Extract Column Name in Mutate_If Call

Extract column name in mutate_if call

You have to use quo instead of enquo

#enquo(.) :
<quosure: empty>
~function (expr) 
{
    enexpr(expr)
}
...

#quo(.) :
<quosure: frame>
~x
<quosure: frame>
~y
<quosure: frame>
~z

With your example :

mutate_if(df, is.numeric, funs({
  lookup_value <- df_lookup %>% pull(quo_name(quo(.)))
  ifelse(is.na(.), lookup_value, .)
}))

# A tibble: 10 x 4
       x     y     z a    
   <int> <int> <int> <chr>
 1     1     1     8 a    
 2     2     2     2 b    
 3     3     3     3 c    
 4     4     5     8 d    
 5     5     1     8 e    
 6     6     2     2 a    
 7     7     3     3 b    
 8     8     5     8 c    
 9     9     1     8 d    
10    10     2     2 e    

Using group_by with mutate_if by column name

You can use mutate_at along with contains from dplyr as follows,

library(dplyr)

 exm_data %>% 
  group_by(group) %>% 
  mutate_at(vars(contains('demo')), funs(mean)) %>% 
  mutate_at(vars(contains('meas')), funs(median))

which gives,

# A tibble: 50 x 5
# Groups:   group [5]
   group    demo_age demo_height meas_score1 meas_score2
   <chr>       <dbl>       <dbl>       <dbl>       <dbl>
 1     d  0.12916082    60.26550   0.1932882  -0.5356818
 2     b -0.31142894    64.50839   0.3219514  -0.4777860
 3     b -0.31142894    64.50839   0.3219514  -0.4777860
 4     a -0.34373403    64.84180   0.1929516  -0.3821047
 5     a -0.34373403    64.84180   0.1929516  -0.3821047
 6     b -0.31142894    64.50839   0.3219514  -0.4777860
 7     d  0.12916082    60.26550   0.1932882  -0.5356818
 8     a -0.34373403    64.84180   0.1929516  -0.3821047
 9     d  0.12916082    60.26550   0.1932882  -0.5356818
10     c -0.05963747    59.07845  -0.2395409  -0.4484245

BONUS You don't need to load stringr

Access the column names in the `mutate_at` to use it for subseting a list

Does this work for you?

library(dplyr)
library(rlang)
df %>% 
  mutate_at(vars(var1,var2),
            .funs = function(x){recode_list %<>% .[[as_label(enquo(x))]]
            recode(x,!!!recode_list)})
## A tibble: 4 x 2
#   var1  var2
#  <dbl> <dbl>
#1     1     0
#2     1    -1
#3     2     1
#4     3     1

I suspect this works while placing the subset recode_list directly into recode does not is because enquo delays evaluation of x until assignment with %<>%. Then !!! can force evaluation after it has been properly evaluated previously.

Edit

Your approach with rlang also works with some modifications:

library(rlang)
df %>%
  mutate_at(vars(var1, var2), function(x) {
    var_name <- rlang::as_label(substitute(x))
    recode(x, !!!recode_list[[var_name]])
  })

Dplyr _if verbs with predicate function referring to the column names & multiple conditions?

You can do:

btest %>%
 select_if(str_detect(names(.), "jcr") & sapply(., is.numeric))

  jcr_fourth
1          6
2          7
3          8
4          9
5         10
6         11

mutate variable if column name contains a string

You should wrap your contains("trait") variable filter into vars() call

my_data %>% 
  mutate_at(vars(contains('trait')), funs(.=='True'))

P.S. I suggest you also drop your if_else() call and just use logical comparison directly

dplyr::mutate_if() with multiple conditions including column class not working

Note that mutate_if is being phased out in favour of across, so the following is perhaps what you want...

df %>% 
    mutate(across(where(is.character) & matches(varnames), ~mean(as.numeric(.))))

  a b c d
1 2 2 4 1
2 2 3 3 5
3 2 4 2 9

Mutate if col name contains 'dat' to date

Instead of mutate_if, we need mutate_at. In the newer version of dplyr, it can be done with mutate and across

library(dplyr) # >= 1.0.0
df1 <- df %>%
    mutate(across(contains('dat'), ~ as.Date(as.character(.), format = '%Y%m%d')))

---

Prior to 1.0.0, mutate_at can be used

df1 <- df %>%
    mutate_at(vars(contains('dat')), ~as.Date(as.character(.), format = '%Y%m%d'))

mutate_if is generally used to check some condition based on the values of the columns, i.e.

df  %>%
      mutate_if(is.numeric, ~ as.Date(as.character(.), format = '%Y%m%d'))

As a reproducible example

head(iris) %>%
         mutate_if(is.numeric, ~ .x * 5)

how to use mutate_if to change values

In this small example, I'm not sure that you actually need mutate_if(). mutate_if is designed to use the _if part to determine which columns to subset and work on, rather than an if condition when modifying a value.

Rather, you can use mutate_at() to select your columns to operate on - either based on their exact name or by using vars(contains('your_string')).

See the help page for more info on the mutate_* functions: https://dplyr.tidyverse.org/reference/mutate_all.html

Here are 3 options, using mutate() and mutate_at():

# using mutate()
tbl %>% 
  mutate(
    b = ifelse(a > 25, NA, b)
  )

# mutate_at - we select only column 'b'
tbl %>% 
  mutate_at(vars(c('b')), ~ifelse(a > 25, NA, .))

# select only columns with 'b' in the col name
tbl %>% 
  mutate_at(vars(contains('b')), ~ifelse(a > 25, NA, .))

Which all produce the same output:

# A tibble: 6 x 2
      a     b
  <dbl> <dbl>
1    10    12
2    20    23
3    30    NA
4    40    NA
5    10    56
6    60    NA

I know it's not mutate_if, but I suspect you don't actually need it.

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