spread_each and margins functions which compliment dplyr and tidyr packages.

margins.R

margins <- 
    function( grouped               #< [grouped_df] A data frame with groups defined.
            , ...                   #< passed to `FUN`
            , FUN=dplyr::summarize  #< Summary function
            , all.name = getOption("margins::all.name", "(All)")
            ){
    "Add margins to summarization"
    g <- groups(grouped)
    com <- lapply(seq(0, length(g)), combn, x=g, simplify=FALSE)
    com <- Reduce(c, com)
    com <- com[order(desc(sapply(com, length)))]
    l <- 
    lapply(com, function(g){
        g2 <- if(length(g)) group_by_(ungroup(grouped), .dots=g) else ungroup(grouped)
        FUN(g2, ...)
    }) %>%
    bind_rows
    for(v in as.character(g)){
        if(v %in% names(l))
            l[[v]] <- xifnotNA(as.character(l[[v]]), all.name)
        else
            l[[v]] <- all.name
    }
    l
}

with_margins <- 
function( FUN                   #< Function to compute one groups and margins.
        , all.name = getOption("margins::all.name", "(All)") #< name to fill in margins.
        ){
    #! Compute marginals 
    #! 
    #! `with_margins` creates another function that performs the given 
    #! `FUN` function over the given groups and all possible marginals
    #! of the groups, including an overall computation.
    #! This will most commonly be used with summarise but can also 
    #! be used with other functions.
    function(.data, ...){
        g   <- groups(.data)
        com <- lapply(seq(0, length(g)), combn, x=g, simplify=FALSE) %>%
               Reduce(c, .) 
        com <- com[order(desc(sapply(com, length)))]

        lapply(com, function(x, .data){
            FUN(group_by_(.data, .dots=x, add=FALSE), ...) %>%
            mutate_( .dots = 
                     structure( replicate(length(setdiff(g, x)), lazyeval::lazy(all.name), simplify=FALSE)
                              , names = as.character(setdiff(g,x))
                              )
                   )
        }, .data) %>%
        bind_rows
    }
}
if(FALSE){#! @example
    .data <- expand.grid( x = c( 'a', 'b', 'c')
                        , y = c( 'd', 'e', 'f')
                        , .rep = 1:10
                        , stringsAsFactors=FALSE
                        ) %>%
            mutate( v = rnorm(90)) %>%
            select(-.rep) %>%
            group_by(x, y)
             
    with_margins(summarise)(.data, N=n(), sum=sum(v))
    margins(.data, N=n(), sum=sum(v))
}

spread_each.R

spread_each <- 
function( data          #< A <data.frame> or <tbl>
        , key           #< unquoted variable name to make into columns
        , ...           #< (sub-)columns of values 
        , fill=NA       #< passed to <spread>.
        , convert=FALSE #< passed to <spread>.
        , drop=FALSE    #< passed to <spread>.
        , sep='.'       #< Separator that goes between the key value and "..." column names.
        ){
    #! wrapper for spread_each_
    spread_each_( data
                , key.col = tidyr:::col_name(substitute(key))
                , .dots = lazyeval::lazy_dots(...)
                , fill=fill, convert=convert, drop=drop, sep=sep
                )
}
    
spread_each_ <- 
function( data          #< A <data.frame> or <tbl>
        , key           #< unquoted variable name to make into columns
        , ...           #< (sub-)columns of values 
        , .dots         #< a <lazy_dots> list.
        , key.col        = tidyr:::col_name(substitute(key))
                        #< the character name of the key column.
        , fill=NA       #< passed to <spread>.
        , convert=FALSE #< passed to <spread>.
        , drop=FALSE    #< passed to <spread>.
        , sep='.'       #< Separator that goes between the key value and "..." column names.
        ){
    #! Spread a key column with multiple sub columns
    #! 
    #! Creates a <tbl> with a  the values of key as columns with 
    #! the variables listed in ... as sub columns.
    dots            <- lazyeval::all_dots(.dots, ...)
    value.cols      <- dplyr:::select_vars_(names(data), dots)
    grouping.cols   <- names(data) %>% setdiff(key.col) %>% setdiff(value.cols)

    grouping.dots <- grouping.cols %>% 
        (lazyeval::as.lazy_dots)(.) %>%
        dplyr:::resolve_vars(tbl_vars(data))
    
    data <- group_by_(data, .dots=grouping.dots)
    
    f <- function(col){
        select.vars <- c(key.col, col, grouping.cols) %>%
                        lapply(as.name) %>%
                        (lazyeval::as.lazy_dots)(.) %>%
                        dplyr:::resolve_vars(tbl_vars(data))
        x <- 
        spread_( data       = select_(data, .dots=select.vars), 
               , key_col    = key.col
               , value_col  = col
               , fill       = fill
               , sep        = NULL
               )
        newcols <- setdiff(names(x), grouping.cols)
        new.names <-  
            structure( newcols %>% lapply(as.name)  %>% lapply(lazyeval::as.lazy)
                     , names=paste(newcols, col, sep=sep)
                     )
        rename_(.data=x, .dots=new.names)
    }
    col.order <- 
        unique(getElement(data, key.col)) %>% as.character() %>%
        lapply(., function(x, env){
            lazyeval::as.lazy(call('starts_with', x), env=env)
        }, env=environment()) %>%
        c(lapply(grouping.cols, lazyeval::as.lazy, env=environment()), .)
    lapply(value.cols, f) %>% 
        Reduce(f=full_join, x=.) %>%
        select_(., .dots=col.order)
}
if(FALSE){#! @example
    data <- expand.grid( x = c( 'a', 'b', 'c')
                       , y = c( 'd', 'e', 'f')
                       , .rep = 1:10
                       , stringsAsFactors=FALSE
                       ) %>%
            mutate( v = rnorm(90)) %>%
            group_by(x, y) %>%
            summarise(N=n(), sum=sum(v))
    # Data is a data.frame with columns x, y, N, and sum.
             
    # Spread column y over columns N and sum
    spread_each(data, y, N, sum)
    # creates a tbl_df dataset with columns:
    #   x, d.N, d.sum, e.N, e.sum, f.N, f.sum
}

Awesome! That margin function is really missing in dplyr.
2 comments on your code:

• function "xifnotNA" is not defined. could you provide the code for it as well?
• somehow one group vanishes using margins-function:

mtcars %>% group_by(cyl, gear, carb) %>% summarise(n=n(), mmpg=mean(mpg)) returns a df with 5 columns
mtcars %>% group_by(cyl, gear, carb) %>% summarise(n=n(), mmpg=mean(mpg)) %>% margins(nn=sum(n), mmpg=weighted.mean(mmpg, n)) only returns 4 columns, carb is missing. do you have any idea how to fix this?