Rasmus Bååth rasmusab

## chat-gtp-api-call.R
# How to call the new (as of 2023-03-01) ChatGTP API from R
# Get your API key over here: https://platform.openai.com/
api_key <- "sk-5-your-actual-api-key-Fvau6" # Don't share this! 😅

library(httr)
library(stringr)

# Calls the ChatGTP API with the given promps and returns the answer
ask_chatgtp <- function(prompt) {
  response <- POST(

## botty-app.html
<!--
  This is a fun and friendly chatbot UI that uses the ChatGPT API.
  It features
  * A retro-styled CRT screen UI
  * A ASCII face that changes based on the bot's current emotion
  * Some light sound effects when the bot is responding
  * Persistent chat history using localStorage
  * All in one single HTML file

  To get going you need a ChatGPT API key to assign to the `openai_api_key` variable.

## hello.Rnw
\documentclass{article}
\begin{document}

<<echo=FALSE,results=tex>>=
cat('\\section{hi}\n')
@


<<echo=FALSE>>=
library(xtable)

## easy-bayesian-bootstrap-in-r.Rmd
---
title: "Easy Bayesian Bootstrap in R"
author: "Rasmus"
date: "04/06/2015"
output:
  html_document:
    keep_md: yes
    self_contained: no
---

## make-iba-cocktails-pdf-gif-datatable.R
# This script takes the scraped IBA cocktails dataset found here:
# https://github.com/rasmusab/iba-cocktails
# And produces a four page PDF with all the IBA cocktails and animated GIFs that
# loops through these, as well.

library(tidyverse)
library(glue)
library(ggtext)
library(gridExtra)

## bowling-t-test.R
bowling_animation_urls <- c(
  "0.01" = "https://i.imgur.com/Kn8CQbj.gif",
  "0.05" = "https://i.imgur.com/HFTKdDL.gif",
  "0.1" = "https://i.imgur.com/8Sw54Mz.gif",
  "1" = "https://i.imgur.com/kjROdhj.gif"
)

# We need to download the animations to the session temp dirercorty to display
# them in the Rstudio Viever pane
bowling_animation_html <- sapply(bowling_animation_urls, function(url) {

## introduction_to_bayes_test_script.R
# Prior to the tutorial make sure that the script below runs without error on your R installation.
# What you need is a working installation of JAGS: http://mcmc-jags.sourceforge.net/
# and the rjags R package that can be installed from R by running
# install.packages("rjags")

# Generating some fake data
set.seed(123)
y <- rbinom(30, size = 1, prob = 0.2015)

# Fitting a simple binomial model using JAGS

## pokemon.csv

          
            #
            Name
            Type 1
            Type 2
            Total
            HP
            Attack
            Defense
            Sp. Atk
            Sp. Def
            Speed
            Generation
            Legendary

            
              1
              Bulbasaur
              Grass
              Poison
              318
              45
              49
              49
              65
              65
              45
              1
              False

            
              2
              Ivysaur
              Grass
              Poison
              405
              60
              62
              63
              80
              80
              60
              1
              False

            
              3
              Venusaur
              Grass
              Poison
              525
              80
              82
              83
              100
              100
              80
              1
              False

            
              3
              VenusaurMega Venusaur
              Grass
              Poison
              625
              80
              100
              123
              122
              120
              80
              1
              False

            
              4
              Charmander
              Fire
              
              309
              39
              52
              43
              60
              50
              65
              1
              False

            
              5
              Charmeleon
              Fire
              
              405
              58
              64
              58
              80
              65
              80
              1
              False

            
              6
              Charizard
              Fire
              Flying
              534
              78
              84
              78
              109
              85
              100
              1
              False

            
              6
              CharizardMega Charizard X
              Fire
              Dragon
              634
              78
              130
              111
              130
              85
              100
              1
              False

            
              6
              CharizardMega Charizard Y
              Fire
              Flying
              634
              78
              104
              78
              159
              115
              100
              1
              False

## the-probability-my-son-will-be-stung-by-a-bumblebee.R
library(tidyverse)
library(purrr)
library(rstan)

### Defining the data ###
#########################
bumblebees <- c(1, 0,  0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
                0, 0, 1, 0, 0, 0, 0, 0, 0)
toddler_steps <- c(26, 16, 37, 101, 12, 122, 90, 55, 56, 39, 55, 15, 45, 8)

## corr_jags.R
library(rjags)
library(mvtnorm) # to generate correlated data with rmvnorm.
library(car) # To plot the estimated bivariate normal distribution.
set.seed(31415)

mu <- c(10, 30)
sigma <- c(20, 40)
rho <- -0.7
cov_mat <- rbind(c(     sigma[1]^2       , sigma[1]*sigma[2]*rho ),
                 c( sigma[1]*sigma[2]*rho,      sigma[2]^2       ))
	# How to call the new (as of 2023-03-01) ChatGTP API from R
	# Get your API key over here: https://platform.openai.com/
	api_key <- "sk-5-your-actual-api-key-Fvau6" # Don't share this! 😅

	library(httr)
	library(stringr)

	# Calls the ChatGTP API with the given promps and returns the answer
	ask_chatgtp <- function(prompt) {
	response <- POST(
	<!--
	This is a fun and friendly chatbot UI that uses the ChatGPT API.
	It features
	* A retro-styled CRT screen UI
	* A ASCII face that changes based on the bot's current emotion
	* Some light sound effects when the bot is responding
	* Persistent chat history using localStorage
	* All in one single HTML file

	To get going you need a ChatGPT API key to assign to the `openai_api_key` variable.
	\documentclass{article}
	\begin{document}

	<<echo=FALSE,results=tex>>=
	cat('\\section{hi}\n')
	@


	<<echo=FALSE>>=
	library(xtable)
	---
	title: "Easy Bayesian Bootstrap in R"
	author: "Rasmus"
	date: "04/06/2015"
	output:
	html_document:
	keep_md: yes
	self_contained: no
	---
	# This script takes the scraped IBA cocktails dataset found here:
	# https://github.com/rasmusab/iba-cocktails
	# And produces a four page PDF with all the IBA cocktails and animated GIFs that
	# loops through these, as well.

	library(tidyverse)
	library(glue)
	library(ggtext)
	library(gridExtra)
	bowling_animation_urls <- c(
	"0.01" = "https://i.imgur.com/Kn8CQbj.gif",
	"0.05" = "https://i.imgur.com/HFTKdDL.gif",
	"0.1" = "https://i.imgur.com/8Sw54Mz.gif",
	"1" = "https://i.imgur.com/kjROdhj.gif"
	)

	# We need to download the animations to the session temp dirercorty to display
	# them in the Rstudio Viever pane
	bowling_animation_html <- sapply(bowling_animation_urls, function(url) {
	# Prior to the tutorial make sure that the script below runs without error on your R installation.
	# What you need is a working installation of JAGS: http://mcmc-jags.sourceforge.net/
	# and the rjags R package that can be installed from R by running
	# install.packages("rjags")

	# Generating some fake data
	set.seed(123)
	y <- rbinom(30, size = 1, prob = 0.2015)

	# Fitting a simple binomial model using JAGS
#	Name	Type 1	Type 2	Total	HP	Attack	Defense	Sp. Atk	Sp. Def	Speed	Generation	Legendary
1	Bulbasaur	Grass	Poison	318	45	49	49	65	65	45	1	False
2	Ivysaur	Grass	Poison	405	60	62	63	80	80	60	1	False
3	Venusaur	Grass	Poison	525	80	82	83	100	100	80	1	False
3	VenusaurMega Venusaur	Grass	Poison	625	80	100	123	122	120	80	1	False
4	Charmander	Fire		309	39	52	43	60	50	65	1	False
5	Charmeleon	Fire		405	58	64	58	80	65	80	1	False
6	Charizard	Fire	Flying	534	78	84	78	109	85	100	1	False
6	CharizardMega Charizard X	Fire	Dragon	634	78	130	111	130	85	100	1	False
6	CharizardMega Charizard Y	Fire	Flying	634	78	104	78	159	115	100	1	False
	library(tidyverse)
	library(purrr)
	library(rstan)

	### Defining the data ###
	#########################
	bumblebees <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
	1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
	0, 0, 1, 0, 0, 0, 0, 0, 0)
	toddler_steps <- c(26, 16, 37, 101, 12, 122, 90, 55, 56, 39, 55, 15, 45, 8)
	library(rjags)
	library(mvtnorm) # to generate correlated data with rmvnorm.
	library(car) # To plot the estimated bivariate normal distribution.
	set.seed(31415)

	mu <- c(10, 30)
	sigma <- c(20, 40)
	rho <- -0.7
	cov_mat <- rbind(c( sigma[1]^2 , sigma[1]sigma[2]rho ),
	c( sigma[1]sigma[2]rho, sigma[2]^2 ))