Obligatory 3Blue1Brown reference https://www.youtube.com/watch?v=zeJD6dqJ5lo
and the Galton Board
https://en.m.wikipedia.org/wiki/Galton_board
(yes, that Galton who invented eugenetics)
Simulating and Visualising the Central Limit Theorem
11–20 of 67 posts
Re: Simulating and Visualising the Central Limit Theorem
#12Looking at the R code in this article, I'm having a hard time understanding the appeal of tidyverse.
Why? The tidyverse is so readable, elegant, compositional, functional and declarative. It allows me to produce a lot more and higher quality than I could without it. ggplot2 is the best visualization software hands down, and dplyr leverages Unix’s famous point free programming style (that reduces the surface area for errors).
I disagree. In this example tidyverse looks convoluted compared to just using an array and apply. ggplot2 is okay but we already had lattice. Lattice does everything ggplot2 does and produces much better-looking plots IMO.
Re: Simulating and Visualising the Central Limit Theorem
#13Looking at the R code in this article, I'm having a hard time understanding the appeal of tidyverse.
Author here; I think I understand where you might be coming from. I find functional nature of R combined with pipes incredibly powerful and elegant to work with.
OTOH in a pipeline, you're mutating/summarising/joining a data frame, and it's really difficult to look at it and keep track of what state the data is in. I try my best to write in a way that you understand the state of the data (hence the tables I spread throughout the post), but I do acknowledge it can be inscrutable.
Re: Simulating and Visualising the Central Limit Theorem
#14Obligatory 3Blue1Brown reference https://www.youtube.com/watch?v=zeJD6dqJ5lo
Very much an inspiration and resource when composing the post.
Re: Simulating and Visualising the Central Limit Theorem
#15Looking at the R code in this article, I'm having a hard time understanding the appeal of tidyverse.
Author here; I think I understand where you might be coming from. I find functional nature of R combined with pipes incredibly powerful and elegant to work with. OTOH in a pipeline, you're mutating/summarising/joining a data frame, and it's really difficult to look at it and keep track of what state the data is in. I try my best to write in a way that you understand the state of the data (hence the tables I spread th…
A "pipe" is simply a composition of functions. Tidyverse adds a different syntax for doing function composition, using the pipe operator, which I don't particularly like. My general objection to Tidyverse is that it tries to reinvent everything but the end result is a language that is less practical and less transparent than standard R.
Re: Simulating and Visualising the Central Limit Theorem
#16Earlier quoted context omitted.
Author here; I think I understand where you might be coming from. I find functional nature of R combined with pipes incredibly powerful and elegant to work with. OTOH in a pipeline, you're mutating/summarising/joining a data frame, and it's really difficult to look at it and keep track of what state the data is in. I try my best to write in a way that you understand the state of the data (hence the tables I spread th…
A "pipe" is simply a composition of functions. Tidyverse adds a different syntax for doing function composition, using the pipe operator, which I don't particularly like. My general objection to Tidyverse is that it tries to reinvent everything but the end result is a language that is less practical and less transparent than standard R.
Can you rewrite some of those snippets in standard R w/o Tidyverse? Curious what it would look like
Re: Simulating and Visualising the Central Limit Theorem
#17The definition under "A Brief Recap" seems incorrect. The sample size doesn't approach infinity, the number of samples does. I'm in a similar situation to the author, I skipped stats, so I could be wrong. Overall great article though.
Re: Simulating and Visualising the Central Limit Theorem
#18[dead]
Re: Simulating and Visualising the Central Limit Theorem
#19Edit: OP confirms there's no AI-generated code, so do ignore me.
The code style - and in particular the *comments - indicate most of the code was written by AI. My apologies if you are not trying to hide this fact, but it seems like common decency to label that you're heavily using AI?
*Comments like this: "# Anonymous function"
Re: Simulating and Visualising the Central Limit Theorem
#20The definition under "A Brief Recap" seems incorrect. The sample size doesn't approach infinity, the number of samples does. I'm in a similar situation to the author, I skipped stats, so I could be wrong. Overall great article though.
Yes indeed, if the sample size approached infinity (and not the number of samples), you would essentially just be calculating the mean of the original distribution.