Use orbital object splicing function to apply orbital prediction in a quosure
aware function such as dplyr::mutate().
Arguments
- x
An orbital object.
Value
a list of quosures.
Details
This function is mostly going to be used for
Dots Injection.
This function is used internally in predict(), but
is also exported for user flexibility. Should be used with !!! as seen in
the examples.
Note should be taken that using this function modifies existing variables and creates new variables, unlike predict() which only returns predictions.
Examples
library(workflows)
library(recipes)
library(parsnip)
rec_spec <- recipe(mpg ~ ., data = mtcars) %>%
step_normalize(all_numeric_predictors())
lm_spec <- linear_reg()
wf_spec <- workflow(rec_spec, lm_spec)
wf_fit <- fit(wf_spec, mtcars)
orbital_obj <- orbital(wf_fit)
orbital_inline(orbital_obj)
#> <list_of<quosure>>
#>
#> $cyl
#> <quosure>
#> expr: ^(cyl - 6.1875) / 1.78592164694654
#> env: global
#>
#> $disp
#> <quosure>
#> expr: ^(disp - 230.721875) / 123.938693831382
#> env: global
#>
#> $hp
#> <quosure>
#> expr: ^(hp - 146.6875) / 68.5628684893206
#> env: global
#>
#> $drat
#> <quosure>
#> expr: ^(drat - 3.5965625) / 0.534678736070971
#> env: global
#>
#> $wt
#> <quosure>
#> expr: ^(wt - 3.21725) / 0.978457442989697
#> env: global
#>
#> $qsec
#> <quosure>
#> expr: ^(qsec - 17.84875) / 1.78694323609684
#> env: global
#>
#> $vs
#> <quosure>
#> expr: ^(vs - 0.4375) / 0.504016128774185
#> env: global
#>
#> $am
#> <quosure>
#> expr: ^(am - 0.40625) / 0.498990917235846
#> env: global
#>
#> $gear
#> <quosure>
#> expr: ^(gear - 3.6875) / 0.737804065256947
#> env: global
#>
#> $carb
#> <quosure>
#> expr: ^(carb - 2.8125) / 1.61519997763185
#> env: global
#>
#> $.pred
#> <quosure>
#> expr: ^20.090625 + (cyl * -0.199023961804221) + (disp *
#> 1.65275221678761) + (hp * -1.47287569912409) + (drat *
#> 0.420851499782799) + (wt * -3.63526678164088) + (qsec *
#> 1.46715321419096) + (vs * 0.160157583474124) + (am *
#> 1.25757032609057) + (gear * 0.483566388425266) + (carb *
#> -0.322101975983201)
#> env: global
#>
library(dplyr)
mtcars %>%
mutate(!!!orbital_inline(orbital_obj))
#> mpg cyl disp hp drat
#> Mazda RX4 21.0 -0.1049878 -0.57061982 -0.53509284 0.56751369
#> Mazda RX4 Wag 21.0 -0.1049878 -0.57061982 -0.53509284 0.56751369
#> Datsun 710 22.8 -1.2248578 -0.99018209 -0.78304046 0.47399959
#> Hornet 4 Drive 21.4 -0.1049878 0.22009369 -0.53509284 -0.96611753
#> Hornet Sportabout 18.7 1.0148821 1.04308123 0.41294217 -0.83519779
#> Valiant 18.1 -0.1049878 -0.04616698 -0.60801861 -1.56460776
#> Duster 360 14.3 1.0148821 1.04308123 1.43390296 -0.72298087
#> Merc 240D 24.4 -1.2248578 -0.67793094 -1.23518023 0.17475447
#> Merc 230 22.8 -1.2248578 -0.72553512 -0.75387015 0.60491932
#> Merc 280 19.2 -0.1049878 -0.50929918 -0.34548584 0.60491932
#> Merc 280C 17.8 -0.1049878 -0.50929918 -0.34548584 0.60491932
#> Merc 450SE 16.4 1.0148821 0.36371309 0.48586794 -0.98482035
#> Merc 450SL 17.3 1.0148821 0.36371309 0.48586794 -0.98482035
#> Merc 450SLC 15.2 1.0148821 0.36371309 0.48586794 -0.98482035
#> Cadillac Fleetwood 10.4 1.0148821 1.94675381 0.85049680 -1.24665983
#> Lincoln Continental 10.4 1.0148821 1.84993175 0.99634834 -1.11574009
#> Chrysler Imperial 14.7 1.0148821 1.68856165 1.21512565 -0.68557523
#> Fiat 128 32.4 -1.2248578 -1.22658929 -1.17683962 0.90416444
#> Honda Civic 30.4 -1.2248578 -1.25079481 -1.38103178 2.49390411
#> Toyota Corolla 33.9 -1.2248578 -1.28790993 -1.19142477 1.16600392
#> Toyota Corona 21.5 -1.2248578 -0.89255318 -0.72469984 0.19345729
#> Dodge Challenger 15.5 1.0148821 0.70420401 0.04831332 -1.56460776
#> AMC Javelin 15.2 1.0148821 0.59124494 0.04831332 -0.83519779
#> Camaro Z28 13.3 1.0148821 0.96239618 1.43390296 0.24956575
#> Pontiac Firebird 19.2 1.0148821 1.36582144 0.41294217 -0.96611753
#> Fiat X1-9 27.3 -1.2248578 -1.22416874 -1.17683962 0.90416444
#> Porsche 914-2 26.0 -1.2248578 -0.89093948 -0.81221077 1.55876313
#> Lotus Europa 30.4 -1.2248578 -1.09426581 -0.49133738 0.32437703
#> Ford Pantera L 15.8 1.0148821 0.97046468 1.71102089 1.16600392
#> Ferrari Dino 19.7 -0.1049878 -0.69164740 0.41294217 0.04383473
#> Maserati Bora 15.0 1.0148821 0.56703942 2.74656682 -0.10578782
#> Volvo 142E 21.4 -1.2248578 -0.88529152 -0.54967799 0.96027290
#> wt qsec vs am
#> Mazda RX4 -0.610399567 -0.77716515 -0.8680278 1.1899014
#> Mazda RX4 Wag -0.349785269 -0.46378082 -0.8680278 1.1899014
#> Datsun 710 -0.917004624 0.42600682 1.1160357 1.1899014
#> Hornet 4 Drive -0.002299538 0.89048716 1.1160357 -0.8141431
#> Hornet Sportabout 0.227654255 -0.46378082 -0.8680278 -0.8141431
#> Valiant 0.248094592 1.32698675 1.1160357 -0.8141431
#> Duster 360 0.360516446 -1.12412636 -0.8680278 -0.8141431
#> Merc 240D -0.027849959 1.20387148 1.1160357 -0.8141431
#> Merc 230 -0.068730634 2.82675459 1.1160357 -0.8141431
#> Merc 280 0.227654255 0.25252621 1.1160357 -0.8141431
#> Merc 280C 0.227654255 0.58829513 1.1160357 -0.8141431
#> Merc 450SE 0.871524874 -0.25112717 -0.8680278 -0.8141431
#> Merc 450SL 0.524039143 -0.13920420 -0.8680278 -0.8141431
#> Merc 450SLC 0.575139986 0.08464175 -0.8680278 -0.8141431
#> Cadillac Fleetwood 2.077504765 0.07344945 -0.8680278 -0.8141431
#> Lincoln Continental 2.255335698 -0.01608893 -0.8680278 -0.8141431
#> Chrysler Imperial 2.174596366 -0.23993487 -0.8680278 -0.8141431
#> Fiat 128 -1.039646647 0.90727560 1.1160357 1.1899014
#> Honda Civic -1.637526508 0.37564148 1.1160357 1.1899014
#> Toyota Corolla -1.412682800 1.14790999 1.1160357 1.1899014
#> Toyota Corona -0.768812180 1.20946763 1.1160357 -0.8141431
#> Dodge Challenger 0.309415603 -0.54772305 -0.8680278 -0.8141431
#> AMC Javelin 0.222544170 -0.30708866 -0.8680278 -0.8141431
#> Camaro Z28 0.636460997 -1.36476075 -0.8680278 -0.8141431
#> Pontiac Firebird 0.641571082 -0.44699237 -0.8680278 -0.8141431
#> Fiat X1-9 -1.310481114 0.58829513 1.1160357 1.1899014
#> Porsche 914-2 -1.100967659 -0.64285758 -0.8680278 1.1899014
#> Lotus Europa -1.741772228 -0.53093460 1.1160357 1.1899014
#> Ford Pantera L -0.048290296 -1.87401028 -0.8680278 1.1899014
#> Ferrari Dino -0.457097039 -1.31439542 -0.8680278 1.1899014
#> Maserati Bora 0.360516446 -1.81804880 -0.8680278 1.1899014
#> Volvo 142E -0.446876870 0.42041067 1.1160357 1.1899014
#> gear carb .pred
#> Mazda RX4 0.4235542 0.7352031 22.59951
#> Mazda RX4 Wag 0.4235542 0.7352031 22.11189
#> Datsun 710 0.4235542 -1.1221521 26.25064
#> Hornet 4 Drive -0.9318192 -1.1221521 21.23740
#> Hornet Sportabout -0.9318192 -0.5030337 17.69343
#> Valiant -0.9318192 -1.1221521 20.38304
#> Duster 360 -0.9318192 0.7352031 14.38626
#> Merc 240D 0.4235542 -0.5030337 22.49601
#> Merc 230 0.4235542 -0.5030337 24.41909
#> Merc 280 0.4235542 0.7352031 18.69903
#> Merc 280C 0.4235542 0.7352031 19.19165
#> Merc 450SE -0.9318192 0.1160847 14.17216
#> Merc 450SL -0.9318192 0.1160847 15.59957
#> Merc 450SLC -0.9318192 0.1160847 15.74222
#> Cadillac Fleetwood -0.9318192 0.7352031 12.03401
#> Lincoln Continental -0.9318192 0.7352031 10.93644
#> Chrysler Imperial -0.9318192 0.7352031 10.49363
#> Fiat 128 0.4235542 -1.1221521 27.77291
#> Honda Civic 0.4235542 -0.5030337 29.89674
#> Toyota Corolla 0.4235542 -1.1221521 29.51237
#> Toyota Corona -0.9318192 -1.1221521 23.64310
#> Dodge Challenger -0.9318192 -0.5030337 16.94305
#> AMC Javelin -0.9318192 -0.5030337 17.73218
#> Camaro Z28 -0.9318192 0.7352031 13.30602
#> Pontiac Firebird -0.9318192 -0.5030337 16.69168
#> Fiat X1-9 0.4235542 -1.1221521 28.29347
#> Porsche 914-2 1.7789276 -0.5030337 26.15295
#> Lotus Europa 1.7789276 -0.5030337 27.63627
#> Ford Pantera L 1.7789276 0.7352031 18.87004
#> Ferrari Dino 1.7789276 1.9734398 19.69383
#> Maserati Bora 1.7789276 3.2116766 13.94112
#> Volvo 142E 0.4235542 -0.5030337 24.36827