[R] Finding non-normal distributions per row of data frame?

[DB1984]

This is my first attempt at this, so hopefully a few kind pointers can get me
going in the right direction...

I have a large data frame of 20+ columns and 20,000 rows. I'd like to
evaluate the distribution of values in each row, to determine whether they
meet the criteria of a normal distribution. I'd loop this over all the rows
in the data frame, and output the summary results to a new data frame.

I have a loop that should run a Shapiro-Wilk test over each row, 

y= data frame 

for (j in 1:nr) {
y.temp<-list(y[j,]) 
testsw <- lapply(y.temp, shapiro.test)
testtable <- t(sapply(testsw, function(x) c(x$statistic, x$p.value)))
 colnames(testtable) <- c("W", "p.value") 
}


but it is currently throwing out an error:
 "Error in `rownames<-`(`*tmp*`, value = "1") : 
  attempt to set rownames on object with no dimensions" 

...which I guess is unrelated to the evaluation of normality, and more
likely a faulty loop?

Any suggestions either for this test, or a better way to evaluate the normal
distribution (e.g. qq-plot residuals for each row) would be greatly
received. Thanks.
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