cfhammill

{ pkgs ? import <nixpkgs> {} }:

let
  inlaDeps = with pkgs.rPackages;
  [ Matrix
  sp
  Deriv
  devtools
  doParallel
  fields

cfhammill

{ pkgs ? import <nixpkgs> {} }:

with pkgs;

mkShell {
  buildInputs = [ git emacs ] ++
  (with rPackages;
  [
   R
   ggplot2

cfhammill

import Data.Vinyl.TypeLevel
import Data.Vinyl
import Data.Vinyl.Functor
import Control.Applicative (liftA2)

type family MapTyCon m xs where
  MapTyCon m '[] = '[]
  MapTyCon m (x ': xs) = m x ': MapTyCon m xs

type family MapTyDeCon (m :: * -> *) (xs :: [*]) where

cfhammill

{-# LANGUAGE Arrows
, OverloadedStrings, QuasiQuotes, ScopedTypeVariables
, TemplateHaskell
#-}

import Control.Arrow   ((>>>), returnA, (&&&), (***)) 
import Control.Exception (Exception (..))
import Data.List (intercalate)
import Data.List.NonEmpty (NonEmpty(..))
import Data.Text as T hiding (intercalate, head)

cfhammill

data S = Sym String | Nil | Tr | Fl | Cons S S deriving (Eq, Show)

data T = Var String |
  Combine T T T |
  Lam String T |
  Eps String T |
  Eval T T     |
  Wrap T       |
  Env [(String, T)] |
  Sexp S  deriving (Eq, Show)

cfhammill

library(rlang)

logged_eval_tidy <-
  function(expr, data, env = caller_env()){
    
    dm <- as_data_mask(data)
    ae <- as_data_mask(list())

    nms <- character(0)
    te <- environment()

cfhammill

---
title: "An Introduction to Graphs"
author: "Chris Hammill"
date: '2015-04-01'
output:
  beamer_presentation:
    theme: "Boadilla"
    fig_crop: false
    keep_tex: true
    template: chris.beamer

cfhammill

Instructions

To use this gist, download the resizeTemplate.beamer file to the same folder and your R markdown presentation.

Then add

template: resizeTemplate.beamer

To your yaml data block after beamer_presentation

cfhammill

I'm currently translating a python statistical package to R and wanted to benchmark the packages side-by-side in an R Markdown document. So I poked around in knitr's internals to figure out how alternative code engines worked. It's pretty straight forward, just find the program you want to use, and pass it the code in your chunk. I wrote a little code snippet to do exactly that in my document (I found using stock python didn't execute my code as expected, so wanted to switch to ipython). Hopefully it may be of use to you.

In order to use the engine in an R Markdown document, download and source ipythonKnitEngine.R then include your python chunks in your document(example below).

```{r, engine = "ipython", engine.path = "path/to/ipython/"}
#Python code to execute
```

The engine.path needs to lead to the folder containing the ipython executable (not the file itself). Currently the engine path must be specified, even if ipython is in your search path, I will try to fix this in the near futu