Lincoln Mullen lmullen

Learning to create a reusable chart function for a scatter plot, following Mike Bostock's "Towards Reusable Charts." Also includes a random data generator.

A quicksort algorithm to demonstrate recursion. Adapted from Norman Matloff, The Art of R Programming (San Francisco: No Starch Press, 2011), 176.

An example of using a matrix to find which cities are closest to one another. The file distances-from-google.r downloads some sample data from Google's Distance Matrix API and converts it from a JSON object to an R matrix. The file distance-matrix.r creates a function to find the closest city in each row of a distance matrix.

Results:

New York is closest to Philadelphia
Los Angeles is closest to Houston

An attribution function for R. Borrowed from Kieran Healy's code.

	tifs_to_project = FileList["*.tif"].exclude(/epsg3857/).ext(".epsg3857.tif")

	desc "Reproject all geotiffs into EPSG:3857"
	multitask :default => tifs_to_project

	rule( /\.epsg3857\.tif$/ => [
	proc {\|task_name\| task_name.sub(/\.epsg3857\.tif$/, '.tif') }
	]) do \|t\|
	system "gdalwarp -t_srs EPSG:3857 -s_srs EPSG:4326 #{t.source} #{t.name}"
	end

	desc "Download the WPA Slave Narratives using wget"
	task :wget_wpa do
	system %{wget --wait 1 --limit-rate=200k -A .jpg,.png,.gif,.txt,.tif,.pdf --no-parent --background --mirror http://memory.loc.gov/mss/mesn/}
	end

	tifs_to_project = FileList["*.tif"].exclude(/epsg3857/).ext(".epsg3857.tif")

	desc "Reproject all geotiffs into EPSG:3857"
	multitask :default => tifs_to_project

	rule( /\.epsg3857\.tif$/ => [
	proc {\|task_name\| task_name.sub(/\.epsg3857\.tif$/, '.tif') }
	]) do \|t\|
	system "gdalwarp -t_srs EPSG:3857 -s_srs EPSG:4326 #{t.source} #{t.name}"
	end

	# Convert text files in cp1252 with dos line endings to files in UTF-8 with
	# Unix line endings
	for file (*.txt) {iconv -f cp1252 -t utf-8 $file -o $file}
	dos2unix *.txt

	library(dplyr)
	library(reshape2)
	library(stringr)
	library(ggplot2)
	library(RColorBrewer)

	# options(stringsAsFactors = FALSE)
	options("scipen"=100, "digits"=4)

	salaries <- read.csv("faculty-salaries-2013-14.csv")

	" Convert pandoc buffer to HTML and copy to system clipboard
	autocmd FileType pandoc nnoremap <buffer> <C-S-x> :write \\| let @+ = system("pandoc -t html " . shellescape(expand("%:p")))<CR>