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# fast R update
rupdate <- function(){
  utils::update.packages(
    ask = FALSE,
    Ncpus = 6,
    repos = "https://cloud.r-project.org"
    )
}

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#' @title {Plot Accrual of patients by month}
#' @description {This function creates a plot of the accrual of patients by month.}
#' @param data A data frame containing the dates column.
#' @param dates The name of the dates column in \code{data}.
#' @return A ggplot object.
#' @export
#' @examples
#' # Create a data frame
#' df <- data.frame(
#'   dates = seq(as.Date("2020-01-01"), as.Date("2020-07-01"), by = "month")

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#' @title {Accrual of Patients in a Clinical Trial}
#' @description {This function plots the expected, randomised and registered number of patients
#' in a clinical trial.}
#' @param start Character. Start date of the trial. Default is "2020-01-01".
#' @param end Character. End date of the trial. Default is "2022-12-31".
#' @param n_target Numeric. Expected number of patients in the trial.
#' @param finished Is patient recruitment finished?
#' @param dates_rando Numeric. Vector of dates of randomised patients.
#' @param dates_regi Numeric. Vector of dates of registered patients.
#' @param x_axis_text Position of X-axis text: 'asis' (default)), 'shifted'.

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report_samplesize_t.test <- function(pwr, drop_out){
  n_raw <- ceiling(pwr$n)
  n_total <- (n_raw + ceiling(n_raw * drop_out))*2
  glue::glue("A two-sample t-test to detect a difference of {round(pwr$delta, 1)} l/min between the groups with an SD of {round(pwr$sd, 1)} l/min has power of {pwr$power*100}% if {ceiling(pwr$n)} patients per arm are randomized. Allowing for a drop-out of {drop_out*100}%  implies that {n_total} patients need to be randomized in total.")
}

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---
title: "Sample Equations used in Statistics"
output: html_document
---

###  Summations

### Without Indices

$\sum x_{i}$

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# Flatten a list returned by the rle() function


library(purrr)


# ARRAY -------------------------------------------------------------------

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st_set_varlabs <- function(data,
                           dict = ITEMDESC,
                           view = modul,
                           keeps = PAGE_TABLE,
                           name = ITEM_COLUMN,
                           label = ITEM_LABEL) {

  library(dplyr, warn.conflicts = FALSE)

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df.VARLABS <- tibble::tribble(
  ~VARNAME,       ~LABEL,
  "PATIENT_ID",   "Patient ID",
  "ARM",          "Randomisation arm",
  "RANDO_DAT",    "Randomisation date"
)