How to select data that have complete cases of a certain column?

Question

I'm trying to get a data frame (just.samples.with.shoulder.values, say) contain only samples that have non-NA values. I've tried to accomplish this using the complete.cases function, but I imagine that I'm doing something wrong syntactically below:

data <- structure(list(Sample = 1:14, Head = c(1L, 0L, NA, 1L, 1L, 1L, 
0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L), Shoulders = c(13L, 14L, NA, 
18L, 10L, 24L, 53L, NA, 86L, 9L, 65L, 87L, 54L, 36L), Knees = c(1L, 
1L, NA, 1L, 1L, 2L, 3L, 2L, 1L, NA, 2L, 3L, 4L, 3L), Toes = c(324L, 
5L, NA, NA, 5L, 67L, 785L, 42562L, 554L, 456L, 7L, NA, 54L, NA
)), .Names = c("Sample", "Head", "Shoulders", "Knees", "Toes"
), class = "data.frame", row.names = c(NA, -14L))

just.samples.with.shoulder.values <- data[complete.cases(data[,"Shoulders"])]
print(just.samples.with.shoulder.values)

I would also be interested to know whether some other route (using subset(), say) is a wiser idea. Thanks so much for the help!

Answer 1

You can try complete.cases too which will return a logical vector which allow to subset the data by Shoulders

data[complete.cases(data$Shoulders), ] 
#    Sample Head Shoulders Knees Toes
#  1      1    1        13     1  324
#  2      2    0        14     1    5
#  4      4    1        18     1   NA
#  5      5    1        10     1    5
#  6      6    1        24     2   67
#  7      7    0        53     3  785
#  9      9    1        86     1  554
# 10     10    1         9    NA  456
# 11     11    1        65     2    7
# 12     12    1        87     3   NA
# 13     13    0        54     4   54
# 14     14    1        36     3   NA

Answer 2

You could try using is.na:

data[!is.na(data["Shoulders"]),]
   Sample Head Shoulders Knees Toes
1       1    1        13     1  324
2       2    0        14     1    5
4       4    1        18     1   NA
5       5    1        10     1    5
6       6    1        24     2   67
7       7    0        53     3  785
9       9    1        86     1  554
10     10    1         9    NA  456
11     11    1        65     2    7
12     12    1        87     3   NA
13     13    0        54     4   54
14     14    1        36     3   NA