These tools are aimed at all levels of user, from the novice to the expert, and are all free to use (with at most a requirement to sign up).
Further suggestions on anything that would be useful or that demonstrate how you're using data are very welcome, please contact us.
We aim to provide our datasets in the useful, standarised and simple formats. Though we spend a lot of time cleaning, tidying and providing our data in various file types, users have individual needs that may require altering fields and values after downloading the data.
If you need to clean messy, real-world data before putting it to use (otherwise known as data 'wrangling') you could use basic software packages such as Excel or even a text editor, but specialist tools also exist to make this easy and straightforward.
Wrangler is an interactive and visual tool that can transform, correct and cross-tabulate irregularly-formatted datasets (e.g. multi-table data files). It's web-based and simple to use, so you don't need to install anything. It also makes predictive suggestions that can help to guide you through the process. There's now a downloadable and supported version called Trifacta.
Originally developed by Google, Open Refine is a powerful tool for making messy datasets clean and useable. It's better than using 'Find & Replace All', helps you to quickly transform data en masse and helps to find and correct errors.
Have you downloaded a dataset in a strange format and don't know where to start with it, or just want to get it into another format? Try Convert CSV to upload or paste in JSON, XML, SQL, HTML, KML and other types of popular data formats and convert to CSV or Excel formats.
Ever download a dataset only to find it's been produced as a near-to-useless PDF? There's a number of 'data scraping' tools that allow you to get useful, tabulated data out, PDF Tables, from ScraperWiki, is one of the best.
If you're a regular wrangler, acqaint yourself with Tidy Data Principles for working with data in the wild.
There are a multitude of ways to visualise data, and alot of it is down to communication, flexibility, interactivity or just personal preference. Data visualisation is becoming an industry in itself, not just the last step in publication, and there is now a massive range of different providers of tools for chart making and presentation.
We'll present some tools and guides for data visualisation, but alot has been done to create libraries and respositories of visualisation that you should check those out too.
Starting at the small end of the scale, Datawrapper allows you to upload data and make a simple chart that you can embed in a website in seconds.
Tableau provides a data storing, analysis, visualisation and web-publication platform in one. You can create full data dashboards, and extend interactivity to other users. There's also the simple, lean version, Tableau Reader.
Built on top of the flexible D3.js charting library, Raw lets you upload and preview your visualisation through a number of innovative charts that you'll be hard-pressed to find anywhere else (dendograms, alluvial flow diagrams, voronoi tessellation charts).
Crunching through numbers to perform data manipulation and calculations is probably the trickiest part of working with data. From the beginner to the expert, here's a few of our favourites that aren't Microsoft Excel.
Fusion Tables is the cousin to Google Sheets, letting you do much of what you can do in Sheets (such as visualisation and importing/exporting data), but also makes it easy to summarise,
Along with Python, R is the programming language of choice for statisticians. It is a complex language with a huge library off extended packages, but R Studio makes it easier to use and there are a number of guides and a broad user and developer community out there.
You can use basic Python functions for data in Python, but the Pandas library provides a fast, structured and easy-to-use approach to working with many different types of data. If you're using Python, you should also check out Bokeh for visualisation.
Our purpose is to improve how the voluntary and community sector uses data. So, if you're stuck or want some advice, we're here to help.