bearloga

suppressMessages({
  # Preamble ========================================
  # ======== Web Scraping ===========================
  library(rvest) # install.packages('rvest')
  library(magrittr)
  # ======== I/O ====================================
  library(httr) # install.packages('httr')
})

html <- read_html("https://wikipedia.org")

bearloga

## Script for scraping Wikipedia for data to use with the geom_flag() prototype
## CONTACT: Mikhail Popov (@bearloga // mikhail[at]mpopov[dot]com)
## URL: https://gist.github.com/bearloga/519a701a6a9bc7c3ba9f

# install.packages("import")
library(rvest) # install.packages("rvest")
library(magrittr)

import::from(dplyr, mutate, select, keep_where = filter, left_join, distinct)

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library(magrittr)
library(dplyr)
library(stringr)

df <- mtcars; df$car <- rownames(df)

df %>%
  mutate(make = str_extract(car, "^[A-Za-z]+\\b")) %>%
 group_by(make) %&gt;%

bearloga

your_handle <- "bearloga"
# Note: must be logged in to twitter to view your own or anyone's list of followers

library(magrittr) # install.packages('magrittr')
library(rvest) # install.packages('rvest')
library(RSelenium) # install.packages('RSelenium')

# Scrolling solution by NicE (http://stackoverflow.com/a/29965233/1091835):
checkForServer()
startServer()

bearloga

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

## Reduce Demo

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#' 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

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

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

# 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(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