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R: Introduction to Data Science (2019)

rafalab.dfci.harvard.edu

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Re: R: Introduction to Data Science (2019)

#121

Post by hello2hello »

hadley wrote: post #70
laylower wrote: post #48

Earlier quoted context omitted.

Amen to that. Can I add the following: - 5 different ways to do wide to long and long to wide over the years even in the tidyverse. - A lot of dependencies to connect to DBs and difficult programs. Rstudio/Posit does have some premium libraries but they should be made free and bundled with the tidyverse to really promote the ecosystem. - Shiny support to save interactive charts and tables. This is a massive problem f…

What are the premium packages you're talking about? As far as I know all of our R packages are 100% open source. I'd love to hear more why you're using webshot etc to talk screenshots of your shiny app. A more typical workflow would be to generate a separate HTML/PDF with quarto/RMarkdown.

PDF in knitr is tied to TeX. Webshot and other capture is better because CSS styles work without translation to TeX.

Re: R: Introduction to Data Science (2019)

#122

Post by jgalt212 »

j7ake wrote: post #118
nomilk wrote: post #14

Earlier quoted context omitted.

About half our team can wrangle and plot as fast as we can think of ideas. It creates an incredibly tight cycle time between us having ideas and getting answers; sometimes many (e.g. 10-20+) of those cycles in a single meeting. Before we used R, it would require someone jotting down things to investigate and reporting back in the next meeting. But we can do ~80% of whatever people can think of on the spot (more invol…

For exploring data, plotting data, and fitting complex models like GLMs and GAMs, using R is essentially as fast as thought at this point. So if you have tabular data, it’s a no brainer to use R. Getting the data into table form is often better suited for python. Fitting models that leverage autograd are also better with python.

> Getting the data into table form is often better suited for python. Fitting models that leverage autograd are also better with python.

Totally agree. If it's in a dataframe, I prefer R. If I get involved pre-dataframe, I prefer Python.

Re: R: Introduction to Data Science (2019)

#123

Post by nequo »

Cosi1125 wrote: post #96
SomeoneFromCA wrote: post #91

Earlier quoted context omitted.

It is de facto.

Why do you think that? (I'm legitimately curious.)

Not parent and I also consider R to be a surprisingly solid general purpose PL but the one reason that I can think of is that formulas are part of the language. Formulas are extremely well-suited for data analysis but odd outside of that.

Re: R: Introduction to Data Science (2019)

#124

Post by Cosi1125 »

nequo wrote: post #123
Cosi1125 wrote: post #96

Earlier quoted context omitted.

Why do you think that? (I'm legitimately curious.)

Not parent and I also consider R to be a surprisingly solid general purpose PL but the one reason that I can think of is that formulas are part of the language. Formulas are extremely well-suited for data analysis but odd outside of that.

Good point. I'd add complex numbers and "everything is a vector" to that.

Of course that doesn't make it a DSL. It simply means that R was designed with a particular application in mind. So was Perl (regular expressions as first-class citizens) or Javascript (DOM manipulation). Not to mention PHP.

Re: R: Introduction to Data Science (2019)

#125

Post by benreesman »

mamonster wrote: post #97
benreesman wrote: post #94

Earlier quoted context omitted.

Likewise interested if a pro has any consulting hours to spare :)

Sorry, unfortunately do not do consulting. In your other comment, you said you are looking to price "weird derivatives". How weird are we talking? If its OTC I won't be able to help anyway, if its standard then I can at least try to point you in the right direction. The fact you mention Black Scholes makes me think it might be something closer to "vanilla" than the other way around.

It’s looking like the goal will be to create downward pressure on derivative beta (especially in the case of a rapidly increasing underlying: big pools of Hopper cards basically).

I have a vague intuition that transaction costs will be sort of cumulatively symmetric: participants who get in quickly will pay a lot per unit time, but conversely, people who VWAP in will get zero-rated on the way out.

There’s a legitimate underlying switching cost, there’s a stability premium thereby, making that equitable for all participants is an interesting problem.

Re: R: Introduction to Data Science (2019)

#126

Post by nequo »

Cosi1125 wrote: post #124
nequo wrote: post #123

Earlier quoted context omitted.

Not parent and I also consider R to be a surprisingly solid general purpose PL but the one reason that I can think of is that formulas are part of the language. Formulas are extremely well-suited for data analysis but odd outside of that.

Good point. I'd add complex numbers and "everything is a vector" to that. Of course that doesn't make it a DSL. It simply means that R was designed with a particular application in mind . So was Perl (regular expressions as first-class citizens) or Javascript (DOM manipulation). Not to mention PHP.

That is true. Also Erlang with the actor model, and Go with its goroutines for network services.

Re: R: Introduction to Data Science (2019)

#127

Post by mamonster »

benreesman wrote: post #125
mamonster wrote: post #97

Earlier quoted context omitted.

Sorry, unfortunately do not do consulting. In your other comment, you said you are looking to price "weird derivatives". How weird are we talking? If its OTC I won't be able to help anyway, if its standard then I can at least try to point you in the right direction. The fact you mention Black Scholes makes me think it might be something closer to "vanilla" than the other way around.

It’s looking like the goal will be to create downward pressure on derivative beta (especially in the case of a rapidly increasing underlying: big pools of Hopper cards basically). I have a vague intuition that transaction costs will be sort of cumulatively symmetric: participants who get in quickly will pay a lot per unit time, but conversely, people who VWAP in will get zero-rated on the way out. There’s a legitimat…

Am I correct in understanding that you have the spot price of Hopper card compute as your underlying and then come up with a pricing equation for some derivative instruments for that?

Re: R: Introduction to Data Science (2019)

#128

Post by benreesman »

mamonster wrote: post #127
benreesman wrote: post #125

Earlier quoted context omitted.

It’s looking like the goal will be to create downward pressure on derivative beta (especially in the case of a rapidly increasing underlying: big pools of Hopper cards basically). I have a vague intuition that transaction costs will be sort of cumulatively symmetric: participants who get in quickly will pay a lot per unit time, but conversely, people who VWAP in will get zero-rated on the way out. There’s a legitimat…

Am I correct in understanding that you have the spot price of Hopper card compute as your underlying and then come up with a pricing equation for some derivative instruments for that?

In a friction free scenario it would be a standard future, yes.

The reality is closer to an option on an FX forward, with a very nasty empirical MC as Q* for the payoff equivalence.

I’m not fancy enough, I know when to sub-contract!

Re: R: Introduction to Data Science (2019)

#129

Post by SomeoneFromCA »

nequo wrote: post #123
Cosi1125 wrote: post #96

Earlier quoted context omitted.

Why do you think that? (I'm legitimately curious.)

Not parent and I also consider R to be a surprisingly solid general purpose PL but the one reason that I can think of is that formulas are part of the language. Formulas are extremely well-suited for data analysis but odd outside of that.

No R is not a "surprisingly solid general purpose PL". Not in comparison to better option such as Ruby, Python, Perl etc.

Re: R: Introduction to Data Science (2019)

#130

Post by SomeoneFromCA »

Cosi1125 wrote: post #124
nequo wrote: post #123

Earlier quoted context omitted.

Not parent and I also consider R to be a surprisingly solid general purpose PL but the one reason that I can think of is that formulas are part of the language. Formulas are extremely well-suited for data analysis but odd outside of that.

Good point. I'd add complex numbers and "everything is a vector" to that. Of course that doesn't make it a DSL. It simply means that R was designed with a particular application in mind . So was Perl (regular expressions as first-class citizens) or Javascript (DOM manipulation). Not to mention PHP.

This is why I said "de facto", not "by design". Yes, you can use it outside stats domain, but it will suck big time for that. PHP used to be like that as well, but later versions look more usable for general scripting.
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