https://github.com/meetps/statistical-computing-interface
R based Statistical Computing Interface for easy statistical analysis and data visualization
https://github.com/meetps/statistical-computing-interface
Last synced: 3 months ago
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R based Statistical Computing Interface for easy statistical analysis and data visualization
- Host: GitHub
- URL: https://github.com/meetps/statistical-computing-interface
- Owner: meetps
- Created: 2015-03-26T17:28:22.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2015-04-02T17:40:17.000Z (about 10 years ago)
- Last Synced: 2025-01-04T03:08:33.126Z (5 months ago)
- Language: JavaScript
- Homepage: http://meetshah1995.github.io/statistical-computing-interface/
- Size: 1.68 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# statistical-computing-interface
R based Statistical Computing Interface for easy statistical analysis and data visualization.
A prototype application written in Django including basic features of displaying graphs from uploaded file data , OpenCPU R Computations etc.
## Features :
* User Registration system
* Secure User Login System to prevent csrf attacks.
* Bar Graph output of uploaded data file (csv for now other formats support soon).
* Line Graph output of uploaded data file (csv for now other formats support soon).
* Mean of columns in the uploaded file displayed using R requests to OpenCPU Servers. (Linear Regression coming soon.)
##Installation
* Clone this repository using `git clone `
* Set up a virual environment in the same directory using `virtualenv `
* Activate your virtualenv using `source //bin/activate`
* Change your directory to statistical-computing-interface
* Install all dependencies using `pip install -r dependencies.txt`
* Collect the static files using `python manage.py collectstatic`
* Start the Django server `python manage.py runserver`
* Open up your browser and open up `127.0.0.1:8080/sciApp/register/` to see the application live.
##Why Django ?
* Thousands of useful scientific and statistical python libraries can be harnessed which is not possible easily in PHP, Node.js.
* Django is utilized by a lot of modern organizations as it provides a lot of scalibity features such as models, templateFillers etc which makes the interface future proof.
* No vulnerability to CSRF attacks.
* Code is in Python hence lesser lines of code.
* Statistical analysis is in pure python , hence future contribution from python community can be easy to get.