artemklevtsov

variables:
  CODECOV_TOKEN: "CODECOV_TOKEN_STRING"
  _R_CHECK_CRAN_INCOMING_: "false"
  _R_CHECK_FORCE_SUGGESTS_: "true"
  APT_PKGS: "libcurl4-openssl-dev libssh2-1-dev libssl-dev libxml2-dev zlib1g-dev git"

before_script:
  - apt-get update
  - apt-get install -y --no-install-recommends ${APT_PKGS}
  - apt-get install -y --no-install-recommends qpdf pandoc pandoc-citeproc

magnusnissel

#This function depends on the knitr and webshot packages, uncomment lines 2-6 below if you need to install them first
#install.packages("knitr")
#install.packages("devtools")
#library(devtools)
#devtools::install_github("wch/webshot")
#webshot::install_phantomjs()

#Feel free to move the library() calls for knitr and webshot outside the function. 

expersso

## Purpose: illustrate use of magick, gganimate, and purrr
## Inspiration:
## https://rud.is/b/2016/07/27/u-s-drought-animations-with-the-witchs-brew-purrr-broom-magick/
## and subsequent discussion on magick vs gganimate:
## https://twitter.com/hrbrmstr/status/758304420224466944
library(purrr)
library(ggplot2)
library(gganimate)
library(animation)
library(magick)

tuttinator

required_packages <- c(
  "dplyr",
  "readr",
  "ggplot2",
  "ggthemes",
  "futile.logger",
  "lubridate"
)

packages_to_install <- required_packages[!(required_packages %in% installed.packages()[, 1])]

aschleg

games.howell <- function(grp, obs) {
  
  #Create combinations
  combs <- combn(unique(grp), 2)
  
  # Statistics that will be used throughout the calculations:
  # n = sample size of each group
  # groups = number of groups in data
  # Mean = means of each group sample
  # std = variance of each group sample

benmarwick

#' Polygon vertices for plotting computed from an object's linear dimensions
#' 
#' This function takes a data frame (one row per object) of object dimensions, 
#' where each object is defined by five linear measurements, and computes coords
#' of polygon vertices at given x and y values (taken from the data frame)
#'
#' @param data data frame with at least seven numeric columns
#' @param x column for x position of polygon centers

dgrapov

#SOM example using wines data set
library(kohonen)
data(wines)
set.seed(7)

#create SOM grid
sommap <- som(scale(wines), grid = somgrid(2, 2, "hexagonal"))

## use hierarchical clustering to cluster the codebook vectors
groups<-3

benmarwick

# somewhat hackish solution to:
# https://twitter.com/EamonCaddigan/status/646759751242620928
# based mostly on copy/pasting from ggplot2 geom_violin source:
# https://github.com/hadley/ggplot2/blob/master/R/geom-violin.r

library(ggplot2)
library(dplyr)


"%||%" <- function(a, b) {

cderv

To insert YAML header for Rnotebook

snippet yaml_notebook_html
	#' ---
	#' title: "${1:title}"
	#' author: "${2:author}"
	#' knit: (function(inputFile, encoding) { rmarkdown::render(inputFile, encoding = encoding, output_file = sprintf("%s_%s.html", format(Sys.Date(), "%y%m%d"), basename(tools::file_path_sans_ext(inputFile))))})
	`r paste("#' date: '\u0060r paste(\u0022", format(Sys.time(), "%d/%m/%Y")," - Dernière MAJ:\u0022, format(Sys.time(), \u0022%d/%m/%Y\u0022))\u0060'", sep = "")`
	#' output : 
	#' html_document:

bearloga

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

## Reduce Demo