R: Averaging columns and conditionally excluding NA data

Question

I'm trying to average across columns, however, because some of the columns are missing data, the average ends of being NA as well. Is there a way to find the mean of a number of columns while excluding any NA data from the calculation?

The code I've used so far is:

### Calculate Bins ###
 {pulse<-transmute(pulse, Question, Type, Student,Bin1=(Rt1+ Rt2 + Rt3+ Rt4)/4 , Bin2= (Rt5+Rt6+Rt7+Rt8)/4 , Bin3= (Rt9+Rt10+Rt11)/3)
 }

However, I don't think this is the best way.My goal is to have three columns with the means of Rt1-Rt4, Rt5-Rt8 and Rt9-Rt11. i.e. something like this:

 Question  Type Student  Bin1  Bin2     Bin3
1        Q   SNR  789331  4.25  4.00 4.666667
2       Q2   SNR  789331  3.75  2.50 3.000000
3       Q8   SNR  789331  4.00  2.50 3.333333
4      Q10   SNR  789331  4.00  2.75 3.333333
5      Q12   SNR  789331  3.50  3.25 3.666667

Any help would be appreciated!

My data is attached below:

> dput(pulse)
structure(list(Question = c("Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12", "Q", 
"Q2", "Q8", "Q10", "Q12", "Q", "Q2", "Q8", "Q10", "Q12"), Type = c("SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", 
"SNR", "SNR", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", 
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS"), Student = c("789331", 
"789331", "789331", "789331", "789331", "805933", "805933", "805933", 
"805933", "805933", "826523", "826523", "826523", "826523", "826523", 
"832929", "832929", "832929", "832929", "832929", "838607", "838607", 
"838607", "838607", "838607", "841903", "841903", "841903", "841903", 
"841903", "843618", "843618", "843618", "843618", "843618", "852125", 
"852125", "852125", "852125", "852125", "876406", "876406", "876406", 
"876406", "876406", "879972", "879972", "879972", "879972", "879972", 
"885650", "885650", "885650", "885650", "885650", "888712", "888712", 
"888712", "888712", "888712", "903303", "903303", "903303", "903303", 
"903303", "796882", "796882", "796882", "796882", "796882", "827911", 
"827911", "827911", "827911", "827911", "830271", "830271", "830271", 
"830271", "830271", "831487", "831487", "831487", "831487", "831487", 
"834598", "834598", "834598", "834598", "834598", "836364", "836364", 
"836364", "836364", "836364", "839802", "839802", "839802", "839802", 
"839802", "855524", "855524", "855524", "855524", "855524", "873527", 
"873527", "873527", "873527", "873527", "885409", "885409", "885409", 
"885409", "885409", "894218", "894218", "894218", "894218", "894218", 
"928026", "928026", "928026", "928026", "928026", "932196", "932196", 
"932196", "932196", "932196", "955389", "955389", "955389", "955389", 
"955389", "956952", "956952", "956952", "956952", "956952", "957206", 
"957206", "957206", "957206", "957206", "957759", "957759", "957759", 
"957759", "957759", "959200", "959200", "959200", "959200", "959200", 
"962490", "962490", "962490", "962490", "962490", "968728", "968728", 
"968728", "968728", "968728", "969005", "969005", "969005", "969005", 
"969005", "971179", "971179", "971179", "971179", "971179", "976863", 
"976863", "976863", "976863", "976863", "981621", "981621", "981621", 
"981621", "981621", "952797", "952797", "952797", "952797", "952797", 
"965873", "965873", "965873", "965873", "965873", "967416", "967416", 
"967416", "967416", "967416", "975424", "975424", "975424", "975424", 
"975424"), Rt1 = c(4, 3, 4, 4, 3, 5, 4, 5, 5, 5, 4, 4, 4, 5, 
5, 4, 4, 4, 4, 3, 5, 5, 5, 5, 5, 2, 3, 4, 3, 4, 4, 5, 5, 4, 4, 
3, 3, 3, 4, 3, 3, 3, 4, 4, 4, 3, 4, 5, 4, 3, 4, 4, 4, 3, 5, 4, 
4, 4, 5, 5, 3, 4, 4, 4, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 4, 5, 3, 4, 4, 4, 3, 3, 5, 4, 4, 2, 2, 3, 4, NA, NA, 
NA, NA, NA, 3, 4, 4, 4, 3, NA, NA, NA, NA, NA, 5, 4, 5, 4, 4, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, 4, 3, 3, 4, 1, 3, 
4, 5, 4, 4, 4, 5, 4, 4, NA, NA, NA, NA, NA), Rt2 = c(4, 4, 4, 
4, 3, 4, 4, 4, 4, 4, 3, 4, 4, 5, 5, 4, 4, 4, 4, 3, 5, 5, 5, 5, 
5, 4, 4, 4, 4, 5, 4, 4, 5, 5, 4, NA, NA, NA, NA, NA, 4, 4, 4, 
4, 4, 3, 4, 4, 5, 3, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 1, 5, 5, 5, 
3, 3, 5, 5, 5, 4, 5, 4, 3, 4, 5, 4, 5, 5, 5, 4, 4, 5, 4, 5, 4, 
5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 3, 4, 3, 4, 3, 5, 5, 5, 5, 5, 3, 
5, 4, 4, 3, 4, 5, 5, 5, 5, 4, 4, 4, 5, 5, 4, 5, 5, 5, 4, 4, 2, 
2, 4, 4, 5, 5, 5, 5, 5, 3, 4, 4, 5, 5, 5, 5, 3, 5, 4, 5, 4, 4, 
5, 4, 5, 2, 3, 4, 3, 4, 3, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 
3, 5, 5, 5, 5, 4, 5, 5, 5, 3, 4, 4, 5, 5, 5, 5, NA, NA, NA, NA, 
NA, NA, 4, 5, 5, 5, NA, NA, NA, NA, NA, 4, 4, 4, 4, 4), Rt3 = c(4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 5, 5, 4, 4, 4, 4, 3, 5, 5, 
5, 5, 5, 4, 5, 4, 4, 4, 5, 4, 5, 5, 4, 4, 4, 4, 4, 3, 4, 3, 4, 
5, 5, 3, 4, 4, 4, 4, 3, 4, 4, 4, 5, NA, NA, NA, NA, NA, 3, 5, 
5, 5, 5, 3, 4, 5, 5, 3, 4, 3, 3, 4, 4, 4, 5, 5, 5, 5, 4, 5, 4, 
4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 1, 3, 1, 4, 1, 4, 5, 5, 5, 
4, 4, 4, 4, 4, 3, 4, 5, 5, 5, 4, 4, 5, 5, 4, 4, 5, 5, 5, 4, 5, 
NA, NA, NA, NA, NA, 4, 4, 5, 5, 5, NA, NA, NA, NA, NA, 5, 4, 
4, 4, 3, 5, 4, 4, 5, 4, NA, NA, NA, NA, NA, 5, 4, 3, 5, 4, 3, 
4, 4, 4, 3, 5, 5, 4, 4, 5, 5, 4, 4, 5, 4, NA, 5, 5, 5, 5, 5, 
4, 4, 5, 5, NA, NA, NA, NA, NA, 5, 5, 5, 5, 5, 5, 5, 4, 3, 4, 
3, 4, 3, 3, 4), Rt4 = c(5, 4, 4, 4, 4, 4, 4, 3, 4, 3, 4, 4, 4, 
5, 5, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, 5, 4, 
4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, NA, NA, NA, NA, NA, 4, 
4, 4, 3, 5, 4, 4, 4, 4, 5, 3, 4, 4, 4, 5, 3, 4, 5, 5, 3, NA, 
NA, NA, NA, NA, 5, 5, 5, 5, 5, 5, 5, 4, 4, 5, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 1, 1, 2, 3, 2, 4, 5, 5, 5, 4, 4, 4, 4, 4, 5, 4, 
5, 5, 5, 5, 5, 5, 4, 4, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
5, 4, 4, 5, 4, NA, NA, NA, NA, NA, 4, 4, 5, 4, 4, 4, 3, 3, 4, 
3, 5, 4, 4, 4, 5, NA, NA, NA, NA, NA, 5, 4, 3, 3, 4, NA, NA, 
NA, NA, NA, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), Rt5 = c(3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 4, 
5, 4, 5, 5, 2, 4, 4, 4, 4, 5, 5, 5, 5, 5, 4, 4, 4, 3, 3, 5, 4, 
4, 4, 5, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 3, 4, 4, 4, 5, 
4, 4, 4, 4, 5, 4, 5, NA, NA, NA, NA, NA, 3, 2, 4, 4, 1, 3, 2, 
3, 5, 4, 5, 5, 5, 5, 5, 4, 5, 4, 5, 4, 4, 4, 4, 4, 5, 3, 4, 3, 
4, 4, 5, 4, 3, 4, 5, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 
4, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, 
3, 3, 5, 5, NA, NA, NA, NA, NA, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 
4, 2, 2, 4, 4, 5, 4, 4, 4, 4, 3, 3, 4, 4, 3, NA, NA, NA, NA, 
NA, 5, 5, 4, 4, 4, NA, NA, NA, NA, NA, 5, 5, 5, 5, 5, 5, 4, 4, 
4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, NA, NA, NA, NA, NA), Rt6 = c(4, 
2, 2, 1, 3, 4, 3, 3, 3, 3, 4, 5, 5, 4, 5, NA, NA, NA, NA, NA, 
5, 4, 4, 4, 5, NA, NA, NA, NA, NA, 5, 4, 4, 4, 5, 3, 3, 4, 4, 
4, 4, 3, 2, 1, 2, 4, 4, 4, 5, 4, 4, 5, 4, 3, 4, 4, 5, 5, 4, 4, 
3, 4, 4, 3, 3, 5, 3, 2, 3, 5, 4, 3, 3, 4, 3, 5, 4, 4, 4, 5, NA, 
NA, NA, NA, NA, 4, 4, 4, 4, 4, 3, 4, 3, 3, 3, 2, 2, 3, 2, 2, 
4, 4, 5, 4, 5, NA, NA, NA, NA, NA, 4, 5, 5, 4, 4, 5, 5, 5, 5, 
5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
3, 2, 4, 3, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2, 4, 4, 
5, 4, 5, 5, 3, 3, 3, 3, 3, NA, NA, NA, NA, NA, NA, 5, 4, 4, 4, 
NA, NA, NA, NA, NA, 5, 3, 4, 4, 5, 4, 3, 4, 4, 3, 4, 4, 4, 3, 
4, 4, 4, 5, 4, 5, NA, NA, NA, NA, NA), Rt7 = c(5, 2, 2, 3, 3, 
4, 3, 3, 3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, 4, 
4, 4, 4, 4, 4, 3, 4, 5, 5, 4, 4, 4, 5, 3, 4, 3, 4, 4, 4, 3, 2, 
2, 3, 4, 4, 4, 4, 4, 5, 5, 4, 4, 4, 5, 4, 5, 4, 5, 3, 4, 4, 4, 
4, 4, 3, 1, 1, 5, NA, NA, NA, NA, NA, 5, 5, 4, 5, 5, 4, 5, 4, 
4, 4, 4, 4, 4, 4, 4, 3, 4, 3, 4, 4, 3, 3, 3, 3, 3, 5, 5, 5, 5, 
4, 4, 4, 4, 4, 5, 4, 5, 5, 3, 4, 5, 5, 5, 5, 5, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 3, 5, 5, 4, 5, 5, 5, 3, 4, 5, 4, 4, 4, 
4, 4, 4, 3, 3, 3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1, 1, 1, 1, 1, 5, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 3, 3, 4, 4, 5, 
3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 1, 1, 1, 1, 5, 5, 5, 4, 4, 3, 2, 
2, 3, 4), Rt8 = c(4, 3, 3, 3, 3, 4, 3, 3, 3, 3, 5, 5, 5, 4, 4, 
NA, NA, NA, NA, NA, 5, 4, 4, 5, 4, 3, 4, 3, 3, 4, 5, 4, 4, 3, 
5, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 3, 5, 
4, 4, 4, 3, 4, 3, 4, 4, 3, 4, 1, 1, 1, 1, 3, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 5, 5, 4, 4, 5, NA, NA, NA, NA, NA, 3, 
4, 3, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 4, 4, 5, 5, 5, 
4, 3, 5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 3, 5, 5, 5, 5, 4, 4, 4, 5, 4, 5, 5, 4, 4, 3, 4, 3, 3, 
3, 3, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 3, 3, 3, 3, 3, 5, 5, 4, 4, 
5, 5, 5, 4, 5, 5, 4, 3, 3, 4, 4, 5, 5, 5, 3, 3, 5, 4, 4, 4, 4, 
3, 2, 2, 2, 2, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA), Rt9 = c(4, 
3, 3, 3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 4, 3, 4, 4, 4, 4, 4, 4, 4, 5, 4, 3, 3, 4, 4, NA, NA, 
NA, NA, NA, 3, 3, 3, 2, 4, 4, 4, 4, 4, 4, 5, 4, 4, 3, 3, 5, 4, 
4, 4, 4, 3, 4, 4, 4, 4, 3, 1, 1, 1, 5, NA, NA, NA, NA, NA, 5, 
5, 5, 5, 5, 5, 5, 5, 4, 5, NA, NA, NA, NA, NA, 3, 4, 3, 3, 4, 
3, 3, 3, 2, 3, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, 4, 5, 5, 4, 4, NA, NA, NA, NA, NA, 5, 4, 3, 4, 4, 4, 3, 3, 
3, 2, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 2, 3, 4, 4, 2, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 4, 1, 1, 1, 1, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), Rt10 = c(5, 3, 3, 3, 4, NA, NA, NA, NA, 
NA, 5, 4, 4, 4, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 5, 4, 4, 3, 4, 4, 3, 3, 3, 4, 4, 3, 2, 3, 4, 
4, 4, 4, 4, 4, 5, 5, 4, 3, 3, 5, 4, 4, 3, 4, 3, 4, 4, 4, 3, 3, 
1, 1, 1, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, 5, 4, 
3, 5, 4, 4, 4, 4, 4, 3, 4, 3, 3, 4, 1, 1, 2, 2, 3, 4, 5, 4, 4, 
4, 4, 4, 4, 3, 4, 4, 4, 4, 2, 5, 4, 4, 4, 3, 5, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, 4, 4, 4, 4, 4, 
4, 3, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 4, 2, 2, 4, 4, 1, 1, 
3, 1, 2, 5, 5, 4, 4, 5, NA, NA, NA, NA, NA, 4, 5, 3, 4, 4, 5, 
5, 5, 5, 5, 4, 4, 4, 4, 4, 5, 3, 3, 2, 4, NA, NA, NA, NA, NA, 
3, 4, 3, 4, 4), Rt11 = c(5, 3, 4, 4, 4, 4, 3, 3, 3, 3, 4, 4, 
4, 4, 5, NA, NA, NA, NA, NA, 4, 4, 3, 3, 4, 3, 5, 5, 5, 5, 5, 
4, 4, 4, 5, 3, 5, 5, 5, 5, 4, 4, 4, 4, 5, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, 5, 5, 5, 4, 4, 4, 5, 5, 4, 5, 5, 3, 4, 5, 
4, NA, NA, NA, NA, NA, 5, 5, 5, 5, 5, 5, 5, 4, 4, 5, 4, 4, 4, 
4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 5, 4, 5, 4, 4, 
5, 4, 4, 4, 3, 3, 5, 5, 5, 5, 5, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, 4, 4, 
4, 5, 5, 4, 5, 5, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1, 1, 1, 2, 3, 5, 5, 4, 4, 5, 5, 5, 5, 5, 5, NA, NA, NA, NA, 
NA, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("Question", "Type", 
"Student", "Rt1", "Rt2", "Rt3", "Rt4", "Rt5", "Rt6", "Rt7", "Rt8", 
"Rt9", "Rt10", "Rt11"), row.names = c(NA, -205L), class = c("tbl_df", 
"tbl", "data.frame"))

Please add the names of any packages you are using. `transmute` is not a base R function. — lmo, Jun 21 '17 at 19:31
See `?mean`. You need `na.rm=T` For the colum Rt1 in your example: `mean(pulse$Rt1, na.rm=T)` — S Rivero, Jun 21 '17 at 19:31
@SRivero Thanks! That helps, but I'm trying to average across columns (i.e. I want the mean of Rt1, RT2 and Rt3) for each student. Is there a way to do that? — Bailey, Jun 21 '17 at 19:37
`Bin1 = rowMeans(pulse[,4:7], na.rm = TRUE); Bin2 = rowMeans(pulse[,8:11], na.rm = TRUE); Bin3 = rowMeans(pulse[,12:14], na.rm = TRUE)` — d.b, Jun 21 '17 at 20:00

score 2 · Answer 1 · answered Jun 21 '17 at 19:36

2

To generate mean of rows:

dataframe <- pulse[(number_of_rows_you_are_interested_in),]   
rowMeans(dataframe, na.rm = TRUE)

answered Jun 21 '17 at 19:36

Piotr

153
7

Thanks! But is there a way to apply this so that it averages columns Rt1-Rt4, Rt5-Rt8 and Rt9-Rt11 to create three new mean columns? I'm a complete R beginner, so additional explanation might be needed and would definitely be appreciated! :) – Bailey Jun 21 '17 at 19:48
@Bailey If you want to average rows from different columns and save result in new column use `mutate` from `dplyr` package. `pulse %>% mutate(new_column1 = mean(c(Rt1:Rt4)), new_column2 = mean(c(Rt5:Rt8)), new_column3 = mean(c(Rt9:Rt11)))` use column numbers instead of column names. – Piotr Jun 21 '17 at 19:55
1

you don't even need dplyr. follow this link https://stackoverflow.com/questions/9490485/how-can-i-get-the-average-mean-of-selected-columns – sweetmusicality Jun 21 '17 at 19:56

score 0 · Answer 2 · answered Jun 21 '17 at 19:37

0

Rt1[!is.na(Rt1)]

The above code returns the reduced dataframe by excluding all NA entries in Rt1

You may use this expression across your columns

answered Jun 21 '17 at 19:37

Raj Padmanabhan

540
5
11

score 0 · Answer 3 · answered Jun 21 '17 at 19:52

I've found complete.cases() to be particularly useful to only give you rows that have no NAs

pulse <- pulse[complete.cases(pulse), ]

and then you should be able to calculate over this dataframe

also, instead of having to manually calculate the average, follow this link's example (which is pretty similar to your question to begin with)

R: Averaging columns and conditionally excluding NA data

3 Answers3