为什么 assign() 在 for() 循环中与 R 中的 dplyr 管道表现奇怪？
Why assign() is behaving oddly in for() loop with dplyr pipes in R?
我需要在我的全局环境中分配的数据帧中循环不同的函数，并将循环的每个 "run" 的输出保存在包含初始名称的新数据帧中。
为此，我将 assign() 与 for() 循环一起使用。它运行良好，除非我使用 dplyr 管道 %>%。该函数本身有效，但分配给输出数据帧的名称存在一些错误。我怎样才能用 %>% 解决这个问题？如果无法修复，我可以将 assign() 更改为另一个函数吗？
这个效果很好：
code1:
for(i in unique(table$V1)){
assign(paste0(i, "_target"),table[grepl(i,table$V1),])
}
解释：选择 "table" 的第 1 列中的唯一条目，并将包含这些条目的行子集化为每个条目的新数据框。输出：新的数据框名称是 "entry name" + "_target"
这不是很好（我想知道为什么）：
code2:
for(i in mget(ls(pattern = "_target"))){
assign(paste0(i, "_slim"),data.frame(i %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C__))))
}
解释：选择全局环境中名称包含“_target”的所有数据帧。在每个数据帧中：它计算与具有相同字符“(Sample.Name)”的条目关联的值“(C__)”的平均值。应该输出：新的数据框名称是 "entry name_target" + "_slim"。实际输出：新数据框呈现相同字符的平均值，但被命名为 "c(aleatory numbers)_slim".
code2 输入：
STA_target <- structure(list(Well = structure(c(8L, 9L, 10L, 21L, 22L, 23L,
33L, 34L, 35L, 46L, 47L, 48L, 58L, 59L, 60L, 73L, 74L, 75L, 85L,
86L, 87L, 97L, 98L, 99L), .Label = c("", "A1", "A10", "A11",
"A12", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "Analysis Type",
"B1", "B10", "B11", "B12", "B2", "B3", "B4", "B5", "B6", "B7",
"B8", "B9", "C1", "C10", "C11", "C12", "C2", "C3", "C4", "C5",
"C6", "C7", "C8", "C9", "Chemistry", "D1", "D10", "D11", "D12",
"D2", "D3", "D4", "D5", "D6", "D7", "D8", "D9", "E1", "E10",
"E11", "E12", "E2", "E3", "E4", "E5", "E6", "E7", "E8", "E9",
"Endogenous Control", "Experiment File Name", "Experiment Run End Time",
"F1", "F10", "F11", "F12", "F2", "F3", "F4", "F5", "F6", "F7",
"F8", "F9", "G1", "G10", "G11", "G12", "G2", "G3", "G4", "G5",
"G6", "G7", "G8", "G9", "H1", "H10", "H11", "H12", "H2", "H3",
"H4", "H5", "H6", "H7", "H8", "H9", "Instrument Type", "Passive Reference",
"Reference Sample", "RQ Min/Max Confidence Level", "Well"), class = "factor"),
Sample.Name = c("Control_in", "Control_in", "Control_in",
"Sample2_in", "Sample2_in", "Sample2_in", "Sample5_in", "Sample5_in",
"Sample5_in", "Sample3_in", "Sample3_in", "Sample3_in", "Control_c",
"Control_c", "Control_c", "Sample2_c", "Sample2_c", "Sample2_c",
"Sample3_c", "Sample3_c", "Sample3_c", "Sample5_c", "Sample5_c",
"Sample5_c"), Target.Name = c("STA", "STA", "STA", "STA",
"STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA",
"STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA",
"STA", "STA"), Task = structure(c(3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L), .Label = c("", "Task", "UNKNOWN"), class = "factor"),
Reporter = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("", "Reporter", "SYBR"), class = "factor"),
Quencher = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("", "None", "Quencher"), class = "factor"),
RQ = structure(c(12L, 12L, 12L, 8L, 8L, 8L, 6L, 6L, 6L, 11L,
11L, 11L, 1L, 1L, 1L, 5L, 5L, 5L, 14L, 14L, 14L, 18L, 18L,
18L), .Label = c("", "0.706286132", "0.714652956", "0.724364996",
"0.7665869", "0.828774512", "0.838611245", "0.846661508",
"0.863589227", "0.896049678", "0.929288268", "1", "1.829339266",
"15.57538891", "17.64183807", "27.67574501", "3.064466953",
"34.78881073", "41.82569504", "8.117406845", "8.884188652",
"RQ"), class = "factor"), RQ.Min = structure(c(9L, 9L, 9L,
7L, 7L, 7L, 8L, 8L, 8L, 10L, 10L, 10L, 1L, 1L, 1L, 2L, 2L,
2L, 21L, 21L, 21L, 17L, 17L, 17L), .Label = c("", "0.032458056",
"0.429091513", "0.460811675", "0.541289926", "0.611138761",
"0.674698055", "0.71383971", "0.742018044", "0.753834546",
"0.772591949", "0.7868222", "0.803419232", "0.820919514",
"0.826185584", "0.989573121", "22.58564949", "27.2142868",
"4.501103401", "4.745172024", "4.843928814", "4.979007244",
"9.076541901", "RQ Min"), class = "factor"), RQ.Max = structure(c(13L,
13L, 13L, 8L, 8L, 8L, 6L, 6L, 6L, 9L, 9L, 9L, 1L, 1L, 1L,
16L, 16L, 16L, 19L, 19L, 19L, 20L, 20L, 20L), .Label = c("",
"0.858568788", "0.910271943", "0.943540215", "0.947846115",
"0.962214947", "0.971821666", "1.062453985", "1.145578504",
"1.162549496", "1.218146205", "1.244680166", "1.347676158",
"14.63914394", "15.85231876", "18.10507202", "20.37916756",
"3.381742954", "50.08181381", "53.58541107", "64.28199768",
"65.58969879", "84.38751984", "RQ Max"), class = "factor"),
C_ = c(25.48042297, 25.4738903, 25.83390617, 25.7304306,
25.78297043, 25.41260529, 25.49670792, 25.52298164, 25.6956234,
25.34812355, 25.51462555, 25.15455437, 0, 0, 0, 32.29237366,
37.10370636, 32.22016525, 29.50172043, 30.18544579, 29.91492081,
25.14842796, 24.89806747, 24.99397278), C_.Mean = c(25.59607506,
25.59607506, 25.59607506, 25.64200401, 25.64200401, 25.64200401,
25.57177162, 25.57177162, 25.57177162, 25.33910179, 25.33910179,
25.33910179, NA, NA, NA, 33.87208176, 33.87208176, 33.87208176,
29.86736107, 29.86736107, 29.86736107, 25.01348877, 25.01348877,
25.01348877), C_.SD = structure(c(21L, 21L, 21L, 20L, 20L,
20L, 12L, 12L, 12L, 19L, 19L, 19L, 1L, 1L, 1L, 31L, 31L,
31L, 23L, 23L, 23L, 14L, 14L, 14L), .Label = c("", "0.039937571",
"0.043110434", "0.049541138", "0.05469643", "0.061177365",
"0.066671595", "0.07365533", "0.079849631", "0.082057081",
"0.095515646", "0.108060829", "0.120047837", "0.126316145",
"0.129658803", "0.130481929", "0.142733917", "0.172286868",
"0.180205062", "0.200392827", "0.205995336", "0.236968249",
"0.344334781", "0.36769405", "0.413046211", "0.445171326",
"0.514641941", "0.640576839", "0.895943522", "0.993181109",
"2.798901796", "C_ SD"), class = "factor"), `_C_` = 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), .Label = c("", "_C_"), class = "factor"),
`_C_.Mean` = structure(c(8L, 8L, 8L, 5L, 5L, 5L, 4L, 4L,
4L, 7L, 7L, 7L, 1L, 1L, 1L, 3L, 3L, 3L, 13L, 13L, 13L, 14L,
14L, 14L), .Label = c("", "_C_ Mean", "-0.577166259", "-0.68969661",
"-0.720502198", "-0.776381195", "-0.85484314", "-0.96064502",
"-1.058534026", "-2.04822278", "-2.545912504", "-3.293611526",
"-4.921841145", "-6.081196308", "0.477069855", "1.373315215",
"2.092705965", "2.244637728", "2.251055479", "2.346632004",
"2.456220627", "2.557917356", "2.729323149", "2.746313095"
), class = "factor"), `_C_.SE` = structure(c(13L, 13L, 13L,
11L, 11L, 11L, 6L, 6L, 6L, 9L, 9L, 9L, 1L, 1L, 1L, 24L, 24L,
24L, 21L, 21L, 21L, 15L, 15L, 15L), .Label = c("", "_C_ SE",
"0.042180877", "0.042606823", "0.048373949", "0.077573851",
"0.088320434", "0.102536619", "0.108728357", "0.113733612",
"0.117972165", "0.144372106", "0.155044988", "0.223316222",
"0.224465802", "0.258952528", "0.300881863", "0.306413502",
"0.319273174", "0.579304695", "0.606897891", "0.635279417",
"0.682336032", "1.643036604"), class = "factor"), HK.Control._C_.Mean = 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), .Label = c("", "HK Control _C_ Mean"
), class = "factor"), HK.Control._C_.SE = 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), .Label = c("", "HK Control _C_ SE"
), class = "factor"), `__C_` = structure(c(12L, 12L, 12L,
16L, 16L, 16L, 18L, 18L, 18L, 13L, 13L, 13L, 1L, 1L, 1L,
19L, 19L, 19L, 7L, 7L, 7L, 10L, 10L, 10L), .Label = c("",
"__C_", "-0.871322632", "-1.61563623", "-3.021018982", "-3.15124011",
"-3.961196184", "-4.140928745", "-4.790550232", "-5.120551586",
"-5.38631773", "0", "0.105801903", "0.15834935", "0.211582825",
"0.240142822", "0.253925949", "0.27094841", "0.383478791",
"0.465211242", "0.484685272", "0.501675308"), class = "factor"),
Automatic.Ct.Threshold = structure(c(3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L), .Label = c("", "Automatic Ct Threshold",
"TRUE"), class = "factor"), Ct.Threshold = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "0.056211855",
"0.208910329", "0.693888608", "0.704941193", "Ct Threshold"
), class = "factor"), Automatic.Baseline = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("", "Automatic Baseline",
"TRUE"), class = "factor"), Baseline.Start = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "3", "Baseline Start"
), class = "factor"), Baseline.End = structure(c(3L, 3L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 13L, 14L, 14L, 8L,
12L, 8L, 6L, 7L, 7L, 3L, 3L, 3L), .Label = c("", "21", "22",
"23", "25", "26", "27", "29", "30", "31", "32", "34", "35",
"39", "Baseline End"), class = "factor"), Efficiency = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "1", "Efficiency"
), class = "factor"), Comments = 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), .Label = c("", "Comments"), class = "factor"),
HIGHSD = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("", "HIGHSD", "N", "Y"), class = "factor"),
NOAMP = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("",
"N", "NOAMP", "Y"), class = "factor"), OUTLIERRG = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "N", "OUTLIERRG",
"Y"), class = "factor"), EXPFAIL = structure(c(3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("", "EXPFAIL", "N", "Y"
), class = "factor")), .Names = c("Well", "Sample.Name",
"Target.Name", "Task", "Reporter", "Quencher", "RQ", "RQ.Min",
"RQ.Max", "C_", "C_.Mean", "C_.SD", "_C_", "_C_.Mean", "_C_.SE",
"HK.Control._C_.Mean", "HK.Control._C_.SE", "__C_", "Automatic.Ct.Threshold",
"Ct.Threshold", "Automatic.Baseline", "Baseline.Start", "Baseline.End",
"Efficiency", "Comments", "HIGHSD", "NOAMP", "OUTLIERRG", "EXPFAIL"
), row.names = c(12L, 13L, 14L, 24L, 25L, 26L, 36L, 37L, 38L,
48L, 49L, 50L, 60L, 61L, 62L, 72L, 73L, 74L, 84L, 85L, 86L, 96L,
97L, 98L), class = "data.frame")
code2 "output":
> dput(`c(8, 9, 10, 21, 22, 23, 33, 34, 35, 46, 47, 48, 58, 59, 60, 73, 74, 75, 85, 86, 87, 97, 98, 99)_slim`)
structure(list(Group.1 = c("Sample2_c", "Sample2_in", "Sample3_c",
"Sample5_in", "Control_c", "Control_in", "Sample5_c", "Sample3_in"
), x = c(33.8720817566667, 25.6420021066667, 29.8673623433333,
25.5717709866667, 0, 25.5960731466667, 25.0134894033333, 25.3391011566667
)), .Names = c("Group.1", "x"), row.names = c(NA, -8L), class = "data.frame")
由于给定的名称，我不知道这是否真的是输出。但预期的输出应该是具有正确名称的内容：STA_slim
感谢您的宝贵时间
首先，我强烈建议您避免在 R 代码中使用 assign()。最好使用 R 中众多 mapping/apply 函数之一在列表中构建相关数据。使用 get/assign 表示您没有以非常 R-like 的方式做事。
你的问题实际上与 dplyr 无关，这是你在循环中循环的问题。当你做
for(i in mget(ls(pattern = "_target"))){
assign(paste0(i, "_slim"),data.frame(i %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C__))))
}
i 不是 data.frame 的名称，因为您 mget() 它是数据框本身。将其粘贴到新名称中没有意义。
为了"fix"这个，你可以做到
for(i in ls(pattern = "_target")){
assign(paste0(i, "_slim"),data.frame(get(i) %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C__))))
}
但即便如此，您的示例数据集中也没有名为 C__ 的列。你有 C_ 或 _C_ 或 __C_（这些名字到底是什么意思？？）。所以你需要解决这个问题。
更好的列表方式是
slim <- lapply(mget(ls(pattern = "_target$")) , function(x) {
x %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C_))
})
我需要在我的全局环境中分配的数据帧中循环不同的函数，并将循环的每个 "run" 的输出保存在包含初始名称的新数据帧中。 为此，我将 assign() 与 for() 循环一起使用。它运行良好，除非我使用 dplyr 管道 %>%。该函数本身有效，但分配给输出数据帧的名称存在一些错误。我怎样才能用 %>% 解决这个问题？如果无法修复，我可以将 assign() 更改为另一个函数吗？
这个效果很好：
code1:
for(i in unique(table$V1)){
assign(paste0(i, "_target"),table[grepl(i,table$V1),])
}
解释：选择 "table" 的第 1 列中的唯一条目，并将包含这些条目的行子集化为每个条目的新数据框。输出：新的数据框名称是 "entry name" + "_target"
这不是很好（我想知道为什么）：
code2:
for(i in mget(ls(pattern = "_target"))){
assign(paste0(i, "_slim"),data.frame(i %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C__))))
}
解释：选择全局环境中名称包含“_target”的所有数据帧。在每个数据帧中：它计算与具有相同字符“(Sample.Name)”的条目关联的值“(C__)”的平均值。应该输出：新的数据框名称是 "entry name_target" + "_slim"。实际输出：新数据框呈现相同字符的平均值，但被命名为 "c(aleatory numbers)_slim".
code2 输入：
STA_target <- structure(list(Well = structure(c(8L, 9L, 10L, 21L, 22L, 23L,
33L, 34L, 35L, 46L, 47L, 48L, 58L, 59L, 60L, 73L, 74L, 75L, 85L,
86L, 87L, 97L, 98L, 99L), .Label = c("", "A1", "A10", "A11",
"A12", "A2", "A3", "A4", "A5", "A6", "A7", "A8", "A9", "Analysis Type",
"B1", "B10", "B11", "B12", "B2", "B3", "B4", "B5", "B6", "B7",
"B8", "B9", "C1", "C10", "C11", "C12", "C2", "C3", "C4", "C5",
"C6", "C7", "C8", "C9", "Chemistry", "D1", "D10", "D11", "D12",
"D2", "D3", "D4", "D5", "D6", "D7", "D8", "D9", "E1", "E10",
"E11", "E12", "E2", "E3", "E4", "E5", "E6", "E7", "E8", "E9",
"Endogenous Control", "Experiment File Name", "Experiment Run End Time",
"F1", "F10", "F11", "F12", "F2", "F3", "F4", "F5", "F6", "F7",
"F8", "F9", "G1", "G10", "G11", "G12", "G2", "G3", "G4", "G5",
"G6", "G7", "G8", "G9", "H1", "H10", "H11", "H12", "H2", "H3",
"H4", "H5", "H6", "H7", "H8", "H9", "Instrument Type", "Passive Reference",
"Reference Sample", "RQ Min/Max Confidence Level", "Well"), class = "factor"),
Sample.Name = c("Control_in", "Control_in", "Control_in",
"Sample2_in", "Sample2_in", "Sample2_in", "Sample5_in", "Sample5_in",
"Sample5_in", "Sample3_in", "Sample3_in", "Sample3_in", "Control_c",
"Control_c", "Control_c", "Sample2_c", "Sample2_c", "Sample2_c",
"Sample3_c", "Sample3_c", "Sample3_c", "Sample5_c", "Sample5_c",
"Sample5_c"), Target.Name = c("STA", "STA", "STA", "STA",
"STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA",
"STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA", "STA",
"STA", "STA"), Task = structure(c(3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L), .Label = c("", "Task", "UNKNOWN"), class = "factor"),
Reporter = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("", "Reporter", "SYBR"), class = "factor"),
Quencher = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), .Label = c("", "None", "Quencher"), class = "factor"),
RQ = structure(c(12L, 12L, 12L, 8L, 8L, 8L, 6L, 6L, 6L, 11L,
11L, 11L, 1L, 1L, 1L, 5L, 5L, 5L, 14L, 14L, 14L, 18L, 18L,
18L), .Label = c("", "0.706286132", "0.714652956", "0.724364996",
"0.7665869", "0.828774512", "0.838611245", "0.846661508",
"0.863589227", "0.896049678", "0.929288268", "1", "1.829339266",
"15.57538891", "17.64183807", "27.67574501", "3.064466953",
"34.78881073", "41.82569504", "8.117406845", "8.884188652",
"RQ"), class = "factor"), RQ.Min = structure(c(9L, 9L, 9L,
7L, 7L, 7L, 8L, 8L, 8L, 10L, 10L, 10L, 1L, 1L, 1L, 2L, 2L,
2L, 21L, 21L, 21L, 17L, 17L, 17L), .Label = c("", "0.032458056",
"0.429091513", "0.460811675", "0.541289926", "0.611138761",
"0.674698055", "0.71383971", "0.742018044", "0.753834546",
"0.772591949", "0.7868222", "0.803419232", "0.820919514",
"0.826185584", "0.989573121", "22.58564949", "27.2142868",
"4.501103401", "4.745172024", "4.843928814", "4.979007244",
"9.076541901", "RQ Min"), class = "factor"), RQ.Max = structure(c(13L,
13L, 13L, 8L, 8L, 8L, 6L, 6L, 6L, 9L, 9L, 9L, 1L, 1L, 1L,
16L, 16L, 16L, 19L, 19L, 19L, 20L, 20L, 20L), .Label = c("",
"0.858568788", "0.910271943", "0.943540215", "0.947846115",
"0.962214947", "0.971821666", "1.062453985", "1.145578504",
"1.162549496", "1.218146205", "1.244680166", "1.347676158",
"14.63914394", "15.85231876", "18.10507202", "20.37916756",
"3.381742954", "50.08181381", "53.58541107", "64.28199768",
"65.58969879", "84.38751984", "RQ Max"), class = "factor"),
C_ = c(25.48042297, 25.4738903, 25.83390617, 25.7304306,
25.78297043, 25.41260529, 25.49670792, 25.52298164, 25.6956234,
25.34812355, 25.51462555, 25.15455437, 0, 0, 0, 32.29237366,
37.10370636, 32.22016525, 29.50172043, 30.18544579, 29.91492081,
25.14842796, 24.89806747, 24.99397278), C_.Mean = c(25.59607506,
25.59607506, 25.59607506, 25.64200401, 25.64200401, 25.64200401,
25.57177162, 25.57177162, 25.57177162, 25.33910179, 25.33910179,
25.33910179, NA, NA, NA, 33.87208176, 33.87208176, 33.87208176,
29.86736107, 29.86736107, 29.86736107, 25.01348877, 25.01348877,
25.01348877), C_.SD = structure(c(21L, 21L, 21L, 20L, 20L,
20L, 12L, 12L, 12L, 19L, 19L, 19L, 1L, 1L, 1L, 31L, 31L,
31L, 23L, 23L, 23L, 14L, 14L, 14L), .Label = c("", "0.039937571",
"0.043110434", "0.049541138", "0.05469643", "0.061177365",
"0.066671595", "0.07365533", "0.079849631", "0.082057081",
"0.095515646", "0.108060829", "0.120047837", "0.126316145",
"0.129658803", "0.130481929", "0.142733917", "0.172286868",
"0.180205062", "0.200392827", "0.205995336", "0.236968249",
"0.344334781", "0.36769405", "0.413046211", "0.445171326",
"0.514641941", "0.640576839", "0.895943522", "0.993181109",
"2.798901796", "C_ SD"), class = "factor"), `_C_` = 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), .Label = c("", "_C_"), class = "factor"),
`_C_.Mean` = structure(c(8L, 8L, 8L, 5L, 5L, 5L, 4L, 4L,
4L, 7L, 7L, 7L, 1L, 1L, 1L, 3L, 3L, 3L, 13L, 13L, 13L, 14L,
14L, 14L), .Label = c("", "_C_ Mean", "-0.577166259", "-0.68969661",
"-0.720502198", "-0.776381195", "-0.85484314", "-0.96064502",
"-1.058534026", "-2.04822278", "-2.545912504", "-3.293611526",
"-4.921841145", "-6.081196308", "0.477069855", "1.373315215",
"2.092705965", "2.244637728", "2.251055479", "2.346632004",
"2.456220627", "2.557917356", "2.729323149", "2.746313095"
), class = "factor"), `_C_.SE` = structure(c(13L, 13L, 13L,
11L, 11L, 11L, 6L, 6L, 6L, 9L, 9L, 9L, 1L, 1L, 1L, 24L, 24L,
24L, 21L, 21L, 21L, 15L, 15L, 15L), .Label = c("", "_C_ SE",
"0.042180877", "0.042606823", "0.048373949", "0.077573851",
"0.088320434", "0.102536619", "0.108728357", "0.113733612",
"0.117972165", "0.144372106", "0.155044988", "0.223316222",
"0.224465802", "0.258952528", "0.300881863", "0.306413502",
"0.319273174", "0.579304695", "0.606897891", "0.635279417",
"0.682336032", "1.643036604"), class = "factor"), HK.Control._C_.Mean = 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), .Label = c("", "HK Control _C_ Mean"
), class = "factor"), HK.Control._C_.SE = 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), .Label = c("", "HK Control _C_ SE"
), class = "factor"), `__C_` = structure(c(12L, 12L, 12L,
16L, 16L, 16L, 18L, 18L, 18L, 13L, 13L, 13L, 1L, 1L, 1L,
19L, 19L, 19L, 7L, 7L, 7L, 10L, 10L, 10L), .Label = c("",
"__C_", "-0.871322632", "-1.61563623", "-3.021018982", "-3.15124011",
"-3.961196184", "-4.140928745", "-4.790550232", "-5.120551586",
"-5.38631773", "0", "0.105801903", "0.15834935", "0.211582825",
"0.240142822", "0.253925949", "0.27094841", "0.383478791",
"0.465211242", "0.484685272", "0.501675308"), class = "factor"),
Automatic.Ct.Threshold = structure(c(3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L), .Label = c("", "Automatic Ct Threshold",
"TRUE"), class = "factor"), Ct.Threshold = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "0.056211855",
"0.208910329", "0.693888608", "0.704941193", "Ct Threshold"
), class = "factor"), Automatic.Baseline = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("", "Automatic Baseline",
"TRUE"), class = "factor"), Baseline.Start = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "3", "Baseline Start"
), class = "factor"), Baseline.End = structure(c(3L, 3L,
4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 13L, 14L, 14L, 8L,
12L, 8L, 6L, 7L, 7L, 3L, 3L, 3L), .Label = c("", "21", "22",
"23", "25", "26", "27", "29", "30", "31", "32", "34", "35",
"39", "Baseline End"), class = "factor"), Efficiency = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "1", "Efficiency"
), class = "factor"), Comments = 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), .Label = c("", "Comments"), class = "factor"),
HIGHSD = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L
), .Label = c("", "HIGHSD", "N", "Y"), class = "factor"),
NOAMP = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("",
"N", "NOAMP", "Y"), class = "factor"), OUTLIERRG = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("", "N", "OUTLIERRG",
"Y"), class = "factor"), EXPFAIL = structure(c(3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("", "EXPFAIL", "N", "Y"
), class = "factor")), .Names = c("Well", "Sample.Name",
"Target.Name", "Task", "Reporter", "Quencher", "RQ", "RQ.Min",
"RQ.Max", "C_", "C_.Mean", "C_.SD", "_C_", "_C_.Mean", "_C_.SE",
"HK.Control._C_.Mean", "HK.Control._C_.SE", "__C_", "Automatic.Ct.Threshold",
"Ct.Threshold", "Automatic.Baseline", "Baseline.Start", "Baseline.End",
"Efficiency", "Comments", "HIGHSD", "NOAMP", "OUTLIERRG", "EXPFAIL"
), row.names = c(12L, 13L, 14L, 24L, 25L, 26L, 36L, 37L, 38L,
48L, 49L, 50L, 60L, 61L, 62L, 72L, 73L, 74L, 84L, 85L, 86L, 96L,
97L, 98L), class = "data.frame")
code2 "output":
> dput(`c(8, 9, 10, 21, 22, 23, 33, 34, 35, 46, 47, 48, 58, 59, 60, 73, 74, 75, 85, 86, 87, 97, 98, 99)_slim`)
structure(list(Group.1 = c("Sample2_c", "Sample2_in", "Sample3_c",
"Sample5_in", "Control_c", "Control_in", "Sample5_c", "Sample3_in"
), x = c(33.8720817566667, 25.6420021066667, 29.8673623433333,
25.5717709866667, 0, 25.5960731466667, 25.0134894033333, 25.3391011566667
)), .Names = c("Group.1", "x"), row.names = c(NA, -8L), class = "data.frame")
由于给定的名称，我不知道这是否真的是输出。但预期的输出应该是具有正确名称的内容：STA_slim
感谢您的宝贵时间
首先，我强烈建议您避免在 R 代码中使用 assign()。最好使用 R 中众多 mapping/apply 函数之一在列表中构建相关数据。使用 get/assign 表示您没有以非常 R-like 的方式做事。
你的问题实际上与 dplyr 无关，这是你在循环中循环的问题。当你做
for(i in mget(ls(pattern = "_target"))){
assign(paste0(i, "_slim"),data.frame(i %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C__))))
}
i 不是 data.frame 的名称，因为您 mget() 它是数据框本身。将其粘贴到新名称中没有意义。
为了"fix"这个，你可以做到
for(i in ls(pattern = "_target")){
assign(paste0(i, "_slim"),data.frame(get(i) %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C__))))
}
但即便如此，您的示例数据集中也没有名为 C__ 的列。你有 C_ 或 _C_ 或 __C_（这些名字到底是什么意思？？）。所以你需要解决这个问题。
更好的列表方式是
slim <- lapply(mget(ls(pattern = "_target$")) , function(x) {
x %>% group_by(Sample.Name) %>% summarise(Mean_dC=mean(C_))
})