r - Aggregate columns in data.table for descriptive statistics -

i looking @ student data set @ individual student level.

what want do descriptive analysis @ faculty degree level.

that students doing 2 degrees (double degrees eg bachelor of , bachelor of science) students generate 2 degrees.

my data looks below. faculty assignments (whether fac1 or fac2) arbitrary.

  studid    fac1     fac2   success sex     ave_mark       1             arts     0     male    65     2       science           1     male    35     3       law               0     male    98     4             science  0     female  55     5       commerce       0     female  20     6       commerce       1     male    80 

this generated

students<-data.table(studid=c(1:6) ,fac1 = c("it","science", "law","it","commerce","commerce"), fac2 = c("arts","","","science","it","it"), success = c(0,1,0,0,0,1), sex=c("male","male","male","female","female","male"), ave_mark=c(65,35,98,55,20,80)) 

how go producing (made figures) create faculty column incorporates both fac1 , fac2 columns? have been trying use lapply function across fac1 , fac2 keep hitting dead ends (ie students[, lapply(.sd, mean), by=agg.by, .sdcols=c('fac1', 'fac2')]

  faculty    mean_success    ave_mark             0.65               65   science    1                  50    law        0.76               50    arts       0.55               50    commerce   0.40               10  

any assistance appreciated.

this seems looking for.

library(reshape2) dt <- melt(students,measure.vars=c("fac1","fac2"),value.name="faculty")[nchar(faculty)>0] dt[,list(mean_success=mean(success),ave_mark=mean(ave_mark)),by=faculty] #     faculty mean_success ave_mark # 1:               0.25       55 # 2:  science         0.50       45 # 3:      law         0.00       98 # 4: commerce         0.50       50 # 5:     arts         0.00       65 

so uses melt(...) function in package reshape2 collapse 2 faculty columns, replicating other columns. unfortunately, results in columns blank faculty, have rid of using [nchar(faculty)>0]. it's simple aggregate based on (new) faculty column.