1. RForcecom: An R package which
provides a connection to Force.com
and Salesforce.com
Takekatsu Hiramura
2014-07-02
The R User Conference 2014 @ UCLA
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2. Agenda
2
1. About me
2. Brief introduction of Force.com and Salesforce.com
3. Overview of RForcecom
4. Features of RForcecom
5. Example of the analysis using RForcecom
-Visualizing consumers’ voice-

3. Agenda
3
1. About me
2. Brief introduction of Force.com and Salesforce.com
3. Overview of RForcecom
4. Features of RForcecom
5. Example of the analysis using RForcecom
-Visualizing consumers’ voice-

4. Takekatsu Hiramura
» IT consultant, Software Engineer and Data scientist
» Private website:
http://thira.plavox.info/
» Blog:
http://hiratake55.wordpress.com/
» R-bloggers:
http://www.r-bloggers.com/author/takekatsu-hiramura/
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5. Agenda
5
1. About me
2. Brief introduction of Force.com and Salesforce.com
3. Overview of RForcecom
4. Features of RForcecom
5. Example of the analysis using RForcecom
-Visualizing consumers’ voice-

6. Specific features of CRM
About Salesforce.com/Force.com
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» “Salesforce.com “ is one of the most famous SaaS (Software-as-a-Service)
based CRM (Customer Relationship Management) service.
» “Force.com” is a application platform of Salesforce.com, and it specifically
called PaaS (Platform-as-a-Service).
Campaign
Management
Contract
Management
Customer
Management
Product
Management
and etc.
Sales
Forecasting
Case
Management
Overview of the Application/Service Architecture
Application Platform
Service
Custom Object
Apex /
VisualForce
Web API
and etc.

7. Agenda
7
1. About me
2. Brief introduction of Force.com and Salesforce.com
3. Overview of RForcecom
4. Features of RForcecom
5. Example of the analysis using RForcecom
-Visualizing consumers’ voice-

8. The RForcecom package
I developed an R package “RForcecom” which provides a connection to
Salesforce.com and Force.com via REST API.
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 Statistical Analysis
 Machine Learning
 Data Manipulation
 Visualization
 Customer Relationship
Management
 Dashboard
 Collaboration Platform
(Chatter,Schedule,ToDo etc.)
RSalesforce.com
Delete
Insert
Update/Upsert
Data extract
SOQL query
Search

9. The CRAN page of the RForcecom
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http://cran.r-project.org/web/packages/RForcecom/

10. Source code is available on GitHub
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https://github.com/hiratake55/RForcecom

11. Agenda
11
1. About me
2. Brief introduction of Force.com and Salesforce.com
3. Overview of RForcecom
4. Features of RForcecom
5. Example of the analysis using RForcecom
-Visualizing consumers’ voice-

12. Features of RForcecom
Execute a SOSL rforcecom.search()
Create a record rforcecom.create()
Retrieve record rforcecom.retrieve()
Update a record rforcecom.update()
Upsert a record rforcecom.upsert()
Delete a record rforcecom.delete()
Retrieve a server timestamp rforcecom.getServerTimestamp()
Execute a SOQL rforcecom.query()
Sign in to the Force.com rforcecom.login()
Feature Function name#
Retrieve object descriptions rforcecom.getObjectDescription()
Retrieve a list of objects rforcecom.getObjectList
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13. Sign in to the Force.com
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> library(RForcecom)
> username <- yourname@yourcompany.com
> password <- "YourPasswordSECURITY_TOKEN”
> instanceURL <- https://xxx.salesforce.com/
> apiVersion <- "26.0“
> session <- rforcecom.login(username, password, instanceURL, apiVersion)

14. Retrieve records
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> objectName <- "Case"
> fields <- c("CaseNumber", "Subject", "Status")
> rforcecom.retrieve(session, objectName, fields, order=c("CaseNumber"),limit=12)
Salesforce.com R

15. Execute a SOQL
> soqlQuery <- "SELECT Id, Name, Industry FROM Account order by CreatedDate"
> rforcecom.query(session, soqlQuery)
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Salesforce.com R

16. Agenda
16
1. About me
2. Brief introduction of Force.com and Salesforce.com
3. Overview of RForcecom
4. Features of RForcecom
5. Example of the analysis using RForcecom
-Visualizing consumers’ voice-

17. RForcecom demo : Visualizing consumers’ voice
» Assume you are a manager at a company and want to know the
consumers’ voice from CRM. Consumers’ voices are stored in
Salesforce.com which registered by their call center staff.
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Call Center Salesforce.com R Managers
Data collection/
Operation
Data
Management
Data Analysis Reporting
Customer
Mgmt.
Case Mgmt.
REST/SOAP
API
API Client
(RForcecom)
NLP
(TreeTagger)
Visualization
(Wordcloud)
…
…

18. Sample Dataset:
Delta’s Twitter Social Customer Support Account
» It is difficult to use actual dataset, so I crawled Delta Airline’s Twitter
account (@DeltaAssist)and stored tweets to Salesforce.com instead
of actual dataset.
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https://twitter.com/DeltaAssist/with_replies

19. Step 1: Retrieving a dataset from Salesforce.com
» Tweets sent to @DeltaAssist are stored in Salesforce.com.
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20. Step 1: Retrieving a dataset from Salesforce.com
» Tweets sent to @DeltaAssist are stored in Salesforce.com.
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21. Step 1: Retrieving a dataset from Salesforce.com
» Load required libraries and sign into Salesforce.com.
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> library(RForcecom)
> username <- yourname@yourcompany.com
> password <- "YourPasswordSECURITY_TOKEN”
> instanceURL <- https://xxx.salesforce.com/
> apiVersion <- "26.0“
> session <- rforcecom.login(username, password, instanceURL, apiVersion)

22. Step 1: Retrieving a dataset from Salesforce.com
» To retrieve dataset with parameters of objectname and field names.
22
> CustomerVoice <-rforcecom.retrieve(session,"CustomerVoice__c",c("TweetDate__c","Tweet__c"))
> head(CustomerVoice$Tweet__c,10)

23. Step 2: Extracting high-frequency keywords
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> library(koRpus)
> temp.file.name<-tempfile()
> write.table(CustomerVoice$Tweet__c,temp.file.name,col.names=F,row.names=F)
> tagged<-treetag(temp.file.name, lang="en",
treetagger="manual",TT.options=list(path="C:/Apps/TreeTagger", preset="en",encoding="UTF-8"))
> tagged.DF<-tagged@TT.res
> head(tagged.DF,10)
» Tag the word class for each words using “koRpus“ package and TreeTagger.

24. Step 2: Extracting high-frequency keywords
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> term<-tagged.DF[tagged.DF$wclass=="noun",]$token
> term<-tolower(term)
> head(term,20)
» Filter “noun” from tagged list

25. Step 2: Extracting high-frequency keywords
25
> term.unique<-unique(term)
> term.freq <- unlist(lapply(term.unique,function(x){length(term[term==x])}))
> termfreq<-data.frame(term=term.unique, freq=term.freq)
> termfreq<-termfreq[order(termfreq$freq,decreasing=T),]
> head(termfreq,10)
» Count frequencies of the terms.

26. Step 3: Visualizing the words as a word cloud
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> library(wordcloud)
> termfreq.top<-head(termfreq, n=100)
> pal <- brewer.pal(8, "Dark2")
> windowsFonts(SegoeUI = "Segoe UI")
> wordcloud(termfreq.top$term, termfreq.top$freq, random.color=T, colors=pal, family="SegoeUI")
» Visualize the terms using wordcloud package. “Flight” is the most frequent.

27. Step 4: Visualize the Buzz-word of the day
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> CustomerVoice.sun<- rforcecom.query(session, "select Tweet__c, TweetDate__c from CustomerVoice__c
where TweetDate__c >= 2014-05-25T00:00:00-04:00and TweetDate__c < 2014-05-26T00:00:00-04:00")
> CustomerVoice.mon <- rforcecom.query(session, "select Tweet__c, TweetDate__c from CustomerVoice__c
where TweetDate__c >= 2014-05-26T00:00:00-04:00and TweetDate__c < 2014-05-27T00:00:00-04:00")
> CustomerVoice.tue <- rforcecom.query(session, "select Tweet__c, TweetDate__c from CustomerVoice__c
where TweetDate__c >= 2014-05-27T00:00:00-04:00and TweetDate__c < 2014-05-28T00:00:00-04:00")
> CustomerVoice.wed <- rforcecom.query(session, "select Tweet__c, TweetDate__c from CustomerVoice__c
where TweetDate__c >= 2014-05-28T00:00:00-04:00and TweetDate__c < 2014-05-29T00:00:00-04:00")
> CustomerVoice.thu <- rforcecom.query(session, "select Tweet__c, TweetDate__c from CustomerVoice__c
where TweetDate__c >= 2014-05-29T00:00:00-04:00and TweetDate__c < 2014-05-30T00:00:00-04:00")
> CustomerVoice.fri <- rforcecom.query(session, "select Tweet__c, TweetDate__c from CustomerVoice__c
where TweetDate__c >= 2014-05-30T00:00:00-04:00and TweetDate__c < 2014-05-31T00:00:00-04:00")
> CustomerVoice.sat<- rforcecom.query(session, "select Tweet__c, TweetDate__c from CustomerVoice__c
where TweetDate__c >= 2014-05-31T00:00:00-04:00and TweetDate__c < 2014-06-01T00:00:00-04:00")
> CustomerVoice.all <- rbind(CustomerVoice.sun, CustomerVoice.mon, CustomerVoice.tue,
CustomerVoice.wed, CustomerVoice.thu, CustomerVoice.fri, CustomerVoice.sat)
» Retreive daily datasets by SOQL.

28. Step 4: Visualize the Buzz-word of the day
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# Tag, Extract noun, Calculate TF
make.treetag<-function(CustomerVoice){
temp.file.name<-tempfile()
write.table(CustomerVoice$Tweet__c,temp.file.name,col.names=F,row.names=F)
tagged<-treetag(temp.file.name, lang="en", treetagger="manual",TT.options=list(path="C:/Apps/TreeTagger",
preset="en",encoding="UTF-8"))
tagged.DF<-tagged@TT.res
# Extract noun, To lower
term<-tagged.DF[tagged.DF$wclass=="noun",]$token
term<-tolower(term)
# Count frequency of term
term.unique<-unique(term)
term.freq <- unlist(lapply(term.unique,function(x){length(term[term==x])}))
termfreq.DF<-data.frame(term=term.unique, freq=term.freq, stringsAsFactors=F)
termfreq.DF<-termfreq.DF[order(termfreq.DF$freq,decreasing=T),]
return(termfreq.DF)
}
# Apply to each dataset
termfreq.sun<-make.treetag(CustomerVoice.sun)
termfreq.mon<-make.treetag(CustomerVoice.mon)
termfreq.tue<-make.treetag(CustomerVoice.tue)
termfreq.wed<-make.treetag(CustomerVoice.wed)
termfreq.thu<-make.treetag(CustomerVoice.thu)
termfreq.fri<-make.treetag(CustomerVoice.fri)
termfreq.sat<-make.treetag(CustomerVoice.sat)
termfreq.all<-make.treetag(CustomerVoice.all)
» Tag, extract noun and calculate the Term Frequency (TF).
*TF (Term Frequency):
number of occurrence of term i in document j

29. 29
Step 4: Visualize the Buzz-word of the day
# Calculate IDF
IDF.documents <- sapply(termfreq.all$term,function(x){
sum(
nrow(termfreq.sun[termfreq.sun$term==x,])>0,
nrow(termfreq.mon[termfreq.mon$term==x,])>0,
nrow(termfreq.tue[termfreq.tue$term==x,])>0,
nrow(termfreq.wed[termfreq.wed$term==x,])>0,
nrow(termfreq.thu[termfreq.thu$term==x,])>0,
nrow(termfreq.fri[termfreq.fri$term==x,])>0,
nrow(termfreq.sat[termfreq.sat$term==x,])>0
)
})
IDF<-data.frame(term=termfreq.all$term,IDF=log(7/IDF.documents))
» Calculate the Inverse Document Frequency (IDF).
*IDF (Inverse Document Frequency):
IDF measures “Term specificity”.
df: number of documents containing term i.
N: Total Number of documents
Reference: http://dovgalecs.com/blog/matlab-simple-tf-idf/
IDF=

30. Step 4: Visualize the Buzz-word of the day
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# Calculate TF and returns TF-IDF
calc.tfidf <- function(termfreq){
# TF
termfreq$TF <- termfreq$freq/sum(termfreq$freq)
# TF-IDF
tfidf.val <- lapply(termfreq$term,function(x){
tf_i <- termfreq[termfreq$term==x,]$TF
idf_i <- IDF[IDF$term==x,]$IDF
return(tf_i * idf_i)
})
tdidf <- data.frame(term=termfreq$term, TFIDF=unlist(tfidf.val))
return(tdidf)
}
tfidf.sun <- calc.tfidf(termfreq.sun)
tfidf.mon <- calc.tfidf(termfreq.mon)
tfidf.tue <- calc.tfidf(termfreq.tue)
tfidf.wed <- calc.tfidf(termfreq.wed)
tfidf.thu <- calc.tfidf(termfreq.thu)
tfidf.fri <- calc.tfidf(termfreq.fri)
tfidf.sat <- calc.tfidf(termfreq.sat)
» Calculate the TF-IDF of each dataset.
*TF-IDF
(Term Frequency–Inverse Document Frequency):
TF-IDF measures how important a word is in a
document.
TF-IDF = TF × IDF

31. Step 4: Visualize the Buzz-word of the day
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# Wordcloud
draw.wordcloud<- function(tfidf,title=""){
png.filename <- paste("wordcloud-", title,".png", sep="")
png(png.filename,width=7,height=7,units="in", res=600)
tfidf <- tfidf[order(tfidf$TFIDF, decreasing=T),]
tfidf.head <- head(tfidf, n=100) # Extract to 100 terms
par(oma = c(0, 1, 2, 1)) # Set margin
pal <- brewer.pal(8, "Dark2")
wordcloud(tfidf.head$term,tfidf.head$TFIDF,random.color=T,colors=pal,main=title) # Plot Wordcloud
par(oma = c(0, 0, 0, 0)) # Unset Margin
title(title) # Add Title
dev.off() # Close File
}
# Plot Wordcloudfor each dataset
draw.wordcloud(tfidf.sun,title="2014-05-25(Sun)")
draw.wordcloud(tfidf.mon,title="2014-05-26(Mon)")
draw.wordcloud(tfidf.tue,title="2014-05-27(Tue)")
draw.wordcloud(tfidf.wed,title="2014-05-28(Wed)")
draw.wordcloud(tfidf.thu,title="2014-05-29(Thu)")
draw.wordcloud(tfidf.fri,title="2014-05-30(Fri)")
draw.wordcloud(tfidf.sat,title="2014-05-31(Sat)")
» Output word clouds as PNG format.

32. Step 4: Visualize the Buzz-word of the day
» These wordclouds are describing the trends of the day.
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33. Step 4: Visualize the Buzz-word of the day
» These wordclouds are describing the trends of the day.
wordcloud of a Sunday
 There are specific location such
as “Vancouver”, “Boston” and
“Phoenix”.
 It seems that this day has more
questions about a route or a
booking than other days or
troubles that happened in
specific airport.
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34. Step 4: Visualize the Buzz-word of the day
» These wordclouds are describing the trends of the day.
wordcloud of a Thursday
 The words “seatback”, “pain”
and ”captains” appeared in the
word cloud.
 It seems that there are troubles
with the fleet, cabin or in-flight
service somewhere.
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35. Step 4: Visualize the Buzz-word of the day
» These wordclouds are describing the trends of the day.
Wordcloud of a Friday
 There are words : “award” and
“platinum”.
 It seems that this day has more
questions about frequent flyer
program than a normal day.
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36. Conclusion
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» I told a brief introduction of SaaS-Based CRM “Salesforce.com” and its
applicationplatform “Force.com”.
» An R package RForcecom has various features for exchanging data
with Salesforce.com/Force.com.
» I made a sample use case of RForcecom using Twitter dataset and
visualized customers’ voice.
» The framework might be applied to for conducting a sentiment
(negative/positive) analysis and for analyzing customer feedback for
specific product or service to improve customer satisfaction.

37. 37
Thank you
» Any Questions?
Takekatsu Hiramura
http://thira.plavox.info/
thira@plavox.info http://rforcecom.plavox.info/