R의 data.frame 열 = FALSE는 다음과 같은 data.frame을 초기화

Question

임 작동하지 : 내가 추가 열을 채우기 위해 지금 원하는 루프에서

combdat <- data.frame(matrix(nrow=50), check.names=FALSE) 

. 이것은 다음과 같이 발생합니다 :

combdat[,mkr] <- mkrgeno 

여기서 mkr은 동일한 문자이고 mkrgeno는 같은 크기의 벡터입니다. 그러나 동일한 mkr에 대한 특정 값이 있습니다. 나는 그들을 지켜야한다. 지금은 그냥 덮어 씁니다. 나는 체크를 설정했다. 이름 = 거짓

아무도 나에게 조언을하지 못한다. 감사

리치

---

좋아, 내가 더 자세히 내 질문을하려고합니다 감사합니다.

임 마커에 대한 특정 정보 목록 markerinfo 갖는

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 

와 이름이 다른 : 표현형

> 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 -2.21.0013199 -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 

> 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 

또 다른 data.frame 것은 유전자형입니다

이제 유전자형과 유전자의 조합 인 새로운 data.frame을 만들고 싶습니다. 표현형. 특히 하나의 열은 markerinfo의 첫 번째 마커의 유전자형이고 다음 열은 표현형의 해당 표현형입니다. 물론 markerinfo의 모든 마커에 대해이 작업을 수행하려고합니다. 그러나 당신이 볼 수 있듯이 중복 형태의 마커가 몇 개 있습니다. 그럼에도 불구하고 이들은 다른 마커로 간주되어야하며 여전히 컬럼이 있어야합니다. 데이터의 추가 처리 때문에이 대체 양식이 필요합니다. 내가 제대로 이해한다면이 당신이 내 질문에

Answer 1

답변을 도울 수 있다면 사전에

덕분에 나는 확실하지 않다. minimal reproducible example을 만드는 방법과 특히 dput을 만드는 방법을 배워야합니다.

나는 data.frames를 markerinfo, genotype, phenotype이라고 부르며 데이터의 일부만 테스트에 사용했습니다. 내 솔루션이 작동하려면 markerinfo의 각 유전자형과 표현형이 해당 데이터 프레임에 있어야합니다. (따라서, 나는 내가 테스트에 사용 된 감소 된 데이터 세트와 함께 작동하도록, markerinfo의 표현형을 변경했다.)

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