment option that groups similar spellings in text or categorical variables.
We've made substantial improvements to our old Network of Columns analysis type. This flow lets you study the links between variables in your dataset.
Choose Models → Cluster Variables to build a project that maps the relationships between variables in your data.
Clustering your variables can be a useful way to understand which factors to feed into a model or to simply grasp which variables are strongly linked to one another.
When setting up a Cluster Variables project, choosing a target variable means that your project will focus on mapping other variables relationships to that target variable.
- Choose a dataset with at least two variables.
- Select Models → Cluster Variables as your analysis type.
- Pick which variables you want to cluster in your project.
- Refine your configuration using the questions in the setup wizard.
- Execute your project and study the relationships between your variables.
Aimed at improving the way you conduct Text Analysis in Graphext, our latest data enrichment groups words with similar spellings. Simply put - the idea is to stop Graphext and Graphex from being considered as two separate entities.
Whether it be typos, misplaced punctuation or a missing letter or two, unintended variation in data is a common - and annoying - occurrence in text analysis. Motivated to overcome this common shortcoming, our team of data scientists and engineers built this algorithm to merge words with similar spellings and made it instantly deployable in Graphext using any type of analysis.
Chose Group similar spellings from the list of enrichment options in your data enrichment tab to start grouping similar text or categorical values. Then, set a threshold to configure the strength of the merges taking place.
- Start building a project using a dataset with a text column.
- Choose an analysis type and open the data enrichment tab.
- Select Group similar spellings from the list of enrichment options.
- Set a threshold to control the strength of your word joinings.
- Continue building your project.
- Open your project and check out the new merged variable.
Redesign of the project setup wizard. Without removing any of our capabilities, we've tidied up the way that flows are presented. We've removed Employees and Survey analysis types and renamed Google Analytics to Marketing Attribution. You can build the same project using the Models analysis type. We've done this to make it simpler to find the right kind of analysis for your project.
- Fixed a bug preventing users from segmenting data using a direct selection of nodes in the Graph.
- Fixed a bug stopping users on some Mac OS from extracting CSV files downloaded from Graphext.
- Fixed an issue causing some minor Graph UI features to overlap on Safari browsers.
- Disabled a users ability to create insights inside of projects embedded on external websites.
- Fixed a bug stopping users from changing the color of a segmentation whilst - at the same time - renaming the segmentation.
Attempting to discover the most influential features of a loan application when considering risk, our team built a model using the features of a loan application to predict whether an applicant would have a good or bad risk rating.
In this guide, María and Paul walk you through the process of building a prediction model that analyzes a dataset of 5110 healthcare patients. The model we help you to build will use factors detailing the lifestyle and existing health conditions of a person in order to predict the likelihood of that person suffering a stroke.
We've been improving the flow of key Graphext features to make them more instinctive to work with. You can now save insights to your recipe so that you don't lose them when you recreate a project. It's also easier to save and edit new segmentations. We're also getting ready to introduce you to some substantial - and colorful - new features next month!
Insights are key. They help store your discoveries and build data-driven narratives. You don't want to lose them if you recreate a project using a different flow. We've added the ability to save insights to your recipe. Choose key aspects of your analysis, toggle the save switch and move your analysis forward without losing your findings.
Insights that you save will appear in the Insights panel of your recreated project. You can change the configuration of your recreated project as much as you like - this won't affect the insights that you save.
- Start by saving some insights in a project you have built.
- Then, click the green recipe icon at the bottom of the insights that you want to save.
- Toggle the switch to save the insight to your recipe.
- Recreate the project using the settings menu located on the top left of your screen.
- Edit your projects configuration until you are happy with the changes. Execute it.
- Head over to the new project's insights panel to inspect your saved insights.
Creating manual or automatic segmentations is a powerful way to discover sub-communities in your data. We've made it easier to customize the properties of segmentations by improving the logic with which you edit a segmentation. We've also added a button to help you cancel the changes that you make and made it simpler to undo steps like renaming or coloring segments.
Segmentations act like a new variable in your dataset, dividing your values along lines that let you see your data from different perspectives. Segmentations are communities in your data and it is important to control how they appear.
Changes we've made to the flow of editing a segmentation are designed to give you greater freedom in renaming, coloring or removing segmentations. Make use of the undo and cancel buttons to control your changes precisely and quickly.
- Create a segmentation in one of your Graphext projects.
- Click on the Edit Segmentation icon from inside the segmentations sidebar card.
- Use the icons to rename, color and remove segments.
- Undo and cancel any changes you make using the icons next to the Save button.
- Fixed a bug preventing sub-cluster region labels from appearing after sub-clustering segments.
- Fixed an issue with the product basket analysis flow.
- Fixed an issue with the network of columns flow.
- Fixed a bug in the keywords flow when using datasets containing URL variables.
- Improved the display of categorical values in your sidebar variable charts. Now - no matter the number of values - categorical variables are always presented in bar charts, not lists.
Andy and María met with Jake to talk about a dataset he's building about himself. From skating to people he sees to whether he eats meat or not - Jake's data diary offers a unique and deeply personal insight into his life. But what makes the difference between good and bad days?
Businesses fighting to understand feedback from hundreds or thousands of customer reviews can analyze what people are saying using NLP techniques but it takes time and resources to do so. This guide is intended to walk you through the process of analyzing customer reviews with Graphext. We will analyze a dataset of 42,656 reviews about 3 Disneyland branches using the Text → Topics flow.
This month, we've been working on making Graphext easier and more intuitive to use. We've added features making it easier to spot relationships between variables using compare charts and inspect data on your Graph quickly by controlling size mapping with greater precision.
We've also added a new type of analysis making it possible to analyze the relationships between recurring items in your data. On top of this, we've extended the list of language support options we have so that you can analyze text written in Turkish and Arabic.
It just got much easier to start analyzing the variables highlighting relationships in your data when you build a prediction model in Graphext.
To make it faster for you to spot key variables, we've re-configured the way that the Compare panel generates charts explaining differences or similarities between values in your dataset.
Now, when you generate compare charts after building a model using the train and predict analysis type, Graphext will automatically display only important variable charts, hiding the variable charts that aren't as relevant to your analysis. You can change this by switching between the new categories of charts that we've created.
Use the dropdown menus at the top of your Compare panel to start toggling between these variable collections.
All - All variables
Target - Modelled variable
Factors - Variables used to create the model
Other Variables - Variables not used as factors or target
Important - Target(s) and Factors
Internal Variables - Variables created by Graphext
None - Select a variable individually
- Start by building a project using the train and predict analysis type.
- Then, from the Compare panel of your project, choose some variables to compare.
- Graphext will automatically display the Important collection of variable charts.
- Toggle between collections using the dropdown menu at the top of your Compare panel.
Node sizes are a great way of exploring quantitative values in your Graph at a glance. You can now control the range of sizes that nodes are given using a sliding scale.
Select the node size icon from the icon collection at the top of your project's Graph to start customizing the range of node sizes presented in your project. You can control the top and bottom of the range using the slider.
You can also control your node sizes from your project settings. If you want to save your node size configuration, you can do this using the node size slider inside of the project settings window.
- Open your project and navigate to the Graph.
- Select the node size icon from the icon list at the top of your Graph.
- Click the three dots from the top right of the variable card.
- Move the range presented in the slider to change the size of your nodes.
We've added a new type of analysis called Co-occurrence. You can find it within the Models section of the project setup wizard.
Co-occurrence analysis lets you find relationships between recurring items in your dataset. It helps you identify which items are most associated with other items.
Whilst it's already possible to conduct co-occurrence analysis with Text and Product Basket analysis types in Graphext, we built this flow to work with any kind of data. Use Models → Co-occurrence analysis to discover the associations between a range of entities which might be people, products or places.
- Upload a dataset containing recurring items.
- Start building your project using the setup wizard.
- Choose Models as your type of analysis.
- Choose Co-occurrence as your sub-analysis type.
- Tell Graphext how you want to aggregate your data and which column contains your items.
- Execute the project and start discovering how items are related.
Our team of engineers and data scientists have been busy improving Graphext's Natural Language Processing capacity so that it is possible to analyze content written in more languages using our text analysis flows.
You can now analyze text written in Arabic and Turkish on top of the existing language support options we already have built-in. We've also made it much easier to extend the list of languages we support!
Read more about language support at Graphext here.
- Start with a dataset containing at least one text field and choose Text or Social Media as your analysis type using the project setup wizard.
- Inside of the Data Extraction tab, choose how you would like to set the language of your text.
- You can set languages manually or by inferring it directly from the text itself.
- That's it. Execute your project and delve into your analysis.
- Fixed a bug causing the building of projects to fail after a user decided to delete data points and recreate the project without these points.
- Solved issues surrounding an inability to save new manual segmentations.
- We've made it easier to build projects using datasets that match our built-in analysis types. When you build a project matching an analysis type, Graphext will make assumptions on the way you want to set up your project. You can still edit the configuration of projects matching your dataset using the new edit button that we've added - instead of moving backwards through the wizard.
Our team set out to build an exceptional football team for less than 100M Euros. Using data provided in the FIFA 2020/2021 dataset - the video game - we built a prediction model in order to find the key performance attributes for each position. Then, we used this to pick out a team of excellent but undervalued players.
Maria and Paul analyzed a dataset of products from a bakery in Edinburgh to discover the associations between menu items. In this video, they walk through the process of conducting a simple product association analysis that could be used for any e-commerce or retail business.