[meztez]

I'm pushing for our actuarial team to transition to more R + Git. After 3 years of preaching, most of the actuaries now use RStudio + git as their primary work tool. It is happening.

What we did :

1) Provide documentation on everything from install to using internal R libraries for ETL.

2) Provide mostly problem free, always updated VMs with RStudio Server/ Shiny Server.

3) Establish an hotline channel for instant help on R or git.

4) A couple members on the team developed really close working relationship with IT and we have great respect for each other work.

What we provide is way better and by being active, we built users trust in the tools.

We are phasing out SAS and proprietary modeling tools. Python never took hold even if we bought Anaconda entreprise. Excel is there to stay for sure but since actuarial student learn R in school, it is easier to onboard new hire.

If you want to go down this path and have a chat, hit me up. I'm in P&C. We use R both in development and production environments. We use it for pricing, spatial contractual obligation, claims assignment and a couple more models.

[wodenokoto]

The current top comment (sibling to the one I’m replying to) argues that keeping Python environments across actuaries/users computers up to date is too difficult.

This is nicely solved by using R server.

I’ve worked in an R server shop, and the experience is really nice. You log on to the server in chrome or Firefox and the browser window basically becomes RStudio and all calculations are done on the server and all code and data also lives on the server which is a huge bonus in terms of data protection. No copies are floating around on peoples laptops and if Johnny is sick and forgot to push his code to git - no worries, it’s all on the r studio server.

I don’t now of a nearly as good Python solution. I think Conda suggests using jupyter lab, and while that is a great environment it’s not great if it’s all you can use.

[disgruntledphd2]

The big problem with notebooks is that you don't have a real REPL. This prevents one from single step debugging and tracing. This is one area where RStudio is much, much better.

The trouble is that so many of the younger DS people are focused on Python, that it makes financial sense to just deal with all its problems. There's also a lot more programming tools (though less statistical modelling tools).

[klelatti]

I'm an actuary with a strong interest in this area - would be very interested to hear more especially on your R vs Python experience.

[meztez]

It came down to IDE, workflow and data.table.

RStudio is an absolute killer solution from the get go. Package management in R is simple and robust. Shiny is the new Excel pivot table on performance enhancing code.

Python has more contributors, more users. It also creates a lot more noise. Business people may feel like it is a a programmer tool. R feel more approachable.

In the end, both are great solutions but we decided on R because we believe in the people contributing to the ecosystem, mostly RStudio. Somewhere down the line, there might be a transition to julia.

[hnracer]

I've used R (3 years) and Python (8+ years) in data science and much prefer Python, because it can do things that aren't just pure data analysis, and because pandas is so amazingly good compared to R's data matrix solutions, in my opinion. I believe that the algorithmic trading industry has gone fully into Python and away from R for these reasons.

[jimmyjimjimmy]

I'm a CPA. When I started learning code, I looked for whatever was most like a spreadsheet. R for the bill, with built-in frames.

[thetwentyone]

I'm on mobile, but do also consider https://JuliaActuary.org (something that I personally have contributed to).

[jgalt212]

R is better if your raw data is already tabular. I prefer Python if the raw data is unstructured / semi-structured. You can make the case that once Python has converted the data to tabular then move to R, but at that point I like the soup to nuts to be in one language.

[stochastastic]

I’ll second klelatti’s question about R vs Python. From my perspective Python is just as practical for actuarial calcs and better for building general purpose tools. Is there a reason Anaconda didn’t click?

[meztez]

GPU integration was broken for a long time. Managing VMs / Environments. The absolutely horrible integration with git/Github.

Having to rebuild your environment from scratch when your workspace crashed. Imagine starting a notebook with a 45 minutes compile time. No go.

One click deploy, let's just forget about it.

[kfk]

I am putting together course aimed at Python beginners in enterprise, I too have experience in Finance. If someone is interested I would love some early feedback, you can contact me my email is in this profile bio.

[ricklamers]

Would love to have a chat about how you’re making R + git more accessible. How do I best reach out?