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SparkSummitDemo

(★ 34)

PySpark Notebook and Shiny App for Demo

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ZIP Download

- BlocPower with Sparkling-checkpoint.ipynb
- BlocPower_T.csv
- CDD-HDD-Features.csv
- clusterEnergyLocation.csv
- CombinedSensorData.csv
- HDD-Features.csv
- script.r
- added files.png
- addFiles.png
- addingcredentials.png
- code snip.png
- dataicon.png
- df_data_1.png
- df_data_2.png
- df_data_3.png
- environment.png
- files.png
- insertCDDHDD.gif
- insertHDD_Features.gif
- loadBlocPower_T.gif
- ManageObjectStorage.png
- new notebook.png
- notebook.png
- objectStoreSetup.png
- Screen Shot 2016-12-22 at 1.01.56 PM.png
- shiny document.png
- sparkSetup.png
- uploaded data.png
BlocPower with Sparkling.ipynb
blocpowertransp.png
README.md
scix.png
shinyDemo.Rmd

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### SparkSummitDemo
---

This repository contains a Python Notebook and R Shiny App created using IBM Data Science Experience.  Sign up or log-in on [Data Science Experience](http://datascience.ibm.com/) and follow along with the steps below.

---

### 1. Data Science Experience Set up
- First [download the repository](https://github.com/IBMDataScience/SparkSummitDemo/archive/master.zip) to your local environment
- Unzip this zip file on your computer so you have a directory with all the assets in the repository.  We will be using the data from the data directory.  
- Log-in to Data Science Experience
- #### Create a project

1. Click on the left hand side "hamburger" icon and then click on My Projects to see a list of your projects. If this is a new account, you should only see a default project.

>  <img src="https://github.com/IBMDataScience/wow-lab-to-production/blob/master/images/my-projects.png?raw=true"/>

2. Click on the create project icon on the top right of the project list.

>  <img src="https://github.com/IBMDataScience/wow-lab-to-production/blob/master/images/create-new-project.png?raw=true" />

3. Type a name for your project. For instance, "DSX Lab". A Spark service and an object storage will be automatically selected as well as a container with a default name. A container is a directory on the object storage. Click on Create.

>  <img src="https://github.com/IBMDataScience/wow-lab-to-production/blob/master/images/create-project.png?raw=true" width="512" height="499" />

- Click on the add data assets + icon

>  <img src="https://github.com/IBMDataScience/wow-lab-to-production/blob/master/images/add-data-asset.png"/>

- Click on the Add file and select each of the files from the data directory of the downloaded zip: BlocPower_T, CDD-HDD_Features, and HDD_Features
- Once the file is loaded, click on Apply to add this file to your project.
- You should now see your 3 files in your project

>  <img src="https://raw.githubusercontent.com/IBMDataScience/SparkSummitDemo/master/Screenshots/uploaded%20data.png"/>

### 2. Running Jupyter Notebook
- Click on New Notebook
- Click the tab for From URL, add a Name for the notebook 
- Copy the URL for the Jupyter notebook - https://raw.githubusercontent.com/IBMDataScience/SparkSummitDemo/master/BlocPower%20with%20Sparkling.ipynb

>  <img src="https://raw.githubusercontent.com/IBMDataScience/SparkSummitDemo/master/Screenshots/new%20notebook.png?raw=true" width="450"/>

- Now you should be in the notebook you loaded from this repository
- Once you are inside the notebook, you need to insert credentials to access the data you uploaded.
- Click inside the blank cell near the top of the notebook (shown in screen shot below)
- Click on BlocPower_T.csv on the right side to Insert Credentials

>  <img src="https://raw.githubusercontent.com/IBMDataScience/SparkSummitDemo/master/Screenshots/addingcredentials.png?raw=true"/>

** If you don't see your files click on this icon: <img src="https://raw.githubusercontent.com/IBMDataScience/SparkSummitDemo/master/Screenshots/dataicon.png?raw=true" width="40" />  **

- After you add the credentials, rename the variable to be called `credentials` in code you added, shown below

>  <img src="https://raw.githubusercontent.com/IBMDataScience/SparkSummitDemo/master/Screenshots/code%20snip.png?raw=true" width = "200" />

- Now you can run each cell to recreate the analysis
- Follow along each of the following steps:
  - Data cleaning
  - Fitting a linear regression model
  - Conducting k-means clustering

### 3. Running the Shiny App - Flex Dashboard
- Open RStudio in Data Science Experience from the left navigation bar 
- Create a new R Markdown Document, select Shiny as the type R Markdown document (note you may be required to download some R packages at this time)

>  <img src="https://github.com/IBMDataScience/SparkSummitDemo/blob/master/Screenshots/shiny%20document.png?raw=true" width = "400" />

- Copy the raw [R Markdown from here](https://raw.githubusercontent.com/IBMDataScience/SparkSummitDemo/master/shinyDemo.Rmd)
- Replace the default content in the new R Markdown file by pasting the code in the file
- Select lines 21 - 65 and execute (This is a one time set up to install all necessary packages)
- Click the "Run Document" button to generate the dashboard from the R script
 - Having popup blockers installed may interfere with launching the dashboard
 - Click the button "Open in Browser" to see the app in a web browser
- Open the app in a browser to interact with it, share the link with anyone