bearloga

notify <- function(msg="Operation complete") {
 
  in.osx <- (Sys.info()['sysname'] == "Darwin")
  in.rstudio <- (Sys.getenv("RSTUDIO") == "1")
  in.rgui <- (Sys.getenv("R_GUI_APP_REVISION") != "")
  
  if (in.rstudio) { # hack to see if running in RStudio
    title <- "RStudio"
    sender <- activate <- "org.rstudio.RStudio"
  }

bearloga

# This file contains two functions:
# 	- combine.samples (used by par.trace.samples)
# 	- par.trace.samples
# and an example at the bottom.

## Author: Mikhail Popov (mikhail [at] mpopov.com)

# install.packages("rjags") # JAGS must be installed on system
# install.packages("doMC") # Unix only

bearloga

library(shiny)
library(ggplot2)

# Download DataTables JS file and TableTools full package from http://datatables.net/download/
# Change the paths appropriately:
addResourcePath('datatables','/Users/yourusername/Downloads/DataTables-1.9.4/media')
addResourcePath('tabletools','/Users/yourusername/Downloads/TableTools-2.1.5/media')

runApp(list(
	ui = basicPage(

bearloga

Dice Combination	Proportion
14, 13, 12, 11, 10, 9	0.0015
16, 15, 14, 13, 12, 11	0.0015
15, 14, 13, 12, 10, 9	0.0014
16, 15, 14, 13, 11, 10	0.0014
14, 13, 13, 12, 11, 10	0.0013
15, 14, 13, 12, 12, 11	0.0013

bearloga

library(base64) # install.packages('base64')
pngfile <- tempfile()
png(pngfile,width=600,height=600,pointsize=12) # adjust as needed
# Plotting code...
dev.off()

# Prints the HTML code to console
cat(img(pngfile))
# Copy and paste the whole output into your HTML source to embed the image

bearloga

# devtools::install_github("RcppCore/Rcpp")
# devtools::install_github("hadley/xml2")
# devtools::install_github("hadley/rvest")
# devtools::install_github("metacran/crandb")
# devtools::install_github('ironholds/practice')
# install.packages('dplyr')

library(rvest); library(crandb)

get_licensing_for_task <- function(task_url) {

bearloga

library(shape) # install.packages('shape')

magic_8ball <- function(x = c("yes","no")) {
	greys <- greycol(50, interval = c(0, 1))
	colours <- shadepalette("#000079", "blue", n = 20)
	emptyplot(xlim = c(-5, 5), ylim = c(-5, 5), col = "white", frame.plot = FALSE)
	filledellipse(rx1 = 4.9, ry1 = 4.9, col = "black")
	filledellipse(rx1 = 2, ry1 = 3.5, col = greys[2:50], angle = -45, dr = .1, mid = c(-2, 2))
	filledellipse(rx1 = 0.25, ry1 = 0.25, col = "white", mid = c(-2, 2))
	filledellipse(rx1 = 2.8, ry1 = 2.8, col = "white")

bearloga

d <- 20
time_pants <- 15 # seconds
# Average human swimming speed: 2 miles per hour

speed_avg <- birk::conv_unit(2, "mph", "m_per_sec") # install.packages('birk')
perc <- seq(0.25, 0.95, 0.01)
speed_slow <- speed_avg * perc

plot(perc * 100, (d / speed_slow)/60, type = "l", las = 1, lwd = 2,
	 xlab = "Speed in pants as % of average speed (2 mph ~ 0.89 m/s). In other words, how much the pants slow you down.",

bearloga

#' Manual classification of observations
#' 
#' \code{classify} launches a Shiny app to manually classify a subset of observations.
#'
#' @param x A character vector.
#' @param btn_labels A character vector of length 2 corresponding to 0 and 1.
#' @return A vector of 0/1 for each element in \code{x}.
#' @export
#' @examples \dontrun{
#'  foo <- sprintf('%s (%.2f miles per gallon)', rownames(mtcars), mtcars$mpg)

bearloga

---
title: "Interactive Presentations"
author: "Mikhail Popov (@bearloga)"
date: "December 4, 2015"
output: ioslides_presentation
runtime: shiny
---

## Reduce Demo