Hello R-Studio Community!
I work for a small/medium size not-for-profit academic research institute. We partner with many universities to conduct observational and experimental studies related to coastal ecology.
Many of these research programs are very synergistic, wherein one program utilizes the data collected by another. We are a fairly young institute and we don't currently have any formalized workflows for 'developing analyses' (credit @hspter). Each program's Primary Investigator (PI) uses an ad-hoc approach for summarizing data and conducting analyses.
I'm keen to push the organization to move towards a coherent, systematic approach to developing analyses and communicating results (R-Studio, tidyverse, GItHUB, LaTeX) that one might see in a more tech oriented data science business. I think there is a lot to be gained by adhering to the principles of reproducibility, open-science, and using a workflow that allows easy integration of other program's work and results.
My question is, how can I be successful in doing this? What challenges will I face? Many of my colleagues use R, but also use Python and Matlab. Certainly there are some general principles that are relevant regardless of programming language, but has anyone experienced push-back from colleagues when being told they have to adhere to some new way of doing things that they have to spend their valuable time to learn?