soedr

Note: $ denotes the start of a command. Don't actually type this.

Steps to Install Anaconda on Windows Ubuntu Terminal

1. Install WSL (Ubuntu for Windows - can be found in Windows Store). I recommend the latest version (I'm using 18.04) because there are some bugs they worked out during 14/16 (microsoft/WSL#785)
2. Go to https://repo.continuum.io/archive to find the list of Anaconda releases
3. Select the release you want. I have a 64-bit computer, so I chose the latest release ending in x86_65.sh. If I had a 32-bit computer, I'd select the x86.sh version. If you accidentally try to install the wrong one, you'll get a warning in the terminal. I chose Anaconda3-5.2.0-Linux-x86_64.sh.
4. From the terminal run wget https://repo.continuum.io/archive/[YOUR VERSION]. Example: $ wget https://repo.continuum.io/archive/Anaconda3-5.2.0-Linux-x86_64.sh
5. Run the installation script: $ bash Anaconda[YOUR VERSION].sh ($ bash Anaconda3-5.2.0-Linux-x86_64.sh)
6. Read the license

soedr

library(tidyverse)
library(Rglpk)
library(glue)

df <- read_csv("data_assignment_regular.csv")
df_with_variability <- read_csv("data_assignment_with_variability.csv")

# Helper Functions ####
SolverLP <- function(model, method = "CPLEX_LP", decimal = 0) {
  model1.lp <- Rglpk_read_file(model, type = method, verbose = F)

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# -*- coding: utf-8 -*-
"""
Sample report generation script from pbpython.com

This program takes an input Excel file, reads it and turns it into a
pivot table.

The output is saved in multiple tabs in a new Excel file.
"""

soedr

import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
import pandas as pd

app = dash.Dash()

df = pd.read_csv(
    'https://gist.githubusercontent.com/chriddyp/'

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Apache Spark installation + ipython notebook integration guide for Mac OS X

Tested with Apache Spark 1.3.1, Python 2.7.9 and Java 1.8.0_45 + workaround for Spark 1.4.x from @enahwe.

Install Java Development Kit

Download and install it from oracle.com

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# A shiny app for estimating the growth of electrical generation over a 100 year time period
# resulting from a one-time investment with reinvestment of proceeds under uncertainty.
#  Programmed by John Yagecic in December 2016
#  JYagecic@gmail.com


library(shiny)
library(ggplot2)

ui<-fluidPage(

soedr

library(data.table)
library(pbapply)
library(pbmcapply)

timeConsumedPbapply <- data.table()
timeConsumedPbmcapply <- data.table()
timeConsumedapply <- data.table()
numCores <- 4L
repeats <- 50
maxNum <- 10