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simulate_pack <- function(...) {
  
  very_rare_pool <- c(paste0("sr", 1:n_super_rare),
                      paste0("ur", 1:n_ultra_rare),
                      paste0("secr", 1:n_secret_rare))
  very_rare_prob_vec <- c(rep(prob_super_rare / n_super_rare, n_super_rare),
                          rep(prob_ultra_rare / n_ultra_rare, n_ultra_rare),
                          rep(prob_secret_rare / n_secret_rare, n_secret_rare))
  
  commons <- sample(paste0("c", 1:n_commons), 7, replace = T)

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get_expected_events <- function(x) {
  #' take in a number of elements in a set
  #' and a probability
  out <- x * log(x) + 0.577 * x + 0.5
  return(out)
}

n_commons <- 48
n_rare <- 20
n_super_rare <- 12

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# Compiled source #
###################
*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #

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vocab <- readxl::read_excel("insert_your_excel.xlsx") %>%
  tidytext::unnest_tokens(word, text) %>%
  dplyr::count(word, sort = TRUE) %>%
  anti_join(stopwords::stop_words) %>%
  dplyr::mutate(french_text = googleLanguageR::gl_translate(word, target = "fr", source = "en")$translatedText)
  
bigram_constructions <- readxl::read_excel("insert_your_excel.xlsx") %>%
  unnest_tokens(bigram, text) %>%
  count(bigram, sort = TRUE) %>%
  mutate(french_text = googleLanguageR::gl_translate(bigram, target = "fr", source = "en")$translatedText)

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#functions.R

create_plot <- function(data) {
  ggplot(data) +
    geom_histogram(aes(x = hp_per_cyl)) +
    theme_bw()
}

validate_data <- function(data) {
  rules <- validator(hp > 0,

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# _targets.R
## installs and loads libraries
pacman::p_load(targets, tidyverse, validate)
source("R/functions.R")
options(tidyverse.quiet = TRUE)
#sets package(s) globally for all subsequent targets
tar_option_set(packages = c("tidyverse"))

list(
  #record state of R environment

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SAFFRONres <- SAFFRON(sample.df)
new_data <- SAFFRONres%>%
  mutate(index = row_number(),
         SAFFRON = log(alphai),
         Bonferroni = log(0.05/index),
         Unadjusted = rep(log(0.05), nrow(.))) %>%
  pivot_longer(cols = c(SAFFRON, Bonferroni, Unadjusted),
               names_to = "adjustment",
               values_to = "alpha")

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sample.df <- data.frame(
id = c('A15432', 'B90969', 'C18705', 'B49731', 'E99902',
    'C38292', 'A30619', 'D46627', 'E29198', 'A41418',
    'D51456', 'C88669', 'E03673', 'A63155', 'B66033'),
pval = c(2.90e-08, 0.06743, 0.01514, 0.08174, 0.00171,
        3.60e-05, 0.79149, 0.27201, 0.28295, 7.59e-08,
        0.69274, 0.30443, 0.00136, 0.72342, 0.54757),
batch = c(rep(1,5), rep(2,6), rep(3,4)))

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server <- function(input, output, session) { 
  
  #reset
  observeEvent(input$reset, {
    updateSelectInput(session, 'cyl')
    updateNumericInput(session, 'disp', value = 100)
    updateNumericInput(session, 'hp', value = 100)
    updateNumericInput(session, "drat", value = 3)
    updateNumericInput(session, "wt", value = 3)
    updateNumericInput(session, "qsec", value = 20)

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body <- dashboardBody(
  h2("The predicted miles per gallon is: "),
  h3(verbatimTextOutput("pred", placeholder = T)),
  tags$head(tags$style("#pred{color: black;
                                 font-size: 20px;
                                 font-family: Source Sans Pro
                                 }")),
  fluidRow(
    infoBox(
      "What", "is this model for?", icon = icon("line-chart"),