将dplyr:：quos（）与列表参数而不是省略号参数一起使用

我正在使用dplyr并尝试创建一个函数，根据分组参数计算p.values。我希望能够有一个参数，可以是任意长度的变量列表，以分组。以下是示例数据集：

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"))

这似乎对quos（）不起作用，但我不明白为什么。 如果能够使用多个独立使用的列表参数（例如，创建一个参数“arrange.by”，这将是一个变量列表，在函数末尾传递给arrange），那将是一件好事

---

如果您能帮助我理解为什么这不起作用，以及我能做些什么，我将不胜感激。

正如@lionel所提到的，@lionel是

dplyr

的主要开发者之一

您希望引用是外部的，由用户显式完成，而不是由函数隐式完成。为此，您可以要求用户使用base:：alist（）、rlang:：exprs（）或dplyr:：vars（）进行引用

你可以这样回答你的问题

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>              

group.t.test2%
do（t.test（x=.$value[[comparison]==水平（[comparison]]）[1]]，
y=.$value[[comparison]==级别（[comparison]]）[2]]）%>%
mutate（p.value.format=symnum（p.value，corr=FALSE，na=FALSE，
切点=c（0,0.0001,0.001,0.01,0.05,1），
符号=c（“****”、“****”、“****”、“*”、NA）））%>%
安排（！！！组变量）
}
grouped.t.test2（dataset=dataset，subset.plot=NULL，comparison='genetic'，
列表（变量，日））
#或
grouped.t.test2（dataset=dataset，subset.plot=NULL，comparison='genetic'，
dplyr:：vars（变量，天））
#一个tibble:8x13
#组：变量，天[8]
可变日估计值估计值1估计值2统计p.值参数
1%CD127+8-3.241.664.90-4.269.93e-412.6
2%CD127+15-24.431.155.5-3.802.88e-3 11.2
3%CD127+22-22.127.449.5-4.605.54e-412.5
4%CD127+30+-28.6 36.8 65.4-5.23 1.36e-4 13.7
5%KLRG1+823.881.257.49.793.11e-712.5
6%KLRG1+15 16.5 73.7 57.2 3.78 2.08e-3 13.8
7%KLRG1+2220.970.149.24.444.82e-414.9
8%KLRG1+30+22.5 76.7 54.2 4.46 6.01e-4 13.4
#…还有5个变量：conf.low，conf.high，
#方法，可选，p.value.format

我对你的意思有点困惑。

c（variable，Day）

不会创建符号向量，它会尝试通过这些名称查找实际对象。你可以尝试在控制台上键入

c（variable，Day）

。也许你的意思是希望传递参数，如

group\u vars=c（“variable”，“Day”）

i.e.作为字符？我的理解是，使用quosures应该允许您捕获函数用户传递的内容，然后将其引用。然后！！！应该在组_by（）中将其取消引用函数。这不是它的工作方式吗？是的，但它的工作方式是使用

..

。您可以使用类似

rlang:：syms

的东西创建符号列表，但这对您的用户来说是更多的工作。通常，您可以通过

..

一次传递一个符号，或者作为字符向量，自己在f中将它们转换为符号我明白了。因此，如果我提供一个字符向量，并使用

rlang:：syms

创建一个符号列表，那么将其传递给

groupby（）的正确方法是什么

？谢谢！看起来它可以与

！！！

取消引用一起使用。这是正确的实现吗？我收到警告消息：

使用此方法对“noquote”元素进行矢量化可能无法保留其属性。
View(grouped.t.test(dataset = dataset, subset.plot = NULL, comparison = 'Genotype', group_vars = c(variable, Day)))

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>