I get flummoxed by some of the simplest of things. In the following code I wanted to extract just a portion of one column in a data.frame called 'a'. I get the right values, but the final entity is padded with NAs which I don't want. 'b' is the extracted column, 'c' is the correct portion of data but has extra NA padding at the end.
How do I best do this where 'c' is ends up naturally only 9 elements long? (ie - the 15 original minus the 6 I skipped)
NumBars = 6
a = as.data.frame(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15))
a[,2] = c(11,12,13,14,15,16,17,18,19,20,21,22,23,24,25)
names(a)[1] = "Data1"
names(a)[2] = "Data2"
{Use 1st column of data only}
b = as.matrix(a[,1])
c = as.matrix(b[NumBars+1:length(b)])
The immediate reason why you're getting NA's is that the sequence operator :
takes precedence over the addition operator +
, as is detailed in the R Language Definition . Therefore NumBars+1:length(b)
is not the same as (NumBars+1):length(b)
. The first adds NumBars
to the vector 1:length(b)
, while the second adds first and then takes the sequence.
ind.1 <- 1+1:3 # == 2:4
ind.2 <- (1+1):3 # == 2:3
When you index with this longer vector, you get all the elements you want, and you also are asking for entries like b[length(b)+1]
, which the R Language Definition tells us returns NA
. That's why you have trailing NA
's.
If
i
is positive and exceedslength(x)
then the corresponding selection isNA
. A negative out of bounds value fori
causes an error.
b <- c(1,2,3)
b[ind.1]
#[1] 2 3 NA
b[ind.2]
#[1] 2 3
From a design perspective, the other solutions listed here are good choices to help avoid this mistake.
It is often easier to think of what you want to remove from your vector / matrix. Use negative subscripts to remove items.
c = as.matrix(b[-1:-NumBars])
c
## [,1]
## [1,] 7
## [2,] 8
## [3,] 9
## [4,] 10
## [5,] 11
## [6,] 12
## [7,] 13
## [8,] 14
## [9,] 15
If your goal is to remove NA
s from a column, you can also do something like
c <- na.omit(a[,1])
Eg
> x
[1] 1 2 3 NA NA
> na.omit(x)
[1] 1 2 3
attr(,"na.action")
[1] 4 5
attr(,"class")
[1] "omit"
You can ignore the attributes - they are there to let you know what elements were removed.
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