grosscol

rosen_height <- function(x,y) {
  100 * (y-x^2)^2 + (1-x)^2
}

gradient <- function(x,y) {
  val_x <- -400*x*(y-x^2)-2*(1-x)
  val_y <- 200*(y-x^2)
  return( c(val_x, val_y) )
}

grosscol

---

#
# This template example assumes a UserPool and UserPoolDomain exist.
# The function of this is to produce a custom resource with an attribute 
#   that can be referenced for DNSName of an Route53::RecordSet AliasTarget.
#   
#   AliasTarget:
#     HostedZone: Z2FDTNDATAQYW2
#     DNSNAME: !GetAtt UPDomain.CloudFrontDistribution

grosscol

options(
  repos = c(CRAN = "https://cran.rstudio.com/"),
  browserNLdisabled = TRUE,
  deparse.max.lines = 2)

# Set lib path
.libPaths( "~/R/x86_64-pc-linux-gnu-library/dev/" )

if (interactive()) {
  suppressMessages(require(devtools))

grosscol

# regarding: https://www.reddit.com/r/Rlanguage/comments/d0ytw7/issues_of_bias_with_rnorm/

# set the RNG seed so the results are reporducible
set.seed(12345)
# Generate 100 target means beteen 0 and 100 from the uniform distribution
generated_means <- runif(100, 0, 100)

# Generate 100 standard deviation values also from uniform distribution
generated_sdev <- runif(100, 1, 20)

grosscol

#!/bin/bash

# Consider the following structure where this script is target.sh
# /tmp/
# ├── target.sh
# └── l1
#     ├── one.sh -> ../target.sh
#     └── l2
#         └── two.sh -> ../one.sh
# From your home directory run:

grosscol

{ 
  "MAPT_data_points":[
    { "performer": "Alice",
      "time": "2019-01-01",
      "measure": "Documentation Rate",
      "performance": "0.90"
    },
    { "performer": "Alice",
      "time": "2019-02-01",
      "measure": "Documentation Rate",

grosscol

library(gtools)

demo_input <- c("fname midname lname","doe john e") #only 2 'names' in this example list.

split_list <- strsplit(demo_input, " |-")

make_combinations <- function(x){
  # Use permutations from the gtools package
  name_grid <- permutations(3,3,x)
  apply(X=name_grid, MARGIN=1, FUN=paste0, collapse=' ')

grosscol

# DrawShape

#' @title Draw Shape
#' @param n height of shape in number of rows
#' @description If n is odd, draw a diamond pattern.
#'   If n is even, draw an hourglass pattern.
#' @note e.g. n = 6
#' #####
#'  ###
#'   #

grosscol

library(readr)
library(dplyr)
library(tibble)

# Input CSV 
input_path <- '/tmp/dahee_coding.csv'

input_df <- read_csv(input_path)

# Veena's observations are row 1

grosscol

library(tibble)
library(dplyr)

count_data <- tibble::tribble(
  ~id, ~numerator, ~denominator,
  "Aly",   14,   20,
  "Aly",   13,   20,
  "Aly",   12,   20,
  "Bob",   11,   20,
  "Bob",   12,   20,