LibGuides: Digital Scholarship Centre: Data visualisation

What is data visualisation?

We create a lot of data. It is estimated that 59 zettabytes were produced in 2020 alone. do you know what a zettabyte looks like? Cisco created the following visualisation of a zettabyte:

The visualisation definitely makes it easier to understand how large a petabyte of data is. And that's the power of data visualisation.

Data visualisation is about presenting data in a visual format to tell a meaningful story. It is a graphical representation of information and data that allow you to quickly see trends and outliers, visualise patterns and relationships, and easily share information. It is storytelling with a purpose.

More about data visualisation

General types of visualisations are:

Charts present information in a tabular, graphical form with data displayed along two axes. It can be in the form of a graph, diagram, or map.
Tables are sets of figures displayed in rows and columns.
Graphs are diagrams of points, lines, segments, curves, or areas that represent certain variables in comparison to each other, usually along two axes at a right angle.
Geospatial visualisations how data in map form using different shapes and colours to show the relationship between pieces of data and specific locations.
Infographics are combinations of visuals and words that represent data and usually use charts or diagrams. Visit our guide on creating infographics using AI.
Dashboards are collections of visualisations and data displayed in one place to help with analysing and presenting data.

More specific types of visualisations are:

Area maps is a form of geospatial visualisation and are used to show specific values set over a map of a country or other geographic location.
Bar charts represent numerical values compared to each other. The length of the bars represent the value of each variable.
Box-and-whisker plots shows the range of values along with the average and interquartile ranges.
Bullet graphs takes a bar chart and adds additional elements to create a compact way to analyse performance against a goal or threshold.
Gannt charts are typically used in project management. These charts are bar charts depicting timelines or tasks.
Heat maps use colour and size to show the values of a measure. It doesn't need to be temperatures!
Highlight tables use colour to categorise similar data to make reading it more intuitive.
Histograms are a type of bar chart that split a continuous measure into different bins to help analyse the distribution.
Line charts are best used to show trends across time intervals. Multiple lines can be used to compare categories within a dimension.
Packed bubble charts use size as the primary visual element to allow for comparison between categories.
Pie charts are circular charts that shows data as a percentage of the whole.
Scatterplots are used to explore the correlation between two measures with independent axes.
Text tables present individual values in rows and columns and mostly used for description or summary.
Treemaps are a type of chart that shows different, related values in the form of rectangles nested together.

Candela
As an open-source suite of web visualization components that make use of the Python language, Candela emphasizes scalable, rich visualizations created with a normalized API for use in real-world data science situations.
Chartbuilder
With Chartbuilder’s intuitive web-based application, creating charts and diagrams is a matter of copy and paste. Easy-to-understand buttons and settings, as well as a consistent display showing what your visualizations look like on mobile and desktop browsers, allow for a clean user experience.
Charted
A clean user interfaces and easy to use. All you have to do is upload a CSV file, or a Google Sheets link, and it’ll generate the chart for you.
Chartist.js
Chartist.js includes a large array of charts that are responsive, animated, and rendered beautifully.
ColorBrewer
In reality, this is not really a data visualization tool on its own but more of an additional tool to enhance your data storytelling. By providing options for colors and styles painted on a complicated map graph, ColorBrewer gives you the chance to experiment and make informed decisions on your data visualizations.
D3 Data-Driven Documents
D3 is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3's emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualisation components and a data-driven approach to DOM manipulation.
Datawrapper
Datawrapper is an extremely easy-to-use data visualization tool for plotting interactive charts. All you need to do is upload your data via a CSV file, choose the chart you want to plot. It's free and no sign-up needed.
Domo
In reality, this is not really a data visualization tool on its own but more of an additional tool to enhance your data storytelling. By providing options for colors and styles painted on a complicated map graph, ColorBrewer gives you the chance to experiment and make informed decisions on your data visualizations.
Dygraphs
Dygraphs is a JavaScript charting library that allows for panning, zooming, and mouse-over actions. It handles and interprets dense data sets very effectively.
Flot
Flot is an easy-to-use charting library that provides very elegant charts and graphs. It allows advanced user-interactions like panning, zooming, resizing, switching a data series on and off, and more.
FusionCharts
Built on JavaScript, FusionCharts provides ready-to-use code snippets to create your own data visualizations based on popular templates from the typical bar and column charts to treemaps and waterfall charts. One of the biggest benefits of using FusionCharts is that it integrates perfectly with popular JavaScript frameworks, as well as front-end and back-end frameworks, allowing for a greater level of customization.
Google Charts
Google Charts is user-friendly and compatible with all browsers and platforms. It covers a wide range of data visualization types — from simple line and bar graphs to complex hierarchical tree maps — making Google charts suitable for almost any project.
Infogram
Beautiful and highly customizable, Infogram features a Microsoft Word-style dashboard that carries the capabilities of Excel and most other visualization platforms. It’s drag-and-drop markdown menu style also allows for solid reports to be created, helping to bring data storytelling to the next level.
Leaflet
Leaflet is an open-source JavaScript library that specializes in mobile-friendly, responsive, and interactive maps. With particularly readable source code and documentation, Leaflet is easy to pick up for beginner coders and works well across different platforms.
Looker Studio (Google)
An extension of Google Charts, Looker Studio provides further functionalities to create full reports based on analytics. These reports are fully customizable with data visualizations and trackable via Google Analytics, giving you a wider view of your data. Most importantly, its integrations with Google’s other products, like Google Sheets and Google Cloud Storage, is crucial for any pre-existing user of Google’s suite of digital solutions.
Palladio
Developed at the Humanities + Design lab at Stanford University, Palladio is a browser-based visualization tool built to handle immense amounts of historical data with relative ease. Palladio is, however, more specialized, focusing on specific case studies and research questions. Some of these features include a gallery view, list view, map view, and a special graph view for illustrating relationships between data points.
PivotTable.js
For those familiar with Microsoft Excel, pivot tables are a complex but powerful tool for distilling and displaying data. An interesting means of data storytelling, PivotTable.js is a free web pivot table that allows users to explore data using an easy drag-and-drop format.
Plotly
An open-source library for R, Python, and JavaScript, Plotly works seamlessly with its enterprise-ready deployment servers to enable easy and code-free collaborations. For those with some knowledge of coding, tutorials and further guidance enhance Plotly’s functionalities.
Polymaps
As its name suggests, Polymaps is for creating catographical data visualizations. It pulls in data from OpenStreetMap, Bing, and other map image providers, while also rendering its own representations.
PowerBI
Turn your data into visuals with advanced data analysis tools, AI capabilities, and a user-friendly report-creation tool. Ideal for creating dashboards.
RAWGraphs
Raw bridges the gap between spreadsheets and vector graphics. It’s built on the D3.js platform. If you’re not a programmer, Raw could be the perfect data visualization tool for you. Raw provides a selection of 16 ready-to-use chart types. Customization is one of the biggest positive aspects of Raw, for it allows you to use your own custom layouts.
Shiny R
Shiny is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards. You can also extend your Shiny apps with CSS themes, html widgets, and JavaScript actions.
Tableau
our data has a story. Share it with the world. Visualize and publish data on topics you care about. Explore and be inspired by creations from like-minded data enthusiasts.
TimelineJS
TimelineJS is a great tool for creating interactive, visually rich timelines without having to write code. TimelineJS has built-in API support for a variety of data sources like Wikipedia, Twitter, SoundCloud, Vine, Google Maps, and YouTube.

Want to capture a trend? Use these visualisations:

Line chart (to capture a numeric variable changing over time)
Multi-line chart (to capture multiple numeric variables over time)
Area chart (to show how a numeric value progresses by shading the area between line and the x-axis)
Stacked area chart (to track the breakdown of a numeric value by subgroups)
Spline chart (a smoothened version of a line chart)

Want to visualise relationships? Use these visualisations:

Bar chart (for the quick comparison of categorical data)
Column chart (for short labels, date ranges, or negatives in values)
Scatter plot (for observing the relationship between two variables and useful for potential correlations between data points)
Connected scatterplot (a scatterplot and line plot combined)
Bubble charts (for visualising data points with 3 dimensions, showing relations between data points using location and size)
World cloud chart (for visualising the most prevalent words that appear in a text)

Part-to-whole visualisations allow you to show sub-categories within a larger category:

Pie chart (for showing parts to the whole data and percentages)
Donut pie chart (for showing parts to the whole data and percentages, but with a hole in the center)
Heat maps (for representing data trends using colour shading)
Stacked column chart (for comparing subcategories with categorical data, or to compare percentages)
Treemap charts (for displaying hierarchically structured data with 2D rectangles whose size is proportional to the value being measured)

These data visualisations allow you to visualise a single data point/value:

Card (for showing and tracking KPIs in dashboards or presentations)
Table chart (for small datasets displaying tabular data in a table)
Gauge chart (for showing relevant KPIs)

Want to capture distributions? Use these visualisations:

Histograms (for the distribution of a variable)
Box plot (for the distribution of a variable using 5 key summary statistics: minimum, first quartile, median, third quartile, and maximum)
Violin plot (for the full distribution of data alongside summary statistics)
Density plot (for the distribution shape of data)

These visualisations allow you to visualise flow:

Sankey chart (for flows in systems)
Chord chart (for weighted relationships or flows between nodes)
Network chart (for relationships)

Where to find visualisations

Here are some places to look for data visualisations:

Data is Beautiful

FiveThirtyEight
FlowingData
Observable
The Pudding
A digital publication explaining ideas debated in culture with visual essays.
Quarts
Good practice and tips on data visualisation
Reuters Graphics
Data visualisations relating to current events, with updates every week.
TheUpshot
Visualisations from the New York Times.

The beauty of data visualisation

Take a look at this video on data visualisation, where David McCandless, the presenter of this TED Talk, illustrates how using visualisation of information allows us to see patterns and connections that matter, and allows us to focus on the information that's important for us.