The mistake here is having an architect who is not shipping product. Architects who's job it is to define 'rules' and 'patterns' without actually impending anything are almost always a bad idea. Just focus on shipping. Have at least one experienced engineer who can guide the development but don't give those decisions over to some 'architect' who is not even going to write 10 lines of code in your codebase
> We had 4 backend developers and a DevOps guy who was already stretched thin.
The mistake here was having an architect full stop. The team is too small, a good tech lead can manage to plan a service with 50k MAU (and way beyond) without an architect. The problem with some companies that get millions in seed funding is that they need to spend the money and they do so by adding roles that shouldn't exist at that stage.
Another favourite antipattern: making devops a bottleneck. Don’t over-engineer production, don’t buy abstraction you can’t afford, and educate your colleagues to lower the bus factor.
Dedicated devops that aren’t co-founders are notorious for cv optimizing: working with cool, but time-consuming stuff they don’t yet master, at the cost of delivery-time risk.
I strongly think that R has outgrown just having statistics as its killer feature. The killer feature of R is data analysis
I have yet to see any software that rivals dplyr, data.table, and ggplot2 in the balance of power and ease of use. It also has all the auxiliary packages you need to fetch your data (DBI, httr, rvest), model it if necessary (parsnip, caret) and visualise it (ggplot2, plotly, shiny)
I know python is more popular here but I would choose R in a heartbeat 19 times out of 20
Possibly. I think R is actually easier to learn for people who have never studied or done programming before.
1. It's easier to get up and running as RStudio is much more 'batteries included' than other popular IDEs, it's harder to get into the case of multiple different python versions, and you install packages through the R interpreter rather than via pip at the command line
2. I would say R data analysis packages are easier to learn than the python equivalents. Because the dataframe is a native structure in R there has been a lot more packages that have tried alternative syntax approaches to try and find the 'optimal' one. Python has really only had pandas, polars, and pyspark (all of which have implemented their own data structures and therefore have focused more on performance than syntax)
3. This doesn't hold if you're studying a language to be a general purpose programmer. Then python is much better. Anything to avoid the hell of the R standard lib. But if you need to do a bit of coding to analyse data and you've never done any before, my vote would be for R.
However, these are thoughts from my own personal anecdotes rather than any pedagogical theory
I haven’t used matlab for 10+ years, but back then Matlab used to provide engineering packages for vibrations and non-linear models approximations, I’d imagine the effort of going Python/ open source code to just to redefine your model for both purposes and then validate and verify the results would be a 10-50 fold cost of paying for a license
Very cool! Are you planning for there to be a corresponding R package that exposes the high level commands? The popularity of the usethis package really showed the power of keeping people within the R interpreter rather than going back and forth with the terminal. This is so important for a language like R that has so many users without much CS training
Yes! Absolutely in the plans to have a corresponding R package. In the meantime, we've created a `.rv` R environment within rv projects that allow users to call things like `.rv$sync()` and `.rv$add("pkg")` from the console. Our internal user bases is primarily not CS based and have found these functions extremely helpful
That’s awesome! I have been playing off and on since 2009!
If you haven’t heard of it, check out The November NetHack Tournament [1]. I played it for the first time in November of last year and almost got a Wizard win (ran out of time) after getting so close with a Monk (got killed by Rodney’s touch of death after he stole my only source of magic resistance).
Drugs or not, your comment was rude and condescending. And trying to brush it off as people being 'sensitive' doesn't change it being rude and condescending
How are you measuring 'nice'? How are you measuring 'performant'?
My assumption would be that if you haven't seen anything, even from the creator, then you just have different expectations to the people who do get value from React
It’s interesting I’ve asked this question several times and no one can point me to one. Performant meaning when react is used it’s not at the bottom in speed and latency[1].
