I was wondering why the planet spends so much money in the $150-billion business process outsourcing industry, especially in voice calls to call centers.
If your iPhone Siri phone can be configured to answer any query, Why can’t it be configured to be a virtual assistant, customer support, marketing outbound or even a super charged call center interactive voice response .
Can we do and run some tests on this?
Some code to read in data from Google Analytics data. Some modifications include adding the SSL authentication code and modifying (in bold) the table.id parameter to choose correct website from a GA profile with many websites
The Google Analytics Package files can be downloaded from http://code.google.com/p/r-google-analytics/downloads/list
It provides access to Google Analytics data natively from the R Statistical Computing programming language. You can use this library to retrieve an R data.frame with Google Analytics data. Then perform advanced statistical analysis, like time series analysis and regressions.
> library(XML)
>
> library(RCurl)
Loading required package: bitops
>
> #Change path name in the following to the folder you downloaded the Google Analytics Package
>
> source(“C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/RGoogleAnalytics.R”)
>
> source(“C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/QueryBuilder.R”)
> # download the file needed for authentication
> download.file(url=”http://curl.haxx.se/ca/cacert.pem”, destfile=”cacert.pem”)
trying URL ‘http://curl.haxx.se/ca/cacert.pem’ Content type ‘text/plain’ length 215993 bytes (210 Kb) opened
URL downloaded 210 Kb
>
> # set the curl options
> curl <- getCurlHandle()
> options(RCurlOptions = list(capath = system.file(“CurlSSL”, “cacert.pem”,
+ package = “RCurl”),
+ ssl.verifypeer = FALSE))
> curlSetOpt(.opts = list(proxy = ‘proxyserver:port’), curl = curl)
An object of class “CURLHandle” Slot “ref”: <pointer: 0000000006AA2B70>
>
> # 1. Create a new Google Analytics API object
>
> ga <- RGoogleAnalytics()
>
> # 2. Authorize the object with your Google Analytics Account Credentials
>
> ga$SetCredentials(“USERNAME”, “PASSWORD”)
>
> # 3. Get the list of different profiles, to help build the query
>
> profiles <- ga$GetProfileData()
>
> profiles #Error Check to See if we get the right website
$profile AccountName ProfileName TableId
1 dudeofdata.com dudeofdata.com ga:44926237
2 knol.google.com knol.google.com ga:45564890
3 decisionstats.com decisionstats.com ga:46751946
$total.results
total.results
1 3
>
> # 4. Build the Data Export API query
>
> #Modify the start.date and end.date parameters based on data requirements
>
> #Modify the table.id at table.id = paste(profiles$profile[X,3]) to get the X th website in your profile
> # 4. Build the Data Export API query
> query <- QueryBuilder() > query$Init(start.date = “2012-01-09”, + end.date = “2012-03-20”, + dimensions = “ga:date”,
+ metrics = “ga:visitors”,
+ sort = “ga:date”,
+ table.id = paste(profiles$profile[3,3]))
>
>
> #5. Make a request to get the data from the API
>
> ga.data <- ga$GetReportData(query)
[1] “Executing query: https://www.google.com/analytics/feeds/data?start-date=2012%2D01%2D09&end-date=2012%2D03%2D20&dimensions=ga%3Adate&metrics=ga%3Avisitors&sort=ga%3Adate&ids=ga%3A46751946”
>
> #6. Look at the returned data
>
> str(ga.data)
List of 3
$ data :’data.frame’: 72 obs. of 2 variables: ..
$ ga:date : chr [1:72] “20120109” “20120110” “20120111” “20120112” … ..
$ ga:visitors: num [1:72] 394 405 381 390 323 47 169 67 94 89 …
$ aggr.totals :’data.frame’: 1 obs. of 1 variable: ..
$ aggregate.totals: num 28348
$ total.results: num 72
>
> head(ga.data$data)
ga:date ga:visitors
1 20120109 394
2 20120110 405
3 20120111 381
4 20120112 390
5 20120113 323
6 20120114 47 >
> #Plotting the Traffic >
> plot(ga.data$data[,2],type=”l”)
Update- Some errors come from pasting Latex directly to WordPress. Here is some code , made pretty-r in case you want to play with the GA api
library(XML) library(RCurl) #Change path name in the following to the folder you downloaded the Google Analytics Package source("C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/RGoogleAnalytics.R") source("C:/Users/KUs/Desktop/CANADA/R/RGoogleAnalytics/R/QueryBuilder.R") # download the file needed for authentication download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem") # set the curl options curl <- getCurlHandle() options(RCurlOptions = list(capath = system.file("CurlSSL", "cacert.pem", package = "RCurl"), ssl.verifypeer = FALSE)) curlSetOpt(.opts = list(proxy = 'proxyserver:port'), curl = curl) # 1. Create a new Google Analytics API object ga <- RGoogleAnalytics() # 2. Authorize the object with your Google Analytics Account Credentials ga$SetCredentials("ohri2007@gmail.com", "XXXXXXX") # 3. Get the list of different profiles, to help build the query profiles <- ga$GetProfileData() profiles #Error Check to See if we get the right website # 4. Build the Data Export API query #Modify the start.date and end.date parameters based on data requirements #Modify the table.id at table.id = paste(profiles$profile[X,3]) to get the X th website in your profile # 4. Build the Data Export API query query <- QueryBuilder() query$Init(start.date = "2012-01-09", end.date = "2012-03-20", dimensions = "ga:date", metrics = "ga:visitors", sort = "ga:date", table.id = paste(profiles$profile[3,3])) #5. Make a request to get the data from the API ga.data <- ga$GetReportData(query) #6. Look at the returned data str(ga.data) head(ga.data$data) #Plotting the Traffic plot(ga.data$data[,2],type="l")
and finally I can download my Google spreadsheet file using-
require(RCurl)
download.file(url=”http://curl.haxx.se/ca/cacert.pem”, destfile=”cacert.pem”)
b <- getURL(url,cainfo=”cacert.pem”)
write.table(b,quote = FALSE, sep = “,”,file=”test.csv”)
Previously (for past 3 5 7 hours)-
The codes at http://blog.revolutionanalytics.com/2011/09/using-google-spreadsheets-with-r-an-update.html dont work thanks to the SSL authentication issue. and the packages at [http://www.omegahat.org/RGoogleDocs/] and [https://r-forge.r-project.org/projects/rgoogledata/] are missing in action.
So I mixed the codes at http://blog.revolutionanalytics.com/2009/09/how-to-use-a-google-spreadsheet-as-data-in-r.html and http://www.brocktibert.com/blog/2012/01/19/358/
and I get this error while using http://thebiobucket.blogspot.in/2012/03/r-function-to-read-data-from-google.html#more
Error in read.table(file = file, header = header, sep = sep, quote = quote, :
more columns than column names
JMP , the visual data exploration, statistical quality control software from SAS Institute launched version 10 of its software today.
Source-http://jmp.com/about/events/webcasts/jmp_webcast.shtml?name=jmp10
JMP 10 includes:
Numerous enhancements to the drag-and-drop Graph Builder, including a new iPad application.
A cutting-edge Control Chart Builder to create process control charts with drag-and-drop ease.
New reliability capabilities, including growth and forecast models.
Additions and improvements for sorting and filtering data, design of experiments, statistical modeling, scripting, add-in and application development, script debugging and more.
From JohnSall’s blog post at http://blogs.sas.com/content/jmp/2012/03/20/discover-more-with-jmp-10/
Much of the development centered on four focus areas:
1. Graph Builder everywhere. The Graph Builder platform itself has new features like Heatmap and Treemap, an elements palette and properties panel, making the choices more visible. But Graph Builder also has some descendents now, including the new Control Chart Builder, which makes creating control charts an interactive process. In addition, some of the drag-and-drop features that are used to change columns in Graph Builder are also available in Distribution, Fit Y by X, and a few other places. Finally, Graph Builder has been ported to the iPad. For the first time, you can use JMP for exploration and presentation on a mobile device for free. So just think of Graph Builder as gradually taking over in lots of places.
2. Expert-driven design.reliability, measurement systems, and partial least squares analyses.
3. Performance. this release has the most new multithreading so far
4. Application development
You can read more here –http://jmp.com/about/events/webcasts/jmpwebcast_detail.shtml?reglink=70130000001r9IP
library(tm)
library(wordcloud)
txt2=”/home/ajay/Desktop/new”
b=Corpus(DirSource(txt2), readerControl = list(language = “eng”))
> b b b tdm m1 v1 d1 wordcloud(d1$word,d1$freq)
Now it seems we need to remove some of the very commonly occuring words like “the” and “and”. We are not using the standard stopwords in english (the tm package provides that see Chapter 13 Text Mining case studies), as the words “we” and “can” are also included .
> b tdm m1 v1 d1 wordcloud(d1$word,d1$freq)
But let’s see how the wordcloud changes if we remove all English Stopwords.
> b tdm m1 v1 d1 wordcloud(d1$word,d1$freq)
and you can draw your own conclusions from the content of this famous speech based on your political preferences.
Politicians can give interesting speeches but they may be full of simple sounding words…..
Citation-
1. Ingo Feinerer (2012). tm: Text Mining Package. R package version0.5-7.1.
Ingo Feinerer, Kurt Hornik, and David Meyer (2008). Text Mining
Infrastructure in R. Journal of Statistical Software 25/5. URL:
http://www.jstatsoft.org/v25/i05/
2. Ian Fellows (2012). wordcloud: Word Clouds. R package version 2.0.
http://CRAN.R-project.org/package=wordcloud
3. You can see more than 100 of Obama’s speeches at http://obamaspeeches.com/
Quote- numbers dont lie, people do.
.
teradataR is a package or library that allows R users to easily connect to Teradata, establish data frames (R data formats) to Teradata and to call in-database analytic functions within Teradata. This allows R users to work within their R console environment while leveraging the in-database functions developed with Teradata Warehouse Miner. This package provides 44 different analytical functions and an additional 20 data connection and R infrastructure functions. In addition, we’ve added a function that will list the stored procedures within Teradata provide the capability to call functions from R.
TeradataR allows R users to leverage all the benefits of in-database processing with Teradata:
Source- http://developer.teradata.com/applications/articles/in-database-analytics-with-teradata-r
This package allows users of R to interact with a Teradata database. R is an open source language for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. Users can use many statistical functions directly against the Teradata system without having to extract the data into memory.
Enhancements included with this new 1.0.1 release include:
From-http://downloads.teradata.com/download/applications/teradata-r
and
A new R package for Red Hat Linux has been added to the teradataR 1.0.1 release. This new package provides the same functionality as in the previously released Windows and Mac OS X packages, but is built for Red Hat Linux. This version was built and tested on Red Hat Linux 6.2 32-bit. (The R version for Red Hat Linux is 2.14.1)
Installing this package is the same as any normal R package; just extract it into your R library area, or use the install.packagescommand with the file path.
from- http://developer.teradata.com/tag/r
and
With plenty of prolific and enthusiastic developers, the number of packages for R is expected to grow tremendously. Statisticians and analysts using these packages will find innovative ways to use data to answer their research and business questions. And as organizations become more willing to rely on open-source software for mission-critical tasks, R is poised to become an essential tool for analyzing our complex world.
Source-http://www.teradatamagazine.com/v09n03/Connections/R-you-ready/
From the user guide-
http://downloads.teradata.com/download/applications/teradata-r
teradataR allows R users to easily connect to Teradata, establish td data frames (virtual R data frames) to
Teradata and to call in-database analytic functions within Teradata. This allows R users to work within their R
console environment while leveraging the in-database functions
A Function List
teradataR-package Allow access to Teradata via R
as.data.frame.td.data.frame Convert td data frame to a data frame
as.td.data.frame Coerce to a td data frame
dim.td.data.frame Dimensions of a td data frame
hist.td.data.frame Histograms
Is.td.data.frame Is an Object a Teradata Data Frame
Is.td.expression Is an Object a Teradata Expression
mean.td.data.frame Arithmetic Mean
median.td.data.frame Median Value
min.td.data.frame Minima
predict.kmeans Kmeans Model Prediction
print.td.data.frame Show contents of a td data frame
sum.td.data.frame Sum of column
summary.td.data.frame Summary of Teradata Data Frame
Td.bincode Create Table of Bincode Values
Td.binomial Binomial Test
Td.binomialsign Binomial Sign Test
Td.call.sp Locate and call stored procedure
Td.cor Correlation Matrix
Td.cov Covariance Matrix
Td.dagostino.pearson D’Agostino Pearson Test
Td.data.frame Teradata Data Frames
Td.f.oneway One way F Test
Td.factanal Factor Analysis
Td.freq Frequency Analysis
Td.hist Histograms
Td.join Join Tables in Teradata
Td.kmeans K-Means Clustering
Td.ks Kolmogorov Smirnov Test
Td.lilliefors Lilliefors Test
Td.merge Merge Rows of Teradata Tables
Td.mode Mode Value of Column
Td.mwnkw Mann-Whitney/Kruskal Wallis Test
Td.nullreplace Replace Null Values
Td.overlap Overlap
Td.quantiles Quantile Values
Td.rank Rank
Td.recode Recode
Td.rescale Rescale Values of Column
Td.sample Sample Rows
Td.shapiro.wilk Shapiro Wilk
Td.sigmoid Sigmoid Transformation
Td.smirnov Smirnov Test
Td.solve Solve a system of equations
Td.stats General Statistics
Td.t.paired T Test Paired
Td.t.unpaired T Test Unpaired
Td.t.unpairedi T Test – Unpaired Indicator
Td.values Values
Td.wilcoxon Wilcoxon Test
Td.zscore Zscore Transformation
tdClose Close connection
tdConnect Connect to Teradata database
tdMetadataDB Set metadata database
tdQuery Query Teradata Database
teradataR Allow access to Teradata via R
[.td.data.frame Extract Teradata Data Frame
[<-.td.data.frame Replace value of Teradata Data Frame
BIG UPDATE-
SAP follows Oracle in using R. I really liked this slideshare and am embedding it here for it to get a wider audience
DECISION STATS 