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local({

    ## Put everything in a separate environment and attach that
    ## environment. Re-use the existing one from the search path it
    ## it's available, so that re-executing this file doesn't add a
    ## new environment to the search path every time.
    custom_env <- tryCatch(as.environment("rct_custom_env"),
                           error=function(...) {
                               attach(NULL, name="rct_custom_env")
                               as.environment("rct_custom_env")

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library(dplyr)
library(tidyr)

d6 <- 1:6
# We'll call the 3 dice X, Y, and D (D is the dragon die)
all_rolls <- expand.grid(X=d6, Y=d6, D=d6) %>%
    mutate(
        ## Sum of all 3 dice; determines if you hit
        sum = X + Y + D,
        ## TRUE if this roll has doubles in it

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library(dplyr)
library(matrixStats)
library(stringr)
library(ggplot2)

## Using point buy table from https://www.d20pfsrd.com/basics-ability-scores/ability-scores/
## Assume anything less than 7 is just -4 points
point_buy_values <-
    c(-4, -4, -4, -4, -4, -4, -4, -2, -1, 0, 1, 2, 3, 5, 7, 10, 13, 17) %>%
    set_names(seq_along(.))

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import mistune
from lxml import html
from snakemake.utils import min_version
min_version('3.7.1')

rule all:
    input: 'presentation.pdf'

rule pdf_to_png:
    input: 'images/{filename}.pdf'

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## This will allow you to use dplyr functions with Bioconductor's
## S4Vectors::DataFrame class. They simply call "as.data.frame" on
## DataFrame objects and then pass them to the methods for
## "data.frame". Note that this entails conversion of S4 vector
## columns to primitive vectors. No attempt is made to convert the
## results back to DataFrame, since such a conversion would not
## restore S4 vector columns that were converted to primitive vectors.

library(S4Vectors)
library(dplyr)

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library(magrittr)
library(dplyr)
library(ggplot2)
library(purrr)
library(forcats)
library(glue)

pledge.amount <- 1
x <- list(
    Old_WorstCase=c(

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Homebrew build logs for argyll-cms on macOS 10.11.6
Build date: 2017-08-20 11:54:00

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;;; bcup-closures.el ---  -*- lexical-binding: t -*-

;; Closure-manipulating function/macros
(defmacro enclose (arg)
  `(lambda () ,arg))
(defmacro enclose-args (&rest args)
  (let ((enclose-exprs
         (mapcar (lambda (arg) `(enclose ,arg)) args)))
    `(list ,@enclose-exprs)))
(defun unenclose (arg) (funcall arg))

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library(rlang)
library(assertthat)
library(magrittr)
library(glue)
library(tibble)

## Takes LHS and RHS as strings
assign_general_ <- function(lhs, rhs, envir) {
    expr <- parse(text=glue("{lhs} <- {rhs}"))
    eval(expr, envir)

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Keybase proof

I hereby claim:

• I am darwinawardwinner on github.
• I am rcthompson (https://keybase.io/rcthompson) on keybase.
• I have a public key ASBVf3s3y1AIHHATFqG8c6WHdw8t4PpcbMIsc2slD2-XBwo

To claim this, I am signing this object: