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r - Using dplyr::quos() with a list argument rather than the ellipsis argument

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r - Using dplyr::quos() with a list argument rather than the ellipsis argument

I am using dplyr and trying to create a function to calculate p.values based on grouping arguments. I would like to be able to have an argument that would be list of any length of variables to group by. Here is the example dataset:

dataset <- structure(list(Experiment = c(170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170222, 170222, 170222, 170222, 
170222, 170222, 170222, 170222, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824, 170824, 170824, 170824, 170824, 
170824, 170824, 170824, 170824), Sample = c("1: FL_496", "1: FL_496", 
"1: FL_496", "1: FL_496", "1: FL_496", "1: FL_496", "1: FL_496", 
"1: FL_496", "2: FL_505", "2: FL_505", "2: FL_505", "2: FL_505", 
"2: FL_505", "2: FL_505", "2: FL_505", "2: FL_505", "3: FL_509", 
"3: FL_509", "3: FL_509", "3: FL_509", "3: FL_509", "3: FL_509", 
"3: FL_509", "3: FL_509", "4: FL_514", "4: FL_514", "4: FL_514", 
"4: FL_514", "4: FL_514", "4: FL_514", "4: FL_514", "4: FL_514", 
"5: cKO_497", "5: cKO_497", "5: cKO_497", "5: cKO_497", "5: cKO_497", 
"5: cKO_497", "5: cKO_497", "5: cKO_497", "6: cKO_504", "6: cKO_504", 
"6: cKO_504", "6: cKO_504", "6: cKO_504", "6: cKO_504", "6: cKO_504", 
"6: cKO_504", "7: cKO_510", "7: cKO_510", "7: cKO_510", "7: cKO_510", 
"7: cKO_510", "7: cKO_510", "7: cKO_510", "7: cKO_510", "8: cKO_515", 
"8: cKO_515", "8: cKO_515", "8: cKO_515", "8: cKO_515", "8: cKO_515", 
"8: cKO_515", "8: cKO_515", "9: cKO_517", "9: cKO_517", "9: cKO_517", 
"9: cKO_517", "9: cKO_517", "9: cKO_517", "9: cKO_517", "9: cKO_517", 
NA, NA, NA, NA, NA, NA, NA, NA, "1: FL_627", "1: FL_627", "1: FL_627", 
"1: FL_627", "1: FL_627", "1: FL_627", "2: FL_628", "2: FL_628", 
"2: FL_628", "2: FL_628", "2: FL_628", "2: FL_628", "3: FL_633", 
"3: FL_633", "3: FL_633", "3: FL_633", "3: FL_633", "3: FL_633", 
"4: FL_636", "4: FL_636", "4: FL_636", "4: FL_636", "4: FL_636", 
"4: FL_636", "5: cKO_620", "5: cKO_620", "5: cKO_620", "5: cKO_620", 
"5: cKO_620", "5: cKO_620", "6: cKO_625", "6: cKO_625", "6: cKO_625", 
"6: cKO_625", "6: cKO_625", "6: cKO_625", "7: cKO_626", "7: cKO_626", 
"7: cKO_626", "7: cKO_626", "7: cKO_626", "7: cKO_626", "8: cKO_634", 
"8: cKO_634", "8: cKO_634", "8: cKO_634", "8: cKO_634", "8: cKO_634", 
"cKO_620", "cKO_620", "cKO_625", "cKO_625", "cKO_626", "cKO_626", 
"cKO_634", "cKO_634", "FL_627", "FL_627", "FL_628", "FL_628", 
"FL_633", "FL_633", "FL_636", "FL_636"), Genotype = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("miR-15/16 FL", 
"miR-15/16 cKO"), class = "factor"), variable = c("% CD127+", 
"% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", 
"% KLRG1+", "% KLRG1+", "% KLRG1+", "% CD127+", "% CD127+", "% CD127+", 
"% CD127+", "% KLRG1+", "% KLRG1+", "% KLRG1+", "% KLRG1+", "% CD127+", 
"% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", 
"% KLRG1+", "% KLRG1+", "% KLRG1+", "% CD127+", "% CD127+", "% CD127+", 
"% CD127+", "% KLRG1+", "% KLRG1+", "% KLRG1+", "% KLRG1+", "% CD127+", 
"% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", 
"% KLRG1+", "% KLRG1+", "% KLRG1+", "% CD127+", "% CD127+", "% CD127+", 
"% CD127+", "% KLRG1+", "% KLRG1+", "% KLRG1+", "% KLRG1+", "% CD127+", 
"% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% CD127+", "% CD127+", "% KLRG1+", "% KLRG1+", 
"% KLRG1+", "% CD127+", "% KLRG1+", "% CD127+", "% KLRG1+", "% CD127+", 
"% KLRG1+", "% CD127+", "% KLRG1+", "% CD127+", "% KLRG1+", "% CD127+", 
"% KLRG1+", "% CD127+", "% KLRG1+", "% CD127+", "% KLRG1+"), 
    value = c(1, 28.7, 40.1, 47.4, 64.1, 69.9, 73.1, 79.42, 0.99, 
    21.72, 33, 56.6, 55.5, 82.9, 84.96, 86.7, 3.94, 43.4, 49.5, 
    60.8, 57.1, 69.8, 71.4, 77.72, 1, 20.56, 28.77, 35.1, 71.07, 
    71.2, 78.16, 84.04, 3.77, 56.9, 60.5, 66.5, 43.7, 50.36, 
    50.8, 51.8, 3.24, 58.2, 59.8, 70.8, 47.9, 58.5, 59.5, 61.3, 
    4.21, 62, 65.7, 73.8, 40, 51.5, 53.1, 55.69, 9.48, 41.7, 
    44, 63, 53.7, 57.31, 60.4, 60.8, 3.84, 34.1, 41.1, 53.2, 
    55.07, 55.3, 62.2, 76.6, NA, NA, NA, NA, NA, NA, NA, NA, 
    12.01, 18.5, 20.99, 66.39, 77.2, 85.6, 12.8, 31.3, 35.11, 
    59.8, 85.5, 89.7, 32.1, 33.3, 34.7, 63.2, 71.6, 80.5, 15.3, 
    17.02, 33.5, 65.54, 82.7, 85.8, 41.61, 51.3, 69.3, 39.81, 
    59, 62, 46.6, 52.1, 67.8, 39.5, 58.8, 66, 52.2, 52.9, 68.7, 
    46, 55.9, 61.6, 45.17, 59.9, 74.3, 31.87, 48.4, 51.2, 6.2, 
    56.34, 4.17, 70.85, 3.54, 59.89, 5.61, 49.71, 1.87, 77.09, 
    0.51, 86.05, 1.8, 80.69, 2.15, 79.43), Day = structure(c(1L, 
    2L, 3L, 4L, 4L, 3L, 2L, 1L, 1L, 3L, 4L, 2L, 2L, 4L, 1L, 3L, 
    1L, 3L, 2L, 4L, 4L, 2L, 3L, 1L, 1L, 3L, 4L, 2L, 4L, 2L, 3L, 
    1L, 1L, 3L, 2L, 4L, 4L, 1L, 2L, 3L, 1L, 3L, 2L, 4L, 4L, 2L, 
    3L, 1L, 1L, 3L, 2L, 4L, 4L, 3L, 2L, 1L, 1L, 3L, 4L, 2L, 2L, 
    1L, 4L, 3L, 1L, 2L, 3L, 4L, 1L, 4L, 3L, 2L, 2L, 3L, 4L, 1L, 
    2L, 3L, 4L, 1L, 3L, 2L, 4L, 3L, 2L, 4L, 2L, 3L, 4L, 3L, 2L, 
    4L, 2L, 3L, 4L, 3L, 2L, 4L, 2L, 3L, 4L, 3L, 4L, 2L, 3L, 2L, 
    4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 4L, 3L, 2L, 
    4L, 3L, 2L, 4L, 3L, 2L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("8", "15", "22", 
    "30+"), class = "factor")), class = "data.frame", row.names = c(NA, 
-144L), .Names = c("Experiment", "Sample", "Genotype", "variable", 
"value", "Day"))

and here is the function I have made that works using ... 

grouped.t.test <- function(dataset, subset.plot, comparison, ...)
  {
  group.by <- quos(...)
  if (is.null(subset.plot)){
    subset.plot <- dataset[['variable']]
    }
  filter(dataset, variable %in% subset.plot) %>%
    group_by(!!!group.by) %>%
    do(tidy(t.test(x = .$value[.[comparison] == levels(.[[comparison]])[1]],
                   y = .$value[.[comparison] == levels(.[[comparison]])[2]]))) %>%
    mutate(p.value.format = symnum(p.value, corr = FALSE, na = FALSE, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), symbols = c("****", "***", "**", "*", NA))) %>%
    arrange(!!!group.by)
  }
View(grouped.t.test(dataset = dataset, subset.plot = NULL, comparison = 'Genotype', variable, Day))

I would like to be able to replace ... with an argument (e.g., group_vars) and call it like this:

View(grouped.t.test(dataset = dataset, subset.plot = NULL, comparison = 'Genotype', group_vars = c(variable, Day)))

This does not seem to work with quos() but I don't understand why. It would be nice to be able to use multiple list arguments that get quosed and used independently (e.g., creating an argument "arrange.by" that would be a list of variables to pass to arrange at the end of the function.

I'd greatly appreciate any help understanding why this doesn't work and what I could do instead!

See Question&Answers more detail:os

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0 votes
by (71.8m points)

As mentioned by @lionel - one of the lead developers of dplyr in this comment

You want the quoting to be external and explicitly done by the user rather than implicitly by your function. To this end you can ask your users to quote with base::alist(), rlang::exprs(), or dplyr::vars()

You can do something like this for your question

grouped.t.test2 <- function(dataset, subset.plot, comparison, group_vars) {

  if (is.null(subset.plot)) {
    subset.plot <- dataset[['variable']]
  }

  filter(dataset, variable %in% subset.plot) %>%
    group_by(!!! group_vars) %>%
    do(tidy(t.test(x = .$value[.[comparison] == levels(.[[comparison]])[1]],
                   y = .$value[.[comparison] == levels(.[[comparison]])[2]]))) %>%
    mutate(p.value.format = symnum(p.value, corr = FALSE, na = FALSE, 
                                   cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), 
                                   symbols = c("****", "***", "**", "*", NA))) %>%
    arrange(!!! group_vars)
}

grouped.t.test2(dataset = dataset, subset.plot = NULL, comparison = 'Genotype', 
               alist(variable, Day))

# or

grouped.t.test2(dataset = dataset, subset.plot = NULL, comparison = 'Genotype', 
               dplyr::vars(variable, Day))

# A tibble: 8 x 13
# Groups:   variable, Day [8]
  variable Day   estimate estimate1 estimate2 statistic p.value parameter
  <fct>    <fct>    <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>
1 % CD127+ 8        -3.24      1.66      4.90     -4.26 9.93e-4      12.6
2 % CD127+ 15      -24.4      31.1      55.5      -3.80 2.88e-3      11.2
3 % CD127+ 22      -22.1      27.4      49.5      -4.60 5.54e-4      12.5
4 % CD127+ 30+     -28.6      36.8      65.4      -5.23 1.36e-4      13.7
5 % KLRG1+ 8        23.8      81.2      57.4       9.79 3.11e-7      12.5
6 % KLRG1+ 15       16.5      73.7      57.2       3.78 2.08e-3      13.8
7 % KLRG1+ 22       20.9      70.1      49.2       4.44 4.82e-4      14.9
8 % KLRG1+ 30+      22.5      76.7      54.2       4.46 6.01e-4      13.4
# ... with 5 more variables: conf.low <dbl>, conf.high <dbl>,
#   method <fct>, alternative <fct>, p.value.format <chr>

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