dplyr change many data types

Question

I have a data frame  dat  lt - data frame fac1   c 1  2                     fac2   c 4  5                     fac3   c 7  8                     dbl1   c  1    2                      dbl2   c  4    5                      dbl3   c  6    7                        To change data types I can use something like l1  lt - c  quot fac1 quot    quot fac2 quot    quot fac3 quot   l2  lt - c  quot dbl1 quot    quot dbl2 quot    quot dbl3 quot   dat   l1   lt - lapply dat   l1   factor  dat   l2   lt - lapply dat   l2   as numeric   with dplyr dat  lt - dat   gt   mutate      fac1   factor fac1   fac2   factor fac2   fac3   factor fac3       dbl1   as numeric dbl1   dbl2   as numeric dbl2   dbl3   as numeric dbl3     is there a more elegant  shorter  way in dplyr  thx Christof

User · Accepted Answer

You can use the standard evaluation version of mutate each  which is mutate each   to change the column classes   dat   gt   mutate each  funs factor   l1    gt   mutate each  funs as numeric   l2

User · Answer

A more general way of achieving column type transformation is as follows   If you want to transform all your factor columns to character columns  e g   this can be done using one pipe   df   gt    mutate each   funs as character      names     sapply    is factor

User · Answer

It s a one-liner with mutate at   dat   gt   mutate at  l1   factor    gt   mutate at  l2   as numeric

User · Answer

Or mayby even more simple with convert from hablar   library hablar   dat   gt      convert fct fac1  fac2  fac3             num dbl1  dbl2  dbl3     or combines with tidyselect   dat   gt      convert fct contains  fac               num contains  dbl

User · Answer

From the bottom of the  mutate each  at least in dplyr 0 5  it looks like  that function  as in  docendo discimus s answer  will be deprecated and replaced with more flexible alternatives mutate if  mutate all  and mutate at   The one most similar to what  hadley mentions in his comment is probably using mutate at   Note the order of the arguments is reversed  compared to mutate each  and vars   uses select   like semantics  which I interpret to mean the  select helpers functions   dat   gt   mutate at vars starts with  fac    funs factor     gt        mutate at vars starts with  dbl    funs as numeric     But mutate at can take column numbers instead of a vars   argument  and after reading through this page  and looking at the alternatives  I ended up using mutate at but with grep to capture many different kinds of column names at once  unless you always have such obvious column names    dat   gt   mutate at grep    fac fctr fckr   colnames     funs factor     gt     mutate at grep    dbl num qty   colnames     funs as numeric     I was pretty excited about figuring out mutate at   grep  because now one line can work on lots of columns   EDIT - now I see matches   in among the select helpers  which handles regex  so now I like this   dat   gt   mutate at vars matches  fac fctr fckr    funs factor     gt     mutate at vars matches  dbl num qty    funs as numeric     Another generally-related comment - if you have all your date columns with matchable names  and consistent formats  this is powerful   In my case  this turns all my YYYYMMDD columns  which were read as numbers  into dates     mutate at vars matches   DT     funs as Date as character    format   Y m d

User · Answer

Since Nick s answer is deprecated by now and Rafael s comment is really useful  I want to add this as an Answer  If you want to change all factor columns to character use mutate if   dat   gt   mutate if is factor  as character    Also other functions are allowed  I for instance used iconv to change the encoding of all character columns    dat   gt   mutate if is character  function x  iconv x  to    ASCII  TRANSLIT       or to substitute all NA by 0 in numeric columns    dat   gt   mutate if is numeric  function x  ifelse is na x   0  x

User · Answer

Dplyr across function has superseded  if   at  and  all  See vignette  quot colwise quot    dat   gt    mutate across all of l1   as factor          across all of l2   as numeric

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