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FROM ubuntu:18.04

RUN apt-get update \
    && apt-get dist-upgrade -y \
    && apt-get install -y wget gcc g++ redis supervisor libgl1 sudo \
        vim less htop \
        webp libwebp-dev \
    && apt-get clean

RUN sudo sed -i '/^bind/s/bind.*/bind 0.0.0.0/' /etc/redis/redis.conf

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library(ggplot2)
theme_set(theme_bw(12))

data1 <- data.frame(location = c(rep(0, 10), 1:100, rep(100, 200), 100:1, rep(0, 10)),
                    style = 1)
#data1$style[(nrow(data1)-30):nrow(data1)] <- 2
data1$idx <- 1:nrow(data1)

plt1 <- ggplot(data1) + geom_line(aes(x = idx, y = location, 
                                      linetype = factor(style)),

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

import csv
import codecs
import cStringIO
import os
from collections import namedtuple

def getParties(parties_file):

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Overview

When you're working on multiple coding projects, you might want a couple different version of Python and/or modules installed. That way you can keep each project in its own sandbox instead of trying to juggle multiple projects (each with different dependencies) on your system's version of Python. This intermediate guide covers one way to handle multiple Python versions and Python environments on your own (i.e., without a package manager like conda). See the Using the workflow section to view the end result.

Use cases

1. Working on 2+ projects that each have their own dependencies; e.g., a Python 2.7 project and a Python 3.6 project, or developing a module that needs to work across multiple versions of Python. It's not reasonable to uninstall/reinstall modules every time you want to switch environments.
2. If you want to execute code on the cloud, you can set up a Python environment that mirrors the relevant

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library(readr)
library(dplyr)
library(ggplot2)

get_first_digit <- function(x) {
    return(substr(x, 1, 1))
}

votes <- read_csv("https://github.com/Prooffreader/election_2016_data/raw/master/data/presidential_general_election_2016_by_county.csv")

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localpath <- "/home/chema/tmp/kaggle/digits/"
if (Sys.info()["sysname"] == "Windows")
    localpath <- "C:/temp/kaggle/digits"

testFile <- file.path(localpath, "test.Rda")
trainFile <- file.path(localpath, "train.Rda")

if(!file.exists(testFile) && !file.exists(trainFile)) {
    testCSV <- file.path(localpath, "test.csv")
    trainCSV <- file.path(localpath, "train.csv")

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library(ggplot2)                                                                
                                                                                
NLINEAS <- 20                                                                   
                                                                                
coords <- data.frame(x1 = runif(NLINEAS, 0, 10),                                
                     x2 = runif(NLINEAS, 0, 10),                                
                     y1 = runif(NLINEAS, 0, 10),                                
                     y2 = runif(NLINEAS, 0, 10))                                
                                                                                
plt1 <- ggplot(coords) + geom_segment(aes(x = x1, xend = x2,                    

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# Timing measurement in R. Vectorized operation on matrix / data.frame
# Author: José María Mateos - jmmateos@ieee.org
#
# For certain vectorized operations, it makes sense to convert your data.frame
# into a matrix. Even if you are using apply, the data frame iteration can be
# real slow.
#
# In this example, I will compute the Euclidean distance for a random vector
# and a random matrix / data.frame of thousands of elements. Operations will be
# done in two different ways: using the apply function over the columns and 

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open("C:\\temp\\barcenas.tif");
run("32-bit");
run("Mean...", "radius=30");
open("C:\\temp\\mariano.tif");
run("32-bit");
run("Duplicate...", "title=mariano-1.tif");
run("Mean...", "radius=20");
imageCalculator("Subtract create 32-bit", "mariano.tif","mariano-1.tif");
imageCalculator("Add create 32-bit", "Result of mariano.tif","barcenas.tif");
selectWindow("barcenas.tif");

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# Eustat data analysis
library(ggplot2)

eustat <- read.csv(file = "/tmp/ilc_di01_1_Data.csv", na.string = ":")
eustat$Value <- gsub(" ", "", eustat$Value)
eustat$Value <- as.numeric(eustat$Value)

spain <- eustat[eustat$GEO == "Spain" & 
                eustat$CURRENCY == "Purchasing Power Standard" & 
                eustat$INDIC_IL == "Top cut-off point",