Hello,
Arun's answer shows that matrices are faster. If your data is all of the
same type, then this might be a point for matrices.
data.frames are better for modeling. You can use the formula interface
to the many modeling functions. For instance, the example below is _not_
possible with a matrix.
set.seed(1234)
dat <- data.frame(x = rnorm(100), A = sample(letters[1:4], 100, TRUE), y
= rnorm(100))
model <- lm(y ~ x + A, data = dat) # not possible with matrix
#predict.lm needs data.frames
newdat <- data.frame(x = c(1,3,4), A = rep("a",3))
predict(model, new = newdat)
There are many other examples like this one. If you are doing data
modeling, use data frames.
Hope this helps,
Rui Barradas
Em 27-06-2013 19:26, Anika Masters escreveu:
When "should" I use a dataframe vs. a matrix? What are the pros and cons?
If I have data of all the same type, am I usually better off using a
matrix and not a dataframe?
What are the advantages if any of using a dataframe vs. a matrix?
(rownames and column names perhaps?)
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