trinker

dat <- data.frame(
    freq = c(37, 88, 47, 89, 28, 68, 65, 98, 68, 86, 50, 87, 59, 82, 45, 65, 52, 32, 59, 47, 68, 78, 39, 31, 64, 68)
)

dat[["sector"]] <- rep(c("U.S. adults", "AAAS scientist"), nrow(dat)/2)

field <- c("Biomedical sciences", "Climate, energy, space sciences")
dat[["field"]] <- factor(rep(field, c(10, 16)), levels=field)

items <-  c(

hkmix

#include <iostream>
#include <iomanip>
#include <random>
#include <string>
#include <thread>
#include <vector>

using std::size_t;

class Point {

slopp

snippet gg
	ggplot(${1:data}, aes(${2:aes})) + 
	  geom_${3:geom}()

snippet shiny
	library(shiny)
	ui <- fluidPage(
		${1:ui}
	)
	

microcai

#pragma once

#include <tuple>
#include <mutex>
#include <boost/asio.hpp>
#include <boost/bind.hpp>
#include <boost/function.hpp>
#include <boost/shared_ptr.hpp>
#include <boost/weak_ptr.hpp>

trinker

library(reshape2)
library(ggplot2)

data(mtcars)
dat <- with(mtcars, data.frame(mpg, cyl, disp, hp, wt, gear))
cor.matrix <- round(cor(dat, use = "pairwise.complete.obs", method = "spearman"), digits = 2)
diag(cor.matrix)<-NA

cor.dat <- melt(cor.matrix)
cor.dat <- data.frame(cor.dat)

lyndametref

R Cheat Sheet : Applying functions

• R Cheat Sheet : Applying functions
  • apply(x,index,function)
  • lapply(x,function)
  • sapply(x,function)
  • tapply(x,y,function)
  • mapply(function,x,y,...)
  • References

whophil

# After Ubuntu 16.04, Systemd becomes the default. 
# It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd

[Unit]
Description=Jupyter Notebook

[Service]
Type=simple
PIDFile=/run/jupyter.pid
ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py

scchan

#include <random>
#include <algorithm>
#include <iostream>
#include <cmath>

// header file for the hc API
#include <hc.hpp>

#define N  (1024 * 500)

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

52cik

npm set registry https://r.npm.taobao.org # 注册模块镜像
npm set disturl https://npm.taobao.org/dist # node-gyp 编译依赖的 node 源码镜像

## 以下选择添加
npm set sass_binary_site https://npm.taobao.org/mirrors/node-sass # node-sass 二进制包镜像
npm set electron_mirror https://npm.taobao.org/mirrors/electron/ # electron 二进制包镜像
npm set ELECTRON_MIRROR https://cdn.npm.taobao.org/dist/electron/ # electron 二进制包镜像
npm set puppeteer_download_host https://npm.taobao.org/mirrors # puppeteer 二进制包镜像
npm set chromedriver_cdnurl https://npm.taobao.org/mirrors/chromedriver # chromedriver 二进制包镜像
npm set operadriver_cdnurl https://npm.taobao.org/mirrors/operadriver # operadriver 二进制包镜像