aammd

matrix_to_df_firstline_header <- function(mat){
  requireNamespace("purrr")
  
  mat %>% 
    ## cut columns into lists
    apply(2, function(s) list(s)) %>% 
    flatten %>% 
    map(flatten_chr) %>% 
    ## set names to the first element of the list
    {set_names(x = map(., ~ .x[-1]),

MilesMcBain

dat <- readr::read_csv("municipality\n- Ticino\n>> Distretto di Bellinzona\n......5001 Arbedo-Castione\n......5002 Bellinzona\n......5003 Cadenazzo\n......5004 Camorino\n")

dat %>% mutate(municipality = gsub(pattern = '[\\.\\>]|^-', 
                                   replacement = "", 
                                   municipality)) %>%
        separate(col = municipality, 
                 sep = '(?<=[0-9])\\s', 
                 into = c ("code","municipality"), 
                 fill = "left")

trestletech

download.file("https://ndownloader.figshare.com/articles/5170705/versions/1", "kd.zip")
unzip("kd.zip")

library(readr)

words <- readr::read_csv("KSP-Word.csv")
users <- readr::read_csv("KSP-User.csv")
entries <- readr::read_csv("KSP-Entry.csv")
keypress <- readr::read_csv("KSP-KeyPress.csv")
pressure <- readr::read_csv("KSP-Pressure.csv")

MilesMcBain

f <- function(...) {
  function(df) with(df, ...)
}

footate <- function(.data, ...) {
  dots <- list(...)

  for (column in names(dots)) {
    .data[[column]] <- dots[[column]](.data)
  }

coolbutuseless

#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#' Create a CSS ruleset
#'
#' Create a CSS ruleset  consisting of a selector and one-or-more property declarations,
#' or, if no \code{.selector} is given, create an inline style string
#'
#' The list of included properties is not a complete list, but rather an
#' abbreviated list from
#' \url{https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_Properties_Reference}

nstrayer

library(tidyverse)

n <-  150
sensitivity_threshold <- 5
data <- tibble(
  a = rgamma(n = n, shape = 5, rate = 0.5),
  b = rgamma(n = n, shape = a/2, rate = 0.5)
)

generate_missing_data <- function(i){

jennybc

---
title: "twee demo"
author: "Jenny Bryan"
date: "17 August, 2014"
output:
  html_document:
    toc: TRUE
    keep_md: TRUE
---

hadley

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply

Pakillo

---
title: "Untitled"
author: "Francisco Rodriguez-Sanchez"
date: "Thursday, January 22, 2015"
output: html_document
---

<style type="text/css">

body, td {

markdly

---
output: 
  html_document:
    theme: cerulean
---

### Example html multiple choice question using Rmarkdown

<!-- Render this Rmarkdown doc to html to make an interactive html / js multiple choice question -->