michaeldorman

library(sf)
library(ggplot2)

# Data
nc = st_read(system.file("shape/nc.shp", package="sf"))
map = nc
dat = nc[5, ]

# Plot 1
ggplot() + 

noamross

library(rtweet) #rtweet API creds should already be set up
library(stringi)
library(dplyr)

friends = get_friends(user="noamross")
followers = get_followers("noamross")
tweeps_id = distinct(bind_rows(friends, followers))
tweeps_info = lookup_users(tweeps_id$user_id)

# A regex for a visit to Durham

mdsumner

#' Title
#'
#' @param lonlatheight matrix or data.frame of lon,lat,height values
#' @param rad radius of sphere
#' @param exag exaggeration to apply to height values (added to radius)
#'
#' @return matrix
#' @export
llh2xyz <- function(lonlatheight, rad = 6378137.0, exag = 1) {
  cosLat = cos(lonlatheight[,2] * pi / 180.0)

tpoisot

#! /usr/bin/env python

import re
import requests
import tempfile
import urllib.request
import random
import sys
import os

dill

# convert mp4 to gif
# converts in.mp4 to out.gif, using 0-20s of the mp4 at resolution 640x480
ffmpeg -ss 00:00:00.000 -i in.mp4 -pix_fmt rgb24 -r 10 -s 640x480 -t 00:00:20.000 out.gif

# alternative mp4 to gif (using imagemagick for the second step)
ffmpeg -i input.mp4 -r 10 output%05d.png
convert output*.png output.gif

# strip audio out of a video file (to aac)

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

sckott

The Natural History Museum just came out with a new data portal, including API access via CKAN! Turns out we already have a CKAN R client in development. Examples? I thought you'd never ask:

Installation

Install latest dev version of taxize

sckott

NOAA oceanographic data (ERDDAP) data in R

NOAA ERDDAP server gives access to apparently 802 datasets

Install and load rnoaa

install_github("ropensci/rnoaa")

mywarr

#!/usr/bin/env python
#
# AccessDump.py
# A simple script to dump the contents of a Microsoft Access Database.
# It depends upon the mdbtools suite:
#   http://sourceforge.net/projects/mdbtools/

import sys, subprocess, os

DATABASE = sys.argv[1]

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results