common data/sample in two dataframes in R - r

Question

I'm trying to compare model-based forecasts from two different models. Model number 2 however requires more non-missing data and has thus more missing values (NA) than model 1.
I am wondering now how I can quickly query both dataframes for non-missing values and identify the common sample? I used to work with excel and the function
=IF(AND(ISVALUE(a1);ISVALUE(b1));then;else)
comes to my mind but I don't know how to do it properly with R.
This is my df from model 1: Every observation is clearly identified by id and time.
(the rownumbers on the left are from my overall dataframe and are identical in both dataframes.)
> head(model1)
id time f1 f2 f3 f4 f5
9 1 1995 16.351261 -1.856662 6.577671 10.7883178 22.5349438
10 1 1996 15.942914 -1.749530 2.894190 0.6058255 1.7057163
11 1 1997 24.187390 15.099166 14.275441 -4.9963831 -0.1866863
12 1 1998 3.101094 -10.455754 -9.674086 -9.8456466 -8.5525140
13 1 1999 33.562234 2.610512 -15.237620 -18.8095980 -17.6351989
14 1 2000 59.979666 -45.106093 -100.352866 -56.6137325 -32.0315737
and this model 2:
> head(model2)
id time meanf1 meanf2 meanf3 meanf4 meanf5
9 1 1995 4.56 5.14 6.05 NA NA
10 1 1996 4.38 4.94 NA NA NA
11 1 1997 4.05 4.51 NA NA NA
12 1 1998 4.07 5.04 6.52 NA NA
13 1 1999 3.61 4.96 NA NA NA
14 1 2000 4.35 4.83 6.46 NA NA
Thank you for your help and hints.

Answer 1

The function complete.cases gives non-missing data across all columns. The sets (f4,meanf4) and (f5,meanf5) have no "common" non-missing values in the sample data, hence have no observations. Is this what you were looking for
#Read Data
model1=read.table(text='id time f1 f2 f3 f4 f5
1 1995 16.351261 -1.856662 6.577671 10.7883178 22.5349438
1 1996 15.942914 -1.749530 2.894190 0.6058255 1.7057163
1 1997 24.187390 15.099166 14.275441 -4.9963831 -0.1866863
1 1998 3.101094 -10.455754 -9.674086 -9.8456466 -8.5525140
1 1999 33.562234 2.610512 -15.237620 -18.8095980 -17.6351989
1 2000 59.979666 -45.106093 -100.352866 -56.6137325 -32.0315737',header=TRUE)
model2=read.table(text=' id time meanf1 meanf2 meanf3 meanf4 meanf5
1 1995 4.56 5.14 6.05 NA NA
1 1996 4.38 4.94 NA NA NA
1 1997 4.05 4.51 NA NA NA
1 1998 4.07 5.04 6.52 NA NA
1 1999 3.61 4.96 NA NA NA
1 2000 4.35 4.83 6.46 NA NA',header=TRUE)
#name indices of f1..f5 = 3..7
#merge data for each f1..f5 and keep only non-missing values using, complete.cases()
DF_list = lapply(3:7,function(x) {
DF=merge(model1[,c(1,2,x)],model2[,c(1,2,x)],by=c("id","time"));
DF=DF[complete.cases(DF),];
return(DF);
})
DF_list
#[[1]]
# id time f1 meanf1
#1 1 1995 16.351261 4.56
#2 1 1996 15.942914 4.38
#3 1 1997 24.187390 4.05
#4 1 1998 3.101094 4.07
#5 1 1999 33.562234 3.61
#6 1 2000 59.979666 4.35
#
#[[2]]
# id time f2 meanf2
#1 1 1995 -1.856662 5.14
#2 1 1996 -1.749530 4.94
#3 1 1997 15.099166 4.51
#4 1 1998 -10.455754 5.04
#5 1 1999 2.610512 4.96
#6 1 2000 -45.106093 4.83
#
#[[3]]
# id time f3 meanf3
#1 1 1995 6.577671 6.05
#4 1 1998 -9.674086 6.52
#6 1 2000 -100.352866 6.46
#
#[[4]]
#[1] id time f4 meanf4
#<0 rows> (or 0-length row.names)
#
#[[5]]
#[1] id time f5 meanf5
#<0 rows> (or 0-length row.names)