[R] when to use & pros/cons of dataframe vs. matrix?

[Rui Barradas]

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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