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 aroundRcppinline,  RInsideRcppArmadilloRcppGSLRcppEigen 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

Register Now!

Instructor Bio

Dirk Eddelbuettel 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.

Interview Markus Schmidberger ,Cloudnumbers.com

Here is an interview with Markus Schmidberger, Senior Community Manager for cloudnumbers.com. Cloudnumbers.com is the exciting new cloud startup for scientific computing. It basically enables transition to a R and other platforms in the cloud and makes it very easy and secure from the traditional desktop/server model of operation.

Ajay- Describe the startup story for setting up Cloudnumbers.com

Markus- In 2010 the company founders Erik Muttersbach (TU München), Markus Fensterer (TU München) and Moritz v. Petersdorff-Campen (WHU Vallendar) started with the development of the cloud computing environment. Continue reading “Interview Markus Schmidberger ,Cloudnumbers.com”

Use R for Business- Competition worth $ 20,000 #rstats

All you contest junkies, R lovers and general change the world people, here’s a new contest to use R in a business application

http://www.revolutionanalytics.com/news-events/news-room/2011/revolution-analytics-launches-applications-of-r-in-business-contest.php

REVOLUTION ANALYTICS LAUNCHES “APPLICATIONS OF R IN BUSINESS” CONTEST

$20,000 in Prizes for Users Solving Business Problems with R

 

PALO ALTO, Calif. – September 1, 2011 – Revolution Analytics, the leading commercial provider of R software, services and support, today announced the launch of its “Applications of R in Business” contest to demonstrate real-world uses of applying R to business problems. The competition is open to all R users worldwide and submissions will be accepted through October 31. The Grand Prize winner for the best application using R or Revolution R will receive $10,000.

The bonus-prize winner for the best application using features unique to Revolution R Enterprise – such as itsbig-data analytics capabilities or its Web Services API for R – will receive $5,000. A panel of independent judges drawn from the R and business community will select the grand and bonus prize winners. Revolution Analytics will present five honorable mention prize winners each with $1,000.

“We’ve designed this contest to highlight the most interesting use cases of applying R and Revolution R to solving key business problems, such as Big Data,” said Jeff Erhardt, COO of Revolution Analytics. “The ability to process higher-volume datasets will continue to be a critical need and we encourage the submission of applications using large datasets. Our goal is to grow the collection of online materials describing how to use R for business applications so our customers can better leverage Big Analytics to meet their analytical and organizational needs.”

To enter Revolution Analytics’ “Applications of R in Business” competition Continue reading “Use R for Business- Competition worth $ 20,000 #rstats”

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
http://www.franklincenterhq.org/2429/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
But with Google Plus API now active

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

Effective limits:

API Per-User Limit Used Courtesy Limit
Google+ API 5.0 requests/second/user 0% 1,000 queries/day
API Calls
Most of the Google+ API follows a RESTful API design, meaning that you use standard HTTP methods to retrieve and manipulate resources. For example, to get the profile of a user, you might send an HTTP request like:

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.

Parameter Name

Value

Description

callback

string

Specifies a JavaScript function that will be passed the response data for using the API with JSONP.

fields

string

Selector specifying which fields to include in a partial response.

key

string

API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.

access_token

string

OAuth 2.0 token for the current user. Learn more about OAuth.

prettyPrint

boolean

If set to “true”, data output will include line breaks and indentation to make it more readable. If set to “false”, unnecessary whitespace is removed, reducing the size of the response. Defaults to “true”.

userIp

string

Identifies the IP address of the end user for whom the API call is being made. This allows per-user quotas to be enforced when calling the API from a server-side application. Learn more about Capping Usage.

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.

Property Name

Value

Description

displayName

string

This is the name of the resource, suitable for displaying to a user.

id

string

This property uniquely identifies a resource. Every resource of a given kind will have a unique id. Even though an id may sometimes look like a number, it should always be treated as a string.

kind

string

This identifies what kind of resource a JSON object represents. This is particularly useful when programmatically determining how to parse an unknown object.

url

string

This is the primary URL, or permalink, for the resource.

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.

 

it would be interesting the first wave of analysis on this new social network and see if it is any different from others, if at all.
After all, an API is only as good as the analysis and applications  that can be done on the data it provides

 

Cloud Computing using Python

I liked the new features in PiCloud , which is a cloud computing way to use Python. Python is increasingly popular as a computational language, and the cloud is the way where HW is headed to atleast as of 2011-12

http://www.picloud.com/

The new features allows you to publish your own functions as urls.

 By publishing your Python functions to URLs. Why would you want to publish a function?

  • To call your Python functions from a programming language other than Python.
  • To use PiCloud from Google AppEngine, which does not support our native client library.
  • To easily setup a scalable RPC system.

Here’s a peek at the interface:

You publish a Python function

cloud.rest.publish(your_func, ‘myfunction’)

We give you a URL Back

https://api.picloud.com/r/2/myfunction/

You make an HTTP request using your method of choice to the URL

curl -k -u ‘key:secret_key’ https://api.picloud.com/r/2/myfunction/

It certainly is an interesting development and I am wondering how other languages can adopt this paradigm as well.
For R, as of now http://www.cloudnumbers.com/ seems to be the only player in the cloud.
It would be exciting to see more players in the cloud statistical analytical space.

 

Using Google Fusion Tables from #rstats

But after all that- I was quite happy to see Google Fusion Tables within Google Docs. Databases as a service ? Not quite but still quite good, and lets see how it goes.

https://www.google.com/fusiontables/DataSource?dsrcid=implicit&hl=en_US&pli=1

http://googlesystem.blogspot.com/2011/09/fusion-tables-new-google-docs-app.html

 

But what interests me more is

http://code.google.com/apis/fusiontables/docs/developers_guide.html

The Google Fusion Tables API is a set of statements that you can use to search for and retrieve Google Fusion Tables data, insert new data, update existing data, and delete data. The API statements are sent to the Google Fusion Tables server using HTTP GET requests (for queries) and POST requests (for inserts, updates, and deletes) from a Web client application. The API is language agnostic: you can write your program in any language you prefer, as long as it provides some way to embed the API calls in HTTP requests.

The Google Fusion Tables API does not provide the mechanism for submitting the GET and POST requests. Typically, you will use an existing code library that provides such functionality; for example, the code libraries that have been developed for the Google GData API. You can also write your own code to implement GET and POST requests.

Also see http://code.google.com/apis/fusiontables/docs/sample_code.html

 

Google Fusion Tables API Sample Code

Libraries

SQL API

Language Library Public repository Samples
Python Fusion Tables Python Client Library fusion-tables-client-python/ Samples
PHP Fusion Tables PHP Client Library fusion-tables-client-php/ Samples

Featured Samples

An easy way to learn how to use an API can be to look at sample code. The table above provides links to some basic samples for each of the languages shown. This section highlights particularly interesting samples for the Fusion Tables API.

SQL API

Language Featured samples API version
cURL
  • Hello, cURLA simple example showing how to use curl to access Fusion Tables.
SQL API
Google Apps Script SQL API
Java
  • Hello, WorldA simple walkthrough that shows how the Google Fusion Tables API statements work.
  • OAuth example on fusion-tables-apiThe Google Fusion Tables team shows how OAuth authorization enables you to use the Google Fusion Tables API from a foreign web server with delegated authorization.
SQL API
Python
  • Docs List ExampleDemonstrates how to:
    • List tables
    • Set permissions on tables
    • Move a table to a folder
Docs List API
Android (Java)
  • Basic Sample ApplicationDemo application shows how to create a crowd-sourcing application that allows users to report potholes and save the data to a Fusion Table.
SQL API
JavaScript – FusionTablesLayer Using the FusionTablesLayer, you can display data on a Google Map

Also check out FusionTablesLayer Builder, which generates all the code necessary to include a Google Map with a Fusion Table Layer on your own website.

FusionTablesLayer, Google Maps API
JavaScript – Google Chart Tools Using the Google Chart Tools, you can request data from Fusion Tables to use in visualizations or to display directly in an HTML page. Note: responses are limited to 500 rows of data.

Google Chart Tools

External Resources

Google Fusion Tables is dedicated to providing code examples that illustrate typical uses, best practices, and really cool tricks. If you do something with the Google Fusion Tables API that you think would be interesting to others, please contact us at googletables-feedback@google.com about adding your code to our Examples page.

  • Shape EscapeA tool for uploading shape files to Fusion Tables.
  • GDALOGR Simple Feature Library has incorporated Fusion Tables as a supported format.
  • Arc2CloudArc2Earth has included support for upload to Fusion Tables via Arc2Cloud.
  • Java and Google App EngineODK Aggregate is an AppEngine application by the Open Data Kit team, uses Google Fusion Tables to store survey data that is collected through input forms on Android mobile phones. Notable code:
  • R packageAndrei Lopatenko has written an R interface to Fusion Tables so Fusion Tables can be used as the data store for R.
  • RubySimon Tokumine has written a Ruby gem for access to Fusion Tables from Ruby.

 

Updated-You can use Google Fusion Tables from within R from http://andrei.lopatenko.com/rstat/fusion-tables.R

 

ft.connect <- function(username, password) {
  url = "https://www.google.com/accounts/ClientLogin";
  params = list(Email = username, Passwd = password, accountType="GOOGLE", service= "fusiontables", source = "R_client_API")
 connection = postForm(uri = url, .params = params)
 if (length(grep("error", connection, ignore.case = TRUE))) {
 	stop("The wrong username or password")
 	return ("")
 }
 authn = strsplit(connection, "\nAuth=")[[c(1,2)]]
 auth = strsplit(authn, "\n")[[c(1,1)]]
 return (auth)
}

ft.disconnect <- function(connection) {
}

ft.executestatement <- function(auth, statement) {
      url = "http://tables.googlelabs.com/api/query"
      params = list( sql = statement)
      connection.string = paste("GoogleLogin auth=", auth, sep="")
      opts = list( httpheader = c("Authorization" = connection.string))
      result = postForm(uri = url, .params = params, .opts = opts)
      if (length(grep("<HTML>\n<HEAD>\n<TITLE>Parse error", result, ignore.case = TRUE))) {
      	stop(paste("incorrect sql statement:", statement))
      }
      return (result)
}

ft.showtables <- function(auth) {
   url = "http://tables.googlelabs.com/api/query"
   params = list( sql = "SHOW TABLES")
   connection.string = paste("GoogleLogin auth=", auth, sep="")
   opts = list( httpheader = c("Authorization" = connection.string))
   result = getForm(uri = url, .params = params, .opts = opts)
   tables = strsplit(result, "\n")
   tableid = c()
   tablename = c()
   for (i in 2:length(tables[[1]])) {
     	str = tables[[c(1,i)]]
   	    tnames = strsplit(str,",")
   	    tableid[i-1] = tnames[[c(1,1)]]
   	    tablename[i-1] = tnames[[c(1,2)]]
   	}
   	tables = data.frame( ids = tableid, names = tablename)
    return (tables)
}

ft.describetablebyid <- function(auth, tid) {
   url = "http://tables.googlelabs.com/api/query"
   params = list( sql = paste("DESCRIBE", tid))
   connection.string = paste("GoogleLogin auth=", auth, sep="")
   opts = list( httpheader = c("Authorization" = connection.string))
   result = getForm(uri = url, .params = params, .opts = opts)
   columns = strsplit(result,"\n")
   colid = c()
   colname = c()
   coltype = c()
   for (i in 2:length(columns[[1]])) {
     	str = columns[[c(1,i)]]
   	    cnames = strsplit(str,",")
   	    colid[i-1] = cnames[[c(1,1)]]
   	    colname[i-1] = cnames[[c(1,2)]]
   	    coltype[i-1] = cnames[[c(1,3)]]
   	}
   	cols = data.frame(ids = colid, names = colname, types = coltype)
    return (cols)
}

ft.describetable <- function (auth, table_name) {
   table_id = ft.idfromtablename(auth, table_name)
   result = ft.describetablebyid(auth, table_id)
   return (result)
}

ft.idfromtablename <- function(auth, table_name) {
    tables = ft.showtables(auth)
	tableid = tables$ids[tables$names == table_name]
	return (tableid)
}

ft.importdata <- function(auth, table_name) {
	tableid = ft.idfromtablename(auth, table_name)
	columns = ft.describetablebyid(auth, tableid)
	column_spec = ""
	for (i in 1:length(columns)) {
		column_spec = paste(column_spec, columns[i, 2])
		if (i < length(columns)) {
			column_spec = paste(column_spec, ",", sep="")
		}
	}
	mdata = matrix(columns$names,
	              nrow = 1, ncol = length(columns),
	              dimnames(list(c("dummy"), columns$names)), byrow=TRUE)
	select = paste("SELECT", column_spec)
	select = paste(select, "FROM")
	select = paste(select, tableid)
	result = ft.executestatement(auth, select)
    numcols = length(columns)
    rows = strsplit(result, "\n")
    for (i in 3:length(rows[[1]])) {
    	row = strsplit(rows[[c(1,i)]], ",")
    	mdata = rbind(mdata, row[[1]])
   	}
   	output.frame = data.frame(mdata[2:length(mdata[,1]), 1])
   	for (i in 2:ncol(mdata)) {
   		output.frame = cbind(output.frame, mdata[2:length(mdata[,i]),i])
   	}
   	colnames(output.frame) = columns$names
    return (output.frame)
}

quote_value <- function(value, to_quote = FALSE, quote = "'") {
	 ret_value = ""
     if (to_quote) {
     	ret_value = paste(quote, paste(value, quote, sep=""), sep="")
     } else {
     	ret_value = value
     }
     return (ret_value)
}

converttostring <- function(arr, separator = ", ", column_types) {
	con_string = ""
	for (i in 1:(length(arr) - 1)) {
		value = quote_value(arr[i], column_types[i] != "number")
		con_string = paste(con_string, value)
	    con_string = paste(con_string, separator, sep="")
	}

    if (length(arr) >= 1) {
    	value = quote_value(arr[length(arr)], column_types[length(arr)] != "NUMBER")
    	con_string = paste(con_string, value)
    }
}

ft.exportdata <- function(auth, input_frame, table_name, create_table) {
	if (create_table) {
       create.table = "CREATE TABLE "
       create.table = paste(create.table, table_name)
       create.table = paste(create.table, "(")
       cnames = colnames(input_frame)
       for (columnname in cnames) {
         create.table = paste(create.table, columnname)
    	 create.table = paste(create.table, ":string", sep="")
    	   if (columnname != cnames[length(cnames)]){
    		  create.table = paste(create.table, ",", sep="")
           }
       }
      create.table = paste(create.table, ")")
      result = ft.executestatement(auth, create.table)
    }
    if (length(input_frame[,1]) > 0) {
    	tableid = ft.idfromtablename(auth, table_name)
	    columns = ft.describetablebyid(auth, tableid)
	    column_spec = ""
	    for (i in 1:length(columns$names)) {
		   column_spec = paste(column_spec, columns[i, 2])
		   if (i < length(columns$names)) {
			  column_spec = paste(column_spec, ",", sep="")
		   }
	    }
    	insert_prefix = "INSERT INTO "
    	insert_prefix = paste(insert_prefix, tableid)
    	insert_prefix = paste(insert_prefix, "(")
    	insert_prefix = paste(insert_prefix, column_spec)
    	insert_prefix = paste(insert_prefix, ") values (")
    	insert_suffix = ");"
    	insert_sql_big = ""
    	for (i in 1:length(input_frame[,1])) {
    		data = unlist(input_frame[i,])
    		values = converttostring(data, column_types  = columns$types)
    		insert_sql = paste(insert_prefix, values)
    		insert_sql = paste(insert_sql, insert_suffix) ;
    		insert_sql_big = paste(insert_sql_big, insert_sql)
    		if (i %% 500 == 0) {
    			ft.executestatement(auth, insert_sql_big)
    			insert_sql_big = ""
    		}
    	}
        ft.executestatement(auth, insert_sql_big)
    }
}

#rstats -Basic Data Manipulation using R

Continuing my series of basic data manipulation using R. For people knowing analytics and
new to R.
1 Keeping only some variables

Using subset we can keep only the variables we want-

Sitka89 <- subset(Sitka89, select=c(size,Time,treat))

Will keep only the variables we have selected (size,Time,treat).

2 Dropping some variables

Harman23.cor$cov.arm.span <- NULL
This deletes the variable named cov.arm.span in the dataset Harman23.cor

3 Keeping records based on character condition

Titanic.sub1<-subset(Titanic,Sex=="Male")

Note the double equal-to sign
4 Keeping records based on date/time condition

subset(DF, as.Date(Date) >= '2009-09-02' & as.Date(Date) <= '2009-09-04')

5 Converting Date Time Formats into other formats

if the variable dob is “01/04/1977) then following will convert into a date object

z=strptime(dob,”%d/%m/%Y”)

and if the same date is 01Apr1977

z=strptime(dob,"%d%b%Y")

6 Difference in Date Time Values and Using Current Time

The difftime function helps in creating differences in two date time variables.

difftime(time1, time2, units='secs')

or

difftime(time1, time2, tz = "", units = c("auto", "secs", "mins", "hours", "days", "weeks"))

For current system date time values you can use

Sys.time()

Sys.Date()

This value can be put in the difftime function shown above to calculate age or time elapsed.

7 Keeping records based on numerical condition

Titanic.sub1<-subset(Titanic,Freq >37)

For enhanced usage-
you can also use the R Commander GUI with the sub menu Data > Active Dataset

8 Sorting Data

Sorting A Data Frame in Ascending Order by a variable

AggregatedData<- sort(AggregatedData, by=~ Package)

Sorting a Data Frame in Descending Order by a variable

AggregatedData<- sort(AggregatedData, by=~ -Installed)

9 Transforming a Dataset Structure around a single variable

Using the Reshape2 Package we can use melt and acast functions

library("reshape2")

tDat.m<- melt(tDat)

tDatCast<- acast(tDat.m,Subject~Item)

If we choose not to use Reshape package, we can use the default reshape method in R. Please do note this takes longer processing time for bigger datasets.

df.wide <- reshape(df, idvar="Subject", timevar="Item", direction="wide")

10 Type in Data

Using scan() function we can type in data in a list

11 Using Diff for lags and Cum Sum function forCumulative Sums

We can use the diff function to calculate difference between two successive values of a variable.

Diff(Dataset$X)

Cumsum function helps to give cumulative sum

Cumsum(Dataset$X)

> x=rnorm(10,20) #This gives 10 Randomly distributed numbers with Mean 20

> x

[1] 20.76078 19.21374 18.28483 20.18920 21.65696 19.54178 18.90592 20.67585

[9] 20.02222 18.99311

> diff(x)

[1] -1.5470415 -0.9289122 1.9043664 1.4677589 -2.1151783 -0.6358585 1.7699296

[8] -0.6536232 -1.0291181 >

cumsum(x)

[1] 20.76078 39.97453 58.25936 78.44855 100.10551 119.64728 138.55320

[8] 159.22905 179.25128 198.24438

> diff(x,2) # The diff function can be used as diff(x, lag = 1, differences = 1, ...) where differences is the order of differencing

[1] -2.4759536 0.9754542 3.3721252 -0.6474195 -2.7510368 1.1340711 1.1163064

[8] -1.6827413

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

12 Merging Data

Deducer GUI makes it much simpler to merge datasets. The simplest syntax for a merge statement is

totalDataframeZ <- merge(dataframeX,dataframeY,by=c("AccountId","Region"))

13 Aggregating and group processing of a variable

We can use multiple methods for aggregating and by group processing of variables.
Two functions we explore here are aggregate and Tapply.

Refering to the R Online Manual at
[http://stat.ethz.ch/R-manual/R-patched/library/stats/html/aggregate.html]

## Compute the averages for the variables in 'state.x77', grouped

## according to the region (Northeast, South, North Central, West) that

## each state belongs to

aggregate(state.x77, list(Region = state.region), mean)

Using TApply

## tapply(Summary Variable, Group Variable, Function)

Reference

[http://www.ats.ucla.edu/stat/r/library/advanced_function_r.htm#tapply]

We can also use specialized packages for data manipulation.

For additional By-group processing you can see the doBy package as well as Plyr package
 for data manipulation.Doby contains a variety of utilities including:
 1) Facilities for groupwise computations of summary statistics and other facilities for working with grouped data.
 2) General linear contrasts and LSMEANS (least-squares-means also known as population means),
 3) HTMLreport for autmatic generation of HTML file from R-script with a minimum of markup, 4) various other utilities and is available at[ http://cran.r-project.org/web/packages/doBy/index.html]
Also Available at [http://cran.r-project.org/web/packages/plyr/index.html],
Plyr is a set of tools that solves a common set of problems:
you need to break a big problem down into manageable pieces,
operate on each pieces and then put all the pieces back together.
 For example, you might want to fit a model to each spatial location or
 time point in your study, summarise data by panels or collapse high-dimensional arrays
 to simpler summary statistics.