nutterb

library(magrittr)
    
    dummy_ifelse <- function(x)
    {
      if (!is.factor(x)) stop("x is not a factor")
      
      lev <- levels(x)
      
      lapply(lev[-1],
             function(l) ifelse(x %in% l, 1, 0)) %>%

nutterb

# Adding lapply versions to http://stackoverflow.com/a/41511889/1017276
library(magrittr)
library(data.table)
library(microbenchmark)
set.seed(pi)

nc = 1e3
nr = 1e2
df_m0 = sample(c(1:10, NA_integer_), nc*nr, replace = TRUE) %>% matrix(nr, nc) %>% data.frame
df_r  = sample(c(1:10), nc*nr, replace = TRUE) %>% matrix(nr, nc) %>% data.frame

nutterb

---
title: "Untitled"
output: html_document
---

```{r}
library(pixiedust)
fit <- lm(mpg ~ qsec * factor(gear) + factor(am), data = mtcars)
```

nutterb

A silly young man from Clyde
In a funeral procession was spied;
When asked, "Who is dead?"
He giggled and said,
"I don't know; I just came for the ride."

(source: https://www.brownielocks.com/Limericks.html)

nutterb

#' @name query_varchar_max
#' @title Query a VARCHAR(MAX) Variable from SQL Server
#'
#' @importFrom RODBCext sqlExecute
#' 
#' @description The RODBC driver to SQL Server (SQL Server Native Client 11.0)
#'   reports the lenght of a VARCHAR(MAX) variable to be zero.  This presents 
#'   difficulties in extracting long text values from the database. Often, the
#'   ODBC will assume a length of 255 characters and truncate the text to that
#'   many characters.  The approach taken here searches the VARCHAR(MAX) variables 

nutterb

# Suppose we have arguments for power and sample size of a two-sample t-test
# (Similar to power.t.test, but vectorized.  See https://github.com/nutterb/StudyPlanning/blob/devel/R/test_t2.R
# delta: Difference of means.  (-Inf, Inf)
# delta0: Difference under the null hypothesis. (-Inf, Inf)
# se: Standard error (0, Inf)
# alpha: significance level (0, 1)
# power: power of the test (0, 1)

### STANDARD ARGUMENT CHECKS ###
# 19 lines of code (259 characters)

nutterb

tidy.microbenchmark <- function(x, unit, ...){
  summary(x, unit = unit)
}

nutterb

quick_summary <- function(df, vars, group = NULL){
  
  group_is_null <- is.null(group)
  
  coll <- checkmate::makeAssertCollection()
  
  checkmate::assert_data_frame(x = df,
                               add = coll)
  
  checkmate::assert_character(x = vars,

nutterb

risk_attack_succeed_sim <- function(attack, defend){
  while(attack >= 2 && defend > 0){
    n_attack <- min(c(3, attack - 1))
    n_defend <- min(c(2, defend))
    
    roll_attack <- sample(1:6, n_attack, replace = TRUE)
    roll_defend <- sample(1:6, n_defend, replace = TRUE)
    
    n_compare <- min(c(n_attack, n_defend))
    

nutterb

atomic_mass <- c( H = 1.008,  He = 4.0026, Li = 6.94,   Be = 9.0122,
                  B = 10.81,   C = 12.011,  N = 14.007,  O = 15.999,
                  F = 18.998, Ne = 20.180, Na = 22.990, Mg = 24.305,
                 Al = 26.982, Si = 28.085,  P = 30.974,  S = 32.06,
                 Cl = 35.45,  Ar = 39.948,  K = 39.098, Ca = 40.078,
                 Sc = 44.956, Ti = 47.867,  V = 50.942, Cr = 51.996,
                 Mn = 54.938, Fe = 55.845, Co = 58.933, Ni = 58.693,
                 Cu = 63.546, Zn = 65.38,  Ga = 69.732, Ge = 72.630,
                 As = 74.922, Se = 78.971, Br = 79.904, Kr = 83.798,
                 Rb = 85.468, Sr = 87.62,   Y = 88.906, Zr = 91.224,