Mutate to transform

The mutate() function creates a new column of data, or overwrite an existing one.

We’ll use a subset of the mtcars dataset for examples.

from siuba import _, group_by, mutate, select
from siuba.data import mtcars

small_cars = mtcars[["mpg", "cyl", "hp"]]

Basics

small_cars >> mutate(mpg_per_cyl = _.mpg / _.cyl)

	mpg	cyl	hp	mpg_per_cyl
0	21.0	6	110	3.500
1	21.0	6	110	3.500
...	...	...	...	...
30	15.0	8	335	1.875
31	21.4	4	109	5.350

32 rows × 4 columns

Replacing columns

When a created column is given the same name as an existing column, it replaces that column in the data.

small_cars >> mutate(mpg = _.mpg - _.mpg.mean(), new_column = 1)

	mpg	cyl	hp	new_column
0	0.909375	6	110	1
1	0.909375	6	110	1
...	...	...	...	...
30	-5.090625	8	335	1
31	1.309375	4	109	1

32 rows × 4 columns

Note that replacement columns are put in the same position as the original columns. For example, in the result above, the mpg column is still in the first position on the left.

Using previous arguments

Arguments can refer to columns that were created in earlier arguments.

small_cars >> mutate(cyl2 = _.cyl * 2, cyl4 = _.cyl2 * 2)

	mpg	cyl	hp	cyl2	cyl4
0	21.0	6	110	12	24
1	21.0	6	110	12	24
...	...	...	...	...	...
30	15.0	8	335	16	32
31	21.4	4	109	8	16

32 rows × 5 columns

In the code above, cyl4 uses the earlier argument cyl2.

Grouped mutates

(small_cars
  >> group_by(_.cyl)
  >> mutate(
       hp_mean = _.hp.mean(),
       demeaned_hp = _.hp - _.hp_mean
     )
  )

(grouped data frame)

	mpg	cyl	hp	hp_mean	demeaned_hp
0	21.0	6	110	122.285714	-12.285714
1	21.0	6	110	122.285714	-12.285714
...	...	...	...	...	...
30	15.0	8	335	209.214286	125.785714
31	21.4	4	109	82.636364	26.363636

32 rows × 5 columns

(small_cars
  >> group_by(_.cyl)
  >> mutate(
       hp_per_cyl = _.hp / _.cyl,
       diff = _.hp_per_cyl - _.hp_per_cyl.shift(1)
     )
  )

(grouped data frame)

	mpg	cyl	hp	hp_per_cyl	diff
0	21.0	6	110	18.333333	NaN
1	21.0	6	110	18.333333	0.000
...	...	...	...	...	...
30	15.0	8	335	41.875000	8.875
31	21.4	4	109	27.250000	-1.000

32 rows × 5 columns