dplyr mutate - How do I pass one row as a function argument?

Question

I'm trying to create a new column in my tibble which collects and formats all words found in all other columns. I would like to do this using dplyr, if possible. Original DataFrame:

df <- read.table(text =      "  columnA     columnB      
                 1            A           Z                    
                 2            B           Y                    
                 3            C           X                    
                 4            D           W                    
                 5            E           V                   
                 6            F           U            "  ) 

As a simplified example, I am hoping to do something like:

df %>%
    rowwise() %>%
    mutate(newColumn = myFunc(.))

And have the output look like this:

       columnA     columnB      newColumn
1            A           Z             AZ        
2            B           Y             BY        
3            C           X             CX        
4            D           W             DW        
5            E           V             EV        
6            F           U             FU       

When I try this in my code, the output looks like:

       columnA     columnB      newColumn
1            A           Z             ABCDEF        
2            B           Y             ABCDEF        
3            C           X             ABCDEF    
4            D           W             ABCDEF    
5            E           V             ABCDEF    
6            F           U             ABCDEF

myFunc should take one row as an argument but when I try using rowwise() I seem to be passing the entire tibble into the function (I can see this from adding a print function into myFunc).

How can I pass just one row and do this iteratively so that it applies the function to every row? Can this be done with dplyr?

Edit:

myFunc in the example is simplified for the sake of my question. The actual function looks like this:

get_chr_vector <- function(row) {

    row <- row[,2:ncol(row)] # I need to skip the first row
    words <- str_c(row, collapse = ' ')
    words <- str_to_upper(words)
    words <- unlist(str_split(words, ' '))
    words <- words[words != '']
    words <- words[!nchar(words) <= 2]
    words <- removeWords(words, stopwords_list) # from the tm library
    words <- paste(words, sep = ' ', collapse = ' ')
}

Answer 1

Take a look at ?dplyr::do and ?purrr::map, which allow you to apply arbitrary functions to arbitrary columns and to chain the results through multiple unary operators. For example,

df1 <- df %>% rowwise %>% do( X = as_data_frame(.) ) %>% ungroup
# # A tibble: 6 x 1
#                  X
# *           <list>
# 1 <tibble [1 x 2]>
# 2 <tibble [1 x 2]>
# ...

Notice that column X now contains 1x2 data.frames (or tibbles) comprised of rows from your original data.frame. You can now pass each one to your custom myFunc using map.

myFunc <- function(Y) {paste0( Y$columnA, Y$columnB )}
df1 %>% mutate( Result = map(X, myFunc) )
# # A tibble: 6 x 2
#                  X    Result
#             <list>    <list>
# 1 <tibble [1 x 2]> <chr [1]>
# 2 <tibble [1 x 2]> <chr [1]>
# ...

Result column now contains the output of myFunc applied to each row in your original data.frame, as desired. You can retrieve the values by concatenating a tidyr::unnest operation.

df1 %>% mutate( Result = map(X, myFunc) ) %>% unnest
# # A tibble: 6 x 3
#   Result columnA columnB
#    <chr>  <fctr>  <fctr>
# 1     AZ       A       Z
# 2     BY       B       Y
# 3     CX       C       X
# ...

If desired, unnest can be limited to specific columns, e.g., unnest(Result).

EDIT: Because your original data.frame contains only two columns, you can actually skip the do step and use purrr::map2 instead. The syntax is very similar to map:

myFunc <- function( a, b ) {paste0(a,b)}
df %>% mutate( Result = map2( columnA, columnB, myFunc ) )

Note that myFunc is now defined as a binary function.

Answer 2

This should work

   df <- read.table(text =      "  columnA     columnB      
                 1            A           Z                    
                 2            B           Y                    
                 3            C           X                    
                 4            D           W                    
                 5            E           V                   
                 6            F           U            "  )  

df %>%
  mutate(mutate_Func = paste0(columnA,columnB))

   columnA columnB mutate_Func
1       A       Z          AZ
2       B       Y          BY
3       C       X          CX
4       D       W          DW
5       E       V          EV
6       F       U          FU