How can you use programming languages to effectively visualize data in journalism and storytelling?

Programming languages offer robust libraries for various aspects of data analysis and storytelling, starting from the data cleansing stages to the data visualization. These libraries empower users to achieve precision, automate procedural tasks, and extract the most valuable insights from their analyses. This, in turn, empowers individuals to make informed decisions grounded in the results.

Data visualization serves as a dynamic component within a BI project, rather than an end product in itself. Just as a newspaper's design evolves to accommodate changing news content, visualizations adapt to the evolving needs of a company. In my experience, the use of programming for visualizations is relatively rare. Consider the example of a fitness app that tracks daily activities, displaying progress through charts. In such cases, using programming for visualizations is expected. However, using programming in BI can often lead to cumbersome project workflows, with many hoops to jump through before reaching a decision. In BI, the ability to swiftly modify and update visualizations is crucial for making informed and timely decisions.

While Python, with its powerful visualization libraries like Plotly, Seaborn, and Matplotlib, is excellent for data visualization, I would highly recommend considering R in combination with D3.js for journalism and storytelling. Personally, I've extensively used Python for data visualization, finding it especially handy for geographical representations. However, when it comes to intricate and interactive visualizations, especially involving geographical data, the combination of R's ggplot2 package and D3.js in R can offer unparalleled flexibility and interactivity, enhancing the storytelling aspect through compelling data visuals.

If you use "no code" platforms like Tableau and Power BI, you may be limited to the visualizations that come as part of the standard features of those platforms. In programming languages used in data visualization, such as the ggplot package in R, you are much less limited, because ggplot provides a language framework for making almost any kind of visualization. In addition, you can use R to do data blending, data quality checks, and data cleanup much more easily than you can do in a standard "no code" business intelligence platform. Fortunately, Tableau and Power BI both contain functionality that allows power users to run R code within the platform. So there's no throwing out the baby with the bathwater.

Programming languages can be powerful tools in journalism for effective data visualization and storytelling. Here’s how they can be used: 1. Data Analysis and Cleaning: Programming languages like Python or R can be used to analyze and clean large datasets, making it easier to extract meaningful insights. 2. Data Visualization: Libraries like Matplotlib, Seaborn (for Python) and ggplot2 (for R) allow journalists to create various types of visualizations, such as charts, graphs, and maps, to make data more understandable. 3. Interactive Visualizations: JavaScript libraries like D3.js or tools like Tableau can be used to create interactive data visualizations that engages readers.

I highly recommend Bokeh library for Python for the less hardcore programmers. Not only does it allow you to create both static and interactive visuals, it is more intuitive than JavaScript for non-programmers and its visuals can be hosted on the web. Further, Bokeh allows automation and customization to the nth level detail of a visualization allowing for highly polished data visuals.

Programming languages are the storyteller's secret weapon in data visualization. They offer unmatched flexibility, turning data into interactive, engaging tales. With R, Python, and JavaScript, you're the conductor, crafting visuals that captivate your audience. Plus, automation and scalability save time, and libraries like D3.js and Plotly bring your vision to life. Imagine pairing these coding skills with BI tools like Metabase or Tableau for an unbeatable combo. In a nutshell, programming languages empower storytellers, offering limitless creativity and enhancing the impact of your data-driven narratives.

If you're publishing on the web then somewhere underneath your code JavaScript is doing the work. But you rarely have to write it since many Python or R packages will do the heavy lifting for you. A good option in the Python ecosystem is to use Streamlit and Altair.

Programming is a general skill, it transfers from language to another. Just like natural languages, the more you learn and the more you practice the easier you will move from one to another. Technology changes fast, today's most in-demand could be obsolete tomorrow. For text manipulation, I recommend python, but if you are into data science and data visualization my goto language is R.

For those seeking user-friendly options in Python for creating visually appealing plots, I highly recommend Bokeh and Plotly libraries. Bokeh stands out for its intuitiveness, making it accessible for non-programmers to generate both static and interactive visuals. Its seamless integration with the web adds to its appeal. Bokeh's automation capabilities and meticulous visualization customization options set it apart, allowing for polished data visuals. On the other hand, Plotly provides an intuitive interface for crafting diverse, interactive plots, making it an excellent choice for both beginners and experienced programmers looking to enhance their data visualization projects

Lately, I've delved into D3.js for visualizations, having coding exp significantly facilitated my D3.js learning curve. Here's some advice to learn coding: 1. No Shortcuts: Take the time to grasp the fundamentals of programming. 2. Begin with Java/C++: These offer structured environments unlike Python. It's simplicity can mask the underlying complexities. 3. Recognize the Long Journey: Coding is difficult to learn and difficult to master compared to BI tools, which are relatively easy to pick up but difficult to excel in. 4. Set Clear Goals: Having a specific objective in mind can significantly ease the learning process. With my exp I know what is possible and not with the current BI tools and I am trying into improve one aspect of it.

Learning programming languages for data visualization involves deliberate practice, as in mastering any skill (Campitelli & Gobet, 2011). Select your path, allocate a few hours weekly, and commit to it. For beginners, "Storytelling with Data" by Natalie Cole is an invaluable resource, offering a solid foundation. Choose a real-world project, either at work or personally, to apply your skills effectively. Building a support system is crucial; join online communities to connect with experts, ask questions, share ideas, and seek feedback. Your learning journey may have its challenges, but with dedication, resources, and a supportive community, you'll become a proficient data visualization storyteller. Good luck! 🚀

I’ve found that there are many resources out there for learning coding languages, and some of my favorites are coursera and udemy, but I found the best way to learn is consistency. Regardless of what language you’re learning the only way to get better is to practice and practice often, especially in the beginning. Once you’ve set these habits in place the only thing left to do is keep learning

There's a wealth of resources out there. Here's the approach that works for embedding learning for me: 1. Do a course to get the basics and theory 2. Watch youtube videos to have a practical example 3. Set myself the challenge to apply my learning 4. Stay curious and follow inspirational content (nightingale dvs). 5. Try to understand how things were made and study Github repo's

To learn programming languages for data visualization in journalism, start by choosing a language like Python, R, or JavaScript based on your goals. Explore relevant libraries and frameworks, engage with online communities, & work on personal data visualization projects. Take online courses, read books, & practice regularly on small projects. Continuous learning, challenges, & contributions to open source will enhance your proficiency. For Python & R, explore courses like "Data Science & Machine Learning Bootcamp," "Applied Data Science with Python," & "Data Analysis for Life Sciences." To master JavaScript for web-based visualizations, you can check out "Data Visualization & Communication with Tableau" & "Programming for Data Science."

Before any analysis and visualisation task, the most important task to be completed is to look at the data. By looking I mean just skim through the data. Look at the different variables. Jot down the questions that you would want the data to answer that is inline with the analysis. Once we have exhausted the number of questions, then go on with the analysis and visualisation.

Plan your act: set goals, know your audience, find reliable data sources, and use the right tools. Design your magic tricks with interactivity and choose the best programming language. Test behind the scenes, optimize for a smooth show, and share your magic. Keep it up-to-date and adjust based on feedback. Remember, honesty is the best policy, and respect data usage rules

Creating a data visualization project is a journey with five stages: planning, initiation, execution, monitoring, and closure. In planning, understand your story, audience, and data sources. Initiation involves sketching your design and choosing the right tools. In execution, pay attention to detail and consider collaborating with experts. Monitoring ensures your visualization stays on track. As you close your project, document its value, costs, and benefits. Share your work with the community for continuous improvement. These stages make your data visualization a compelling narrative.

Analysis must always precede visualization. You must have a clear idea of what your data set is & what each individual variable truly means before you start visualizing. Visualizations can help you "see into" a data set, but you have to make sure that what you're seeing is an accurate reflection of the data set & not simply something you're reading into it. Then, once you have double-checked your analysis, you can make a data visualization suitable to show to other stakeholders outside the analysis team. First, declutter anything extraneous you added while you did your analysis. Second, focus stakeholder attention on the most insightful findings with the greatest impact, while placing less emphasis on what is less important.

Uno de los aspectos más delicados a la hora de generar visualizaciones de datos, no son tanto las herramientas o el lenguaje, sino tener la claridad respecto de lo que se desea mostrar. Me pasó empezar muchas veces con una idea aparentemente clara, que cuando se empieza a implementar se queda a mitad de camino. Mi consejo es hacer primero unos dibujos sobre hojas para tratar de obtener una vista previa de lo que estaremos mostrando, luego validarlo con personas que puedan colaborar y una vez que eso está listo, recién pasar al diseño en sí. Las ideas iniciales muchas veces parecen estar completas pero al final descubrimos que siempre pueden mejorar.

Coding data visualizations is like painting with numbers—creative and precise. Start by picking the right chart type, colors, and labels while avoiding clutter and misleading elements. Be a coding maestro by mastering the documentation, syntax, and standards of your chosen language or framework. Comment your code, use clear variable names, and keep your code organized. Testing is your best friend—detect bugs, enhance performance, and boost readability. In data visualization, your code is the brush that brings your data to life, telling a compelling story with every line.

One trick I use with data visualizations that I need to put into presentations is to save my visualizations in a scalable vector graphic format, denoted with the svg file extension. If you use svg & you need to resize the visualization to fit your presentation format, you can then use a free open-source vector graphics editor, such as Inkscape, to do what you need to do to make your infographic visually engaging. These vector graphics editors are especially useful if you need to make visualizations conform to the brand standard of your company.

To showcase your data visualization effectively: Choose a fitting platform (web, dashboard, presentation), prioritize interactivity and accessibility, provide a clear narrative, opt for appropriate visualization types, focus on aesthetics, ensure performance optimization (especially for the web), and gather feedback for iterative improvements.

1/ - Select a platform that suits your project's goals and audience. Options include a personal website, social media, data visualization platforms (e.g., Tableau, Power BI), or even print publications. - Consider the format - interactive web-based visualizations, static images, animated gifs, or video infographics. - Add annotations, captions, and labels to your visualizations to provide context and guide understanding. - Encourage audience engagement by including interactive elements in your visualizations, such as tooltips, filters, or interactive dashboards. - Enable comments or feedback mechanisms on the platform you use.

By harnessing the power of machine learning algorithms to unearth hidden patterns and trends, complemented by the nuanced decision-making abilities of human analysts, organizations can not only enhance their data-driven strategies but also derive a deeper, more actionable understanding of complex data ecosystems. This collaborative approach not only streamlines processes but also affords a more comprehensive view of data, ultimately leading to more informed and strategic decision-making in an increasingly data-centric world.

Selecting a suitable tool,one that aligns with your skill level and comfort, is a pivotal step. Once you've done that, the next phase is choosing the right visual for your narrative.Know that a powerful visualization should not need extensive context.Try the "BUS" principle to cut through the noise- it must be Big, Useful, and Surprising. Amidst numerous insights at your disposal,only a few will meet these criteria and seamlessly integrate with your story.Remember to prioritize interpretability of your visual. Notably,a source of inspiration and knowledge that stands out is David McCandles,with his TED talks,books, and the invaluable resource that is his website,"InformationIsBeautiful." The Wall Street Journal is another wonderful example.

Programming languages like Python allow us to create interactive data visualizations, such as a live election map that updates in real-time, enriching storytelling and engaging readers.

One thing that I have found very useful, is that as you proceed with data, you may be lost in it. but if you know your data and verified and cleaned and put it together. you will relax and the magic begins.

Coding is the foundation of data visualization. It prepares and processes data for visual representation, enabling developers and data scientists to create interactive visualizations using languages like Python, R, and JavaScript. This approach integrates multiple data sources to form comprehensive, multidimensional visualizations that can update in real time. These visualizations easily scale with growing data complexity, ensuring future-proof solutions. The power of data visualization lies in its ability to quickly identify trends and correlations, aiding in informed decision-making.

The decision to do data visualization using a programming language vs. a drag & drop tool is an important one, with some key trade-offs. One of the biggest trade-offs is time to market. If you need a data viz immediately and have little programming skills, then drag and drop tools like Tableau or PowerBI can be a good approach in some cases. However, the trade-off here is the limit one quickly faces with drag & drop tools, which may mean delivering only a half-baked solution or no solution. In my experience, PowerBI has a lot of limits - one of which is a message that it can't handle more than 1 million records! Tableau is less limiting, but its just a matter of time before hitting a wall - e.g., try building a flow chart in Tableau.