jrosell

library(targets)
library(tibble)
library(readr)
library(glmnet)

# Step 1: load raw data
load_raw_data <- function() {
  Sys.sleep(10) # simulate slow load
  tibble(

jrosell

library(tidyverse)
library(tidymodels)
set.seed(123)
n <- 10000
Evento1 <- rbinom(n, size = 1, prob = 0.7) 
Evento2 <- rbinom(n, size = 1, prob = 0.6) 
p_target <- ifelse(Evento1 == 1, 0.7, 0.2)
p_target <- p_target + ifelse(Evento2 == 1, 0.3, -0.05)
p_target <- pmin(pmax(p_target, 0.01), 0.99) # limitar entre 0 y 1

jrosell

# getUser cfg =
#   lookup "username" cfg >>= \uname ->
#   lookup "age" cfg >>= \ageStr ->
#   readMaybe (trim ageStr) >>= \age ->
#   lookup "email" cfg >>= \emailRaw ->
#   validateEmail (trim emailRaw) >>= \email ->
#   Just (User uname age email)

# 1. Using atomic vectors -----

jrosell

# Refrence: https://www.biobits.be/biofunctor/2025/07/30/what-s-r-vector-victor/
# Laboratory experiments are often performed in 96-well plastic plates, with 8 rows (labeled A-H) and 12 columns (labeled 1-12). Each microwell is a separate micro-experiment (labeled A1-H12).

rows <- LETTERS[1:8]
columns <- 1:12 |> sprintf(fmt = "%02i")

# Recycled vectors is not what we want here.
paste0(rows, columns) 
#>  [1] "A01" "B02" "C03" "D04" "E05" "F06" "G07" "H08" "A09" "B10" "C11" "D12"

jrosell

 

# 1. Preparations & helper functions -----

rlang::check_installed(c("tidyverse", "repurrrsive", "DBI", "duckdb", "ollamar", "ellmer", "glue", "testthat"))
library(tidyverse)
library(glue)
library(repurrrsive)
library(DBI)
library(duckdb)

jrosell

load(url("https://github.com/topepo/FES/raw/refs/heads/master/Data_Sets/Ischemic_Stroke/stroke_data.RData"))

rlang::check_installed(c("tidyverse", "tidymodels", "corrplot"))
library(tidyverse)
library(tidymodels)

VC_preds <- 
  c("CALCVol", "CALCVolProp", "MATXVol", "MATXVolProp", "LRNCVol", 
    "LRNCVolProp", "MaxCALCArea", "MaxCALCAreaProp", "MaxDilationByArea", 

jrosell

rlang::check_installed(c("vitals", "ellmer", "dplyr", "ggplot2"))

library(vitals)
library(ellmer)
library(dplyr)
library(ggplot2)

eval_df <- tibble(
  input = c("What's 2+2?", "What's 2+3?", "What's 2+4?"),
  target = c("4", "5", "6")

jrosell

# An experiment creating new functions with type validation using formula notation in R.
# For example, for two double argugment inputs and integer output the formula would be: integer ~ double + double

library(rlang)
library(testthat)

type_check_fn <- function(type_name) {
  switch(as.character(type_name),
         "integer" = is.integer,
         "double" = is.double,

jrosell

rm(list = ls()); rstudioapi::getActiveDocumentContext()$path |> 
  parsermd::parse_rmd(parse_yaml = FALSE) |> 
  parsermd::rmd_select("setup") |> 
  parsermd::as_document() |> 
  purrr::keep(\(x) !stringr::str_detect(x, '^```|^#')) |> 
  parse(text = _) |> 
  eval()

jrosell

function main() {
  var spreadsheetUrl = 'https://docs.google.com/spreadsheets/d/id/edit?usp=sharing';
  var spreadsheet = SpreadsheetApp.openByUrl(spreadsheetUrl);
  
  var pivotedSheet = getOrCreateSheet(spreadsheet, 'PivotedConversionActions');  // New pivoted data sheet
  
  var firstDate = '2022-01-01';
  var startDate = getDateNDaysAgo(200);
  var endDate = getDateNDaysAgo(1);