library(rlang)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(purrr)
#>
#> Attaching package: 'purrr'
#> The following objects are masked from 'package:dplyr':
#>
#> contains, order_by
#> The following objects are masked from 'package:rlang':
#>
#> %||%, %@%, as_function, flatten, flatten_chr, flatten_dbl,
#> flatten_int, flatten_lgl, invoke, is_atomic, is_bare_atomic,
#> is_bare_character, is_bare_double, is_bare_integer,
#> is_bare_list, is_bare_logical, is_bare_numeric,
#> is_bare_vector, is_character, is_double, is_empty, is_formula,
#> is_function, is_integer, is_list, is_logical, is_null,
#> is_scalar_atomic, is_scalar_character, is_scalar_double,
#> is_scalar_integer, is_scalar_list, is_scalar_logical,
#> is_scalar_vector, is_vector, list_along, prepend, rep_along,
#> set_names, splice
# this works
mtcars %>% mutate(x = reduce(list(am, gear, carb), `+`))
#> mpg cyl disp hp drat wt qsec vs am gear carb x
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 9
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 9
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 6
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 4
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 7
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 6
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 6
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 8
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 8
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 6
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 6
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 6
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 7
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 7
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 7
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 6
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 7
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 6
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 4
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 5
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 5
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 7
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 5
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 6
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 8
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 8
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 10
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 12
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 14
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 7
# so does this
mtcars %>% mutate(x = pmap(list(am, gear, carb), sum))
#> mpg cyl disp hp drat wt qsec vs am gear carb x
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 9
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 9
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 6
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 4
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 7
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 6
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 6
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 8
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 8
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 6
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 6
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 6
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 7
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 7
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 7
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 6
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 7
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 6
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 4
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 5
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 5
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 7
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 5
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 6
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 8
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 8
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 10
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 12
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 14
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 7
# what if we store in a variable?
x <- colnames(mtcars) %>% tail(3)
x
#> [1] "am" "gear" "carb"
# I think this should work, but it doesn't
mtcars %>% mutate(x = reduce(!!syms(x), `+`))
#> Error in mutate_impl(.data, dots): Evaluation error: non-numeric argument to binary operator.
# especially odd since it emits _exactly_ what I did before
expr(mtcars %>% mutate(x = reduce(!!syms(x), `+`)))
#> mtcars %>% mutate(x = reduce(list(am, gear, carb), `+`))
# this _does_ work, but feels like a workaround
mtcars %>% mutate(x = reduce(select(., x), `+`))
#> mpg cyl disp hp drat wt qsec vs am gear carb x
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 9
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 9
#> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 6
#> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 4
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 5
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 4
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 7
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 6
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 6
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 8
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 8
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 6
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 6
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 6
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 7
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 7
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 7
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 6
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 7
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 6
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 4
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 5
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 5
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 7
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 5
#> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 6
#> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 8
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 8
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 10
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 12
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 14
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 7
devtools::session_info(c("rlang", "dplyr", "purrr"))
#> Session info --------------------------------------------------------------
#> setting value
#> version R version 3.4.1 (2017-06-30)
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> tz America/New_York
#> date 2017-08-02
#> Packages ------------------------------------------------------------------
#> package * version date source
#> assertthat 0.2.0 2017-04-11 CRAN (R 3.4.0)
#> BH 1.62.0-1 2016-11-19 CRAN (R 3.4.0)
#> bindr 0.1 2016-11-13 cran (@0.1)
#> bindrcpp * 0.2 2017-06-17 CRAN (R 3.4.0)
#> dplyr * 0.7.2 2017-07-20 CRAN (R 3.4.1)
#> glue 1.1.1 2017-06-21 CRAN (R 3.4.0)
#> lazyeval 0.2.0 2016-06-12 CRAN (R 3.4.0)
#> magrittr 1.5 2014-11-22 CRAN (R 3.4.0)
#> pkgconfig 2.0.1 2017-03-21 cran (@2.0.1)
#> plogr 0.1-1 2016-09-24 cran (@0.1-1)
#> purrr * 0.2.2.2 2017-05-11 CRAN (R 3.4.0)
#> R6 2.2.1 2017-05-10 cran (@2.2.1)
#> Rcpp 0.12.11 2017-05-22 cran (@0.12.11)
#> rlang * 0.1.1 2017-05-18 CRAN (R 3.4.0)
#> tibble 1.3.3 2017-05-30 Github (tidyverse/tibble@b2275d5)
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August 2, 2017 20:03
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tidyeval not behaving when used inside purrr and dplyr
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Added a not-quite-right-feeling workaround based on
selectnow working with character vectors.