rowMeans function in dplyr

rowMeans function needs at least two dimensions but DATA[,-1:-3] is just one row.

[1]     2     4    60    80   200   400   600 10000 12000

You can get the Result by below code

DATA%>%
        group_by(SITE, DATE) %>% 
        ungroup() %>% 
        mutate(NAYSA = rowMeans(.[,3:4]))

  SITE DATE STUFF STUFF2  NAYSA
1    A    1     1      2    1.5
2    A    1     2      4    3.0
3    A    2    30     60   45.0
4    A    2    40     80   60.0
5    B    3   100    200  150.0
6    B    3   200    400  300.0
7    B    3   300    600  450.0
8    C    4  5000  10000 7500.0
9    C    4  6000  12000 9000.0

You need the rowwise function in dplyr to do that. Your data is random (because of sample) so it produces different results but you will see that it works:

library(dplyr)
  group_by(DATA, SITE, DATE) %>%
  mutate(STUFF=sample(STUFF,replace= TRUE), STUFF2 = sample(STUFF2,replace= TRUE))%>%
  rowwise() %>%
  mutate(NAYSA = mean(c(STUFF,STUFF2)))

Output:

Source: local data frame [9 x 5]
Groups: <by row>

  SITE DATE STUFF STUFF2  NAYSA
1    A    1     1      2    1.5
2    A    1     2      2    2.0
3    A    2    30     80   55.0
4    A    2    30     60   45.0
5    B    3   200    600  400.0
6    B    3   300    200  250.0
7    B    3   100    600  350.0
8    C    4  5000  12000 8500.0
9    C    4  6000  10000 8000.0

As you see it calculates the rowwise mean per row, according to STUFF and STUFF2

@GregF Yep....ungroup() was the key. Thanks.

Working code

RESULT = group_by(DATA, SITE, DATE) %>% 
  mutate(STUFF = sample(STUFF,replace= TRUE), 
         STUFF2 = sample(STUFF2,replace= TRUE)) %>% 
  ungroup() %>% 
  mutate(NAYSA = rowMeans(.[,-1:-2]))

Now that dplyr has introduced across, this can be accomplished with across and base R's rowMeans. The following code will take the row-wise average of columns starting with the string "STUFF":

DATA %>% 
  mutate(NAYSA = rowMeans(across(starts_with("STUFF"))))