Question

If we have his data recentely used here:

data <- data.frame(name = rep(letters[1:3], each = 3), 
                   var1 = rep(1:9), var2 = rep(3:5, each = 3))

  name var1 var2
1    a    1    3
2    a    2    3
3    a    3    3
4    b    4    4
5    b    5    4
6    b    6    4
7    c    7    5
8    c    8    5
9    c    9    5

we can look for rows where var2 == 4.

data[data[,3] == 4 ,] # equally data[data$var2 == 4 ,]

#  name var1 var2
#4    b    4    4
#5    b    5    4
#6    b    6    4

or rows where both var1 and var2 ==4

data[data[,2] == 4 &  data[,3] == 4,]

#  name var1 var2
#4    b    4    4

what I dont get is why this:

data[ data[ , 2:3 ] == 4 ,]

gives this:

     name var1 var2
4       b    4    4
NA   <NA>   NA   NA
NA.1 <NA>   NA   NA
NA.2 <NA>   NA   NA

#I would still hope to get 
 #  name var1 var2
#4    b    4    4

Where do the NAs come from?

Answer 1

Your data[,2:3]==4 is the following :

R> data[,2:3]==4
       var1  var2
 [1,] FALSE FALSE
 [2,] FALSE FALSE
 [3,] FALSE FALSE
 [4,]  TRUE  TRUE
 [5,] FALSE  TRUE
 [6,] FALSE  TRUE
 [7,] FALSE FALSE
 [8,] FALSE FALSE
 [9,] FALSE FALSE

Then you try to index the rows of your data frame with this matrix. To do this, R seems to first convert your matrix to a vector :

R> as.vector(data[,2:3]==4)
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[12] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE

It then selects the rows of data based on this vector. The 4th TRUE value selects the 4th row, but the three others TRUE values select “out of bounds” rows, so they return NA‘s.