I'm sure this is kind of basic, but I'd just like to really understand the logic of R data structures here.
If I subset a matrix by index out of bounds, I get exactly that error:
m <- matrix(data = c("foo", "bar"), nrow = 1)
m[2,]
# Error in m[2, ] : subscript out of bounds
If I do the same do a data frame, however, I get all NA
rows:
df <- data.frame(foo = "foo", bar = "bar")
df[2,]
# foo bar
# NA <NA> <NA>
If I subset into a non-existent data frame column I get the familiar
df[, 3]
# Error in `[.data.frame`(df, , 3) : undefined columns selected
I know (roughly) that data frame rows are weird and to be treated carefully, but I don't quite see the connection to the above behavior.
Can someone explain why R behaves in this way for non-existent df rows?
Update
To be sure, giving NA
on out-of-bounds subsets, is normal R behavior for 1D vectors:
vec <- c("foo", "bar")
vec[3]
# [1] NA
So in a way, the weird one out here is matrix subsetting, not dataframe subsetting, depending from where you're starting out.
Still the different 2D subsetting behavior (m[2, ]
vs df[2, ]
) might strike a dense user (as I am right now) as inconsistent.
l1 <- list('foo', 'bar')
. Now try the following: 1)l1[3]
, 2)l1[[3]]
– Sotosis.atomic(a_matrix)
yieldsTRUE
while for a list or a data.frame this condition is not satisfied. – dd_rookie