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    Essential tips and tools for beginning data journalism

    Posted By Alastair Otter

    As the world of journalism changes many journalists are looking to learn new skills; skills better suited to an industry that is increasingly digitised and visual.  For many that probably entails learning something about data journalism and visualisation. But, if you’re from a strictly printed words background, the change can be daunting.

    For a start there is an ever-growing list of data journalism tools that are available which can be daunting. The question becomes, where to start?

    There is no single right answer. What you need to do is to decide what it is you want to achieve, and your particular working circumstances. If you work in a newsroom and your primary output is in a newspaper then you probably don’t need to learn to make interactive graphics. But if you work online then you may want to learn some data visualisation tools.

    The important thing to understand here is that no matter what kind of journalism you do you can benefit by learning some basic data journalism techniques. And don’t be fooled by the all-to-often portrayal of data journalists as code hackers. There is a place for great programmers but you don’t have to be a programmer to be a data journalist.

    What follows is an opinionated list of tools worth taking the time to explore. Most of these are tools I have come to rely on for a range of different projects, such as data driven stories like this. This is not a comprehensive list of tools, just a shortlist that makes up a good toolbox.

    Part 1: The data journalism basics

    Spreadsheets

    Yes, you can’t escape it. Spreadsheets are the core tool for any data journalism project. Too often journalists fall back on the old pretense that they’re no good with maths. You don’t need a PhD in mathematics to use a spreadsheet but a basic understanding of averages, means, medians and the ability to work with a spreadsheet will boost your reporting skills. If you’re completely new to spreadsheets there are many tutorials online that will have you up and running in no time.

    For most people the first thing they think of when they hear spreadsheets is Excel, which is a great option but by no means the only one. Google Sheets is preferred by many spreadsheet newcomers because its simplified set of options give them the bits they need without the huge array of functions in Excel. If you want something free but powerful, Libre Office spreadsheets is one of the best options.

    Document organisation and collaboration

    One of the challenges in doing data journalism is how to manage large numbers of documents without losing your way. Again, Google Drive is a good starting point. Drive stores all of your documents in the cloud and makes it easy to share these easily with other users. Drive also has built in version tracking, although it’s not immediately obvious, which means you can go back to previous versions of a document if you end up in a data dead end or if you make a mistake.

    While Drive has a ton of uses, sometimes you need something a little more focused on the task at hand. Which is where Document Cloud comes in. Document Cloud is also an online document storage service but it has a number of features that make it a great tool for data journalism. One of the most useful of these is the ability to upload PDFs to Document Cloud and have it convert these to text for you. Not only that but Document Cloud also indexes documents and over time it becomes possible to search across all your stored documents for particular words or names. Document Cloud includes annotations, it can build timelines from documents and makes it easy to embed portions of documents into your online stories. Also, multiple users can collaborate on the documents. Your newsroom will need to apply for an account but the service itself is free for news organisations.

    If you’re looking for something a little different to Document Cloud or Google Drive then it’s worth taking a look at Git and Github. Git has largely been the domain of programmers but increasingly journalists and other writers are turning to Git/hub for a range of reasons. Git is a version control system. You can create files, edit those while being able to revert to previous versions at any point. You can also “branch” files which means creating a second or third version of your files which you can experiment with. If these experiments work out you can then “merge” the changes back into your main files. If not you can dump the experiment and switch back to your original files. If you’re keen to try out Git and Github then do yourself a favour and watch Daniel Shiffman’s entertaining Git and Github for Poets YouTube series.

     

     

    Collecting and cleaning data

    The other reality about data journalism is that it is a rare occasion when you get to deal with clean data. Either you’ll be dealing with dozens of PDF files that need to be converted into something useful and verified. Or you’ll have a dump of messy CSV or excel files.

    If you’re looking to convert PDFs into text/numbers there are dozens of good tools that do good to excellent conversions. The problem is that PDFs are tricky things and your success converting them is largely based on how they are created. PDFs that were created directly from spreadsheets are typically easier to convert than PDFs that are actually made by scanning in a document and then saving to to PDF. More often than not you’ll deal with this latter type, especially if you’re getting leaked data.

    If you’ve got a Document Cloud account this should be your first stop because it has PDF conversion built in. If you’re looking to convert just a portion of a PDF, or multiple similar portions of a document then try Tabula. With a little bit of practice Tabula can be made to do pretty reliable PDF conversions, even if your data is spread throughout multiple documents.

    There are also a number of online PDF conversion tools that work with varying degrees of success. One of the more popular is CometDocs which does conversion to multiple file formats. Zamzar offers a similar service. If you’re looking for something a little more robust then Nitro is worth testing. Nitro offers a free online PDF conversion service but it is also available as a paid-for desktop application. It’s not cheap but it’s very capable if you’re dealing with multiple documents on a regular basis.

    Once you’ve got your data probably need to clean it. If the data is not too messy or detailed then a spreadsheet is a good starting place. But, if you’ve got a file with hundreds or thousands of rows and multiple problems then Open Refine is the tool of choice. Open Refine used to be called Google Refine and it makes it relatively easy clean up dirty datasets. One of its strengths is its ability to work with just portions of your dataset at time. For my money, if you’re going to commit to learn anything then Refine would my choice. Once you’re over the initial learning curve and you discover the power of Refine you won’t look back and there are some good introductory tutorials available for Open Refine.

    A tool similar to Open Refine is Data Wrangler which aims to make it as easy as possible to clean up and manipulate large data sets. I’m not overly familiar with Data Wrangler so my preference is for Open Refine but I mention it because it looks to be a promising tool.

     

    Part 2: Analysing and visualising data

    Once you’ve got your data cleaned and sorted you’ll want to see what the data is telling you. If you’ve read anything about data journalism you’ve probably heard someone say that you need to interview your data like you would interview a source. Just because you’ve got a set of data doesn’t mean you have a story. What you need to do is look at the data in multiple different ways to see what stands out. Also, when you do this you might well spot anomalies in the data, a sudden spike or dip in values. Sometimes these are the stories but often these are the result of a problem in your data.

    One of the easiest tools for doing a quick visualisation or two is Google Sheets. Exel or Libre Office could also be used but Google Sheets is perhaps the easiest of the tools when you’re looking for a quick chart. It’s worth looking at your data in multiple different views to see what the patterns look like.

    An initial view of Vaal Dam levels for every day of the past year. Visualising it this way makes it easy to spot anomalies or missing data points. Those sudden spikes are very likely errors in the data rather than actual spikes.

     

    Another way to do initial visualisation is with one of a number of online tools. One of the easiest to use is Datawrapper which outputs your charts in multiple different ways. It’s a useful way to switch between different views quickly to get a sense of what works well. There are a few other services online, such as RAW or Quartz’s Atlas charts which produce good results.

    Once you’ve got an idea of what you want to do then it’s time to start creating. Most of the programs mentioned above will produce embeddable versions of the charts you’ve made but they may be limited in adding other elements like images, text areas or extra labels. For that you’ll need to look at some other tools.

    Piktochart and Infogram are among the best and easiest at doing this. Both make it easy to combine charts with other visual elements, and if you start with one of the pre-built templates you’ll have something decent looking in next to no time.

    If you’re looking for something more detailed with more than just a few default chart types then you should probably try out Tableau Public which is free and extremely powerful. It can build everything from the simplest charts to complete interlinked dashboards. But be warned, the initial learning curve can be a little daunting for first-timers. If you’re serious about data visualisation then take the time to learn more about Tableau Public. But if you just want the occasional chart to dress up a story then stick with one of the other options.

    Part 3: Maps and mapping

    If you do any kind of data journalism you’re bound to come across geographic data. Which brings up the issue of mapping tools, some of which are simple point and click affairs while others border on the arcane. So you need to think carefully about what you’re trying to achieve with geographic data.

    Too often the first instinct is to plot the points on a map. Which is worth doing in the initial exploratory stages in almost all cases, but often a map is not the best way to illustrate the point of a story. For example, having a map with 200 points all clustered around a small area is often not the most informative way to display data. While shaded contiguous areas to indicate some sort of distribution can be far more effective.

    Having said that, a good map done right can add huge amounts to a data story, so what are the best tools?

    Once again Google is a good starting point. Google My Maps is one of the simplest tools to use. It’s pretty intuitive to use and makes it easy to look up geographic points, draw lines and shape on maps and even add driving directions. If you just want to illustrate where or how something happened geographically then there is no better place to start.

    A step up from My Maps is Google Fusion Tables. This is also part of the Google Drive suite of tools. Fusion Tables in fact does a lot more than just make maps, though that is one of its strengths. Fusion Tables also make it easy to filter data sets, do some cleaning up of data, merge multiple datasets into one and a fair amount more. It’s a little tricky at first but is a good choice when you’re dealing with larger data sets.

    If you’re really getting into this mapping thing and you want a bit more than the previous two options then CartoDB is your next step. Carto is all about maps and it has the potential to make excellent maps with multiple layers and different designs so long as you’re prepared to put in a little initial work. Personally I find Carto an excellent choice for mocking up a quick sample map or merging sets of data to include geographic points. It makes it pretty simple to visualise larger sets of data and make decisions about where you should go with your project. Carto also makes it easy to export the cleaned and fixed datasets into many formats which makes it easy to use in other applications.

    Undersea cables
    The world’s undersea cables as viewed in Mapshaper.org

    There are literally dozens of other applications for making maps some of which are extremely powerful but often also very complex. ArcGIS is popular tool, as is the open source QGIS application but both are aimed at fairly experienced mappers so the learning curve can be steep. If you’re keen to try your hand at making your own map styles then Mapbox is great for that. Mapshaper.org  is another of my most commonly used maps tools because it makes it easy to get a quick visual representation of the data in your map files and it also makes it easy to simplify map shapes, something that can be extremely useful in keeping download times down.

    In conclusion

    Data journalism is a broad area of work with place for many different skills. Some might favour the visualisation side of data journalism while others may prefer the mapping side. No matter what you prefer doing or what the limitations of your newsroom are there is always something more to be learned about data journalism. The recommended route would be to start with the basics above and then gradually move into some of the more detailed areas.

    From experience the best way to learn to become better at data journalism is to practice. Find a real world dataset and see what you can make out of it. It’s only when you’re working in a real world scenario that you’ll really learn the ins and out of good data analysis.

    Comments, thoughts, feedback? You can find me on Twitter or leave a comment below.

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