我有两个dfs - 基准返回和allreturns（下面的示例），我想创建一个第三个df，它返回一个减去的值allreturns-benchmarkretur，匹配allreturns的每一行的“startmonth”值，如以及在两个dfs中都一致的colname（V1：V12）。
此问题类似于Excel中的两个条件的索引匹配，其中我在行侧与startmonth匹配，在列侧与“V month”匹配（此处为colnames）。
因此，例如，如果在所有的返回中我有一个2005年1月的startmonth并且V3中该行的值为5，并且2005年1月行和第V3列的基准返回为3，那么新行中所有返回的行的值df将是3.依此类推。
实际的allreturns数据帧非常大，所以我担心在减去之前合并，并且想知道这是否是最可行的方法，或者我是否可以一步减去元素。
感谢您的帮助！
所有回报：
structure(list(startmonth = structure(c(2012, 2012.08333333333,
2012.16666666667, 2012.25, 2012.33333333333, 2012.41666666667,
2012.5, 2012.58333333333, 2012.66666666667, 2012.75), class = "yearmon"),
V1 = c(0.0284247303, 0.0010087300099999, 0.0022169700300001,
-0.01468004942, 0.00102045998000011, 0.00591265976000011,
0.0199267303899999, 0.0417483615900001, 0.00335606009, 0.00334482998000007
), V2 = c(0.0294621331884797, 0.00322793636420049, -0.012495624619603,
-0.0136745698429375, 0.0069391533726606, 0.0259572101269254,
0.0625070003256281, 0.0452445316901551, 0.00671211552040374,
0.0314807661806089), V3 = c(0.0317444198847785, -0.0114994993211506,
-0.0114879159244523, -0.00784276316178301, 0.0270041584010525,
0.0687892427111718, 0.0660728376647666, 0.0487406969361834,
0.0349424776135903, 0.0430893809414579), V4 = c(0.0165983608120608,
-0.0104907741199978, -0.00564318030266497, 0.0119276866011795,
0.0698798993604133, 0.0723761636332563, 0.0696386700530516,
0.0781496303938529, 0.0465900515831383, 0.0514300069951692
), V5 = c(0.0176357587550031, -0.00464014273798818, 0.0141710999549016,
0.0541740095643377, 0.0734704805917499, 0.0759630795752142,
0.0996336273987892, 0.090283471493128, 0.054958669279074,
0.0744177901533509), V6 = c(0.0236526927561311, 0.0151941247787009,
0.056511081749947, 0.057711880885752, 0.0770610568378784,
0.106135386898253, 0.112009256278245, 0.099001465266642,
0.0780236008188706, 0.0925232124743112), V7 = c(0.0440507439776798,
0.0575768161840056, 0.0600567964260508, 0.0612497472951408,
0.107264153793045, 0.118584188660589, 0.120900971163225,
0.123029319908411, 0.0961897860250946, 0.0862168299189658
), V8 = c(0.0876381519555687, 0.06112610752891, 0.0636025061792393,
0.0910094612943682, 0.119725659059378, 0.127528477230367,
0.145407622386291, 0.141953913583335, 0.0898622388709431,
0.0799104502736558), V9 = c(0.0912883309497081, 0.0646753939459335,
0.0934281965725043, 0.103288031047544, 0.128679074917691,
0.152180027943971, 0.164709321337116, 0.135362201702614,
0.0835346946365931, 0.076045245502748), V10 = c(0.0949385048758928,
0.0945311704081673, 0.105733987546834, 0.11211001031858,
0.153355781552244, 0.171595851273483, 0.157986258190939,
0.128770492863592, 0.0796565180062141, 0.069524282329458),
V11 = c(0.125642923731963, 0.10684937460315, 0.114575524881519,
0.136424461680214, 0.172791417952441, 0.164833036942378,
0.151263198147072, 0.124730408712938, 0.0731136700886696,
0.0680519003357034), V12 = c(0.138311269558968, 0.115699830661878,
0.138943880653118, 0.155574781977547, 0.166021702439959,
0.158070225731925, 0.147142608328795, 0.117914407614703,
0.0716363466924688, 0.086773098425986)), .Names = c("startmonth",
"V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10",
"V11", "V12"), class = "data.frame", row.names = c(NA, 10L))
基准回报：
structure(list(startmonth = structure(c(2005, 2005.08333333333,
2005.16666666667, 2005.25, 2005.33333333333, 2005.41666666667,
2005.5, 2005.58333333333, 2005.66666666667, 2005.75, 2005.83333333333,
2005.91666666667, 2006, 2006.08333333333, 2006.16666666667, 2006.25,
2006.33333333333, 2006.41666666667, 2006.5, 2006.58333333333,
2006.66666666667, 2006.75, 2006.83333333333, 2006.91666666667,
2007, 2007.08333333333, 2007.16666666667, 2007.25, 2007.33333333333,
2007.41666666667, 2007.5, 2007.58333333333, 2007.66666666667,
2007.75, 2007.83333333333, 2007.91666666667, 2008, 2008.08333333333,
2008.16666666667, 2008.25, 2008.33333333333, 2008.41666666667,
2008.5, 2008.58333333333, 2008.66666666667, 2008.75, 2008.83333333333,
2008.91666666667, 2009, 2009.08333333333, 2009.16666666667, 2009.25,
2009.33333333333, 2009.41666666667, 2009.5, 2009.58333333333,
2009.66666666667, 2009.75, 2009.83333333333, 2009.91666666667,
2010, 2010.08333333333, 2010.16666666667, 2010.25, 2010.33333333333,
2010.41666666667, 2010.5, 2010.58333333333, 2010.66666666667,
2010.75, 2010.83333333333, 2010.91666666667, 2011, 2011.08333333333,
2011.16666666667, 2011.25, 2011.33333333333, 2011.41666666667,
2011.5, 2011.58333333333, 2011.66666666667, 2011.75, 2011.83333333333,
2011.91666666667, 2012, 2012.08333333333, 2012.16666666667, 2012.25,
2012.33333333333, 2012.41666666667, 2012.5, 2012.58333333333,
2012.66666666667, 2012.75, 2012.83333333333, 2012.91666666667,
2013, 2013.08333333333, 2013.16666666667, 2013.25, 2013.33333333333,
2013.41666666667, 2013.5, 2013.58333333333, 2013.66666666667,
2013.75, 2013.83333333333, 2013.91666666667, 2014, 2014.08333333333,
2014.16666666667, 2014.25, 2014.33333333333, 2014.41666666667,
2014.5, 2014.58333333333, 2014.66666666667, 2014.75, 2014.83333333333
), class = "yearmon"), V1 = c(0.83740290789865, 0.175849379040301,
0.115745179820806, 0.588703967630863, 0.663440374424681, 0.122854199260473,
0.329111734172329, 0.386456901207566, 0.328031520592049, 0.91476626554504,
0.838957223575562, 0.482753940392286, 0.203118846053258, 0.832898694789037,
0.569874424720183, 0.85476457583718, 0.873173910891637, 0.599100088467821,
0.990275882417336, 0.360726963961497, 0.366468257969245, 0.213221889222041,
0.736634878907353, 0.770827260101214, 0.78438491653651, 0.443004491971806,
0.0431681957561523, 0.362984816078097, 0.933778994018212, 0.890268561197445,
0.0288882539607584, 0.704298130935058, 0.0533608365803957, 0.965311451349407,
0.797033908311278, 0.702818523859605, 0.615030416054651, 0.783928481163457,
0.0931849081534892, 0.693762929877266, 0.709550492931157, 0.527024435112253,
0.463098735082895, 0.975416449131444, 0.418448116164654, 0.716492718551308,
0.900618054671213, 0.937315494287759, 0.371745373355225, 0.876447483897209,
0.638362225377932, 0.230460224673152, 0.84510417538695, 0.709547182312235,
0.135460949270055, 0.86736886552535, 0.0408105747774243, 0.59634031355381,
0.378888477338478, 0.400274467887357, 0.73448179056868, 0.0201477019581944,
0.786700234282762, 0.145882265642285, 0.722347423899919, 0.495645943330601,
0.8204965388868, 0.944398637628183, 0.920839294558391, 0.244003586936742,
0.770501866471022, 0.608119423966855, 0.599260926479474, 0.316019677789882,
0.594015010166913, 0.862004647962749, 0.248981321696192, 0.251351157668978,
0.86709617683664, 0.477806584443897, 0.308428246993572, 0.930116105359048,
0.820579398889095, 0.407806032337248, 0.00576947652734816, 0.205415812321007,
0.503058285918087, 0.421452875249088, 0.171408428112045, 0.927357386332005,
0.736080843256786, 0.0634949475061148, 0.628076422959566, 0.825837341602892,
0.860992948058993, 0.637153514893726, 0.496678973315284, 0.532001776387915,
0.742080342955887, 0.279888727236539, 0.702802252722904, 0.20291490922682,
0.761654698755592, 0.413414426380768, 0.0555722850840539, 0.125917585333809,
0.545490033458918, 0.19256273820065, 0.242046363884583, 0.0174444266594946,
0.380734569625929, 0.735217718640342, 0.346396475099027, 0.997578310314566,
0.544119880301878, 0.610004446236417, 0.643721028929576, 0.0819557739887387,
0.666767429094762), V2 = c(0.325795059790835, 0.0280552506446838,
0.563847373938188, 0.968374367104843, 0.0397681496106088, 0.409115779446438,
0.568448322825134, 0.498488057404757, 0.725102471420541, 0.745882571907714,
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0.740428047254682, 0.447626604698598, 0.136078626848757, 0.343137193005532,
0.994878244586289, 0.569102957844734, 0.264083825284615, 0.0172849236987531,
0.902412769151852, 0.237230452476069, 0.342157111503184, 0.940635218983516,
0.355561858741567, 0.0913543966598809, 0.730372012127191, 0.424820594489574,
0.862009244738147, 0.673514985479414, 0.0558020728640258, 0.769041493767872,
0.429111589910462, 0.814037810778245, 0.485316230216995, 0.407698386581615,
0.0245183343067765, 0.38149883877486, 0.939248957671225, 0.206260631326586,
0.773903579683974, 0.955869939411059, 0.930366629268974, 0.497170565184206,
0.232823575846851, 0.119068749016151, 0.188956700265408, 0.769491335609928,
0.487886165967211, 0.249603169038892, 0.61157290590927, 0.967520525446162,
0.669223197968677, 0.0509379308205098, 0.630447809584439, 0.93567297863774,
0.296445045853034, 0.231306056724861, 0.664361900882795, 0.570044811815023,
0.988849612651393, 0.96225642086938, 0.567738547222689, 0.562166472664103,
0.382391084451228, 0.87047664122656, 0.553916300181299, 0.348980325739831,
0.861658359179273, 0.824328954331577, 0.602889677742496, 0.108061951119453,
0.510826006531715, 0.787226195679978, 0.209260584786534, 0.467020642245188,
0.916463258676231, 0.810527657391503, 0.259206298971549, 0.383183149620891,
0.399636579910293, 0.517378240358084, 0.277383098145947, 0.0540329110808671,
0.334274391178042, 0.762725146021694, 0.624413190409541, 0.664978531654924,
0.877504705451429, 0.133810454979539, 0.303127287654206, 0.401904000202194,
0.333468962227926, 0.880315525457263, 0.693340804660693, 0.104616170749068,
0.726646555587649, 0.787352371029556, 0.805738570401445, 0.842183391330764,
0.203002500347793, 0.4008568006102, 0.764609909849241, 0.68458701716736,
0.826388106215745, 0.458343503531069, 0.681572974193841, 0.816049969289452,
0.99422895279713, 0.586194961098954, 0.106009811395779, 0.564460329711437,
0.763485615840182, 0.641962635098025, 0.556110577424988, 0.624630474718288,
0.0865604616701603), V3 = c(0.103272917214781, 0.145110326353461,
0.139097063103691, 0.704820184968412, 0.499401112552732, 0.183894460555166,
0.142426387872547, 0.654179619625211, 0.231400778284296, 0.07779724849388,
0.639861593721434, 0.329660887364298, 0.207785484148189, 0.349164958344772,
0.918049042113125, 0.561889169970527, 0.801518984371796, 0.374053293140605,
0.279966498259455, 0.211821462959051, 0.988277795957401, 0.208132355008274,
0.358667319407687, 0.489217024296522, 0.235113901318982, 0.0665953925345093,
0.682081600185484, 0.923956993501633, 0.951206840341911, 0.153028624830768,
0.457171397982165, 0.497927478514612, 0.464426794555038, 0.700641778064892,
0.350401868345216, 0.239867100957781, 0.495585883036256, 0.659481293288991,
0.121178397443146, 0.966434765374288, 0.781230272026733, 0.191542416810989,
0.144150232896209, 0.649679786758497, 0.990280882688239, 0.182715938193724,
0.990455492865294, 0.0560185567010194, 0.258451806847006, 0.745103346649557,
0.389899141388014, 0.0482800761237741, 0.0293514989316463, 0.393075271975249,
0.53582366858609, 0.522070667007938, 0.802251521963626, 0.292497304268181,
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0.951305341674015, 0.21787527250126, 0.728856807108968, 0.932554795406759,
0.530237913830206, 0.146221859846264, 0.220554714323953, 0.133143884129822,
0.845931079704314), V4 = c(0.828090658877045, 0.244675925467163,
0.617208154639229, 0.675795117858797, 0.921914914855734, 0.777150499867275,
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0.333197562023997, 0.574352362193167, 0.4974719658494, 0.190328923054039,
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0.97094266442582, 0.879881211323664, 0.308937143767253, 0.579827836947516,
0.496185180498287), V5 = c(0.923095905454829, 0.762332424288616,
0.972339176572859, 0.422411200823262, 0.940925979521126, 0.540574067272246,
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0.660041945520788, 0.0605304769705981, 0.894808513810858, 0.766446133842692,
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0.0906804192345589, 0.863758355844766, 0.81235816469416, 0.754735963186249,
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0.921317730797455)), .Names = c("startmonth", "V1", "V2", "V3",
"V4", "V5", "V6", "V7", "V8", "V9"), class = "data.frame", row.names = c(NA,
119L))
答案 0 :(得分：1)
这是我得到的合并，然后仅限于每个df的前两列的结果：
> merge(Benret, Allret, by=1)[c(1,2,3,11,12)]
startmonth V1.x V2.x V1.y V2.y
1 Jan 2012 0.005769477 0.27738310 0.02842473 0.029462133
2 Feb 2012 0.205415812 0.05403291 0.00100873 0.003227936
3 Mar 2012 0.503058286 0.33427439 0.00221697 -0.012495625
4 Apr 2012 0.421452875 0.76272515 -0.01468005 -0.013674570
5 May 2012 0.171408428 0.62441319 0.00102046 0.006939153
6 Jun 2012 0.927357386 0.66497853 0.00591266 0.025957210
7 Jul 2012 0.736080843 0.87750471 0.01992673 0.062507000
8 Aug 2012 0.063494948 0.13381045 0.04174836 0.045244532
9 Sep 2012 0.628076423 0.30312729 0.00335606 0.006712116
10 Oct 2012 0.825837342 0.40190400 0.00334483 0.031480766
计算差异并省略输入：
res <- merge(Benret, Allret, by=1)[c(1,2,3,11,12)]
> res$diffV1 <- with(res, V1.x - V1.y)
> res$diffV2 <- with(res, V2.x - V2.y)
> res[ -(2:5) ]
startmonth diffV1 diffV2
1 Jan 2012 -0.02265525 0.24792096
2 Feb 2012 0.20440708 0.05080497
3 Mar 2012 0.50084132 0.34677002
4 Apr 2012 0.43613292 0.77639972
5 May 2012 0.17038797 0.61747404
6 Jun 2012 0.92144473 0.63902132
7 Jul 2012 0.71615411 0.81499771
8 Aug 2012 0.02174659 0.08856592
9 Sep 2012 0.62472036 0.29641517
10 Oct 2012 0.82249251 0.37042323