maandag 18 september 2017


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. 

Working and installing Jupyter works the easiest in Ubuntu. Python is already installed, and installing Jupyter on top of Python is not rocket science. Just follow the manual. If you want a quick introduction into the working of Jupyter an Youtube instruction is helpful. . Before using an R kernel in Jupyter you first have to install the kernel in R already installed. Here is some background and a guide for installing.
In the screen dump below I am using data about municipalities in the Netherlands. For visualization I am using and running a python 2 kernel in Jupyter, for the data analysis I am using the R kernel. The whole analysis is describes in markdown.
The strength of Jupyter is the use of many different languages: From Ruby to Javascript in different kernels if needed.

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