In R how to use mutate with glue to create columns

Question

This is my code

col_vec <- vector(length = 11)
my_df <- mtcars
for (i in 1:11) {
  col_vec[i]<- glue::glue("col{i}")
  colnames(my_df) <- col_vec

}

I want to do this bellow for all columns but I would like to use glue package:

my_df %>% mutate(up_down = as.numeric(col1) - lag(as.numeric(col1),
                                             default = as.numeric(col1)[1]))  

I tried this, but it didnt work:

for (i in 1:11) {

  my_df %>% mutate(glue::glue("up_down_{i} = as.numeric(col{i}) - lag(as.numeric(col{i}),
                                                    default = as.numeric(col{i})[1]))"))


}

The output should give me for each col_* a column that calculate the difference between rows.

So, I would have 22 columns as a result

Answer 1

We can use across more easily without having to assign with glue on the lhs . The new columns are automatically added once we modify the .names

library(dplyr)
my_df2 <- my_df %>% 
   mutate(across(starts_with('col'), ~ .x -
     lag(.x, default = first(.x)), .names = "up_down_{.col}"))

-output

> head(my_df2)
                  col1 col2 col3 col4 col5  col6  col7 col8 col9 col10 col11 up_down_col1 up_down_col2 up_down_col3 up_down_col4 up_down_col5
Mazda RX4         21.0    6  160  110 3.90 2.620 16.46    0    1     4     4          0.0            0            0            0         0.00
Mazda RX4 Wag     21.0    6  160  110 3.90 2.875 17.02    0    1     4     4          0.0            0            0            0         0.00
Datsun 710        22.8    4  108   93 3.85 2.320 18.61    1    1     4     1          1.8           -2          -52          -17        -0.05
Hornet 4 Drive    21.4    6  258  110 3.08 3.215 19.44    1    0     3     1         -1.4            2          150           17        -0.77
Hornet Sportabout 18.7    8  360  175 3.15 3.440 17.02    0    0     3     2         -2.7            2          102           65         0.07
Valiant           18.1    6  225  105 2.76 3.460 20.22    1    0     3     1         -0.6           -2         -135          -70        -0.39
                  up_down_col6 up_down_col7 up_down_col8 up_down_col9 up_down_col10 up_down_col11
Mazda RX4                0.000         0.00            0            0             0             0
Mazda RX4 Wag            0.255         0.56            0            0             0             0
Datsun 710              -0.555         1.59            1            0             0            -3
Hornet 4 Drive           0.895         0.83            0           -1            -1             0
Hornet Sportabout        0.225        -2.42           -1            0             0             1
Valiant                  0.020         3.20            1            0             0            -1

---

If we want to use a for loop, it would need assignment ( <- ) to original dataset for creating new columns. In addition, we need to evaluate ( !! ) while assigning with := ) (instead of eval/parse )

my_df3 <- my_df
for(i in 1:11) {
    my_df3 <- my_df3 %>%
            mutate(!! glue::glue("up_down_{i}") := 
                 !! rlang::sym(glue::glue("col{i}")) -
                 lag(!!rlang::sym(glue::glue("col{i}")), default = 
                      first(!!rlang::sym(glue::glue("col{i}")))))
}

-output

> head(my_df3)
                  col1 col2 col3 col4 col5  col6  col7 col8 col9 col10 col11 up_down_1 up_down_2 up_down_3 up_down_4 up_down_5 up_down_6 up_down_7
Mazda RX4         21.0    6  160  110 3.90 2.620 16.46    0    1     4     4       0.0         0         0         0      0.00     0.000      0.00
Mazda RX4 Wag     21.0    6  160  110 3.90 2.875 17.02    0    1     4     4       0.0         0         0         0      0.00     0.255      0.56
Datsun 710        22.8    4  108   93 3.85 2.320 18.61    1    1     4     1       1.8        -2       -52       -17     -0.05    -0.555      1.59
Hornet 4 Drive    21.4    6  258  110 3.08 3.215 19.44    1    0     3     1      -1.4         2       150        17     -0.77     0.895      0.83
Hornet Sportabout 18.7    8  360  175 3.15 3.440 17.02    0    0     3     2      -2.7         2       102        65      0.07     0.225     -2.42
Valiant           18.1    6  225  105 2.76 3.460 20.22    1    0     3     1      -0.6        -2      -135       -70     -0.39     0.020      3.20
                  up_down_8 up_down_9 up_down_10 up_down_11
Mazda RX4                 0         0          0          0
Mazda RX4 Wag             0         0          0          0
Datsun 710                1         0          0         -3
Hornet 4 Drive            0        -1         -1          0
Hornet Sportabout        -1         0          0          1
Valiant                   1         0          0         -1
> dim(my_df3)
[1] 32 22

Answer 2

The below answer should work,

Basically, we use mutate() and across() to create new columns, that take a lag difference of a column and apply that across to each numeric column. We then use the .names argument in the across to give each new lag column a new name.

 my_df %>% 
   mutate(across(where(is.numeric),list(lag=~.x-lag(.x)),.names= "{.col}_{.fn}"))