clatan

Not a fan of abandoning Rstudio notebooks in favor of jupyter i always found them inferior.

I can read, thak you, and no it doesn't.

It describes 3 new generics in base R that help their new S7 system.

It all seems motivated by better interop with python which is 'neat' but really doesn't seem like a critical necessity of the language. I guess it's more of a tactical thing where they're trying to make it easier for python users to eventually try R. Or for R users that work alongside python users to not abandon R.

I trust the authors immensely but i don't see what yet another class system in R solves. That's on me, but I'd like to understand more of what motivates this effort.

In other words OOP can be great for tooling, but doesn't make much sense for what R is meant to be used for -interactive analysis- in every day work.

R's mess of OOP systems works great, S3 is "fine" for just dispatching 'methods' based on attributes, one doesn't even know it's happening in base R ALL the time.

R flexibility also makes it possible to build your own class system. i.e. modern ggplot2 has its own ggproto object system.

Don't look into stremio

I'm an old R user forced to mostly use python because that's what the team uses.

R is so much better than python in many areas concerning data pipelines: connecting with external database systems through an unified API, superior data munging utilities, as well as plotting, a more comprehensive (obviously) statistical analysis toolset.

I even find rmarkdown vastly superior to jupyter.

But IMO the best reason to use R rather tha python is that its tools will make you approach the problem as a statistician rather than a programmer.

A good DS is one who can understand the problem and tackle it using data, not someone who knows engineering well.

The sooner SQL is phased out in favor of something more akin to Hadley Wickham's dplyr the better. Don't particularly like the syntax of this but it's the right direction.

I work at an ISP. We use Citrix for our VPN...

Everyone assumes things that aren't true, not just "junior" data scientists. There is not standard methodology for non gaussian non independent random variables.

Yeah... No thank you. I mostly look at/work in code

the .rmd is just text. But the output is cached separately, RStudio can load the full notebook, code and output. So it seems to me it's only a matter of how to integrate the .rmd with the output data so it can be displayed in interfaces other than RStudio.

Plus you get the option of rendering an HTML which IMO is no different than having a jupyter file.

I wonder how the HN crowd feels about H2O.ai

I wish SQL would die as a query language. This is one of the reasons why, writing a query in the syntactic order is counter to how you should reason about getting and shaping your data.

They do use that information, it's not necessarilly sketchy. They run analytics just like everyone else. In fact, one could argue Google's huge push for https was primarily motivated to deprive service providers of valuable data that Google has anyway.

Cancel culture is real

That quote you chose is as straightforward as it gets.

How about page rank instead of only using number of citations

Mexican here, I agree it's becoming (if not already) a failed state. Only thing holding it together is that not everyone is a pos and that big capitals do like pretending to have a rule of law.

Do be hard on yourselves, because the rest of the world isn't so proudly uneducated.

The alternatives aren't US companies, they are NOKIA and Ericsson

How about an evaluation period. People still do that.

An understandable desicion when the US government has been persecuting you for more than a decade.

No, he's absolutely right. And your idealization of 'progressives' is completely laughable.

There's a recent 99% invisible episode about this:

https://99percentinvisible.org/episode/the-house-that-came-i...

Yeah... No. This doesn't solve anything.

It's only the best resource it there

This is not new however, companies have been trying to find ways to deprive others from data. For example, Google's strong push for https adoption was mostly a play to deprive ISPs from detailed web browsing data.

You'll also need a strong background in statistics if you wanna bring value to a company other than "machine learn all the things".

I'd even argue that - for data science - it's more important than any ammout of years doing engineering.

There's a lot more stuff to do with ML and make money than ad targeting