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# Joseph R. Mihaljevic
# July 2013
# (Partial) Bayesian analysis of variance, accounting for heteroscedasticity
# Generate some artificial data:
# Normally distributed groups, but heteroscedastic
a <- rnorm(25, mean=8, sd=10)
b <- rnorm(50, mean=5, sd=2)
c <- rnorm(25, mean=3, sd=.1)
d <- rnorm(25, mean=11, sd=3)
e <- rnorm(50, mean=13, sd=2)

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# Here are a few methods for getting text from PDF files. Do read through 
# the instructions carefully! NOte that this code is written for Windows 7,
# slight adjustments may be needed for other OSs

# Tell R what folder contains your 1000s of PDFs
dest <- "G:/somehere/with/many/PDFs"

# make a vector of PDF file names
myfiles <- list.files(path = dest, pattern = "pdf",  full.names = TRUE)

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## Function to use GDAL to project coordinate reference system

# See http://www.gdal.org/gdalwarp.html for additional details 
# `resampling` can be 'near' (nearest neighbour), 'bilinear', 'cubic', or
# 'lanczos' (Lanczos windowed sinc resampling). 
# `extent` should be a bbox object or a vector of c(xmin, ymin, xmax, ymax)
# `of` is the output format (use GDAL short name as given by the name field of
# gdalDrivers(), or at http://www.gdal.org/formats_list.html)
# `extension` is the output extension corresponding to the primary file
# `ot` is the output type (see http://www.gdal.org/gdal_translate.html)

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---
title: "A few methods for making tables in rmarkdown"
output: html_document
---

Updates: 

Packages that have appeared since my original look into this, and seem great:

https://github.com/yihui/printr

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Converting a Word Document to Markdown in One Move

The Problem

A lot of important government documents are created and saved in Microsoft Word (*.docx). But Microsoft Word is a proprietary format, and it's not really useful for presenting documents on the web. So, I wanted to find a way to convert a .docx file into markdown.

Installing Pandoc

On a mac you can use homebrew by running the command brew install pandoc.

The Solution

Pakillo

Notes:

• I've tried to break up in to separate pieces, but it's not always possible: e.g. knowledge of data structures and subsetting are tidy intertwined.

• Level of Bloom's taxonomy listed in square brackets, e.g. http://bit.ly/15gqPEx. Few categories currently assess components higher in the taxonomy.

Programming R curriculum

Data structures

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---
title: "Untitled"
author: "Jeff W. Hollister"
date: "2/2/2016"
output: beamer_presentation
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)

Pakillo

library("shiny")
library("shinyjs")


ui <- shinyUI(pageWithSidebar(
  titlePanel("", "3D Perspective Plot for Interaction Effects"),
  sidebarPanel(
    tabsetPanel(
      tabPanel("Data", 

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`simulate.gamm` <- function(object, nsim = 1, seed = NULL, newdata,
                            freq = FALSE, unconditional = FALSE, ...) {
    if (!exists(".Random.seed", envir = .GlobalEnv, inherits = FALSE))
        runif(1)
    if (is.null(seed))
        RNGstate <- get(".Random.seed", envir = .GlobalEnv)
    else {
        R.seed <- get(".Random.seed", envir = .GlobalEnv)
        set.seed(seed)
        RNGstate <- structure(seed, kind = as.list(RNGkind()))

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---
title: "Auto-numbering and cross-referencing of figures and tables in rmarkdown"
output: html_document
---

TODO: check this out: https://github.com/adletaw/captioner

Here's how to use:

1. Copy and paste the first two code chunks in this document to the top of your document