Making
visualizations like maps or charts is the end product for many data
journalism research. When you want to explain the structure of the
research and the different steps of the data journalism research,
problems emerge. Or when you want to make the research more
transparent by sharing the outcome of the different steps of the
research. For example I am using R for analysis and plotly for
visualizations; for showing the different steps I am have a text
describing the whole process in markdown. During the lecture or a
training you have to switch from one application to another. Jupyter
notebooks solves this problem, because by using different kernels in
the notebook you can show text in markdown, calculations in R, and
visualizations in plotly. You can also share the notebook with the
data, so anybody can after downloading follow the research process
step by step.
