We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
    cybersecurity efforts
    How Behavioral Analytics and AI Are Redefining Cybersecurity for Boca Raton Businesses
    14 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: R –Refcards and Basic I/O Operations
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > R –Refcards and Basic I/O Operations
Data Mining

R –Refcards and Basic I/O Operations

Editor SDC
Editor SDC
4 Min Read
R –Refcards and Basic I/O Operations
Photo by marionbrun on Pixabay (https://pixabay.com/photos/doctor-surgeon-operation-650534/)
SHARE

While working with a large number of files for data processing, I used the following R commands for data processing. Given that everyone needs to split as well merge and append data – I am just giving some code on splitting data based on parameters , and appending data as well as merging data.

 

Splitting Data Based on a Parameter.

The following divides the data into subsets which contain either Male or anything else in different datasets.

More Read

Does Data Mining Require a PhD? Probably Not, But The New York Times Hired One
Does Data Mining Require a PhD? Probably Not, But The New York Times Hired One
The interoperability of social networks
Ning and Wordframe
Want to Get Certified in Data Mining?
Workforce Planning and HR Analytics

Input and Subset

Note the read.table command assigns the dataset name X in R environment from the file reference (path denoted by ….)

x <- read.table(....)
rowIndx <- grep("Male", x$col)
write.table(x[rowIndx,], file="match")
write.table(x[-rowIndx,], file="nomatch")

Suppose we need to divide the dataset into multiple data sets.


X17 <- subset(X, REGION == 17)

This is prefered to the technique -
attach(X)
X17 = X[REGION == 17,]

 

Output

For putting the files back to the Windows environment you can use-

write.table(x,file="",row.names=TRUE,col.names=TRUE,sep=" ")

Append

Lets say you have a large number of data files ( say csv files )

that you need to append (assuming the files are in same syrycture)

after performing basic operations on them.

 

>setwd("C:\\Documents and Settings\\admin\\My Documents\\Data")

Note this changes the working folder to folder you want it to be,

note the double slashes which are needed to define the path

>list.files(path = ".", pattern = NULL, all.files = FALSE, full.names = FALSE,

+     recursive = FALSE, ignore.case = FALSE)

The R output would be something like below

 

 [1] "cal1.csv"                                     "cal2.csv"                                           

[3] "cal3.csv"                                     "cal4.csv"                                           

[5] "cal5.csv"                                     "cal6.csv"                                           

[7] "cal7.csv"                                     "cal8.csv"

 

Now you can use the file.append command for succesively appending the second file

to the first file.

If writing a lot of similar code is a tedium use the & (concatenate) function

in excel to create the code.Note the Formula Bar (B7=A7&C7&D7&E7)

Excel is useful because it is good in click and drag repetitive text and

concatenation is easily done.

 

The output would be something like

>file.append("cal1.csv","cal2.csv")
[1] TRUE
>file.append("cal1.csv","cal3.csv")
[1] TRUE
>file.append("cal1.csv","cal4.csv")
[1] TRUE
>file.append("cal1.csv","cal5.csv")
[1] TRUE
>file.append("cal1.csv","cal6.csv")
[1] TRUE
>file.append("cal1.csv","cal7.csv")
[1] TRUE
>file.append("cal1.csv","cal8.csv")
[1] TRUE

 

Note all data here gets appended to filecal1.csv

This should be a good starting point for you to trying out R.

For a Reference Sheet, here is an excellent reference sheet from Tom Short,

and it is aptly called the Short Refcard

(http://cran.r-project.org/doc/contrib/Short-refcard.pdf)

Note- Experienced analytics people are best served by

www.rforsasandspssusers.com

Anyways MeRRy ChRistmas !

 

Short Refcard

Publish at Scribd or explore others: Mathematics Science R

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article examines AI agents that escalate from legitimate data retrieval to attempted intrusions
OpenAI’s Government Website Incidents Raise a Hard Question for AI Agents: When Should They Stop?
Artificial Intelligence News Security
Flat editorial illustration: The article's core relationship is the alignment between customer behavioral data (visit frequency,
Data-Driven Loyalty: How Restaurants Use Behavioral Analytics to Optimize Revenue
Exclusive
Flat editorial illustration: The article's core relationship is that reliable eCommerce attribution depends on a unified, well-st
How eCommerce Data Teams Can Build Attribution That Holds Up
Big Data Exclusive
Flat editorial illustration: The article's core relationship is the contrast between fragmented inherited data infrastructure (wh
Data Stack Consolidation as a Data Quality and Governance Strategy for Mid-Market Teams
Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Information, Intelligence and Process:  Combining Forces to Better Answer Business Needs
Business IntelligenceData MiningData QualityData VisualizationPolicy and Governance

Information, Intelligence and Process: Combining Forces to Better Answer Business Needs

16 Min Read
Data ManagementData MiningData Quality

The Data Is In: Finding Affordable Car Loans

5 Min Read
How Your Hadoop Distribution Could Lose Your Data Forever
AnalyticsBig DataBusiness IntelligenceCloud ComputingData MiningData QualityData VisualizationData WarehousingHadoopITMapReduceOpen SourceSecuritySocial DataSoftwareSQLUnstructured DataWorkforce Data

How Your Hadoop Distribution Could Lose Your Data Forever

0 Min Read
Target, Pregnancy, and Predictive Analytics, Part II
AnalyticsBest PracticesData MiningMarketingModelingStatistics

Target, Pregnancy, and Predictive Analytics, Part II

0 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?