martinctc

# estimate sample size via power analysis
from statsmodels.stats.power import TTestIndPower

# parameters for power analysis
effect = 0.8
alpha = 0.05
power = 0.8

# perform power analysis
analysis = TTestIndPower()

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#' @title
#' Rank a data frame by grouping variable using base R
#'
#' @description
#' This function ranks a specified column in a data frame by group using entirely base R functions. 
#' The underlying function is `rank()`, where additional arguments can be passed with `...`. 
#' The grouping variable is specified as a string using the argument `group_var`, and the variable to rank is 
#' specified using the argument `rank_var`. The operation is analogous to using `group_by()` followed by 
#' `mutate()` in {dplyr}.
#' See example below using the base dataset `iris`. 

martinctc

# multiply values based on weights

wtest <-
  data.frame(
    x = c("cats", "dogs", "birds", "cats"),
    y = c(1, 2, 3, 2)
  )


wtest[rep(seq_len(nrow(wtest)), wtest$y),]

martinctc

Sub FilterTwoSeriesCopyAndPaste()

    Dim S1_String, S2_String As String
    
    'READ VALUES FROM SETTING SHEET -------------------------------------------------------
    
    S1_String = Worksheets("Settings").Range("B2").Value
    S2_String = Worksheets("Settings").Range("B3").Value
    
    Debug.Print S1_String

martinctc

# Load packages -----------------------------------------------------------


library(tidyverse)

# Path where all Rd files live --------------------------------------------

man_path <-
  here::here("man")

martinctc

## Implement Information Value in base?

iris_iv <-
  iris %>%
  mutate(IsSetosa = ifelse(Species == "setosa", 1, 0))

## Information
infoTables <- 
  Information::create_infotables(
    data = iris_iv,

martinctc

# Regular R CMD check
devtools::check()

# Check R-hub
devtools::check_rhub()

# Cross-platform R CRAN check
rhub::check_for_cran()

# Return a character vector with the names of dependencies - CRAN only

martinctc

## Date expansion
validweeks <-
  unique(clean_wbq_totalview$Date) %>%
  purrr::map(function(x){
      seq(x, x + 6, by = "days")
  }) %>%
  purrr::reduce(c)

martinctc

import_to_fst <- function(path){
  
  temp_df <- import_wpa(path)
  
  path_fst <- gsub(pattern = "csv$",
                   replacement = "fst",
                   x = path)
  
  fst::write_fst(temp_df, path_fst)
  

martinctc

library(tidyverse)

10:30 %>%
  map(function(x){
    
    prob_v <-
      c(x, 
        100 - 2*x,
        100 - x - (100 - 2*x))