Underscore example:
_.each([1, 2, 3], function(num) { alert(num); });
plotOptions: { | |
line : { | |
dataLabels : { | |
enabled : true, | |
formatter: function() { | |
var first = this.series.data[0], | |
last = this.series.data[this.series.data.length - 1]; | |
if ((this.point.category === first.category && this.point.y === first.y) || | |
(this.point.category === last.category && this.point.y === last.y)) { | |
return this.point.y; |
# .png with Windows GDI versus .png with cairographics | |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("ggplot2", "RColorBrewer", "Cairo") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.us.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Generate some data | |
nn <- 100 | |
myData <- data.frame(X = rnorm(nn), |
# Truly the most ridiculous thing I could think of. | |
doInstall <- TRUE # Change to FALSE if you don't want packages installed. | |
toInstall <- c("XML", "png", "devtools", "RCurl") | |
if(doInstall){install.packages(toInstall, repos = "http://cran.r-project.org")} | |
lapply(toInstall, library, character.only = TRUE) | |
# Some helper functions, lineFinder and makeTable | |
source_gist("818983") | |
source_gist("818986") |
#!/bin/bash | |
NAME="hello_app" # Name of the application | |
DJANGODIR=/webapps/hello_django/hello # Django project directory | |
SOCKFILE=/webapps/hello_django/run/gunicorn.sock # we will communicte using this unix socket | |
USER=hello # the user to run as | |
GROUP=webapps # the group to run as | |
NUM_WORKERS=3 # how many worker processes should Gunicorn spawn | |
DJANGO_SETTINGS_MODULE=hello.settings # which settings file should Django use | |
DJANGO_WSGI_MODULE=hello.wsgi # WSGI module name |
# Helper functions that allow string arguments for dplyr's data modification functions like arrange, select etc. | |
# Author: Sebastian Kranz | |
# Examples are below | |
#' Modified version of dplyr's filter that uses string arguments | |
#' @export | |
s_filter = function(.data, ...) { | |
eval.string.dplyr(.data,"filter", ...) | |
} |
An example of how to animate a d3-cloud word cloud.
Based on https://github.com/jasondavies/d3-cloud/blob/master/examples/simple.html.
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) %>% |
If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?
I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:
# Twitter Topic Modeling Using R | |
# Author: Bryan Goodrich | |
# Date Created: February 13, 2015 | |
# Last Modified: April 3, 2015 | |
# | |
# Use twitteR API to query Twitter, parse the search result, and | |
# perform a series of topic models for identifying potentially | |
# useful topics from your query content. This has applications for | |
# social media, research, or general curiosity | |
# |