If your are interested in being introduced to some basic Data Science Engineering concepts and applications, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R. Additionally, if you are interested in using Python with Spark, you can have a look at our pySpark notebooks.
For these series of notebooks, we have used Jupyter with the IRkernel R kernel. You can find installation instructions for you specific setup here. Have also a look at Andrie de Vries post Using R with Jupyter Notebooks that includes instructions for installing Jupyter and IRkernel together.
A good way of using these notebooks is by first cloning the repo, and then
starting your Jupyter in pySpark mode. For example,
if we have a standalone Spark installation running in our
localhost with a
maximum of 6Gb per node assigned to IPython:
MASTER="spark://127.0.0.1:7077" SPARK_EXECUTOR_MEMORY="6G" IPYTHON_OPTS="notebook --pylab inline" ~/spark-1.5.0-bin-hadoop2.6/bin/pyspark
Notice that the path to the
pyspark command will depend on your specific
installation. So as requirement, you need to have
Spark installed in
the same machine you are going to start the
IPython notebook server.
For more Spark options see here. In general it works the rule of passign options
described in the form
Every year, the US Census Bureau runs the American Community Survey. In this survey, approximately 3.5 million households are asked detailed questions about who they are and how they live. Many topics are covered, including ancestry, education, work, transportation, internet use, and residency. You can directly to the source in order to know more about the data and get files for different years, longer periods, individual states, etc.
In any case, the starting up notebook will download the 2013 data locally for later use with the rest of the notebooks.
The idea of using this dataset came from being recently announced in Kaggle as part of their Kaggle scripts datasets. There you will be able to analyse the dataset on site, while sharing your results with other Kaggle users. Highly recommended!
Where we download our data locally and start up a SparkR cluster.
About loading our data into SparkSQL data frames using SparkR.
Different operations we can use with SparkR and
DataFrame objects, such as data selection and filtering, aggregations, and sorting. The basis for exploratory data analysis and machine learning.
How to explore different types of variables using SparkR and ggplot2 charts.
About linear models using SparkR, its uses and current limitations in v1.5.
An Exploratory Data Analysis of the 2013 American Community Survey dataset, more concretely its geographical features.
Contributions are welcome! For bug reports or requests please submit an issue.
Feel free to contact me to discuss any issues, questions, or comments.
This repository contains a variety of content; some developed by Jose A. Dianes, and some from third-parties. The third-party content is distributed under the license provided by those parties.
The content developed by Jose A. Dianes is distributed under the following license:
Copyright 2016 Jose A Dianes Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.