Not clear to me what your successive rows indicate in your output.
However, you may get something similar to what you want using the reshape2
library:
library(reshape2)
dta1 <- read.csv( text=
"Tool,Step_Number,Data1,Data2
A,1,0,1
A,2,3,2
A,3,2,3
B,1,3,2
B,2,1,2
B,3,3,2
")
# put into long form
dta1l <- melt(dta1,c("Tool","Step_Number"))
# re-cast to wide form
# assuming first row is for tool A, second row is for tool B, etc.
dta2 <- dcast(dta1l,Tool~variable+Step_Number)
On Sun, 14 Oct 2012, siddu479 wrote:
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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