[R] transforming a .csv file column names as per a particular column rows using R code

[arun]

Hi,

If you don't want the column "Tool" to be repeated, try this:


dat1<-read.table(text="
Tool Step_Number Data1 Data2
A 1 0 1
A 2 3 1
A 3 2 1
B 1 3 2
B 2 1 2
B 3 3 2
",sep="",header=TRUE,stringsAsFactors=FALSE)    


 with(dat1,aggregate(cbind(Data1,Data2),list(Tool=Tool),function(x) x))
  Tool Data1.1 Data1.2 Data1.3 Data2.1 Data2.2 Data2.3
#1    A       0       3       2       1       1       1
#2    B       3       1       3       2       2       2A.K.



----- Original Message -----
From: siddu479 <onlyfordigitalstuff at gmail.com>
To: r-help at r-project.org
Cc: 
Sent: Sunday, October 14, 2012 12:13 PM
Subject: [R] transforming a .csv file column names as per a particular column rows using R code

Hello all,
I have a .csv file like below.
Tool,Step_Number,Data1,Data2... etc up to 100 columns.
A,1,0,1
A,2,3,1
A,3,2,1
.
.
B,1,3,2
B,2,1,2
B,3,3,2
.
.
...... so on upto 50 rows 
where the column "*Tool*" has distinct steps in second column
"*Step_Number*",but both have same entries in Step_Number column.
I want the output like below.

Tool_1,Data1_1,Data2_1,Tool_2,Data1_2,Data2_2,Tool_3,Data1_3,Data2_3... so
on
A,0,1,A,3,1,A,2,1
B,3,2,B,1,2,B,3,2
......
so on.

basically I am transposing entire data based on a specific column row values
and renaming the column headers.
I have a shell script based on awk which can do this task, but the script is
taking exceptionally higher processing time.

So I am looking for a script in R which can save the time.
"Please revert to me if the problem description is not clear."

Regards
Sidda




-----
Sidda
Business Analyst Lead
Applied Materials Inc.

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