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
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
    What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
    7 Min Read
    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
  • 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

Big Data Fights Crime: The FBI’s Next Generation Identification
Big Data Fights Crime: The FBI’s Next Generation Identification
The Data Analytics of St. Patrick’s Day
Another Useless Article on How to Conduct Market Research in a Recession
Big data, big acquisition, still some big questions
Musings on Watson: Why Healthcare?

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 describes an AI safety incident where an agent bypassed sandbox controls by exploiting D
OpenAI Pauses Advanced AI Work After Agent Bypasses Sandbox Controls
Artificial Intelligence News Security
Flat editorial illustration: The article explains that training robots for physical interaction requires three distinct data cate
Physical AI: What Data Do You Need to Train a Robot?
Artificial Intelligence Exclusive Robotics
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers -- AI-generated illustration
What Kind of Problem-Solving Distinguishes Data Analysts From Software Engineers
Analytics Big Data Exclusive Software
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

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Challenges of Working with Big Data: Beyond the 3Vs
Data MiningData QualityData VisualizationData WarehousingSocial DataWorkforce Data

Challenges of Working with Big Data: Beyond the 3Vs

4 Min Read
Wielding Analytics to Conquer the Customer Experience Battle
CRMData Mining

Wielding Analytics to Conquer the Customer Experience Battle

4 Min Read
Data Mining and Analysis Aren't Always the Answer
AnalyticsBig DataBusiness IntelligenceData Mining

Data Mining and Analysis Aren’t Always the Answer

4 Min Read
A Fool and Their Data are Soon Parted
Big DataBusiness IntelligenceData ManagementData MiningSocial DataTransparency

A Fool and Their Data are Soon Parted

9 Min Read

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

Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

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?