counting -99.9 in r - r

Question

I am new in using R and I have a question for which I am trying to find the answer. I have a file organized as follows (it has thousands of rows but I just show a sample for simplicity):
YEAR Month day S1 T1 T2 R1
1965 3 2 11.7 20.6 11.1 18.8
1965 3 3 14.0 16.7 3.3 0.0
1965 3 4 -99.9 -99.9 -99.9 -99.9
1965 3 5 9.2 5.6 0.0 -99.9
1965 3 6 10.1 6.7 0.0 -99.9
1965 3 7 9.7 7.2 1.1 0.0
I would like to know for each column (T1, T2, and R1) in which Year, Month and Day the -99.9 are located; e.g. from 1980/1/3 to 1980/1/27 there are X -99.9 for T1, from 1990/2/10 to 1990/3/30 there are Y-99.9 for T1....and so on. Same for T2, and R.
How can do this in R?
This is only one file like this but I have almost 2000 files with the same problem (I know I need to loop it) but if I know how to do it for one file then I will just create a loop.
I really appreciate any help. Thank you very much in advance for reading and helping!!!

Answer 1

I took the liberty of renaming your last dataframe column "R1"
lapply(c('T1', 'T2', 'R1'), function(x) { dfrm[ dfrm[[x]]==-99.9 , # rows to select
1:3 ] }# columns to return
)
#-------------
[[1]]
YEAR Month day
3 1965 3 4
[[2]]
YEAR Month day
3 1965 3 4
[[3]]
YEAR Month day
3 1965 3 4
4 1965 3 5
5 1965 3 6
It wasn't clear whether you wanted the values or counts (and I don't think you can have both in the same report.) If you wanted to name the entries:
> misdates <- .Last.value
> names(misdates) <- c('T1', 'T2', 'R1')
If you wanted a count:
lapply(misdates, NROW)
$T1
[1] 1
$T2
[1] 1
$R1
[1] 3
(You might want to learn how to use NA values. Using numbers as missing values is not recommended data management.)

Answer 2

If I understand correctly, you want to obtain how many "-99.9"s you get per month AND by column,
Here's my code for S1 using plyr. You'll note that I expanded your example to get one more month of data.
library(plyr)
my.table <-read.table(text="YEAR Month day S1 T1 T2 R1
1965 3 2 11.7 20.6 11.1 18.8
1965 3 3 14.0 16.7 3.3 0.0
1965 3 4 -99.9 -99.9 -99.9 -99.9
1965 3 5 9.2 5.6 0.0 -99.9
1965 3 6 10.1 6.7 0.0 -99.9
1965 3 7 9.7 7.2 1.1 0.0
1966 1 7 -99.9 7.2 1.1 0.0
1966 1 8 -99.9 7.2 1.1 0.0
", header=TRUE, as.is=TRUE,sep = " ")
#Create a year/month column to summarise per month
my.table$yearmonth <-paste(my.table$YEAR,"/",my.table$Month,sep="")
S1 <-count(my.table[my.table$S1==-99.9,],"yearmonth")
S1
yearmonth freq
1 1965/3 1
2 1966/1 2