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Tag Archives: tutorial
Using R with MySQL #rstats
A brief tutorial to working with R and MySQL. MySQL belongs to Oracle is one of the most widely used databases now.
http://www.mysql.com/downloads/mysql/ or (http://www.mysql.com/downloads/mirror.php?id=403831)
Click Install -use default options, remember to note down the password=XX
2.Download the ODBC connector from http://www.mysql.com/downloads/connector/odbc/5.1.htmlThe Data Sources (ODBC) can be located from the Control Panel in Windows7
Install ODBC Connector by double clicking the .msi file downloaded in Step 2-
Check this screenshot in ODBC Connectors to verify-
Note this is the Drivers tab in ODBC Data Source Administrator
Click the System DSN and Configure MySQL using the add button
Use the configuration options shown exactly here. The user is root, the TCP/IP Server is local host, use the same password in Step 1 and the Database is MySQLTest the connection
Click OK to finish this step.
Click the User DSN tab (and repeating the step immediately above -Add, and Configure the connection using options The user is root, the TCP/IP Server is local host, use the same password in Step 1 and the Database is MySQL , Test the connection and OK to add the connection
3. Download the MySQL workbench from http://www.mysql.com/downloads/workbench/
This is very helpful to configuring the database
http://www.mysql.com/downloads/mirror.php?id=403983#mirrors
Create a new table using the options in the screenshots below
Open Connection
You can create a new table using the options as below,
Once created you can also add new variables (using the Columns Tab)
MySQL allows you create new columns very easily
The SQL commands are automatically generated.
Click Apply to execute the changes to the Database.
Now we start R
Type the commands in the screenshot below to create a connection to the Database in MySQL
> library(RODBC)
> odbcDataSources()
> ajay=odbcConnect(“MySQL”,uid=”root”,pwd=”XX”)
> ajay
> sqlTables(ajay)
>tested=sqlFetch(ajay,”host”)
Note- this is a brief tutorial for beginners without getting into too many complexities of database administration and management, to start using R and MySQL.
Rcpp Workshop in San Francisco Oct 8th
Rcpp Workshop in San Francisco Oct 8th
Following the successful one-day master class on Rcpp preceding this year’s R/Finance conference, a full-day master class on Rcpp and related topics which will be held on Saturday, October 8, in San Francisco.
Join Dirk Eddelbuettel for six hours of detailed and hands-on instructions and discussions aroundRcpp, inline, RInside, RcppArmadillo, RcppGSL, RcppEigen and other packages—in an intimate small-group setting.
The full-day format allows combining an introductory morning session with a more advanced afternoon session while leaving room for sufficient breaks. We plan on having about six hours of instructions, a one-hour lunch break and two half-hour coffee breaks (and lunch and refreshments will be provided).
Morning session: “A Hands-on Introduction to R and C++”
The morning session will provide a practical introduction to the Rcpp package (and other related packages). The focus will be on simple and straightforward applications of Rcpp in order to extend R and/or to significantly accelerate the execution of simple functions.
The tutorial will cover the inline package which permits embedding of self-contained C, C++ or FORTRAN code in R scripts. We will also discuss RInside, to easily embed the R engine code in C++ applications, as well as standard Rcpp extension packages such as RcppArmadillo and RcppEigen for linear algebra (via highly expressive templated C++ libraries) and RcppGSL.
Afternoon session: “Advanced R and C++ Topics”
The afternoon tutorial will provide a hands-on introduction to more advanced Rcpp features. It will cover topics such as writing packages that use Rcpp, how Rcpp modules and the new R ReferenceClasses interact, and how Rcpp sugar lets us write C++ code that is often as expressive as R code. Another possible topic, time permitting, may be writing glue code to extend Rcpp to other C++ projects.
We also expect to leave some time to discuss problems brought by the class participants.
October 8, 2011 – San Franciso
AMA Executive Conference Center
@ the Marriott Hotel
55 4th Street, 2nd Level
San Francisco, CA 94103
Tel. 415-442-6770
Instructor Bio
| Dirk E has been contributing packages to CRAN for nearly a decade. Among these are RQuantLib, digest, littler, random, RPostgreSQL, as well the Rcpp family of packages comprising Rcpp, RInside, RcppClassic, RcppExamples, RcppDE, RcppArmadillo and RcppEigen. He maintains the CRAN Task Views for Finance as well as High-Performance Computing, and is a founding co-organiser of the annual R / Finance conferences in Chicago. He has Ph.D. in Financial Econometrics from EHESS (Paris), and works in Chicago as a Quantitative Strategist. |
Google Plus API- statistical text mining anyone
For the past year and two I have noticed a lot of statistical analysis using #rstats /R on unstructured text generated in real time by the social network Twitter. From an analytic point of view , Google Plus is an interesting social network , as it is a social network that is new and arrived after the analytic tools are relatively refined. It is thus an interesting use case for evolution of people behavior measured globally AFTER analytic tools in text mining are evolved and we can thus measure how people behave and that behavior varies as the social network and its user interface evolves.
And it would also be a nice benchmark to do sentiment analysis across multiple social networks.
Some interesting use cases of using Twitter that have been used in R.
- Using R to search Twitter for analysis
- Text Data Mining With Twitter And R
- TWITTER FROM R… SURE, WHY NOT!
- A package called TwitteR
- slides from my R tutorial on Twitter text mining #rstats
- Generating graphs of retweets and @-messages on Twitter using R and Gephi
The Console lets you see and manage the following project information:
- Activated APIs - Activate one or more APIs to enable traffic monitoring, filtering, and billing, and API-specific pages for your project. Read more about activating APIs here.
- Traffic information - The Console reports traffic information for each activated API. Additionally, you can cap or filter usage by API. Read more about traffic reporting and request filtering here.
- Billing information - When you activate billing, your activated APIs can exceed the courtesy usage quota. Usage fees are billed to the Google Checkout account that you specify. Read more about billing here.
- Project keys - Each project is identified by either an API key or an OAuth 2.0 token. Use this key/token in your API requests to identify the project, in order to record usage data, enforce your filtering restrictions, and bill usage to the proper project. You can use the Console to generate or revoke API keys or OAuth 2.0 certificates to use in your application. Read more about keys here.
- Team members - You can specify additional members with read, write, or ownership access to this project’s Console page. Read more about team members here.
| Google+ API | Courtesy limit: 1,000 queries/day |
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Effective limits:
| API | Per-User Limit | Used | Courtesy Limit | |
|---|---|---|---|---|
| Google+ API | 5.0 requests/second/user | 0% | 1,000 queries/day |
API Calls
GET https://www.googleapis.com/plus/v1/people/userId
Common Parameters
Different API methods require parameters to be passed either as part of the URL path or as query parameters. Additionally, there are a few parameters that are common to all API endpoints. These are all passed as optional query parameters.
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Data Formats
Resources in the Google+ API are represented using JSON data formats. For example, retrieving a user’s profile may result in a response like:
{
"kind": "plus#person",
"id": "118051310819094153327",
"displayName": "Chirag Shah",
"url": "https://plus.google.com/118051310819094153327",
"image": {
"url": "https://lh5.googleusercontent.com/-XnZDEoiF09Y/AAAAAAAAAAI/AAAAAAAAYCI/7fow4a2UTMU/photo.jpg"
}
}
Common Properties
While each type of resource will have its own unique representation, there are a number of common properties that are found in almost all resource representations.
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Pagination
In requests that can respond with potentially large collections, such as Activities list, each response contains a limited number of items, set by maxResults(default: 20). Each response also contains a nextPageToken property. To obtain the next page of items, you pass this value of nextPageToken to the pageTokenproperty of the next request. Repeat this process to page through the full collection.
For example, calling Activities list returns a response with nextPageToken:
{
"kind": "plus#activityFeed",
"title": "Plus Public Activities Feed",
"nextPageToken": "CKaEL",
"items": [
{
"kind": "plus#activity",
"id": "123456789",
...
},
...
]
...
}
To get the next page of activities, pass the value of this token in with your next Activities list request:
https://www.googleapis.com/plus/v1/people/me/activities/public?pageToken=CKaEL
As before, the response to this request includes nextPageToken, which you can pass in to get the next page of results. You can continue this cycle to get new pages — for the last page, “nextPageToken” will be absent.
Amazon Ec2 goes Red Hat
message from Amazing Amazon’s cloud team- this will also help for #rstats users given that revolution Analytics full versions on RHEL.
—————————————————-
on-demand instances of Amazon EC2 running Red Hat Enterprise Linux (RHEL) for as little as $0.145 per instance hour. The offering combines the cost-effectiveness, scalability and flexibility of running in Amazon EC2 with the proven reliability of Red Hat Enterprise Linux.
Highlights of the offering include:
- Support is included through subscription to AWS Premium Support with back-line support by Red Hat
- Ongoing maintenance, including security patches and bug fixes, via update repositories available in all Amazon EC2 regions
- Amazon EC2 running RHEL currently supports RHEL 5.5, RHEL 5.6, RHEL 6.0 and RHEL 6.1 in both 32 bit and 64 bit formats, and is available in all Regions.
- Customers who already own Red Hat licenses will continue to be able to use those licenses at no additional charge.
- Like all services offered by AWS, Amazon EC2 running Red Hat Enterprise Linux offers a low-cost, pay-as-you-go model with no long-term commitments and no minimum fees.
For more information, please visit the Amazon EC2 Red Hat Enterprise Linux page.
which is
Amazon EC2 Running Red Hat Enterprise Linux
Amazon EC2 running Red Hat Enterprise Linux provides a dependable platform to deploy a broad range of applications. By running RHEL on EC2, you can leverage the cost effectiveness, scalability and flexibility of Amazon EC2, the proven reliability of Red Hat Enterprise Linux, and AWS premium support with back-line support from Red Hat.. Red Hat Enterprise Linux on EC2 is available in versions 5.5, 5.6, 6.0, and 6.1, both in 32-bit and 64-bit architectures.
Amazon EC2 running Red Hat Enterprise Linux provides seamless integration with existing Amazon EC2 features including Amazon Elastic Block Store (EBS), Amazon CloudWatch, Elastic-Load Balancing, and Elastic IPs. Red Hat Enterprise Linux instances are available in multiple Availability Zones in all Regions.
Pricing
Pay only for what you use with no long-term commitments and no minimum fee.
On-Demand Instances
On-Demand Instances let you pay for compute capacity by the hour with no long-term commitments.
| Standard Instances | Red Hat Enterprise Linux |
|---|---|
| Small (Default) | $0.145 per hour |
| Large | $0.40 per hour |
| Extra Large | $0.74 per hour |
| Micro Instances | Red Hat Enterprise Linux |
| Micro | $0.08 per hour |
| High-Memory Instances | Red Hat Enterprise Linux |
| Extra Large | $0.56 per hour |
| Double Extra Large | $1.06 per hour |
| Quadruple Extra Large | $2.10 per hour |
| High-CPU Instances | Red Hat Enterprise Linux |
| Medium | $0.23 per hour |
| Extra Large | $0.78 per hour |
| Cluster Compute Instances | Red Hat Enterprise Linux |
| Quadruple Extra Large | $1.70 per hour |
| Cluster GPU Instances | Red Hat Enterprise Linux |
| Quadruple Extra Large | $2.20 per hour |
Pricing is per instance-hour consumed for each instance type. Partial instance-hours consumed are billed as full hours.
and
Available Instance Types
Standard Instances
Instances of this family are well suited for most applications.
Small Instance – default*
1.7 GB memory
1 EC2 Compute Unit (1 virtual core with 1 EC2 Compute Unit)
160 GB instance storage
32-bit platform
I/O Performance: Moderate
API name: m1.small
Large Instance
7.5 GB memory
4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each)
850 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.large
Extra Large Instance
15 GB memory
8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each)
1,690 GB instance storage
64-bit platform
I/O Performance: High
API name: m1.xlarge
Micro Instances
Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.
Micro Instance
613 MB memory
Up to 2 EC2 Compute Units (for short periodic bursts)
EBS storage only
32-bit or 64-bit platform
I/O Performance: Low
API name: t1.micro
High-Memory Instances
Instances of this family offer large memory sizes for high throughput applications, including database and memory caching applications.
High-Memory Extra Large Instance
17.1 GB of memory
6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each)
420 GB of instance storage
64-bit platform
I/O Performance: Moderate
API name: m2.xlarge
High-Memory Double Extra Large Instance
34.2 GB of memory
13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each)
850 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.2xlarge
High-Memory Quadruple Extra Large Instance
68.4 GB of memory
26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: m2.4xlarge
High-CPU Instances
Instances of this family have proportionally more CPU resources than memory (RAM) and are well suited for compute-intensive applications.
High-CPU Medium Instance
1.7 GB of memory
5 EC2 Compute Units (2 virtual cores with 2.5 EC2 Compute Units each)
350 GB of instance storage
32-bit platform
I/O Performance: Moderate
API name: c1.medium
High-CPU Extra Large Instance
7 GB of memory
20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each)
1690 GB of instance storage
64-bit platform
I/O Performance: High
API name: c1.xlarge
Cluster Compute Instances
Instances of this family provide proportionally high CPU resources with increased network performance and are well suited for High Performance Compute (HPC) applications and other demanding network-bound applications. Learn more about use of this instance type for HPC applications.
Cluster Compute Quadruple Extra Large Instance
23 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cc1.4xlarge
Cluster GPU Instances
Instances of this family provide general-purpose graphics processing units (GPUs) with proportionally high CPU and increased network performance for applications benefitting from highly parallelized processing, including HPC, rendering and media processing applications. While Cluster Compute Instances provide the ability to create clusters of instances connected by a low latency, high throughput network, Cluster GPU Instances provide an additional option for applications that can benefit from the efficiency gains of the parallel computing power of GPUs over what can be achieved with traditional processors. Learn more about use of this instance type for HPC applications.
Cluster GPU Quadruple Extra Large Instance
22 GB of memory
33.5 EC2 Compute Units (2 x Intel Xeon X5570, quad-core “Nehalem” architecture)
2 x NVIDIA Tesla “Fermi” M2050 GPUs
1690 GB of instance storage
64-bit platform
I/O Performance: Very High (10 Gigabit Ethernet)
API name: cg1.4xlarge
Getting Started
To get started using Red Hat Enterprise Linux on Amazon EC2, perform the following steps:
- Open and log into the AWS Management Console
- Click on Launch Instance from the EC2 Dashboard
- Select the Red Hat Enterprise Linux AMI from the QuickStart tab
- Specify additional details of your instance and click Launch
- Additional details can be found on each AMI’s Catalog Entry page
The AWS Management Console is an easy tool to start and manage your instances. If you are looking for more details on launching an instance, a quick video tutorial on how to use Amazon EC2 with the AWS Management Console can be found here .
A full list of Red Hat Enterprise Linux AMIs can be found in the AWS AMI Catalog.
Support
All customers running Red Hat Enterprise Linux on EC2 will receive access to repository updates from Red Hat. Moreover, AWS Premium support customers can contact AWS to get access to a support structure from both Amazon and Red Hat.
Resources
- Red Hat solution provider page in EC2
- Red Hat Images in the Amazon Machine Image catalog
- Cloud Computing with Red Hat
About Red Hat
Red Hat, the world’s leading open source solutions provider, is headquartered in Raleigh, NC with over 50 satellite offices spanning the globe. Red Hat provides high-quality, low-cost technology with its operating system platform, Red Hat Enterprise Linux, together with applications, management and Services Oriented Architecture (SOA) solutions, including the JBoss Enterprise Middleware Suite. Red Hat also offers support, training and consulting services to its customers worldwide.
also from Revolution Analytics- in case you want to #rstats in the cloud and thus kill all that talk of RAM dependency, slow R than other softwares (just increase the RAM above in the instances to keep it simple)
,or Revolution not being open enough
http://www.revolutionanalytics.com/downloads/gpl-sources.php
GPL SOURCES
Revolution Analytics uses an Open-Core Licensing model. We provide open- source R bundled with proprietary modules from Revolution Analytics that provide additional functionality for our users. Open-source R is distributed under the GNU Public License (version 2), and we make our software available under a commercial license.
Revolution Analytics respects the importance of open source licenses and has contributed code to the open source R project and will continue to do so. We have carefully reviewed our compliance with GPLv2 and have worked with Mark Radcliffe of DLA Piper, the outside General Legal Counsel of the Open Source Initiative, to ensure that we fully comply with the obligations of the GPLv2.
For our Revolution R distribution, we may make some minor modifications to the R sources (the ChangeLog file lists all changes made). You can download these modified sources of open-source R under the terms of the GPLv2, using either the links below or those in the email sent to you when you download a specific version of Revolution R.
Download GPL Sources
| Product | Version | Platform | Modified R Sources |
| Revolution R Community | 3.2 | Windows | R 2.10.1 |
| Revolution R Community | 3.2 | MacOS | R 2.10.1 |
| Revolution R Enterprise | 3.1.1 | RHEL | R 2.9.2 |
| Revolution R Enterprise | 4.0 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.0.1 | RHEL | R 2.11.1 |
| Revolution R Enterprise | 4.1.0 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.2 | Windows | R 2.11.1 |
| Revolution R Enterprise | 4.2 | RHEL | R 2.11.1 |
| Revolution R Enterprise | 4.3 | Windows & RHEL | R 2.12.2 |
Revolution releases R Windows for Academics for free
Image via Wikipedia
Based on the official email from them, God bless the merry coders at Revo-
Revolution Analytics has just released Revolution R Enterprise 4.3 for 32-bit and 64-bit Windows, a significant step forward in enterprise data analytics. It features an updated RevoScaleR package for scalable, fast (multicore), and extensible data analysis with R. Revolution R Enterprise 4.3 for Windows also provides R 2.12.2, and includes an enhanced R Productivity Environment (RPE), a full-featured integrated development environment with visual debugging capabilities. Also available is an updated Windows release of our deployment server solution, RevoDeployR 1.2, designed to help you deliver R analytics via the Web.
As a registered user of the Academic version of Revolution R Enterprise for Windows, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.3 today. You can install Revolution R Enterprise 4.3 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.
Top ten business analytics graphs Bar Charts (3/10)
Basically a bar chart shows rectangular bars with length proportional to the quantities being described. It helps to see relative quantities between various category types.
The barplot() command is used for making Bar Plots, while hist() is used for histograms. You can also use the plot() command with type=h to create histograms-The official R manual also suggests that Dot plots using dotchart () are a reasonable substitute for bar plots.
A very simple easy to understand tutorial for basic bar plots is at http://msenux.redwoods.edu/math/R/barplot.php
The difference between the three main functions that can be used for these charts are shown below-
> VADeaths
Rural Male Rural Female Urban Male Urban Female
50-54 11.7 8.7 15.4 8.4
55-59 18.1 11.7 24.3 13.6
60-64 26.9 20.3 37.0 19.3
65-69 41.0 30.9 54.6 35.1
70-74 66.0 54.3 71.1 50.0
> plot(VADeaths,type=”h”)
> dotchart(VADeaths)
Youtube's variance in interface/s for sharing
Youtube seems to have a different interface for sharing a channel, a playlist or an individual song. Also it seems to be missing out on revenue from Itunes (or maybe it isnt). and it seems to promoting Facebook and Twitter to the expense of other social media sharing buttons which can be only seen when you click share more (or maybe the buttons/social media channels change based on sharing activity analytics 
)
on a slightly different note read my techie tutorial on boosting your youtube channel views
http://decisionstats.com/2010/09/10/creating-an-anonymous-bot/
Creating an Anonymous Bot
See the following interface snapshots/views-
youtube song share expanded
youtube song share default
youtube playlist share
youtube channel share
Related Articles
- Optimizing Your Brands YouTube Channel (ignitesocialmedia.com)
- 7 Little Known Tricks That Will Get You More YouTube Views (socialtimes.com)
- Youtube Channel (nmtp06fauzank.wordpress.com)
- YouTube Obsessed! (valerieraynerants.wordpress.com)
