Karthik Ram karthik

What is this?

A scratchpad for a classifer of software libraries focussing on a number of areas that will guide users to make more informed decisions about whether to include a library within their own project.

See librariesio/libraries.io#1486 for more information.

Themes/Questions

There are a number of key themes here that we need to look at. Within each I have posed a number of questions that might be of interest to a developer or maintainer.

Code

What scale is this project? - Looking at things like novel LoC, #dependencies and LoC in dependencies perhaps.

	library(patchwork)
	library(ggplot2)
	library(purrr)
	library(dplyr)
	library(glue)
	map(
	5:8,
	~iris %>%
	filter(Petal.Length < .x) %>%
	ggplot() +

	# not possible to change labels via V4
	# https://platform.github.community/t/mutations-for-label/2144

	# list labels
	current_labels <- gh::gh("/repos/:owner/:repo/labels",
	owner = "ropensci",
	repo = "unconf18")
	current_labels <- purrr::map_chr(current_labels, "name")

	# delete all labels

	# =============================================================================
	# @file Makefile
	# @brief Makefile for generating previews of the paper
	# @author Michael Hucka <[email protected]>
	# @license Please see the file named LICENSE in the project directory
	# @website https://github.com/casics/dassie
	# =============================================================================

	# Change the following values to match your configuration.
	# .............................................................................

	# Using concaveman in R using V8
	concaveman <- function(d){
	library(V8)
	ctx <- v8()
	ctx$source('https://www.mapbox.com/bites/00222/concaveman-bundle.js')
	jscode <- sprintf(
	"var points = %s;var polygon = concaveman(points);",
	jsonlite::toJSON(d, dataframe = 'values')
	)
	ctx$eval(jscode)

	library(tidyverse)
	library(plotly)

	# I'm being lazy, please don't do this
	# setwd("~/Downloads/Version 2_0_1/")

	d <- read_csv('GEOSTAT_grid_POP_1K_2011_V2_0_1.csv') %>%
	rbind(read_csv('JRC-GHSL_AIT-grid-POP_1K_2011.csv') %>%
	mutate(TOT_P_CON_DT = '')) %>%
	mutate(

	# data from http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat
	# Originally seen at http://spatial.ly/2014/08/population-lines/

	# So, this blew up on both Reddit and Twitter. Two bugs fixed (southern Spain was a mess,
	# and some countries where missing -- measure twice, submit once, damnit), and two silly superflous lines removed after
	# @hadleywickham pointed that out. Also, switched from geom_segment to geom_line.

	# The result of the code below can be seen at http://imgur.com/ob8c8ph

	library(tidyverse)

	# Should work on all Debian based distros with systemd; tested on Ubuntu 16.04+.
	# This will by default install all plugins; you can customize this behavior on line 6. Selecting too many plugins can cause issues when downloading.
	# Run as root (or sudo before every line) please. Note this is not designed to be run automatically; I recommend executing this line by line.

	apt install curl
	curl https://getcaddy.com \| bash -s personal dns,docker,dyndns,hook.service,http.authz,http.awses,http.awslambda,http.cache,http.cgi,http.cors,http.datadog,http.expires,http.filemanager,http.filter,http.forwardproxy,http.geoip,http.git,http.gopkg,http.grpc,http.hugo,http.ipfilter,http.jekyll,http.jwt,http.locale,http.login,http.mailout,http.minify,http.nobots,http.prometheus,http.proxyprotocol,http.ratelimit,http.realip,http.reauth,http.restic,http.upload,http.webdav,net,tls.dns.auroradns,tls.dns.azure,tls.dns.cloudflare,tls.dns.cloudxns,tls.dns.digitalocean,tls.dns.dnsimple,tls.dns.dnsmadeeasy,tls.dns.dnspod,tls.dns.dyn,tls.

	#' Convert a dplyr expression to a list of step objects
	separate_steps <- function(expr, iscall=FALSE) {
	if (iscall) {
	call <- expr
	} else {
	call <- match.call()[["expr"]]
	}

	len <- length(call)
	if (len == 1) {