Hey the easiest way to do this is with the apply() function.
Assume you have 25 rows of data and 6 columns labeled A1 through A6.
data <- data.frame(A1=rnorm(25,50,4),A2=rnorm(25,50,4),A3=rnorm(25,50,4),
A4=rnorm(25,50,4),A5=rnorm(25,50,4),A6=rnorm(25,50,4))
You can use the apply function to find the standard deviation of each row columns 1 through 6 with the code below. The first argument is your data object. The second argument is an integer specifying either 1 for rows or 2 for columns (This is the direction the function will be applied to the data frame). The final argument is the function you wish to apply to your data frame (such as mean or standard deviation (sd) in this case. See the code below.
apply(data[,1:6],1,sd)
Indexing can be used to limit the number of rows or columns of data passed to the apply function. This is done by entering a vector of numbers for either the rows or columns you are interested in within brackets after your data object.
data[c(row.vector),c(column.vector)]
Say you only want to know the sd of the first 3 columns.
apply(data[,1:3],1,sd)
Now lets see the sd of columns 4 through 6 and rows 1 through 10
apply(data[1:10,4:6],1,sd)
Just for good measure lets find the sd of each column
apply(data,2,sd)
Notice that the sd is close to 4, which, is what I specified when I generated the pseudo-random data for columns A1 through A6.
Hope this helps