Use dynamic variable names in dplyr

Question

I want to use dplyr  mutate   to create multiple new columns in a data frame  The column names and their contents should be dynamically generated  Example data from iris  library dplyr  iris  lt - as tibble iris   I ve created a function to mutate my new columns from the Petal Width variable  multipetal  lt - function df  n        varname  lt - paste  quot petal quot   n   sep  quot   quot       df  lt - mutate df  varname   Petal Width   n      problem arises here     df    Now I create a loop to build my columns  for i in 2 5        iris  lt - multipetal df iris  n i     However  since mutate thinks varname is a literal variable name  the loop only creates one new variable  called varname  instead of four  called petal 2 - petal 5   How can I get mutate   to use my dynamic name as variable name

User · Answer

While I enjoy using dplyr for interactive use, I find it extraordinarily tricky to do this using dplyr because you have to go through hoops to use lazyeval::interp(), setNames, etc. workarounds.

Here is a simpler version using base R, in which it seems more intuitive, to me at least, to put the loop inside the function, and which extends @MrFlicks's solution.

multipetal <- function(df, n) {
   for (i in 1:n){
      varname <- paste("petal", i , sep=".")
      df[[varname]] <- with(df, Petal.Width * i)
   }
   df
}
multipetal(iris, 3)

User · Answer

Another alternative  use    inside quotation marks to easily create dynamic names  This is similar to other solutions but not exactly the same  and I find it easier  library dplyr  library tibble   iris  lt - as tibble iris   multipetal  lt - function df  n      df  lt - mutate df   quot petal  n  quot     Petal Width   n      problem arises here   df    for i in 2 5      iris  lt - multipetal df iris  n i    iris  I think this comes from dplyr 1 0 0 but not sure  I also have rlang 4 7 0 if it matters

User · Answer

In the new release of dplyr  0 6 0 awaiting in April 2017   we can also do an assignment      and pass variables as column names by unquoting      to not evaluate it   library dplyr   multipetalN  lt - function df  n         varname  lt - paste0  petal    n        df   gt            mutate   varname    Petal Width   n       data iris   iris1  lt - tbl df iris   iris2  lt - tbl df iris   for i in 2 5         iris2  lt - multipetalN df iris2  n i          Checking the output based on  MrFlick s multipetal applied on  iris1   identical iris1  iris2    1  TRUE

User · Answer

I am also adding an answer that augments this a little bit because I came to this entry when searching for an answer  and this had almost what I needed  but I needed a bit more  which I got via  MrFlik  s answer and the R lazyeval vignettes   I wanted to make a function that could take a dataframe and a vector of column names  as strings  that I want to be converted from a string to a Date object   I couldn t figure out how to make as Date   take an argument that is a string and convert it to a column  so I did it as shown below   Below is how I did this via SE mutate  mutate     and the  dots argument   Criticisms that make this better are welcome   library dplyr   dat  lt - data frame a  leave alone                     dt  2015-08-03 00 00 00                     dt2  2015-01-20 00 00 00      This function takes a dataframe and list of column names   that have strings that need to be   converted to dates in the data frame convertSelectDates  lt - function df  dtnames character 0         for  col in dtnames            varval  lt - sprintf  as Date  s    col          df  lt - df   gt   mutate   dots  setNames list varval   col             return df     dat  lt - convertSelectDates dat  c  dt    dt2    dat   gt   str

User · Answer

Since you are dynamically building a variable name as a character value  it makes more sense to do assignment using standard data frame indexing which allows for character values for column names  For example  multipetal  lt - function df  n        varname  lt - paste  quot petal quot   n   sep  quot   quot       df  varname    lt - with df  Petal Width   n      df    The mutate function makes it very easy to name new columns via named parameters  But that assumes you know the name when you type the command  If you want to dynamically specify the column name  then you need to also build the named argument   dplyr version  gt   1 0 With the latest dplyr version you can use the syntax from the glue package when naming parameters when using     So here the    in the name grab the value by evaluating the expression inside  multipetal  lt - function df  n      mutate df   quot petal  n  quot     Petal Width   n      dplyr version  gt   0 7 dplyr starting with version 0 7 allows you to use    to dynamically assign parameter names  You can write your function as    --- dplyr version 0 7 --- multipetal  lt - function df  n        varname  lt - paste  quot petal quot   n   sep  quot   quot       mutate df    varname    Petal Width   n     For more information  see the documentation available form vignette  quot programming quot    quot dplyr quot     dplyr   gt  0 3  amp   lt 0 7  Slightly earlier version of dplyr   gt  0 3  lt 0 7   encouraged the use of  quot standard evaluation quot  alternatives to many of the functions  See the Non-standard evaluation vignette for more information  vignette  quot nse quot     So here  the answer is to use mutate    rather than mutate   and do    --- dplyr version 0 3-0 5--- multipetal  lt - function df  n        varname  lt - paste  quot petal quot   n   sep  quot   quot       varval  lt - lazyeval  interp  Petal Width   n  n n      mutate  df   dots  setNames list varval   varname       dplyr  lt  0 3 Note this is also possible in older versions of dplyr that existed when the question was originally posed  It requires careful use of quote and setName    --- dplyr versions  lt  0 3 --- multipetal  lt - function df  n        varname  lt - paste  quot petal quot   n   sep  quot   quot       pp  lt - c quote df   setNames list quote Petal Width   n    varname       do call  quot mutate quot   pp

User · Answer

With rlang 0 4 0 we have curly-curly operators        which makes this very easy    library dplyr  library rlang   iris1  lt - tbl df iris   multipetal  lt - function df  n       varname  lt - paste  petal   n   sep         mutate df    varname      Petal Width   n     multipetal iris1  4     A tibble  150 x 6     Sepal Length Sepal Width Petal Length Petal Width Species petal 4             lt dbl gt         lt dbl gt          lt dbl gt         lt dbl gt   lt fct gt       lt dbl gt    1          5 1         3 5          1 4         0 2 setosa      0 8   2          4 9         3            1 4         0 2 setosa      0 8   3          4 7         3 2          1 3         0 2 setosa      0 8   4          4 6         3 1          1 5         0 2 setosa      0 8   5          5           3 6          1 4         0 2 setosa      0 8   6          5 4         3 9          1 7         0 4 setosa      1 6   7          4 6         3 4          1 4         0 3 setosa      1 2   8          5           3 4          1 5         0 2 setosa      0 8   9          4 4         2 9          1 4         0 2 setosa      0 8  10          4 9         3 1          1 5         0 1 setosa      0 4       with 140 more rows   We can also pass quoted unquoted variable names to be assigned as column names    multipetal  lt - function df  name  n       mutate df    name      Petal Width   n     multipetal iris1  temp  3     A tibble  150 x 6     Sepal Length Sepal Width Petal Length Petal Width Species  temp             lt dbl gt         lt dbl gt          lt dbl gt         lt dbl gt   lt fct gt     lt dbl gt    1          5 1         3 5          1 4         0 2 setosa  0 6     2          4 9         3            1 4         0 2 setosa  0 6     3          4 7         3 2          1 3         0 2 setosa  0 6     4          4 6         3 1          1 5         0 2 setosa  0 6     5          5           3 6          1 4         0 2 setosa  0 6     6          5 4         3 9          1 7         0 4 setosa  1 2     7          4 6         3 4          1 4         0 3 setosa  0 900   8          5           3 4          1 5         0 2 setosa  0 6     9          4 4         2 9          1 4         0 2 setosa  0 6    10          4 9         3 1          1 5         0 1 setosa  0 3         with 140 more rows   It works the same with  multipetal iris1   temp   3

User · Answer

Here s another version  and it s arguably a bit simpler    multipetal  lt - function df  n        varname  lt - paste  petal   n  sep          df lt -mutate  df   dots setNames paste0  Petal Width   n   varname       df    for i in 2 5        iris  lt - multipetal df iris  n i      gt  head iris  Sepal Length Sepal Width Petal Length Petal Width Species petal 2 petal 3 petal 4 petal 5 1          5 1         3 5          1 4         0 2  setosa     0 4     0 6     0 8       1 2          4 9         3 0          1 4         0 2  setosa     0 4     0 6     0 8       1 3          4 7         3 2          1 3         0 2  setosa     0 4     0 6     0 8       1 4          4 6         3 1          1 5         0 2  setosa     0 4     0 6     0 8       1 5          5 0         3 6          1 4         0 2  setosa     0 4     0 6     0 8       1 6          5 4         3 9          1 7         0 4  setosa     0 8     1 2     1 6       2

User · Answer

You may enjoy package friendlyeval which presents a simplified tidy eval API and documentation for newer casual dplyr users   You are creating strings that you wish mutate to treat as column names  So using friendlyeval you could write   multipetal  lt - function df  n      varname  lt - paste  petal   n   sep        df  lt - mutate df    treat string as col varname     Petal Width   n    df    for i in 2 5      iris  lt - multipetal df iris  n i      Which under the hood calls rlang functions that check varname is legal as column name    friendlyeval code can be converted to equivalent plain tidy eval code at any time with an RStudio addin

User · Answer

After a lot of trial and error  I found the pattern UQ rlang  sym  some string here     really useful for working with strings and dplyr verbs  It seems to work in a lot of surprising situations    Here s an example with mutate  We want to create a function that adds together two columns  where you pass the function both column names as strings  We can use this pattern  together with the assignment operator     to do this      Take column  name1   add it to column  name2   and call the result  new name  mutate values  lt - function new name  name1  name2     mtcars   gt        mutate UQ rlang  sym new name       UQ rlang  sym name1      UQ rlang  sym name2      mutate values  test    mpg    cyl     The pattern works with other dplyr functions as well  Here s filter       filter a column by a value  filter values  lt - function name  value     mtcars   gt        filter UQ rlang  sym name      value    filter values  gear   4    Or arrange       transform a variable and then sort by it  arrange values  lt - function name  transform     mtcars   gt        arrange UQ rlang  sym name     gt    UQ rlang  sym transform      arrange values  mpg    sin     For select  you don t need to use the pattern  Instead you can use         select a column  select name  lt - function name     mtcars   gt        select   name    select name  mpg

[r] Use dynamic variable names in `dplyr`

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