r - error in calculation of relative frequency of groups based on different combinations -

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i calculate frequency , relative frequency of categorical variables based on different combinations. have calculated frequency , not successful in piping output relative frequency calculation. me in identifying error ?

# random generation of values categorical data set.seed(33) df <- data.frame(cat1 = sample( letters[1:2], 100, replace=true ),                  cat2 = sample( letters[3:5], 100, replace=true ),                 cat3 = sample( letters[2:4], 100, replace=true ),                 var1 = sample( letters[1:3], 100, replace=true ),                  var2 = sample( letters[3:8], 100, replace=true ),                 var3 = sample( letters[2:3], 100, replace=true ),                 vre1 = sample( letters[2:7], 100, replace=true ),                  vre2 = sample( letters[1:5], 100, replace=true ),                 ref3 = sample( letters[2:9], 100, replace=true ),                 con1 = runif(100,0,100),                 con2 = runif(100,23,45))  # calculating frequency library(dplyr) cat.names <- c('var1','var3','vre2','ref3') df %>% group_by(cat1, cat3) %>% summarise_each(funs(n = n()), one_of(cat.names))  # piping calculate relative frequency/percentage df %>% group_by(cat1, cat3) %>% summarise_each(funs(n = n()), one_of(cat.names)) %>% mutate(freq = n / sum(n))  # error error: invalid 'type' (closure) of argument  #expected output     cat1    cat3    var1.freq   var3.freq   vre2.freq   ref3.freq   var1.rfreq  var3.rfreq  vre2.rfreq  ref3.rfreq 1     b   8   8   8   8   0,153846154 0,153846154 0,153846154 0,153846154 2     c   27  27  27  27  0,519230769 0,519230769 0,519230769 0,519230769 3     d   17  17  17  17  0,326923077 0,326923077 0,326923077 0,326923077 4   b   b   16  16  16  16  0,333333333 0,333333333 0,333333333 0,333333333 5   b   c   12  12  12  12  0,25    0,25    0,25    0,25 6   b   d   20  20  20  20  0,416666667 0,416666667 0,416666667 0,416666667

try

 df1 <- df %>%           group_by(cat1, cat3) %>%           summarise_each(funs(n()), one_of(cat.names))  df2 <- df1 %>%             group_by(cat1) %>%              mutate_each(funs(./sum(.)), var1:ref3)  bind_cols(df1, df2[-(1:2)])

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