alexpghayes

library(tidyverse)

x <- seq(0, 10, length.out = 100)

unobserved <- tibble(
  x = x, 
  true_curve1 = sin(x),
  true_curve2 = tanh(x) - 0.5,
 coin = as.logical(rbinom(length(x), size = 1, prob = 0.5)),

chrisalbon

# -*- coding: utf-8 -*-

""" Deletes all tweets below a certain retweet threshold.
"""

import tweepy
from datetime import datetime

# Constants
CONSUMER_KEY = ''

noamross

#!/bin/bash

# Usage: mkproj [projectname]
# projectname defaults to name of current directory

template="Version: 1.0

RestoreWorkspace: No
SaveWorkspace: No
AlwaysSaveHistory: Yes

seanjtaylor

veekaybee

Wholesome Data Science

Data science has a really bad reputation recently. Between Facebook's privacy violations , facial scanning at kiosks in restaurants, and racism in algorithms, there are a lot of cases where surveillance, invasion of privacy, and unethical algorithms are dominating the news.

These cases are really important to make public, study, and prevent. But it's just as important to collect examples of good use cases of data science (that are not hyperbolized or PR fluff) so we can focus on those as an industry, and learn about what makes them work, as well.

Have some? Make some? Feel free to leave a comment or edit.

Examples

hrbrmstr

library(purrr)
library(dplyr)
library(XML)

read_plist <- safely(readKeyValueDB)
safe_compare <- safely(compareVersion)

apps <- list.dirs(c("/Applications", "/Applications/Utilities"), recursive=FALSE)

# if you have something further than this far down that's bad you're on your own

hrbrmstr

ghelp <- function(topic, in_cran=TRUE) {
  
  require(htmltools) # for getting HTML to the viewer
  require(rvest)     # for scraping & munging HTML

  # github search URL base
  base_ext_url <- "https://github.com/search?utf8=%%E2%%9C%%93&q=%s+extension%%3AR"
  ext_url <- sprintf(base_ext_url, topic)

  # if searching with user:cran (the default) add that to the URL  

noamross

library(twitteR)
library(rlist)
library(pipeR)
library(stringi)

# Authenticate with twitter
# consumer/access keys and secrets for the twitter API must be defined elsewhere
setup_twitter_oauth(consumer_key, consumer_secret, access_token, access_secret)

# Get all my followers and followees

abresler

library(dplyr)
library(tidyr)
library(magrittr)
library(ggplot2)
"http://academic.udayton.edu/kissock/http/Weather/gsod95-current/NYNEWYOR.txt" %>%
	read.table() %>% data.frame %>% tbl_df -> data
names(data) <- c("month", "day", "year", "temp")

data %>%
	group_by(year, month) %>%

hadley

library("shiny")

x <- runApp(shinyApp(
  fluidPage(
    "Password:", 
    tags$input(id = "password", type = "password"),
    actionButton("done", "Done")
  ),
  function(input, output) {
    observe({