dplyr - using mutate() like rowmeans()

Question

I can\'t find the answer anywhere.

I would like to calculate new variable of data frame which is based on mean of rows.

For example:

data &l         


        

Answer 1

I think this is the dplyr-ish way. First, I'd create a function:

my_rowmeans = function(...) Reduce(`+`, list(...))/length(list(...))

Then, it can be used inside mutate:

data %>% mutate(rms = my_rowmeans(a, b))

#    id a b c rms
# 1 101 1 2 3 1.5
# 2 102 2 2 3 2.0
# 3 103 3 2 3 2.5

# or

data %>% mutate(rms = my_rowmeans(a, b, c))

#    id a b c      rms
# 1 101 1 2 3 2.000000
# 2 102 2 2 3 2.333333
# 3 103 3 2 3 2.666667

---

To deal with the possibility of NAs, the function must be uglified:

my_rowmeans = function(..., na.rm=TRUE){
  x = 
    if (na.rm) lapply(list(...), function(x) replace(x, is.na(x), as(0, class(x)))) 
    else       list(...)

  d = Reduce(function(x,y) x+!is.na(y), list(...), init=0)

  Reduce(`+`, x)/d
} 

# alternately...

my_rowmeans2 = function(..., na.rm=TRUE) rowMeans(cbind(...), na.rm=na.rm)

# new example

data$b[2] <- NA  
data %>% mutate(rms = my_rowmeans(a,b,na.rm=FALSE))

   id a  b c rms
1 101 1  2 3 1.5
2 102 2 NA 3  NA
3 103 3  2 3 2.5

data %>% mutate(rms = my_rowmeans(a,b))

   id a  b c rms
1 101 1  2 3 1.5
2 102 2 NA 3 2.0
3 103 3  2 3 2.5

The downside to the my_rowmeans2 is that it coerces to a matrix. I'm not certain that this will always be slower than the Reduce approach, though.