jeroenjanssens

#!/usr/bin/env python3

import json
import string

prefix_key = "b" # My prefix key in tmux is Ctrl-B
prefix_hex = "02" # The hex code that iTerm sends (corresponds to Ctrl-B)

keys_upper = string.ascii_uppercase + r'!@#$%^&*()_+{}:"|<>?~'
keys_lower = string.ascii_lowercase + r"1234567890-=[];'\,./`"

jeroenjanssens

#!/bin/bash
# Hacked together by JeroenJanssens.com on 2013-12-10
# Requires: https://github.com/joewalnes/websocketd
# Run: websocketd --devconsole --port 8080 ./chat.sh

echo "Please enter your name:"; read USER
echo "[$(date)] ${USER} joined the chat" >> chat.log
echo "[$(date)] Welcome to the chat ${USER}!"
tail -n 0 -f chat.log --pid=$$ | grep --line-buffered -v "] ${USER}>" &
while read MSG; do echo "[$(date)] ${USER}> ${MSG}" >> chat.log; done

jeroenjanssens

#' Cache the result of an expression.
#'
#' Use \code{options(cache.path = "...")} to change the cache directory (which
#' is the current working directory by default).
#'
#' @param expr expression to evaluate
#' @param key basename for cache file
#' @param ignore_cache evalute expression regardless of cache file?
#' @return result of expression or read from cache file
#'

jeroenjanssens

#!/usr/bin/env RScript
messages <- purrr::map_chr(devtools::test(),
                           list("results", 1, "message"))
q("no", status = sum(messages != "success"))

jeroenjanssens

If your R script uses senstive information such as a password, then it's best to keep this in a seperate file (and perhaps outside the project's repository). Moreover, if you're giving a live demo using RStudio, then you should avoid putting this senstive information in your global environment.

If you put it in a YAML file, say .my_project.yaml, which may look as follows:

---
api_service:
  username: foo
  password: bar123!

jeroenjanssens

Some jq examples translated from https://github.com/jsonlines/guide

jq can be downloaded from https://stedolan.github.io/jq/download/.

curl https://raw.githubusercontent.com/jsonlines/guide/master/datagov100.json > data.json

$ < data.json jq '.name' | head -n 6

jeroenjanssens

library(tidyverse)
library(magrittr)
library(stringr)
library(readxl)

directory <- "bunch/of/excel/files"

# Get an overview of all the Excel files and their sheets
sheets <-
  data_frame(file = list.files(directory, full.names = TRUE),

jeroenjanssens

library(rvest)

html_more_nodes <- function(session, css, more_css) {
  xml2:::xml_nodeset(c(
    html_nodes(session, css),
    tryCatch({
      html_more_nodes(follow_link(session, css = more_css),
                      css, more_css)
    }, error = function(e) NULL)
  ))

jeroenjanssens

library(tidyverse)

keep_top_n <- function(df, col, n = 10) {
  semi_join(df, head(count_(df, col, sort = TRUE), n))
}

data(mpg)
# All car models
mpg %>% nrow()
# Just car models of top three manufacturers

jeroenjanssens

Back in the old days, when many data sets were still small, stem-and-leaf plots were a popular method of representing quantitative data. The example data shown in the text area comes from the cover of John Tukey's Exploratory Data Analysis. The stem-and-leaf plot updates as you change the data. Try adding fractions and negative values. Hover over the leaves to see the original values.