这是一个 Dataframe ，对每个结果变量进行了三次重复比较（因此子多级为12）

A tibble: 36 x 10
   s     .y.   group1          group2             n1    n2     p p.signif p.adj p.adj.signif
 * <chr> <chr> <chr>           <chr>           <int> <int> <dbl> <chr>    <dbl> <chr>       
 1 E     value new_value_for_8 new_value_for_6    25    25 0.877 ns       1     ns          
 2 E     value new_value_for_8 new_value_for_4    25    25 0.929 ns       1     ns          
 3 E     value new_value_for_6 new_value_for_4    25    25 0.948 ns       1     ns          
 4 F     value new_value_for_8 new_value_for_6    25    25 0.735 ns       1     ns          
 5 F     value new_value_for_8 new_value_for_4    25    25 0.738 ns       1     ns          
 6 F     value new_value_for_6 new_value_for_4    25    25 0.501 ns       1     ns          
 7 G     value new_value_for_8 new_value_for_6    25    25 0.808 ns       0.808 ns          
 8 G     value new_value_for_8 new_value_for_4    25    25 0.101 ns       0.303 ns          
 9 G     value new_value_for_6 new_value_for_4    25    25 0.161 ns       0.321 ns          
10 H     value new_value_for_8 new_value_for_6    25    25 0.964 ns       0.964 ns

我刚刚为三个重复测量中的每一个创建了一个包含12个元素的列表，其中包含以下相对统计数据：

my_comparisons <- list(E = comparisons[1:3,], 
                       F = comparisons[4:6,], 
                       G = comparisons[7:9,], 
                       H = comparisons[10:12,], 
                       I = comparisons[13:15,], 
                       J = comparisons[16:18,], 
                       K =comparisons[19:21,], 
                       L = comparisons[22:24,], 
                       M = comparisons[25:27,], 
                       N = comparisons[28:30,], 
                       O  = comparisons[31:33,], 
                       P = comparisons[34:36,])

得到以下结果

[[E]]

    # A tibble: 3 x 10
      s     .y.   group1          group2             n1    n2     p p.signif p.adj p.adj.signif
      <chr> <chr> <chr>           <chr>           <int> <int> <dbl> <chr>    <dbl> <chr>       
    1 E     value new_value_for_8 new_value_for_6    25    25 0.877 ns           1 ns          
    2 E     value new_value_for_8 new_value_for_4    25    25 0.929 ns           1 ns          
    3 E     value new_value_for_6 new_value_for_4    25    25 0.948 ns           1 ns  
    [[F]] .... and so on

因为你可以看到在信号列，根据我分裂列表有一些常见的字体（例如P3，FCz，LPPearly等）.我想使用一个迭代函数，如lapply（），一些循环或map（）函数，以自动化这个列表的创建.
先谢了
这里是原始数据集

> dput(head(df, 10))
structure(list(A = 1:10, C = c("Maybe", "Maybe", "Maybe", "Maybe", 
"Maybe", "Maybe", "Maybe", "Maybe", "Maybe", "Maybe"), D = structure(c(1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L), .Label = c("new_value_for_8", 
"new_value_for_6", "new_value_for_4"), class = "factor"), E = c(988.368784828308, 
988.856158671407, 996.004085290553, 999.685844324618, 1000.23888564896, 
1005.03749946898, 999.786378084971, 997.039675082569, 998.028313183065, 
997.168905747014), F = c(994.834756009939, 994.468875098246, 
1000.62150212342, 1002.23100741241, 1003.96990710863, 1007.75899775608, 
998.699806256246, 996.401009591011, 998.076594704249, 1002.19344184533
), G = c(1011.88022669726, 1012.10534266625, 1012.9554415821, 
1015.09810043606, 1015.40462298842, 1016.67103699915, 1003.13771453335, 
999.9107434841, 1002.15365554737, 1013.67789244066), H = c(988.221495702721, 
990.850727928741, 992.418094914622, 995.984841639886, 993.398346143465, 
997.971380355398, 1004.4672957051, 1002.54036572775, 1002.2292388993, 
999.116379988893), I = c(994.035709684742, 994.890815628412, 
997.18267770374, 998.564426335124, 996.851278420874, 1000.16039368502, 
1003.52155765272, 1002.1043798945, 1002.7069399281, 1005.49897156208
), J = c(1008.23981597718, 1009.51261484649, 1009.42367409926, 
1005.06332653216, 1005.02619159395, 1009.07903916629, 1007.56089165218, 
1005.49719893791, 1004.91476855238, 1013.03209535721), K = c(994.327042030287, 
995.608170991922, 997.033470393412, 1000.15918365269, 998.216388150646, 
1001.97377908784, 1003.17401220482, 1001.60211665164, 1002.27932356239, 
1002.41479226363), L = c(999.225538268699, 999.349990537239, 
1001.14010250645, 1001.51403741206, 1000.25571835554, 1003.76051565494, 
1002.74763442988, 1001.09116707486, 1003.29833843754, 1006.55857216695
), M = c(1009.99385579756, 1011.12126521731, 1010.6989716872, 
1003.7899021821, 1004.59413830322, 1008.52123662618, 1006.34418311104, 
1004.1077131243, 1004.94124365003, 1011.89121961563), N = c(999.801263745036, 
996.838989582336, 1000.89599227983, 1003.11042068113, 1002.27800090558, 
1003.83846437952, 1000.70169995102, 1001.75290674649, 998.660833714301, 
1006.69246804854), O = c(1002.96437294923, 997.870867692911, 
1002.94619035116, 1003.44844607015, 1003.02403433836, 1004.70457675466, 
999.880559826981, 1000.66826545719, 999.59436981446, 1007.32640154038
), P = c(1006.28027312932, 1005.24535230967, 1007.68162285336, 
1001.08242973466, 1002.99896314, 1005.36085942954, 1001.22060069797, 
1000.43007709819, 1000.47666761108, 1008.73650967215)), row.names = c(NA, 
10L), class = "data.frame")