Analytical environments

Analytical environments are layered abstractions, and may be understood differently both by those belonging or not belonging to them, and by varieties of communities taken as belonging to a given analytical environment. It is helpful to see the role of evolving communities and often symbiotic relationships between participants as a lens that will help us grasp the "life" of analytical environments. This life is also implicit in the experiences of participants, be they users, developers, recipients of research output, students, or providers of key underlying software libraries. In this chapter, a partly autobiographical approach has been adopted in order to position this reading of how analytical environments may be understood as outcomes of willed actions. These willed actions, to provide software among other things for teaching and research, do not limit themselves to achieving pre-defined goals. In the cases used, Python and R, the reach and influence of the language platforms has evolved greatly over the twenty years work has been progressing. In addition, the communities of users and developers have matured and grown, although the pressing need for bringing in younger and more diverse contributors is recognised. We also know too little about the impact of these analytical environments, as citation practice with regard to software tools such as Python or R packages has not been encouraging in the social sciences. Going forward, and as part of the movement towards reproducible research, it would be interesting to encourage journal editors to require article authors to document their work by citing the software used. Unfortunately, as of now it is not possible to give a systematic overview of who uses which analytical environment for what, so this chapter will follow the chosen partly autobiographical approach, rendering the claims advanced largely subjective.