data.table - R: Roll up column values containing NA's by sum while grouping by ID's -

i have data frame got

id <- c("a","a","a","a","b","b","b","b")  type <- c(45,45,46,46,45,45,46,46) point_a <- c(10,na,30,40,na,80,na,100)  point_b <- c(na,32,43,na,65,11,na,53) df <- data.frame(id,type,point_a,point_b)      id  type    point_a point_b 1     45        10    na 2     45        na    32 3     46        30    43 4     46        40    na 5   b   45        na    65 6   b   45        80    11 7   b   46        na    na 8   b   46       100    53 

while learnt post, roll data id , 1 column.

i using sqldf sum rows , group id , type. while job me, slow on bigger dataset.

    df1 <- sqldf("select id, type, sum(point_a) point_a, sum(point_a) point_a                    df                    group id, type") 

please suggest usage of other techniques solve problem. have started learning dplyr & plyr packages , find interesting not knowing how apply here.

desired output

    id  type    point_a point_b 1     45        10    32 2     46        70    43 3   b   45        80    76 4   b   46       100    53 

using dplyr:

df %>% group_by(id, type) %>% summarise_each(funs(sum(., na.rm = t))) 

or

df %>%    group_by(id, type) %>%    summarise(point_a = sum(point_a, na.rm = t),              point_b = sum(point_b, na.rm = t)) 

or

f <- function(x) sum(x, na.rm = t)   df %>%    group_by(id, type) %>%    summarise(point_a = f(point_a),              point_b = f(point_b)) 

which gives:

#source: local data frame [4 x 4] #groups: id # #  id type point_a point_b #1    45      10      32 #2    46      70      43 #3  b   45      80      76 #4  b   46     100      53