R语言 如何为每个独立变量建立斯皮尔曼相关矩阵？

but5z9lq  于 2023-02-26  发布在  其他
关注(0)|答案(3)|浏览(165)

我请求帮助制作一个斯皮尔曼相关矩阵，我可以在每个条件下可视化TAC（因变量）和浓度之间是否存在相关性？如果可能，包括p.adjust。
我所寻找的矩阵类型是一个易于阅读的，其中包括斯皮尔曼的p和p值。我感谢任何人谁可以帮助我或指出我在正确的方向。
以下是我数据框：

> str(table5)
'data.frame':   280 obs. of  5 variables:
 $ treatment    : chr  "control" "control" "control" "control" ...
 $ concentration: num  0 0 0 0 0 0 0 0 0 0 ...
 $ day          : chr  "day 00" "day 00" "day 00" "day 00" ...
 $ TAC          : num  0.0135 0.0162 0.0146 0.0153 0.0128 ...
 $ conditions   : Factor w/ 15 levels "controlday 00",..: 1 1 1 1 1 1 1 1 2 2 ...
> dput(table5)
structure(list(treatment = c("control", "control", "control", 
"control", "control", "control", "control", "control", "control", 
"control", "control", "control", "control", "control", "control", 
"control", "control", "control", "control", "control", "control", 
"control", "control", "control", "control", "control", "control", 
"control", "control", "control", "control", "control", "control", 
"control", "control", "control", "control", "control", "control", 
"control", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", "nZn", 
"nZn", "nZn", "nZn", "nZn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", "Zn", 
"Zn", "Zn", "Zn", "Zn"), concentration = c(0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100), day = c("day 00", 
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00", 
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07", 
"day 07", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14", 
"day 14", "day 14", "day 21", "day 21", "day 21", "day 21", "day 21", 
"day 21", "day 21", "day 21", "day 28", "day 28", "day 28", "day 28", 
"day 28", "day 28", "day 28", "day 28", "day 00", "day 00", "day 00", 
"day 00", "day 00", "day 00", "day 00", "day 00", "day 07", "day 07", 
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 14", 
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14", 
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21", 
"day 21", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28", 
"day 28", "day 28", "day 00", "day 00", "day 00", "day 00", "day 00", 
"day 00", "day 00", "day 00", "day 07", "day 07", "day 07", "day 07", 
"day 07", "day 07", "day 07", "day 07", "day 14", "day 14", "day 14", 
"day 14", "day 14", "day 14", "day 14", "day 14", "day 21", "day 21", 
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 28", 
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28", 
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00", 
"day 00", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07", 
"day 07", "day 07", "day 14", "day 14", "day 14", "day 14", "day 14", 
"day 14", "day 14", "day 14", "day 21", "day 21", "day 21", "day 21", 
"day 21", "day 21", "day 21", "day 21", "day 28", "day 28", "day 28", 
"day 28", "day 28", "day 28", "day 28", "day 28", "day 00", "day 00", 
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 07", 
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07", 
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14", 
"day 14", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21", 
"day 21", "day 21", "day 28", "day 28", "day 28", "day 28", "day 28", 
"day 28", "day 28", "day 28", "day 00", "day 00", "day 00", "day 00", 
"day 00", "day 00", "day 00", "day 00", "day 07", "day 07", "day 07", 
"day 07", "day 07", "day 07", "day 07", "day 07", "day 14", "day 14", 
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 21", 
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21", 
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28", 
"day 28", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00", 
"day 00", "day 00", "day 07", "day 07", "day 07", "day 07", "day 07", 
"day 07", "day 07", "day 07", "day 14", "day 14", "day 14", "day 14", 
"day 14", "day 14", "day 14", "day 14", "day 21", "day 21", "day 21", 
"day 21", "day 21", "day 21", "day 21", "day 21", "day 28", "day 28", 
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28"), 
    TAC = c(0.0134723395589115, 0.0161888871061509, 0.0146337654145718, 
    0.0153067871292595, 0.012800314735395, 0.0160841665978896, 
    0.0140621616691814, 0.0135425580967982, 0.0132198270328205, 
    0.0138496077219653, 0.0135775493518084, 0.0126333962864469, 
    0.0164821881641534, 0.0132516331108305, 0.0157791571175251, 
    0.0129960024291699, 0.0146323678504021, 0.0134451215151322, 
    0.0143262838325461, 0.0153573779185249, 0.0139773746147923, 
    0.0159350865128266, 0.0156720782857077, 0.0155096081292032, 
    0.013476349735956, 0.0140104181996115, 0.0129878390010014, 
    0.0147239859165112, 0.015160930718777, 0.0148955399340424, 
    0.013274378116328, 0.0153663044374496, 0.0145472559523844, 
    0.0132898660703847, 0.0139871399975842, 0.0124985111701027, 
    0.0149240276338179, 0.0129573902698069, 0.0147729343794709, 
    0.0128674264777598, 0.0147815872982594, 0.0139767796824041, 
    0.0144185398405766, 0.0155799146991459, 0.0135417909851351, 
    0.015988596586438, 0.0139603963976125, 0.0126397298299191, 
    0.013297964384596, 0.012347536157165, 0.0152573470818857, 
    0.0136566619097667, 0.0125192707022401, 0.0141156296691061, 
    0.0139603724286662, 0.0141388938152221, 0.0127749097766803, 
    0.0142082519110294, 0.0149398326676766, 0.0143207529313558, 
    0.0144381103787128, 0.0149147414885484, 0.0139224295866318, 
    0.0161358891403436, 0.0151690152511571, 0.0120945286936824, 
    0.0153132383654698, 0.0131770823852777, 0.0136750345235747, 
    0.0129352436377984, 0.0162120454010317, 0.0155409171425954, 
    0.0135940425474181, 0.0142951343511937, 0.0143779323175896, 
    0.0136891451722703, 0.0140286347004686, 0.0122667606250391, 
    0.0152446224172418, 0.013442306549535, 0.0129068996979612, 
    0.0147404146947943, 0.013688825582269, 0.0130193063055386, 
    0.01285971255513, 0.0151660181611206, 0.0138280467330508, 
    0.0135147736966651, 0.0158580706409006, 0.0149366602534351, 
    0.0106554950909403, 0.0179654260106192, 0.0120425346368713, 
    0.0145387164119486, 0.0139546280207597, 0.0121871897075845, 
    0.0150418870034593, 0.0148117380734173, 0.0139690179111281, 
    0.0170751257982307, 0.0129661477952429, 0.0144612227917873, 
    0.0146065893466387, 0.0126241343210384, 0.0170751257982307, 
    0.0130964557093226, 0.0134570968344701, 0.0165480203562944, 
    0.0151921149184481, 0.0130666062376204, 0.012722050697886, 
    0.0155582048904096, 0.0125288074742436, 0.016985639190516, 
    0.0176528351294189, 0.0138432089287227, 0.013890319218671, 
    0.017035215335001, 0.0168839977227436, 0.0133203267470888, 
    0.013892777179513, 0.0155216139064973, 0.0130076218759369, 
    0.013903958340264, 0.0135000204009635, 0.0148519977852621, 
    0.0153029154169557, 0.0141832966293512, 0.0176005510379328, 
    0.0180687740940438, 0.0177789446952697, 0.0182099087520794, 
    0.0184723827329167, 0.022483746075728, 0.0196648164641345, 
    0.0170131886149416, 0.0215058343136062, 0.0211259597744559, 
    0.0196373761289472, 0.0206737739206, 0.020532594441278, 0.0193494766153245, 
    0.0211617300063814, 0.0213333413267872, 0.0202163436360403, 
    0.0236752367085596, 0.0231873026647459, 0.0228522660496144, 
    0.0238366734630018, 0.0264524093818515, 0.0268093919646026, 
    0.0252668406573153, 0.0258403852690662, 0.0223986018317785, 
    0.0272147558779617, 0.0225116847733454, 0.0247724813762193, 
    0.022691182948792, 0.0235805783268122, 0.0270689051186104, 
    0.0126334908832258, 0.0164665820507107, 0.0129386884401034, 
    0.0119158011756844, 0.0130928729787235, 0.0149940706645974, 
    0.0129535502638655, 0.0162831996423606, 0.0176755444192191, 
    0.0161755659998132, 0.0174173101524856, 0.0155714069341957, 
    0.01433383826834, 0.0143819293817603, 0.0185494616259894, 
    0.0140319779691521, 0.0144114680062016, 0.0174497227904159, 
    0.0180907703704672, 0.0157478259355293, 0.0158958906812569, 
    0.0147163839619763, 0.0146701443994308, 0.0180369287296324, 
    0.0149336258279806, 0.0186097801562105, 0.0137231521985133, 
    0.0153650910635747, 0.0138998273293687, 0.0155199902217533, 
    0.0163903022171882, 0.015754928008943, 0.0171808546793322, 
    0.0154244829039175, 0.0134954450270778, 0.0147187179502944, 
    0.0160939056001929, 0.0145497150558122, 0.0154571534643691, 
    0.015511148172344, 0.0132885919777709, 0.0138910418368534, 
    0.0152496449072613, 0.0132820365830201, 0.013480084079182, 
    0.016683045565325, 0.0176337406920335, 0.0151657804062655, 
    0.0125455114843902, 0.0118102856445592, 0.0116410665300014, 
    0.0146556231989517, 0.014464999427952, 0.0121229802720933, 
    0.0146834533301593, 0.0121645122630423, 0.0136816673389857, 
    0.0135984961089614, 0.0164906141382343, 0.0149265724276527, 
    0.0163311308492402, 0.017967595623527, 0.0143263172313383, 
    0.0145117513172078, 0.0149694356038913, 0.0136478358101476, 
    0.0148523043836901, 0.0140267859486034, 0.0136857372651645, 
    0.0161384954212, 0.0171836598216303, 0.0165288287203719, 
    0.0163703032374203, 0.0149628937118673, 0.0167639896711626, 
    0.0144140290861155, 0.0164700832677882, 0.017097353142466, 
    0.0177233791174971, 0.016410406871025, 0.0145656397252108, 
    0.0127795571441824, 0.0139787766512734, 0.0145603577832239, 
    0.0130325210010334, 0.0157142193796273, 0.0165295708322065, 
    0.0154878492755022, 0.0176888974165639, 0.0186435561581489, 
    0.0177330425080685, 0.0182856446463086, 0.0219973970170363, 
    0.0217533371623466, 0.0176290655250839, 0.0202192044566584, 
    0.01917805317661, 0.0186277616395779, 0.0170154664932417, 
    0.0195884686724334, 0.0201420675026667, 0.0183148068985733, 
    0.020836323932372, 0.0207067552945439, 0.018534989031893, 
    0.019680916901509, 0.0219673944081694, 0.0236890701508884, 
    0.0235543150426157, 0.0234233849979097, 0.0210565415662947, 
    0.0232511101944444, 0.0227186732866978, 0.0225332903957415, 
    0.0234773944195847, 0.0229988542468931, 0.022618525386521, 
    0.0197686090869307, 0.0186686467858637, 0.0189525178016395
    ), conditions = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 6L, 
    6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
    8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 6L, 6L, 6L, 6L, 
    6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 
    8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 
    10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 
    11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 
    13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 
    14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 
    12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 
    13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 
    15L, 15L, 15L, 15L, 15L, 15L, 15L), levels = c("controlday 00", 
    "controlday 07", "controlday 14", "controlday 21", "controlday 28", 
    "nZnday 00", "nZnday 07", "nZnday 14", "nZnday 21", "nZnday 28", 
    "Znday 00", "Znday 07", "Znday 14", "Znday 21", "Znday 28"
    ), class = "factor")), class = "data.frame", row.names = c(NA, 
-280L))
r

3条答案

按热度按时间
p4tfgftt

p4tfgftt1#

您可以按conditions对 Dataframe 进行split，使用lapply从每个子 Dataframe 中获取cor.test，并从每个子 Dataframe 中创建一行 Dataframe （组/相关性/p值），然后将其rbind到单个 Dataframe 中。

do.call(rbind, lapply(split(table5, table5$conditions), function(d) { 
  x <- cor.test(d$concentration, d$TAC, method = 'spearman')
  data.frame(group = d$conditions[1], cor = x$estimate, 
             p = scales::pvalue(x$p.value, add_p = TRUE))
})) |> `rownames<-`(NULL)
#>            group         cor       p
#> 1  controlday 00          NA    <NA>
#> 2  controlday 07          NA    <NA>
#> 3  controlday 14          NA    <NA>
#> 4  controlday 21          NA    <NA>
#> 5  controlday 28          NA    <NA>
#> 6      nZnday 00 -0.05160468 p=0.811
#> 7      nZnday 07  0.70034929 p<0.001
#> 8      nZnday 14  0.71509349 p<0.001
#> 9      nZnday 21  0.73720978 p<0.001
#> 10     nZnday 28  0.78144237 p<0.001
#> 11      Znday 00  0.23590713 p=0.267
#> 12      Znday 07  0.46444216 p=0.022
#> 13      Znday 14  0.56765153 p=0.004
#> 14      Znday 21  0.65611670 p<0.001
#> 15      Znday 28  0.81830286 p<0.001

前四组都具有NA值，因为concentration的值对于整个组为0，因此标准差为0。

赞(0）分享  回复(0）举报  2023-02-26
ih99xse1

ih99xse12#

使用tidyverse-假设OP表示cor.test，因为正在询问P值。按"治疗"、"天"条件分组，将cor.test应用于TAC、浓度，将list输出转换为tibble，broom::tidy和unnest位于tibble列

library(dplyr) # version >= 1.1.0
library(tidyr)
table5 %>%
   reframe(cor = broom::tidy(cor.test(TAC, concentration,
    method = "spearman")), .by = c("treatment", "day", "conditions")) %>% 
   unnest(where(is_tibble))
  • 输出
# A tibble: 15 × 8
   treatment day    conditions    estimate statistic     p.value method                          alternative
   <chr>     <chr>  <fct>            <dbl>     <dbl>       <dbl> <chr>                           <chr>      
 1 control   day 00 controlday 00  NA            NA  NA          Spearman's rank correlation rho two.sided  
 2 control   day 07 controlday 07  NA            NA  NA          Spearman's rank correlation rho two.sided  
 3 control   day 14 controlday 14  NA            NA  NA          Spearman's rank correlation rho two.sided  
 4 control   day 21 controlday 21  NA            NA  NA          Spearman's rank correlation rho two.sided  
 5 control   day 28 controlday 28  NA            NA  NA          Spearman's rank correlation rho two.sided  
 6 nZn       day 00 nZnday 00      -0.0516     2419.  0.811      Spearman's rank correlation rho two.sided  
 7 nZn       day 07 nZnday 07       0.700       689.  0.000139   Spearman's rank correlation rho two.sided  
 8 nZn       day 14 nZnday 14       0.715       655.  0.0000860  Spearman's rank correlation rho two.sided  
 9 nZn       day 21 nZnday 21       0.737       604.  0.0000396  Spearman's rank correlation rho two.sided  
10 nZn       day 28 nZnday 28       0.781       503.  0.00000654 Spearman's rank correlation rho two.sided  
11 Zn        day 00 Znday 00        0.236      1757.  0.267      Spearman's rank correlation rho two.sided  
12 Zn        day 07 Znday 07        0.464      1232.  0.0222     Spearman's rank correlation rho two.sided  
13 Zn        day 14 Znday 14        0.568       994.  0.00381    Spearman's rank correlation rho two.sided  
14 Zn        day 21 Znday 21        0.656       791.  0.000499   Spearman's rank correlation rho two.sided  
15 Zn        day 28 Znday 28        0.818       418.  0.00000103 Spearman's rank correlation rho two.sided
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dzjeubhm

dzjeubhm3#

你可以试试这个R基溶液：

ll <- split(df, df$condition)
sprtest <- lapply(ll, function(x) cor.test(x$TAC, x$concentration, method = "spearman")[c("estimate", "p.value")])
do.call(rbind, lapply(sprtest, unlist))

或者，在底数R中，可以使用by来避免split ting：

by(df, df$condition, FUN = function(x) 
  unlist(cor.test(x$TAC, x$concentration, method = "spearman")[c("estimate", "p.value")]))
do.call(rbind, xx)

无论哪种方式，输出都是：

#               estimate.rho      p.value
# controlday 00           NA           NA
# controlday 07           NA           NA
# controlday 14           NA           NA
# controlday 21           NA           NA
# controlday 28           NA           NA
# nZnday 00      -0.05160468 8.107384e-01
# nZnday 07       0.70034929 1.386591e-04
# nZnday 14       0.71509349 8.597702e-05
# nZnday 21       0.73720978 3.964055e-05
# nZnday 28       0.78144237 6.541909e-06
# Znday 00        0.23590713 2.671011e-01
# Znday 07        0.46444216 2.222910e-02
# Znday 14        0.56765153 3.812867e-03
# Znday 21        0.65611670 4.987260e-04
# Znday 28        0.81830286 1.031562e-06
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