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# XML package demo.
# Create barplot of distribution of first letters
# in the Mondial database.
# RS15082015

library(XML)
library(ggplot2)

# Load data.
xml <- xmlTreeParse("~/Temp/mondial-3.0.xml", useInternalNodes = TRUE)  

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find . -maxdepth 1 -type d -exec git --git-dir={}/.git --work-tree=$PWD/{} pull origin master \;

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# Question on treating "NA" values.
# RS25072017

# PROBLEM STATEMENT: Remove the "?" values in the data set, and turn them into "n". ---------------------

require(h2o)

# Modify below, as needed.
h2o.init(startH2O = FALSE)

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# Data manipulation with H2O
# RS25072017

require(h2o)

h2o.init()


# 1. The R way. ---------------------

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import pandas as pd
import numpy as np

# I want to clean col2. This records equipemnts.
data = {'col1': [1, 2, 4], 'col2': ["e70", "e70 e80 ua", "b20"]}

# Create a Pandas data frame.
df = pd.DataFrame(data)

# Quick helper function to flatten a nested list.

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Docker - How to cleanup (unused) resources

Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...

delete volumes

// see: https://github.com/chadoe/docker-cleanup-volumes

$ docker volume rm $(docker volume ls -qf dangling=true)

$ docker volume ls -qf dangling=true | xargs -r docker volume rm

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startup_message off
vbell off
hardstatus alwayslastline
hardstatus string '%{gk}[ %{G}%H %{g}][%= %{wk}%?%-Lw%?%{=b kR}(%{W}%n*%f %t%?(%u)%?%{=b kR})%{= kw}%?%+Lw%?%?%= %{g}]%{=y C}[%d/%m %c]%{W}'

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# Generate word clouds from R.
# RS10062015

# May need to install the below packages.

#req <- c("wordcloud", "tm")
#install.packages(req)

library(tm)
library(wordcloud)

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version: '2'
services:
  kaggle:
    image: kaggle/python
    container_name: kaggle
    command: jupyter notebook --allow-root --no-browser --ip="0.0.0.0" --notebook-dir=/notebooks --NotebookApp.token='' --allow-root
    user: root
    ports:
      - "8888:8888"
    volumes: