dplyr/rlang: parse_expr with multiple expressions
We can use the triple-bang operator with the plural form parse_exprs and a modified e2 expression to parse multiple expressions (see ?parse_quosures):
Explanation:
- Multiple expressions in
e2need to be separated either by;or by new lines. - From
?quasiquotation: The!!!operator unquotes and splices its argument. The argument should represents a list or a vector.
e2 = "vs + am ; am +vs";
mtcars %>% mutate(!!!parse_exprs(e2))
# mpg cyl disp hp drat wt qsec vs am gear carb vs + am am + vs
#1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1 1
#2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1 1
#3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 2 2
#4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1 1
#5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 0 0
#6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1 1
#7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0 0
#8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1 1
#9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1 1
#10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1 1
#11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 1 1
#12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 0 0
#13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 0 0
#14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 0 0
#15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 0 0
#16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 0 0
#17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 0 0
#18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 2 2
#19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 2 2
#20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 2 2
#21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 1 1
#22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 0 0
#23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 0 0
#24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 0 0
#25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 0 0
#26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 2 2
#27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 1 1
#28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 2 2
#29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 1 1
#30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 1 1
#31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 1 1
#32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 2 2