主頁 > .NET開發 > 如何通過迭代方法或管道運算子從嵌套資料集中洗掉真正極端的例外值

如何通過迭代方法或管道運算子從嵌套資料集中洗掉真正極端的例外值

2021-10-21 05:29:03 .NET開發

我正在嘗試從這個嵌套資料集中洗掉例外值

df_join
# A tibble: 12 x 2
# Groups:   signals [12]
   signals     data             
   <chr>       <list>           
 1 P3FCz       <tibble [75 x 5]>
 2 P3Cz        <tibble [75 x 5]>
 3 P3Pz        <tibble [75 x 5]>
 4 LPPearlyFCz <tibble [75 x 5]>
 5 LPPearlyCz  <tibble [75 x 5]>
 6 LPPearlyPz  <tibble [75 x 5]>
 7 LPP1FCz     <tibble [75 x 5]>
 8 LPP1Cz      <tibble [75 x 5]>
 9 LPP1Pz      <tibble [75 x 5]>
10 LPP2FCz     <tibble [75 x 5]>
11 LPP2Cz      <tibble [75 x 5]>
12 LPP2Pz      <tibble [75 x 5]>

它的全部內容如下：

> dput(head(df_join))
structure(list(signals = c("P3FCz", "P3Cz", "P3Pz", "LPPearlyFCz", 
"LPPearlyCz", "LPPearlyPz"), data = list(structure(list(ID = structure(c(1L, 
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 
6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 
11L, 12L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 
16L, 16L, 16L, 17L, 17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 
20L, 20L, 21L, 21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 
24L, 25L, 25L, 25L), .Label = c("01", "04", "06", "07", "08", 
"09", "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
"22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-11.6312151716924, -11.1438413285935, 
    -3.99591470944713, -0.314155675382471, 0.238885648959708, 
    5.03749946898385, -0.213621915029167, -2.96032491743069, 
    -1.97168681693488, -2.83109425298642, 1.09291198163802, -6.692991645215, 
    4.23849942428043, 2.9898889629932, 3.5510699900835, 9.57481668808606, 
    5.4167795618285, 1.7067607715475, -6.13036076093477, -2.82955734597919, 
    -2.50672211111696, 0.528517585832501, 8.16418133488309, 1.88777321897925, 
    -7.73588468896919, -9.83058052401056, -6.97442700196932, 
    1.27327945355082, 2.11962397764132, 0.524299677616254, -1.83310726842883, 
    0.658810483381172, -0.261373488428192, 4.37524298634374, 
    0.625555654900511, 3.19617639836154, 0.0405517582137798, 
    -3.29357103412113, -0.381435057304614, -5.73445509910268, 
    -6.1129152355645, -2.45744234877604, 2.95352732001065, 0.527721249096473, 
    1.91803490989119, -3.46703346467546, -2.40438419043702, -5.35374408162217, 
    -7.27028665849262, -7.1532211375959, -5.39955520296854, 2.65765002364624, 
    0.372495441513391, 6.24433066412776, 1.85698518142405, -0.564454675803529, 
    -0.068523080368053, -7.04782633579147, -4.52263283590558, 
    -6.62134671432544, 4.56661945182626, 3.05859761335498, 2.02997952225347, 
    -6.10523962206958, -0.521871236969702, -3.97851995684846, 
    -2.61258020387919, -4.13974828699279, -3.9210032516844, -4.63162466544638, 
    -4.36762718685405, -6.71005969834916, -4.22719611676328, 
    -0.229916506217565, -5.69725200870146)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)), structure(list(
    ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 
    9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 
    13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
    17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 
    21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 
    25L, 25L), .Label = c("01", "04", "06", "07", "08", "09", 
    "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
    "22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-5.16524399006139, -5.53112490175437, 
    0.621502123415388, 2.23100741241039, 3.96990710862955, 7.75899775608441, 
    -1.30019374375434, -3.59899040898949, -1.92340529575071, 
    2.19344184533265, 5.87900720863083, -5.92378937757888, 2.44958531767688, 
    3.10043497883256, 1.65779442628225, 13.7118233181713, 6.86178446511352, 
    5.31481098188172, -4.13240668697805, 0.162182285588285, 0.142083484505352, 
    5.42592103255673, 14.5496375672716, 4.52018125654081, -2.40677805475299, 
    -5.3832670295207, -1.55736964635117, 3.48359241788107, 4.23167123533126, 
    2.00051785325202, 1.48755216347718, 2.37269462739372, 1.30346907198835, 
    3.89476490634811, 1.87516303240986, 4.36353100770575, 1.9413417416824, 
    -2.22114447555529, -0.015852062711641, -2.76146409940467, 
    -3.51627712447581, 1.01799377568815, 1.74783962328435, 1.1303870721987, 
    2.16398550183836, -3.31557794753334, -1.83920975041768, -6.06703163736936, 
    -8.1566939611461, -9.23030396302541, -4.35545141573936, 0.906302081219897, 
    0.45401759063429, 3.80236232314171, 4.0336657306528, 2.0185967445137, 
    0.835589319243251, -4.6805488231028, -1.20746167339041, -5.50475999427345, 
    4.96594373869991, 4.1349308440931, 3.00187233307059, -5.61465293602653, 
    0.544596077279702, -5.20450410570445, -0.0325220589039272, 
    -2.28038421035601, -2.01375702882255, -1.6547144697087, -0.619979893871085, 
    -4.48258340054462, -1.42281778522059, 2.62315679073783, -4.13736508533355
    )), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-75L)), structure(list(ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 
8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 
13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 21L, 
21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 25L, 25L
), .Label = c("01", "04", "06", "07", "08", "09", "10", "11", 
"12", "13", "15", "16", "17", "18", "19", "21", "22", "23", "25", 
"27", "28", "30", "44", "46", "49"), class = "factor"), GR = 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, 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, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), 
    SES = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), COND = structure(c(1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", 
    "NEG-NOC", "NEU-NOC"), class = "factor"), value = c(11.8802266972569, 
    12.1053426662461, 12.955441582096, 15.0981004360619, 15.4046229884164, 
    16.671036999147, 3.13771453335467, -0.0892565159000666, 2.15365554736525, 
    13.6778924406572, 14.3862738306396, 6.86762877785576, 7.47946451329025, 
    8.93405130318593, 8.45962311067909, 23.4166601996042, 15.1868092142896, 
    9.97183712753913, 6.267521071803, 10.142198458411, 10.6320358418368, 
    12.9998037913548, 20.7052065690674, 11.8852179570666, 15.7899796085713, 
    7.50729833890206, 14.3076172484818, 9.93797956768228, 10.7693238464384, 
    5.04681800218272, 5.16656503460515, 7.87875085817396, 2.29899409536951, 
    10.0135486953849, 5.48278706243332, 7.81908431468528, 8.64382513728869, 
    3.35777109534179, 3.47474629234488, 4.35678644331281, 3.47085321062162, 
    6.56231512354717, 4.93825547529124, 7.33985613752315, 6.81966900599588, 
    6.54487921689425, 7.25872117706077, 1.10301223694429, -0.856423579793706, 
    -0.887835692028378, -0.931653372049331, 5.6617683754256, 
    2.29939831067085, 5.1554825066748, 6.59026080217083, 3.0741733363644, 
    1.80359068950898, 1.63892755704177, 3.857933716935, 0.769316188513939, 
    10.7031907391191, 9.53278894637555, 8.01071628743378, 6.04891324234645, 
    11.1964453850602, 3.46633322373091, 14.4393884282958, 11.2339563353478, 
    7.74933708914689, 7.1182095475238, 7.39260082121406, 0.627435381320771, 
    9.15473202689768, 13.6559037433263, 7.14786907480758)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)), structure(list(
    ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 
    9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 
    13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
    17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 
    21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 
    25L, 25L), .Label = c("01", "04", "06", "07", "08", "09", 
    "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
    "22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-11.7785042972793, -9.14927207125904, 
    -7.58190508537766, -4.01515836011381, -6.60165385653499, 
    -2.02861964460179, 4.46729570509601, 2.54036572774646, 2.22923889930115, 
    -0.883620011106743, -2.63569087592267, -2.0629672230873, 
    1.14544537612393, 2.08056674659401, 0.0422658298956365, 13.2986259796748, 
    5.06669915366333, 3.93467692474742, 0.0229069420708053, 4.31923128857779, 
    0.237726051904304, 1.89972383690448, 3.2371880079134, 0.318100791495115, 
    -8.08292381883298, -5.73174008540523, -15.7998485301436, 
    1.75469999857951, 0.677370118816266, -1.8397955509895, 2.55445787016256, 
    -0.380810453692585, 0.62462329496673, 2.61316333850434, 2.68202480583985, 
    1.76690658846479, 0.148635887703097, -0.958853757041888, 
    -3.17305964093897, -7.82526758429289, -6.58557573679886, 
    -4.39207076049089, 2.36752476749952, 0.594715760553033, -0.29794568443312, 
    -4.5365387390683, 0.196832250811775, -2.70852853745588, 0.498995124872827, 
    0.165171574219401, 0.269498974991661, 0.901948386281446, 
    -2.45955661653299, 1.63525170542944, 0.155897732673534, 1.8491735212703, 
    -0.856727109535223, -1.16182571974245, 1.07658425742917, 
    -2.21433585407388, 4.3385479368043, 4.40588599635354, 0.127710423625772, 
    -6.26956613362656, -1.17658595005389, -7.25886366924741, 
    -0.888293709383838, -2.14177059335841, -2.42141595261389, 
    -2.958120275175, -5.1274001953303, -5.32347488769128, -4.41290818553442, 
    -1.21404719262173, -4.23649270310915)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)), structure(list(
    ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
    4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 
    9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 
    13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
    17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 
    21L, 21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 
    25L, 25L), .Label = c("01", "04", "06", "07", "08", "09", 
    "10", "11", "12", "13", "15", "16", "17", "18", "19", "21", 
    "22", "23", "25", "27", "28", "30", "44", "46", "49"), class = "factor"), 
    GR = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), SES = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), 
    COND = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", "NEG-NOC", "NEU-NOC"
    ), class = "factor"), value = c(-5.96429031525769, -5.10918437158799, 
    -2.81732229625975, -1.43557366487622, -3.14872157912645, 
    0.160393685024631, 3.52155765271648, 2.10437989449921, 2.70693992810407, 
    5.49897156207812, 5.81171180245335, -1.37301251388987, -0.434363848460157, 
    2.87987510596148, -1.27152670283348, 17.2093269365993, 7.79412746755931, 
    8.11964589961276, 4.95253363860044, 9.50695673265293, 4.15235381401148, 
    6.1294488368639, 8.01447499455337, 0.783414018677801, -1.24197194087055, 
    -0.487178595894761, -9.79031812534203, 4.22150266269492, 
    4.20139847550095, 0.208005397351335, 4.19096721581768, 0.815283302847055, 
    1.48137456347872, 2.0809543999959, 4.35199943309111, 2.84860039832237, 
    3.05879540677983, 2.11976068962167, -0.269002712326028, -2.77155065610474, 
    -2.59002218694999, 0.17928456999128, 2.24515223348079, 1.88805943988563, 
    -0.0920286086411814, -2.00968595029144, 2.59427260100332, 
    -1.27622011197768, 0.588399071755827, -1.43982473126936, 
    1.96978732491278, -0.338674980283045, -1.86484698930706, 
    -0.0154791822607025, 2.55036185373462, 4.42520405730058, 
    -0.599156247027551, 1.60091251589958, 4.7367320574401, -0.192490723623988, 
    4.8452288234686, 5.71745745981867, 1.02554478706585, -4.5951256708181, 
    1.1704842909792, -7.42770276334892, 3.15655538248828, -0.639830772856786, 
    -0.345116641695513, -0.0391030568720636, -2.61585906518491, 
    -2.71685194532693, -1.7348388034111, 1.00287124847525, -2.4844653851482
    )), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-75L)), structure(list(ID = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 
8L, 8L, 9L, 9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 
13L, 13L, 13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 
17L, 17L, 18L, 18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 21L, 
21L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 25L, 25L
), .Label = c("01", "04", "06", "07", "08", "09", "10", "11", 
"12", "13", "15", "16", "17", "18", "19", "21", "22", "23", "25", 
"27", "28", "30", "44", "46", "49"), class = "factor"), GR = 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, 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, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "RP", class = "factor"), 
    SES = 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, 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, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L), .Label = "V", class = "factor"), COND = structure(c(1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("NEG-CTR", 
    "NEG-NOC", "NEU-NOC"), class = "factor"), value = c(8.23981597718437, 
    9.51261484648731, 9.42367409925817, 5.06332653216481, 5.02619159395405, 
    9.07903916629231, 7.56089165217984, 5.49719893790597, 4.91476855238182, 
    13.0320953572069, 10.8414516494484, 5.86927622259489, 3.25309970442897, 
    4.6847880297099, 2.71096740085175, 25.567439566524, 16.3241813617706, 
    13.0990192799703, 11.9200281736866, 14.6901305277101, 9.67397418905514, 
    10.2974302220899, 12.0768070828642, 5.9401530589224, 12.4817579327688, 
    12.419526465857, 1.00612108990875, 9.63063375751153, 10.5631237176538, 
    3.08031473770521, 3.35694102903017, 4.28046277054405, -0.133592200169464, 
    6.9103658689166, 7.64737651416791, 6.75669517393108, 8.5369185279747, 
    7.08645126073423, 4.47409706618326, 4.39617687043259, 3.27924738047746, 
    6.06169418872804, 5.34939694712468, 5.58288092654703, 4.85729686493463, 
    7.38032829587839, 11.7259526759912, 4.95764559864061, 6.24066579989613, 
    3.49843659402445, 4.07498375647916, 3.55732294589389, 1.33918111568512, 
    0.956782967443242, 2.32002496709926, 3.15289777246607, -0.832211906889126, 
    6.39254974438057, 7.0533787627062, 2.97245026797807, 6.23573445580928, 
    7.6052386193207, 2.98791225155534, 3.10850022259445, 8.12060882554471, 
    -0.00459651443883508, 13.5899217198075, 9.93070913311253, 
    8.10285456644801, 5.04464304009428, 2.02262615478956, 1.0510618938653, 
    5.62233873107127, 10.1193593084848, 5.87476640145049)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -75L)))), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -6L), groups = structure(list(
    signals = c("LPPearlyCz", "LPPearlyFCz", "LPPearlyPz", "P3Cz", 
    "P3FCz", "P3Pz"), .rows = structure(list(5L, 4L, 6L, 2L, 
        1L, 3L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L), .drop = TRUE))
>

我試圖檢查例外值的存在如下：

outliers_table <- df_join %>%
  unnest() %>% 
  dplyr::select(COND, signals, value) %>% 
  group_by(COND) %>%  #it is the equivalent to use as grouping variable the time
  identify_outliers(value)

那轉身 那轉身

A tibble: 30 x 5
   COND    signals     value is.outlier is.extreme
   <fct>   <chr>       <dbl> <lgl>      <lgl>     
 1 NEG-CTR P3FCz       -11.6 TRUE       FALSE     
 2 NEG-CTR P3Cz         13.7 TRUE       FALSE     
 3 NEG-CTR P3Pz         15.1 TRUE       FALSE     
 4 NEG-CTR P3Pz         13.7 TRUE       FALSE     
 5 NEG-CTR P3Pz         23.4 TRUE       TRUE      
 6 NEG-CTR P3Pz         15.8 TRUE       FALSE     
 7 NEG-CTR P3Pz         14.4 TRUE       FALSE     
 8 NEG-CTR LPPearlyFCz -11.8 TRUE       FALSE     
 9 NEG-CTR LPPearlyCz   17.2 TRUE       FALSE     
10 NEG-CTR LPPearlyPz   25.6 TRUE       TRUE

如果我有興趣洗掉所有這些真正極端的值，我該怎么做才能使用一些迭代函式或一些 if 陳述句？請考慮其他替代方案，以防它更容易（也為了保留我通過添加另一個 %>% 命令列撰寫的命令）來撰寫 for 回圈或其他一些函式。

提前致謝

uj5u.com熱心網友回復：

好的。讓我們一起一步一步來。據我了解，您非常擔心您的資料（我將其保存在變數中df）中存在例外值甚至極端值。首先，我們將從您的資料中僅提取一個分組的標題并過濾COND ==" NEG-NOC "

library(tidyverse)
library(rstatix)
library(outliers)

data = df$data[[1]] %>% filter(COND=="NEG-NOC")

現在讓我們考慮我們將使用哪種例外值識別方法。我們可以boxplot為此使用該功能。

boxplot.stats(data$value)$out
#[1] 8.164181

這很好，但它只會以向量形式為我們提供例外值。第二種方法是使用identify_outliers. 這給了我們一個tibble但仍然只有那些具有這些例外值的行。

data %>% identify_outliers(variable = "value")
# # A tibble: 1 x 7
# ID    GR    SES   COND    value is.outlier is.extreme
# <fct> <fct> <fct> <fct>   <dbl> <lgl>      <lgl>     
#   1 11    RP    V     NEG-NOC  8.16 TRUE       FALSE

好吧，讓我們使用包中的outlier函式 outliers。這可以給我們一個邏輯向量。

outlier(data$value, opposite = T)
#[1] 8.164181
outlier(data$value, opposite = T, logical = T)
# [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#[22] FALSE FALSE FALSE FALSE

但是，這兩種方法都無法幫助您決定如何處理這些例外值。請閱讀此仔細。如您所見，您有三個選項可供選擇：插補、上限、預測。你會選擇哪一個？我選擇了封頂。所以我寫了一個小函式來識別例外值、極值并在封頂后額外回傳你的值。

fOutCapp = function(data){
  x = data$value
  qnt = quantile(x, probs=c(.25, .75), na.rm = T)
  caps = quantile(x, probs=c(.05, .95), na.rm = T)
  H = 1.5 * IQR(x, na.rm = T)
  He = 3 * IQR(x, na.rm = T)
  is.outlier = (x < (qnt[1] - H)) | (x > (qnt[2]   H))
  x[x < (qnt[1] - H)] <- caps[1]
  x[x > (qnt[2]   H)] <- caps[2]
  data %>% group_by(COND) %>% 
    mutate(
      is.outlier = is.outlier,
      is.extreme = (x < (qnt[1] - He)) | (x > (qnt[2]   He)),
      cap.value = x
    )
}

讓我們看看它是否有效

data %>% fOutCapp() %>% filter(is.outlier)
# A tibble: 1 x 8
# ID    GR    SES   COND    value is.outlier is.extreme cap.value
# <fct> <fct> <fct> <fct>   <dbl> <lgl>      <lgl>          <dbl>
#   1 11    RP    V     NEG-NOC  8.16 TRUE       FALSE           4.95
data %>% fOutCapp()
# A tibble: 25 x 8
# ID    GR    SES   COND      value is.outlier is.extreme cap.value
# <fct> <fct> <fct> <fct>     <dbl> <lgl>      <lgl>          <dbl>
#   1 01    RP    V     NEG-NOC -11.1   FALSE      FALSE        -11.1  
# 2 04    RP    V     NEG-NOC   0.239 FALSE      FALSE          0.239
# 3 06    RP    V     NEG-NOC  -2.96  FALSE      FALSE         -2.96 
# 4 07    RP    V     NEG-NOC   1.09  FALSE      FALSE          1.09 
# 5 08    RP    V     NEG-NOC   2.99  FALSE      FALSE          2.99 
# 6 09    RP    V     NEG-NOC   5.42  FALSE      FALSE          5.42 
# 7 10    RP    V     NEG-NOC  -2.83  FALSE      FALSE         -2.83 
# 8 11    RP    V     NEG-NOC   8.16  TRUE       FALSE          4.95 
# 9 12    RP    V     NEG-NOC  -9.83  FALSE      FALSE         -9.83 
# 10 13    RP    V     NEG-NOC   2.12  FALSE      FALSE          2.12 
# ... with 15 more rows

但是請注意，變數中的資料data分組在變數之后 COND。因此，讓我們再撰寫一個小函式來fOutCapp對每個組進行處理。

fOutCappGroup = function(data) data %>% group_by(COND) %>% 
  group_modify(~fOutCapp(.x))

df$data[[1]] %>% fOutCappGroup()
# # A tibble: 75 x 8
# # Groups:   COND [3]
# COND    ID    GR    SES     value is.outlier is.extreme cap.value
# <fct>   <fct> <fct> <fct>   <dbl> <lgl>      <lgl>          <dbl>
#   1 NEG-CTR 01    RP    V     -11.6   FALSE      FALSE        -11.6  
# 2 NEG-CTR 04    RP    V      -0.314 FALSE      FALSE         -0.314
# 3 NEG-CTR 06    RP    V      -0.214 FALSE      FALSE         -0.214
# 4 NEG-CTR 07    RP    V      -2.83  FALSE      FALSE         -2.83 
# 5 NEG-CTR 08    RP    V       4.24  FALSE      FALSE          4.24 
# 6 NEG-CTR 09    RP    V       9.57  FALSE      FALSE          9.57 
# 7 NEG-CTR 10    RP    V      -6.13  FALSE      FALSE         -6.13 
# 8 NEG-CTR 11    RP    V       0.529 FALSE      FALSE          0.529
# 9 NEG-CTR 12    RP    V      -7.74  FALSE      FALSE         -7.74 
# 10 NEG-CTR 13    RP    V       1.27  FALSE      FALSE          1.27 
# # ... with 65 more rows

答對了。一切都很好。現在我們只需要做一個簡單的變異。

df %>% group_by(signals) %>% 
  mutate(data = map(data, ~fOutCappGroup(.x))) %>% 
  unnest(data)

輸出

# A tibble: 450 x 9
# Groups:   signals [6]
   signals COND    ID    GR    SES     value is.outlier is.extreme cap.value
   <chr>   <fct>   <fct> <fct> <fct>   <dbl> <lgl>      <lgl>          <dbl>
 1 P3FCz   NEG-CTR 01    RP    V     -11.6   FALSE      FALSE        -11.6  
 2 P3FCz   NEG-CTR 04    RP    V      -0.314 FALSE      FALSE         -0.314
 3 P3FCz   NEG-CTR 06    RP    V      -0.214 FALSE      FALSE         -0.214
 4 P3FCz   NEG-CTR 07    RP    V      -2.83  FALSE      FALSE         -2.83 
 5 P3FCz   NEG-CTR 08    RP    V       4.24  FALSE      FALSE          4.24 
 6 P3FCz   NEG-CTR 09    RP    V       9.57  FALSE      FALSE          9.57 
 7 P3FCz   NEG-CTR 10    RP    V      -6.13  FALSE      FALSE         -6.13 
 8 P3FCz   NEG-CTR 11    RP    V       0.529 FALSE      FALSE          0.529
 9 P3FCz   NEG-CTR 12    RP    V      -7.74  FALSE      FALSE         -7.74 
10 P3FCz   NEG-CTR 13    RP    V       1.27  FALSE      FALSE          1.27 
# ... with 440 more rows

這樣你的句子就完成了。我們不僅識別了例外值，而且還對它們應用了上限。現在決定是使用value變數還是cap.value變數進行進一步分析。決定權在你。

uj5u.com熱心網友回復：

如果您的函式根據給定值是否為例外值identify_outliers回傳TRUE/ FALSE，value則您可以使用filter(identify_outliers(value))并繼續。

轉載請註明出處，本文鏈接：https://www.uj5u.com/net/327641.html

標籤：r 循环 if 语句 迭代 离群值

上一篇：一個方法如何在app.use()中作為回呼作業？

下一篇：groupby在R中的類字符列上進行匯總

標籤雲
其他(157675) Python(38076) JavaScript(25376) Java(17977) C(15215) 區塊鏈(8255) C＃(7972) AI(7469) 爪哇(7425) MySQL(7132) html(6777) 基礎類(6313) sql(6102) 熊猫(6058) PHP(5869) 数组(5741) R(5409) Linux(5327) 反应(5209) 腳本語言(PerlPython)(5129) 非技術區(4971) Android(4554) 数据框(4311) css(4259) 节点.js(4032) C語言(3288) json(3245) 列表(3129) 扑(3119) C++語言(3117) 安卓(2998) 打字稿(2995) VBA(2789) Java相關(2746) 疑難問題(2699) 细绳(2522) 單片機工控(2479) iOS(2429) ASP.NET(2402) MongoDB(2323) 麻木的(2285) 正则表达式(2254) 字典(2211) 循环(2198) 迅速(2185) 擅长(2169) 镖(2155) 功能(1967) .NET技术(1958) Web開發(1951) python-3.x(1918) HtmlCss(1915) 弹簧靴(1913) C++(1909) xml(1889) PostgreSQL(1872) .NETCore(1853) 谷歌表格(1846) Unity3D(1843) for循环(1842)

熱門瀏覽
  • WebAPI簡介

    Web體系結構： 有三個核心：資源(resource)，URL(統一資源識別符號)和表示 他們的關系是這樣的：一個資源由一個URL進行標識，HTTP客戶端使用URL定位資源，表示是從資源回傳資料，媒體型別是資源回傳的資料格式。 接下來我們說下HTTP. HTTP協議的系統是一種無狀態的方式，使用請求/ ......

    uj5u.com 2020-09-09 22:07:47 more
  • asp.net core 3.1 入口：Program.cs中的Main函式

    本文分析Program.cs 中Main()函式中代碼的運行順序分析asp.net core程式的啟動，重點不是剖析原始碼，而是理清程式開始時執行的順序。到呼叫了哪些實體，哪些法方。asp.net core 3.1 的程式入口在專案Program.cs檔案里，如下。ususing System; us ......

    uj5u.com 2020-09-09 22:07:49 more
  • asp.net網站作為websocket服務端的應用該如何寫

    最近被websocket的一個問題困擾了很久，有一個需求是在web網站中搭建websocket服務。客戶端通過網頁與服務器建立連接，然后服務器根據ip給客戶端網頁發送資訊。 其實，這個需求并不難，只是剛開始對websocket的內容不太了解。上網搜索了一下，有通過asp.net core 實作的、有 ......

    uj5u.com 2020-09-09 22:08:02 more
  • ASP.NET 開源匯入匯出庫Magicodes.IE Docker中使用

    Magicodes.IE在Docker中使用 更新歷史 2019.02.13 【Nuget】版本更新到2.0.2 【匯入】修復單列匯入的Bug，單元測驗“OneColumnImporter_Test”。問題見（https://github.com/dotnetcore/Magicodes.IE/is ......

    uj5u.com 2020-09-09 22:08:05 more
  • 在webform中使用ajax

    如果你用過Asp.net webform， 說明你也算是.NET 開發的老兵了。WEBform應該是2011 2013左右，當時還用visual studio 2005、 visual studio 2008。后來基本都用的是MVC。 如果是新開發的專案，估計沒人會用webform技術。但是有些舊版 ......

    uj5u.com 2020-09-09 22:08:50 more
  • iis添加asp.net網站，訪問提示：由于擴展配置問題而無法提供您請求的

    今天在iis服務器配置asp.net網站，遇到一個問題，記錄一下： 問題：由于擴展配置問題而無法提供您請求的頁面。如果該頁面是腳本，請添加處理程式。如果應下載檔案，請添加 MIME 映射。 WindowServer2012服務器，添加角色安裝完.netframework和iis之后，運行aspx頁面 ......

    uj5u.com 2020-09-09 22:10:00 more
  • WebAPI-處理架構

    帶著問題去思考，大家好！ 問題1：HTTP請求和回傳相應的HTTP回應資訊之間發生了什么？ 1：首先是最底層，托管層，位于WebAPI和底層HTTP堆疊之間 2：其次是 訊息處理程式管道層，這里比如日志和快取。OWIN的參考是將訊息處理程式管道的一些功能下移到堆疊下端的OWIN中間件了。 3：控制器處理 ......

    uj5u.com 2020-09-09 22:11:13 more
  • 微信門戶開發框架-使用指導說明書

    微信門戶應用管理系統，采用基于 MVC + Bootstrap + Ajax + Enterprise Library的技術路線，界面層采用Boostrap + Metronic組合的前端框架，資料訪問層支持Oracle、SQLServer、MySQL、PostgreSQL等資料庫。框架以MVC5，... ......

    uj5u.com 2020-09-09 22:15:18 more
  • WebAPI-HTTP編程模型

    帶著問題去思考，大家好！它是什么？它包含什么？它能干什么？ 訊息 HTTP編程模型的核心就是訊息抽象，表示為：HttPRequestMessage,HttpResponseMessage.用于客戶端和服務端之間交換請求和回應訊息。 HttpMethod類包含了一組靜態屬性： private stat ......

    uj5u.com 2020-09-09 22:15:23 more
  • 部署WebApi隨筆

    一、跨域 NuGet參考Microsoft.AspNet.WebApi.Cors WebApiConfig.cs中配置： // Web API 配置和服務 config.EnableCors(new EnableCorsAttribute("*", "*", "*")); 二、清除默認回傳XML格式 ......

    uj5u.com 2020-09-09 22:15:48 more
最新发布
  • C#多執行緒學習(二) 如何操縱一個執行緒

    <a href="https://www.cnblogs.com/x-zhi/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/2943582/20220801082530.png" alt="" /></...

    uj5u.com 2023-04-19 09:17:20 more
  • C#多執行緒學習(二) 如何操縱一個執行緒

    C#多執行緒學習(二) 如何操縱一個執行緒 執行緒學習第一篇：C#多執行緒學習(一) 多執行緒的相關概念 下面我們就動手來創建一個執行緒，使用Thread類創建執行緒時，只需提供執行緒入口即可。（執行緒入口使程式知道該讓這個執行緒干什么事） 在C#中，執行緒入口是通過ThreadStart代理（delegate）來提供的 ......

    uj5u.com 2023-04-19 09:16:49 more
  • 記一次 .NET某醫療器械清洗系統 卡死分析

    <a href="https://www.cnblogs.com/huangxincheng/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/214741/20200614104537.png" alt="" /&g...

    uj5u.com 2023-04-18 08:39:04 more
  • 記一次 .NET某醫療器械清洗系統 卡死分析

    一：背景 1. 講故事 前段時間協助訓練營里的一位朋友分析了一個程式卡死的問題，回過頭來看這個案例比較經典，這篇稍微整理一下供后來者少踩坑吧。 二：WinDbg 分析 1. 為什么會卡死 因為是表單程式，理所當然就是看主執行緒此時正在做什么? 可以用 ~0s ; k 看一下便知。 0:000> k # ......

    uj5u.com 2023-04-18 08:33:10 more
  • SignalR, No Connection with that ID，IIS

    <a href="https://www.cnblogs.com/smartstar/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/u36196.jpg" alt="" /></a>...

    uj5u.com 2023-03-30 17:21:52 more
  • 一次對pool的誤用導致的.net頻繁gc的診斷分析

    <a href="https://www.cnblogs.com/dotnet-diagnostic/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/3115652/20230225090434.png" alt=""...

    uj5u.com 2023-03-28 10:15:33 more
  • 一次對pool的誤用導致的.net頻繁gc的診斷分析

    <a href="https://www.cnblogs.com/dotnet-diagnostic/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/3115652/20230225090434.png" alt=""...

    uj5u.com 2023-03-28 10:13:31 more
  • C#遍歷指定檔案夾中所有檔案的3種方法

    <a href="https://www.cnblogs.com/xbhp/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/957602/20230310105611.png" alt="" /></a&...

    uj5u.com 2023-03-27 14:46:55 more
  • C#/VB.NET：如何將PDF轉為PDF/A

    <a href="https://www.cnblogs.com/Carina-baby/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/2859233/20220427162558.png" alt="" />...

    uj5u.com 2023-03-27 14:46:35 more
  • 武裝你的WEBAPI-OData聚合查詢

    <a href="https://www.cnblogs.com/podolski/" target="_blank"><img width="48" height="48" class="pfs" src="https://pic.cnblogs.com/face/616093/20140323000327.png" alt="" /><...

    uj5u.com 2023-03-27 14:46:16 more

Copyright © 2010-2020 有解無憂