我正在初始化data.frame，如下所示：
combdat <- data.frame(matrix(nrow=50), check.names=FALSE)
在循环中，我现在想要填写其他列。这是这样的：
combdat[,mkr] <- mkrgeno
其中mkr是某个字符，而mkrgeno是相同大小的矢量。但是mkr的某些值是相同的。我需要保留它们。现在他们只会被覆盖。虽然我设置了check.names = FALSE
有人对我有建议。 感谢
富
好的，谢谢，我会尝试更详细地提出我的问题。
我的列表markerinfo包含有关标记的某些信息：
> markerinfo
marker chr pos lod pheno
1 c1m22 1 213.2983 9.1495699 RAPGEF2
2 c4m14 4 131.0000 8.5438345 CACNA1E
3 c1m8 1 63.0000 9.0002544 CACNB3
4 c3m22 3 228.0000 7.1775450 RASA2
5 c1m31 1 305.0000 6.4748053 CACNG6
6 c3m22 3 230.3826 6.5638616 PRKCG
7 c4m11 4 103.0000 6.3592497 CACNA1B
8 c4m26 4 256.0000 8.5450810 CACNA1F
9 c4m14 4 139.0000 5.3257424 CACNG3
10 c2m1 2 0.0000 7.8765658 CACNA1G
11 c2m2 2 13.0000 10.0825268 PRKCA
12 c2m16 2 159.0000 9.2080541 CACNA1D
13 c4m20 4 191.7279 7.2340899 SOS2
14 c2m3 2 16.0000 5.9131295 CACNG5
15 c3m22 3 230.3826 6.7322605 CACNA1A
16 c3m8 3 75.4555 1.1470464 RASGRF1
17 c3m8 3 70.0000 1.9991043 MRAS
18 c1m30 1 288.2238 1.8443845 RRAS2
19 c4m16 4 157.0000 2.1455832 RASGRP3
20 c3m30 3 320.0000 1.9721441 HRAS
21 c1m10 1 90.0000 1.8833757 RASGRF2
22 c3m16 3 161.6888 2.1163401 NRAS
23 c3m20 3 201.9852 2.6265899 RASGRP1
24 c3m30 3 319.4977 1.3677933 KRAS
25 c3m22 3 230.3826 0.7012214 RASGRP2
另一个data.frame是基因型：
c3m1 c3m2 c3m3 c3m4 c3m5 c3m6 c3m7 c3m8 c3m9 c3m10 c3m11 c3m12 c3m13 c3m14 c3m15 c3m16 c3m17 c3m18 c3m19 c3m20 c3m21 c3m22 c3m23 c3m24 c3m25 c3m26 c3m27 c3m28
V1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2
V2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
V3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
V4 1 1 1 1 2 2 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1
V5 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1
V6 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
V7 2 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2
V8 2 2 2 2 2 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
V9 2 2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 2 2 2
V10 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2
V11 1 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
V12 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
V13 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1
V14 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 2
V15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1
V16 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
V17 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
V18 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 2
V19 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
V20 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1
V21 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1
V22 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
V23 2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2
V24 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2
V25 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
V26 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1
V27 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
V28 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2
V29 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
V30 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
V31 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
V32 1 1 1 1 1 1 1 1 2 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
V33 2 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1
V34 1 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 2
V35 1 1 1 1 1 1 2 2 2 2 1 1 2 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1
V36 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1
V37 2 2 2 1 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1
V38 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1
V39 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1
V40 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1
V41 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
V42 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
V43 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
V44 2 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1
V45 2 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 1
V46 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
V47 1 2 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
V48 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 2 2 2
V49 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
V50 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2
c3m29 c3m30 c3m31 c3m32
V1 2 2 2 2
V2 1 1 1 1
V3 2 2 2 1
V4 1 1 1 1
V5 1 1 2 2
V6 1 1 1 2
V7 2 2 2 2
V8 1 1 1 1
V9 2 2 2 2
V10 2 2 2 2
V11 2 2 2 2
V12 1 2 2 2
V13 2 2 2 2
V14 2 2 2 2
V15 1 1 1 1
V16 1 1 1 1
V17 1 1 1 1
V18 2 2 2 2
V19 2 1 1 1
V20 1 1 1 1
V21 2 2 2 2
V22 1 1 1 1
V23 2 2 2 2
V24 2 2 2 2
V25 2 2 2 2
V26 1 1 1 1
V27 1 1 1 1
V28 2 2 1 1
V29 1 1 1 1
V30 1 1 2 2
V31 2 2 2 2
V32 1 1 2 2
V33 1 1 2 2
V34 1 1 1 1
V35 1 1 1 1
V36 1 1 1 1
V37 1 1 1 1
V38 1 2 2 2
V39 1 1 1 1
V40 1 1 1 1
V41 2 2 2 2
V42 2 1 1 1
V43 1 1 1 1
V44 1 1 1 1
V45 2 2 2 2
V46 1 1 2 2
V47 2 2 2 2
V48 2 2 2 2
V49 2 2 2 1
V50 2 2 2 2
另一个名称为：phenotype
> phenotype
RAPGEF2 CACNA1E CACNB3 RASA2 CACNG6 PRKCG CACNA1B CACNA1F CACNG3 CACNA1G PRKCA CACNA1D SOS2
1 -0.247595001 0.053503367 -0.236269632 -0.198393959 0.30226149 0.034393665 0.13201747 -0.055123952 -0.4775578 -0.16406024 -0.601510801 -0.74241018 0.003437553
2 0.076554542 -0.296400594 -0.204362787 -0.083725326 -0.51309205 0.008746035 0.49724817 -0.141674911 -1.5563250 -0.28751925 -0.152694444 -0.83115868 -0.520475369
3 -0.327202333 0.001312523 0.013790261 0.074576720 0.23008238 -0.050573176 -1.04673228 -0.330784609 0.5481467 -0.84388147 -0.743829290 -0.55692338 -0.542878574
4 -0.007847655 -0.138671725 -0.149620332 -0.819362934 -0.11386931 -0.041000430 -0.16221890 0.157342905 -0.4304658 -0.04136305 0.140892816 -1.43966569 -0.502489598
5 -0.288891373 -0.453451438 -0.203315372 -0.432877782 -0.32638230 -0.079509208 -1.07767644 -0.239044759 -2.6685509 -0.34506117 -0.601583079 0.06418028 -0.447845591
6 -0.819438873 -0.186128793 0.010946957 -0.541158848 -0.05467246 -0.091256991 -0.14121849 0.120465369 -0.7412188 -1.45824366 -0.742750372 -0.65559390 -0.024424118
7 0.056713692 0.099570795 -0.140081980 -0.249675499 -0.54844575 -0.142449430 -0.17804642 -0.193517791 -2.8180865 -0.37995253 0.521983735 -0.13506427 -0.496292115
8 -0.415822882 -0.234501809 0.045971377 -0.501303875 0.10064320 -0.123989099 -0.18390119 -0.272184476 -2.3932719 -0.17459784 0.041698873 -0.58292029 -0.030478251
9 0.216626060 -0.055785714 0.102465484 -0.296597579 -0.63187464 -0.043925124 -0.73290772 0.086194905 1.2253629 -0.26787759 -0.186213820 0.21540883 -0.409209752
10 -0.172889555 -0.359033332 0.059976873 -0.122362142 -0.57597543 -0.039439871 -0.37358470 0.046816519 -0.2194930 0.44557540 -0.008582745 0.04681091 -0.151858633
11 -0.799370277 -0.390225142 0.092905430 -0.539360659 -0.46040156 -0.080978159 -1.35509517 -0.183647290 -2.0786691 -0.53105091 -0.537946690 0.15503760 -0.250068125
12 0.165948180 0.084380299 0.072471995 -0.257986088 -0.31888913 -0.113180297 0.09108201 0.081261902 0.4084345 -0.32413522 -0.410390899 -0.52705454 -1.311315609
13 -0.151699952 -0.345160750 0.024127039 -0.199062545 -0.35710011 -0.101713530 -1.52298182 -0.131191677 -1.4151031 0.13075608 -0.112750159 -0.09761248 -0.448443675
14 -0.064050378 0.058414370 -0.049131860 -0.438722188 0.46253165 -0.085058699 -0.48949571 0.312177213 -0.4383044 -0.13332403 -0.952470633 -0.10016991 -0.738450721
15 -0.252830650 -0.021360957 -0.054884002 -0.132821999 0.24029851 0.032595174 -0.28201065 -0.134742072 -1.2429264 -0.20965743 -0.266581307 -0.65461311 -0.026886166
16 -0.302939138 -0.237659778 -0.173316135 -0.433111666 -0.49102642 -0.169569976 -0.14919939 -0.024873565 -1.7566415 -0.11697234 0.250150721 -1.00971694 -0.314707578
17 -0.027397752 -0.220213983 -0.020104605 -0.260175395 0.36690904 -0.015439485 -1.64675598 -0.341331701 1.1341947 0.19718194 0.040220128 0.21718090 -1.082049767
18 -0.084826002 0.075130631 0.085664240 -0.516533930 0.05420691 -0.111368755 -0.54866864 -0.246852143 -0.1673859 0.54867571 -0.491091471 -0.64419595 -0.417058365
19 0.076420274 -0.198417039 -0.209613388 0.275960810 0.20461276 -0.016330089 -2.44087703 0.016533904 -0.9745876 -0.32916054 -0.886846124 -0.03904152 0.423648190
20 -0.341758547 0.027599210 -0.238241196 -0.122481806 -0.53322283 -0.041335840 -0.09748360 0.109385536 0.7184183 -0.42004508 -0.297868841 0.02331034 -0.176874436
21 -0.729225854 -0.366947864 -0.151319971 -0.766507590 -0.93109904 -0.120188998 -0.82125694 -0.069669901 -1.8344670 0.19827344 -0.121866097 -0.64905504 -0.309849450
22 -0.375156253 0.023848706 -0.084361744 -0.444354626 -0.66319529 -0.062962171 -1.20604478 0.168518715 -1.5501544 0.47227482 -0.209564431 -0.47454099 -0.057838134
23 -0.254021124 0.169933007 -0.110124957 -0.321290108 -0.25074586 -0.002748504 -1.67191531 -0.213003128 -0.6702960 0.06601284 0.419818706 -0.24339589 -0.900376250
24 -0.115377716 -0.069793465 -0.082424787 -0.207820569 -0.62402649 -0.057047717 -0.28566344 -0.343388680 -0.9703774 -0.05548410 -0.226484770 -0.73331271 -0.699834400
25 -0.049844861 -0.005899354 -0.014298567 -0.058495200 -1.32936915 -0.080242402 0.21312235 -0.469668455 0.3296792 -0.40816963 0.169496411 -0.06951457 0.678321997
26 -0.722770403 0.103085237 -0.107956995 -0.453234395 -0.79713145 -0.010894595 -0.02192121 -0.183129347 -0.4671715 -0.24454782 0.140808502 -0.16672267 -0.297979736
27 0.177156038 -0.352948087 0.126134036 0.009680394 -0.53116648 0.083284652 -2.56881648 0.040743856 -1.3867899 -0.30346968 -0.943847562 -1.27918873 0.074066589
28 0.031014348 -0.096368514 -0.191044463 -0.150761960 -0.34080995 -0.082406406 0.81100676 0.081447585 -0.4011565 0.46952945 -0.126056643 -0.39482906 0.487768932
29 -0.175479066 -0.406803418 0.060241581 -0.630242987 -0.04177606 -0.099102694 -1.77644280 -0.220901308 -1.0807459 0.25538082 -0.127072554 -0.28244767 0.077844220
30 0.067184617 0.135066792 0.061038582 -0.005188869 -0.28276832 0.002666423 1.11312551 -0.261943690 0.9199570 -0.65210434 -0.308977705 -0.74132895 -0.089614346
31 0.077892704 -0.235195609 -0.067162872 -0.207711784 0.02699528 -0.005653163 -1.61297664 -0.338387970 -0.2485027 -0.10887056 0.343968213 0.09719695 -0.452561385
32 0.142403286 -0.026388719 -0.065678040 -0.362428853 -0.19390021 -0.130526170 -1.21100755 -0.350326700 -1.2818116 -0.72894545 -0.654865598 -0.75242740 -0.379810157
33 -0.001080476 -0.290697156 0.011388500 0.139363744 0.27888665 -0.100895638 0.39220173 -0.346996776 -0.7863979 -0.52910994 -0.558958463 0.31595835 -0.710613795
34 0.224116945 -0.185072933 0.086483429 -0.348059767 -0.25522243 -0.126570401 -2.48462353 -0.402525824 -1.8282210 -0.71284302 0.003787240 0.33055507 -0.485361798
35 -0.254131666 -0.181962657 0.134810146 -0.144177046 -0.42946649 0.006665253 -1.31883436 -0.233832760 -0.8644715 0.02096703 -0.386481233 -0.72159749 0.091061479
36 0.078173409 0.069614224 -0.027333201 -0.338889055 -0.08953657 -0.048366331 -1.05945722 -0.005647055 -0.5515289 -0.99689326 -0.499325729 0.25250542 -0.630618039
37 -0.383263187 0.050446587 0.042835279 -0.187032348 0.10888308 -0.044352563 -0.14934550 -0.123438315 1.1205628 -0.59339281 -0.824166347 -0.50010055 0.362946526
38 0.155765532 -0.095113895 -0.028232352 -0.341382444 -0.28993519 -0.063198747 -0.74942280 -0.262175258 0.3796110 -0.64149439 0.038476888 -0.15428205 -0.070443511
39 -0.352871059 -0.154463839 -0.040044333 -0.215973910 -0.70080752 -0.030485881 -1.59167190 -0.018228487 -2.7482696 -0.81423002 -0.990327664 0.02797165 -0.961506882
40 -0.027887194 -0.500539888 0.101565681 0.026081728 -0.37318368 0.030271868 -1.56720146 0.114323657 -0.9604690 -0.83847006 -0.616284751 -0.22106937 -0.817229295
41 -0.116324675 0.141997059 0.011066622 -0.637030608 -0.06816308 -0.139064501 -0.21884155 -0.133162057 0.3200013 0.40302112 0.196245908 -0.44456908 -0.060186732
42 -0.011563437 -0.097908807 0.010180963 -0.356297511 0.25810039 -0.053495480 -1.23448236 -0.075325095 -2.1873328 0.25853977 0.024608949 -0.24320912 -0.865864499
43 -0.473180079 -0.175778274 -0.153653640 -0.492266908 -0.72545341 -0.089492114 -1.52409341 -0.113111386 -1.8098738 -0.23081989 -0.143859625 -0.33247673 -0.930370376
44 -0.301982544 -0.276093471 -0.172829397 -0.165867999 -0.09716023 -0.074000281 -1.29494575 -0.284384336 -0.3640354 -0.98837691 -0.583165895 -0.22244048 -0.389223572
45 -0.035837132 0.089487455 0.043398895 -0.261321417 -0.14740720 0.086069259 0.50424191 -0.435393685 -0.6916679 0.08837666 -0.764933697 -0.15527777 -0.180500006
46 -0.283577759 0.033526022 -0.053893390 -0.276804767 -0.38757922 0.049021497 1.11676571 -0.165603000 -1.4368988 0.08869823 0.165745244 -0.43123024 -0.409150399
47 -0.579455295 0.045838903 -0.174331523 -0.503703045 -0.51013334 -0.018538629 0.24724654 -0.382273065 -0.2014670 -0.67669484 -0.653328789 0.46375442 -0.481676959
48 -0.308546234 -0.047014302 -0.005449878 -0.350135893 -0.16086990 -0.090971861 0.11738860 -0.360362823 -0.3117357 -0.92804263 -0.430577252 0.38097823 -0.426938081
49 -0.165320629 -0.436561117 -0.022108887 -0.412614936 0.20412609 0.003279052 -0.77152209 0.211526672 -0.5851201 -0.18290809 -0.284230585 0.30449400 -0.666071768
50 -0.142082710 0.195017303 -0.121702032 0.077439475 -0.47426071 0.055089372 -0.82942407 -0.249394753 0.5139078 -0.52850805 -0.707774591 0.02486043 -0.529796003
CACNG5 CACNA1A RASGRF1 MRAS RRAS2 RASGRP3 HRAS RASGRF2 NRAS RASGRP1 KRAS RASGRP2
1 -1.08126573 -0.10466468 0.16163511 5.2884330 1.4807031 1.367194844 -5.3632946 8.854311810 -1.590394 5.46299955 3.39043935 -0.6188210
2 -0.20103987 0.02859079 -4.04956365 6.8065804 9.7156082 2.358011759 7.5529682 -1.371397362 5.512496 2.38105873 -4.31024938 -2.9758226
3 -0.56279304 -0.49473575 0.93100155 13.3018509 4.7819748 -0.830227776 7.1269586 1.639458379 5.579675 1.92566166 -10.04349925 -3.9823054
4 -0.17721434 -0.13495743 -4.18967059 7.7963292 2.4795673 0.849823268 16.4843104 1.625120794 2.538493 -1.96693411 -1.06650587 2.9583095
5 -0.21284845 -0.41776136 11.57622331 7.8696230 25.3334550 0.525216862 21.7506102 1.804542827 27.144583 1.33103943 14.91107071 4.3580818
6 0.26966929 -0.57921249 -3.81118227 -1.7711352 2.6537342 2.381451473 0.3413279 0.002745248 11.787951 -2.72785260 5.81449916 1.1492321
7 -0.05721931 -0.61373510 3.20661730 17.0161591 8.3848898 9.128073635 10.0460744 7.427485748 6.423633 8.58609614 5.14330065 0.2455554
8 -0.23483474 -0.30007284 7.44882239 -4.1520715 2.5809601 0.007694412 14.4026853 6.009882772 1.973626 5.85650616 -4.99508071 1.4778224
9 -0.30401185 -0.23601064 0.61950230 2.1421284 15.4745282 -0.515190084 5.7490335 -3.364087292 12.305191 0.68371891 0.70766236 0.7915359
10 0.03069795 0.17789637 5.48077430 0.1797954 1.5320631 -0.612153126 -11.1569228 2.314820314 5.364269 -1.03632032 7.25132489 3.9454336
11 -0.67484374 -0.24910596 2.32388243 17.9765927 0.9794240 10.700691074 7.1050062 4.714036496 15.891228 -4.31287607 7.62253612 5.1733717
12 -0.23985708 -0.38664533 5.49113542 6.9358357 20.5868853 -0.490459011 19.0955840 -0.187311045 -11.228341 -2.01774050 2.46021292 2.9611938
13 -0.17565650 -0.26472802 -3.66145265 12.2382531 18.9846037 -1.676584866 15.2614596 4.241818360 11.685053 -1.41970648 -8.67808713 5.7914843
14 -0.66123979 -0.73403494 -1.47051990 -9.4605317 7.9187982 1.649205050 5.5260746 2.236724615 -5.689584 0.08904104 -7.61932439 -2.3718501
15 -0.25993662 -0.36155808 0.47540397 0.2766627 -12.6713829 3.527719828 16.7505891 -4.031521995 -6.139259 1.16441221 -6.18252342 1.0479288
16 -0.03146197 -0.45563938 7.13155463 -8.8844448 10.2941475 6.470602700 11.8131578 2.036032005 -3.021039 1.27196373 16.98691230 5.9919408
17 0.15062972 -0.14899016 -5.17104361 27.1526356 10.6209803 8.107969651 11.0779712 5.968404297 13.698359 2.93330073 20.28969711 -1.1704762
18 -0.25937867 -0.39833982 -0.43088475 -5.6251327 11.3899990 -0.318345728 3.1713730 0.760007843 5.409240 -3.68088307 -10.11778528 4.8975433
19 -0.87451283 -0.05959917 -2.53664942 29.4869423 19.1536567 -4.591416100 27.3860278 3.156809354 7.025175 4.72109032 26.79484568 2.0115602
20 -0.67964587 0.27642731 -7.18238442 5.0073861 9.8321189 0.380995576 1.9077432 -0.585178489 -3.439573 2.59522601 -8.74681890 -0.6800699
21 -0.49629567 -0.56934938 2.23942230 22.8194269 5.2645346 -2.428571330 6.1776451 2.611162565 18.775754 1.10296129 12.87445853 -4.3216192
22 0.00724720 -0.70303883 -1.43444204 1.8773895 9.1167518 3.582722007 9.7741579 0.028240658 4.460745 2.27952502 14.99544664 -0.9230170
23 -0.10643328 -0.67769320 6.75704004 3.0189378 -0.9081308 2.255448682 9.2941211 2.151332408 -2.619788 1.16186606 -5.75794077 3.8895972
24 0.35597240 0.06858421 -1.72085135 10.4151256 -0.1591937 1.167127427 4.6532448 0.296189520 -10.270647 -0.35558702 16.91723551 -1.0866788
25 0.10449039 0.22289001 6.69617230 6.2155570 10.8483718 -1.374067174 3.7386102 1.255906864 5.792042 6.56478190 5.65215300 4.2867125
26 0.11049705 -0.26850303 -2.60011742 1.7766863 -7.9563835 -1.795606943 2.2133029 -4.103202628 5.503321 -1.80881337 6.71979360 5.2476183
27 -0.43247910 0.06570798 3.12944595 26.3058088 23.7036553 1.572823145 41.4230817 -3.123108372 44.661343 6.00690771 12.20911459 9.3681238
28 -0.06832140 -0.47558618 -0.05898754 10.2791424 -1.2785850 -1.881395391 -7.0972730 0.283137062 11.300423 -5.42201881 7.69205240 3.6647710
29 -0.14796844 -0.31242843 -7.13439956 8.8376481 13.4659132 1.461275344 4.1133381 2.784203145 12.496497 0.41425657 6.27234388 3.2425929
30 0.33267383 0.07562561 4.30418636 11.5135055 -5.5710269 0.595018978 20.2956727 -1.999030542 23.338891 1.79473828 25.14227894 3.5624672
31 -0.13135999 0.03429504 3.12945679 -1.7988365 -0.8664450 -0.925567331 -3.7275570 -3.950239410 7.792904 -2.94586593 -4.80659759 -5.1385471
32 -0.65278805 -0.24207506 -0.80329023 -1.5381825 -7.0147661 -1.371024797 11.1363243 -0.703554423 9.848548 0.77097874 -0.01193523 2.0874871
33 -0.27298162 0.36527044 -0.44873371 -3.2108142 15.4038635 5.626084802 7.3734731 -0.818813872 -2.329578 -1.22258273 5.73140635 7.6681611
34 0.24697363 0.04004560 3.55251026 10.9369448 17.4436080 4.964061402 -4.1149183 -0.594522702 30.979488 1.34426338 10.79636312 3.7373761
35 -0.23005566 0.07016680 8.61098096 7.2749938 6.1983372 -1.931047305 11.2845415 -0.255800684 -12.768165 0.65177004 7.72055325 -9.8395187
36 0.07745730 -0.07007581 4.21970890 16.3408506 13.6502613 -2.764005594 4.7150426 -3.352393845 7.726116 1.05046858 -11.41243533 -2.5015196
37 -0.68399493 0.23974508 -0.17544534 -5.5184731 5.8961029 -4.510778693 17.5402976 4.658695314 3.495335 4.32696570 6.21866892 2.9641552
38 -0.04013683 -0.78642712 -3.96729208 3.4475599 -1.2403075 2.536697158 -7.7241472 4.334766041 -9.963346 -0.64687173 17.34032967 1.8524765
39 -0.44495174 -0.19879868 1.92668453 6.8470802 21.4526006 0.455531935 27.9513567 -1.370725185 1.955942 3.59422972 24.79601058 -4.6690074
40 -0.13325651 -0.17514241 -2.82513210 21.0013199 -2.2907174 -1.494103403 18.4596623 2.297606605 -2.724228 2.31410400 0.75443901 0.1896653
41 -0.04049955 -0.30950401 1.08764034 12.0828373 3.2890383 5.742280231 -11.8575537 1.698274301 -2.021231 1.42562103 0.06413767 2.3617709
42 0.11173966 -0.66458170 7.85442282 9.1662041 30.2460296 1.990946110 16.4452737 5.687569677 11.302004 8.06994470 23.60159352 -3.6748499
43 0.22047452 -0.53158026 0.50466780 19.9152823 8.9427850 -0.637162403 11.3976456 4.603380514 8.462772 -1.49806588 15.98236455 2.5163547
44 -0.36319770 -0.22408093 2.86754885 1.5941018 -7.0354188 0.740816157 5.6042852 -1.145312539 -4.309770 -4.60556357 8.99063162 4.1639967
45 -0.45275458 -0.08379418 -5.95422943 16.4861889 15.9877620 -0.807411042 8.0873218 4.025147480 -3.494243 1.36140592 0.17167116 0.5730415
46 -0.02849445 -0.22411911 3.18637465 7.3235045 12.1141402 -2.049762449 -5.7373841 1.660312041 16.389530 4.32823877 2.31488480 -1.0958932
47 -0.46860175 -0.13260285 4.40493794 8.4949938 3.9516605 -1.243255229 -1.6795379 -0.013959038 4.140808 -3.39817037 4.27670204 -1.6862091
48 -0.41927264 -0.70467223 3.69590189 -6.4179034 -2.8701968 2.692561594 20.7038768 0.392052464 -2.993030 1.25742496 -5.18694095 -6.7182529
49 -0.02718469 -0.35311492 1.12532546 0.4862352 0.3023580 -1.603408864 1.2115986 0.845596944 9.048511 3.92056012 -8.67131197 -2.3896462
50 -0.32380034 0.06106854 3.30870522 -4.9429947 15.9727621 -0.159746543 7.7858779 1.608172511 4.614853 1.15746997 -3.63746568 -1.5704711
现在我想创建一个新的data.frame，它是基因型和表型的组合。特别地，一列是第一标记的基因型（在markerinfo中），下一列是表型中的相应表型。我想对markerinfo中的所有标记做这个。但是正如您所看到的，有一些重复形式的标记。然而，这些应该被视为不同的标记并且仍然具有列。由于数据的进一步处理，我需要这种交替形式。
如果这有助于您回答我的问题，请提前致谢
答案 0 :(得分：1)
我不确定我是否理解正确。您需要了解如何创建minimal reproducible example，特别是如何使用dput。
我调用了data.frames markerinfo，genotype，phenotype并仅使用了一小部分数据进行测试。为了使我的解决方案起作用，markerinfo中的每个基因型和表型必须存在于相应的data.frames中。 （因此，我不得不改变markerinfo中的表型，使其适用于我用于测试的简化数据集。）
result <- lapply(seq_along(markerinfo$marker),function(i) {
x <- as.character(markerinfo$marker[i])
res <- cbind(genotype[,x],phenotype[,as.character(markerinfo[i,"pheno"])])
colnames(res) <- c(paste('geno',x,sep="_"),paste('pheno',as.character(markerinfo[i,"pheno"]),sep="_"))
res
}
)
result <- do.call('cbind',result) #combine lists
head(result)
geno_c3m22 pheno_CACNA1E geno_c3m22 pheno_CACNA1F geno_c3m16 pheno_CACNA1G geno_c3m20 pheno_RAPGEF2
[1,] 2 0.053503367 2 -0.05512395 2 -0.16406024 2 -0.247595001
[2,] 2 -0.296400594 2 -0.14167491 2 -0.28751925 2 0.076554542
[3,] 2 0.001312523 2 -0.33078461 2 -0.84388147 2 -0.327202333
[4,] 1 -0.138671725 1 0.15734291 1 -0.04136305 1 -0.007847655
[5,] 1 -0.453451438 1 -0.23904476 2 -0.34506117 1 -0.288891373
[6,] 1 -0.186128793 1 0.12046537 2 -1.45824366 1 -0.819438873
#this is a matrix, use as.data.frame to turn it into a data.frame