listwise_delete.md
In supernova: Judd, McClelland, & Ryan Formatting for ANOVA Output

it leaves a message when rows with missing cases are excluded

Code
  listwise_delete(df_missing)
Message
  3 cases removed due to missing value(s).
Output
                       mpg cyl  disp  hp drat    wt  qsec vs am gear carb
  Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
  Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
  Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
  Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
  Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
  Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
  Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
  Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
  Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
  Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
  Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
  Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
  Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
  Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
  Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
  Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
  Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
  Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
  Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
  AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
  Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
  Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
  Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
  Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
  Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
  Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
  Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
  Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
  Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

it refits an lm with missing data with a message about the new call

Code
  listwise_delete(model)
Message
  Refitting to remove 3 cases with missing value(s)
  i lm(formula = mpg ~ hp * disp, data = listwise_delete(df_missing, 
      c("mpg", "hp", "disp")))
Output

  Call:
  lm(formula = mpg ~ hp * disp, data = listwise_delete(df_missing, 
      c("mpg", "hp", "disp")))

  Coefficients:
  (Intercept)           hp         disp      hp:disp  
   40.0122448   -0.0968780   -0.0737020    0.0002847

it works in a pipe

Code
  get_data_with_missing() %>% lm(mpg ~ hp * disp, data = .) %>% listwise_delete()
Message
  Refitting to remove 3 cases with missing value(s)
  i lm(formula = mpg ~ hp * disp, data = listwise_delete(., c("mpg", 
  "hp", "disp")))
Output

  Call:
  lm(formula = mpg ~ hp * disp, data = listwise_delete(., c("mpg", 
  "hp", "disp")))

  Coefficients:
  (Intercept)           hp         disp      hp:disp  
   40.0122448   -0.0968780   -0.0737020    0.0002847