我有两个列表，我想使用map(., map(., ...)（或其他方法）进行映射，并将两个列表合并在一起。
也就是说，我想在shap_score中创建一个新列，该列由列表BIAS0组成。 BIAS0只有一个观察值，但是我试图在shap_score的新列中重复观察。
shap_score列表之一的预期输出为：
holiday temp wind ... BIAS0
1 -0.2875051 -0.004131595 -0.68942320 0.0023
2 -0.2613214 0.075470507 0.15884757 0.0023
3 -0.2788497 0.148605376 0.84752327 ... 0.0023
4 -0.2852835 -0.011284750 0.11122661 0.0023
5 -0.2827380 0.012600235 -0.08916214 0.0023
6 -0.2799886 0.055347286 -0.29320240 0.0023
哪个代表2016-01-01。
lst <- list(`5` = list(`2016-01-01` = list(shap_score = structure(list(
holiday = c(-0.287505149841309, -0.261321395635605, -0.27884966135025,
-0.285283505916595, -0.282738029956818, -0.279988586902618
), temp = c(-0.00413159467279911, 0.075470507144928, 0.14860537648201,
-0.0112847499549389, 0.0126002347096801, 0.0553472861647606
), wind = c(-0.689423203468323, 0.158847570419312, 0.847523272037506,
0.111226610839367, -0.089162141084671, -0.29320240020752),
humidity = c(0.0878895819187164, 0.115549802780151, 0.0808247774839401,
0.13438692688942, 0.168190106749535, 0.0706237256526947),
barometer = c(-0.0442701764404774, -0.0273909661918879, -0.110819362103939,
-0.0135176228359342, 0.017435971647501, -0.00947459880262613
), weekday = c(0.081529937684536, 0.111831456422806, 0.0768596306443214,
0.0925759300589561, 0.0789813026785851, 0.086820051074028
), weekend = c(0, 0, 0, 0, 0, 0), workday_on_holiday = c(-0.00449116015806794,
-0.00492851622402668, -0.00990568101406097, -0.00535996304824948,
-0.00591197935864329, -0.00449116015806794), weekend_on_holiday = c(0,
0, 0, 0, 0, 0), protocol_active = c(0, 0, 0, 0, 0, 0), text_fog = c(0.0545360967516899,
0.070147879421711, 0.0239687878638506, 0.0693793371319771,
0.0703154131770134, 0.0456382371485233), text_light_rain = c(0.0205550380051136,
0.0347262620925903, 0.0337300077080727, 0.0276936888694763,
0.0274705961346626, 0.0205550380051136), text_mostly_cloudy = c(-0.0035081070382148,
-0.0106095243245363, -0.00892648566514254, 0.00776881678029895,
-0.0245751272886992, -0.0141745395958424), text_passing_clouds = c(0.0114779146388173,
0.0142141683027148, 0.00865811575204134, 0.0236570592969656,
0.0147861018776894, 0.014693571254611), text_rain = c(0.0248998813331127,
-0.000406149541959167, -0.032954890280962, -0.000589479866903275,
0.00113414553925395, -0.000406149541959167), text_scattered_clouds = c(-0.0810607820749283,
-0.0777786746621132, -0.172431215643883, -0.0851063579320908,
-0.0796751528978348, -0.0967930480837822), text_sunny = c(0.00860885996371508,
0.00318501936271787, 0.00632673315703869, 0.00091076479293406,
0.00572518166154623, 0.0106474123895168), month_1 = c(0.0764073804020882,
0.163439929485321, 0.0846812948584557, 0.157616153359413,
0.0762593746185303, 0.0853563472628593), month_2 = c(0.0455782376229763,
0.0182493720203638, 0.0024642909411341, 0.0399442575871944,
0.0477627664804459, 0.0225149858742952), month_3 = c(0.00336221815086901,
0.00445762183517218, -0.0292761437594891, -0.000451473635621369,
0.0162581335753202, 0.00577702885493636), month_4 = c(-0.0627421438694,
-0.173753097653389, -0.0831228122115135, -0.159142002463341,
-0.144872710108757, -0.132649913430214), month_5 = c(-0.0746728777885437,
-0.147042363882065, -0.0870080813765526, -0.154210984706879,
-0.0838618874549866, -0.105986662209034), month_6 = c(-0.032955776900053,
-0.00787918921560049, -0.00770710874348879, -0.00865088775753975,
-0.0083921467885375, -0.0374950207769871), month_7 = c(-0.0100117931142449,
-0.0129543459042907, -0.0147038688883185, -0.0122463470324874,
-0.0122463470324874, -0.0128087708726525), month_8 = c(-0.0355697833001614,
-0.0451076440513134, -0.0443550981581211, -0.0451076440513134,
-0.0451076440513134, -0.036579642444849), month_9 = c(0.0187235474586487,
0.0188821014016867, 0.0159602463245392, 0.0188821014016867,
0.0190539490431547, 0.0190539490431547), month_10 = c(0.071361169219017,
0.0771452113986015, 0.0575473643839359, 0.076715886592865,
0.0704263001680374, 0.0722210109233856), month_11 = c(0.0580858998000622,
0.0873404517769814, 0.057171318680048, 0.0863843932747841,
0.110879138112068, 0.0858354195952415), month_12 = c(0.15059332549572,
0.250447928905487, 0.0866759195923805, 0.179991602897644,
0.115858882665634, 0.179295092821121)), row.names = c(NA,
-6L), class = "data.frame"), mean_shap_score = structure(list(
. = c(0.360876396982335, 0.247068853404152, 0.135545302893898,
0.123760943490689, 0.108000884954977, 0.107575414474356)), row.names = c("wind",
"holiday", "month_10", "month_12", "temp", "text_scattered_clouds"
), class = "data.frame"), BIAS0 = structure(list(BIAS = 0.00230497121810913), row.names = c(NA,
-1L), class = "data.frame")), `2016-01-02` = list(shap_score = structure(list(
holiday = c(-0.272332519292831, -0.340749680995941, -0.29936483502388,
-0.262564092874527, -0.244395390152931, -0.24520780146122
), temp = c(0.134692505002022, 0.111628539860249, 0.0722469985485077,
0.0883198082447052, 0.0662637650966644, 0.0605684258043766
), wind = c(0.143159076571465, 0.776454508304596, 0.118829421699047,
-0.0511538982391357, -0.242683932185173, -0.280367434024811
), humidity = c(0.129436731338501, 0.0423655584454536, 0.0963118970394135,
0.143828451633453, 0.0356308110058308, 0.125916793942451),
barometer = c(-0.035864919424057, -0.0873878672719002, -0.0221946015954018,
-0.00450283708050847, -0.00959178991615772, 0.0137610333040357
), weekday = c(0.114081330597401, 0.0863068029284477, 0.0976831987500191,
0.0876984223723412, 0.0789007022976875, 0.0907445028424263
), weekend = c(0, 0, 0, 0, 0, 0), workday_on_holiday = c(-0.00402905605733395,
-0.00453507993370295, -0.00482375361025333, -0.00508680287748575,
-0.00402905605733395, -0.00376600702293217), weekend_on_holiday = c(0,
0, 0, 0, 0, 0), protocol_active = c(0, 0, 0, 0, 0, 0), text_fog = c(0.11833580583334,
0.0388534627854824, 0.115720294415951, 0.120073936879635,
0.0912353321909904, 0.0499505363404751), text_light_rain = c(0.0520697049796581,
0.0487560331821442, 0.0497864708304405, 0.0607201047241688,
0.0531328953802586, 0.050849661231041), text_mostly_cloudy = c(-0.0203497968614101,
-0.0203211829066277, -0.0203497968614101, -0.0237297993153334,
-0.0105950376018882, -0.0152477817609906), text_passing_clouds = c(0.00878167618066072,
0.000530663761310279, 0.0143402041867375, 0.0136684160679579,
0.00791957695037127, 0.00819030683487654), text_rain = c(-0.00133128161542118,
-0.0482824929058552, -0.0013865182409063, 0.000755225541070104,
-0.0013865182409063, -0.0013865182409063), text_scattered_clouds = c(-0.0839326903223991,
-0.166067808866501, -0.104155018925667, -0.0906047150492668,
-0.109635159373283, -0.0941797941923141), text_sunny = c(0.00581001630052924,
0.00568450707942247, 0.00266551366075873, 0.0067223091609776,
0.0134993428364396, 0.0094219297170639), month_1 = c(0.109191954135895,
0.0550585612654686, 0.123545065522194, 0.0814228802919388,
0.0547124668955803, 0.0466522350907326), month_2 = c(0.00459166103973985,
0.00601228745654225, 0.0340671129524708, 0.0411521792411804,
0.015175917185843, -0.00683757895603776), month_3 = c(0.00803727563470602,
-0.0509188324213028, 0.00803727563470602, 0.00101890868972987,
-0.000487668992718682, 0.00101890868972987), month_4 = c(-0.172974124550819,
-0.14289490878582, -0.172974124550819, -0.174231216311455,
-0.179455310106277, -0.174041390419006), month_5 = c(-0.114909239113331,
-0.0815929844975471, -0.114909239113331, -0.0847142040729523,
-0.119625508785248, -0.0925337672233582), month_6 = c(-0.0256863348186016,
-0.0171801336109638, -0.0256863348186016, -0.0254424661397934,
-0.0288356766104698, -0.0256347432732582), month_7 = c(-0.00124008720740676,
-0.000914117030333728, -0.00219869031570852, -0.00219869031570852,
0.00217337417416275, 0.00217337417416275), month_8 = c(-0.0525347702205181,
-0.0453627146780491, -0.0501848794519901, -0.05464031919837,
-0.0498320944607258, -0.047506932169199), month_9 = c(0.0249587465077639,
0.0150943985208869, 0.026452062651515, 0.0224876832216978,
0.021443136036396, 0.021443136036396), month_10 = c(0.0767439901828766,
0.0637840330600739, 0.076332226395607, 0.0759803578257561,
0.0758688449859619, 0.0741193741559982), month_11 = c(0.0604153387248516,
0.0535821802914143, 0.047236505895853, 0.129973396658897,
0.0794788673520088, 0.0823308974504471), month_12 = c(0.260544240474701,
0.104838013648987, 0.194464340806007, 0.136765509843826,
0.1962860673666, 0.228263437747955)), row.names = c(NA, -6L
), class = "data.frame"), mean_shap_score = structure(list(. = c(0.34787637969349,
0.263833619147267, 0.137870378298983, 0.126111664515006, 0.105620052684184,
0.0995344268686366)), row.names = c("wind", "holiday", "month_12",
"month_10", "month_5", "temp"), class = "data.frame"), BIAS0 = structure(list(
BIAS = 0.00215145945549011), row.names = c(NA, -1L), class = "data.frame"))),
`6` = list(`2016-01-01` = list(shap_score = structure(list(
holiday = c(-0.01831397973001, -0.0278525706380606, -0.0545091107487679,
-0.0852110907435417, -0.0915239304304123, -0.0201904978603125
), temp = c(0.0619977787137032, 0.240116253495216, 0.30674609541893,
0.0303262006491423, -0.0750823989510536, 0.0211294107139111
), wind = c(-0.980749845504761, 0.299531370401382, 0.994995355606079,
0.362696558237076, 0.219618409872055, -0.307217717170715
), humidity = c(0.0190069638192654, 0.0383657030761242,
-0.0103078661486506, 0.386975973844528, 0.0216114148497581,
0.0369361937046051), barometer = c(-0.0764741972088814,
-0.0111954780295491, -0.132382750511169, 0.0809806659817696,
0.282660692930222, 0.0890090838074684), weekday = c(0.0523185282945633,
0.0715024098753929, 0.10260059684515, 0.0199155658483505,
0.0760686323046684, 0.0664166510105133), weekend = c(0,
0, 0, 0, 0, 0), workday_on_holiday = c(-0.00574090098962188,
-0.0297964438796043, -0.027728658169508, -0.107685536146164,
-0.110589444637299, -0.0184716582298279), weekend_on_holiday = c(0,
0, 0, 0, 0, 0), protocol_active = c(0, 0, 0, 0, 0, 0),
text_fog = c(0.0996797382831573, 0.106587544083595, 0.115442782640457,
0.0638781487941742, 0.0548176616430283, 0.102432563900948
), text_light_rain = c(0.000621219805907458, 0.000409607600886375,
-0.0062907743267715, 0.00471668830141425, 0.0146307684481144,
0.00518008414655924), text_mostly_cloudy = c(-0.1487677693367,
-0.130784466862679, -0.150917783379555, -0.151641234755516,
-0.0495412796735764, -0.129829555749893), text_passing_clouds = c(0.0073356693610549,
0.0170528776943684, -0.00333861657418311, 0.0242675002664328,
0.00929313059896231, 0.00433033425360918), text_rain = c(0.00624925876036286,
0.0306352786719799, 0.031936164945364, 0.0310601592063904,
0.0337994284927845, 0.0347826145589352), text_scattered_clouds = c(-0.0313251540064812,
-0.00891299080103636, -0.0361328050494194, -0.0527477525174618,
0.00748181901872158, -0.0359514877200127), text_sunny = c(0.00310508860275149,
0.00278438744135201, 0.00266698561608791, 0.00256888521835208,
0.0030412464402616, 0.00211086357012391), month_1 = c(0.0124078718945384,
0.115149907767773, 0.0342674888670444, 0.0768248960375786,
0.0654645636677742, 0.0636573806405067), month_2 = c(0.00906078889966011,
0.00684765540063381, 0.00684765540063381, 0.0043728593736887,
0.00523211201652884, 0.00523211201652884), month_3 = c(-0.00860895030200481,
0.0395329669117928, -0.00739339413121343, 0.0254243556410074,
0.0387462340295315, 0.000762063078582287), month_4 = c(0.0156710669398308,
-0.0731847062706947, -0.059317272156477, -0.0797693580389023,
-0.063595674932003, -0.0707821249961853), month_5 = c(0.0155536076053977,
-0.00135426537599415, -0.000712861423380673, 0.00186582200694829,
0.00462800171226263, 0.00815873499959707), month_6 = c(-0.00416068313643336,
-0.00582700222730637, -0.00630857422947884, -0.00630857422947884,
-0.00371318752877414, -0.0028462684713304), month_7 = c(-0.0765896588563919,
-0.0602815672755241, -0.0606993623077869, -0.0679243355989456,
-0.0981804504990578, -0.143634766340256), month_8 = c(-0.0274758692830801,
-0.029958751052618, -0.0222828015685081, -0.0231222733855247,
-0.0426335595548153, -0.0394838415086269), month_9 = c(0.0263891816139221,
0.0408347435295582, 0.0264717359095812, 0.0400248542428017,
0.0434771627187729, 0.0341347306966782), month_10 = c(0.0503858253359795,
0.0485818050801754, 0.0558837354183197, 0.0611822977662086,
0.056214764714241, 0.0590351931750774), month_11 = c(0.0138247236609459,
0.0193855240941048, 0.0193508081138134, 0.0653398260474205,
0.0657737776637077, 0.0138247236609459), month_12 = c(0.0957958102226257,
0.135555163025856, 0.124385118484497, 0.165062874555588,
0.177980005741119, 0.212877199053764)), row.names = c(NA,
-6L), class = "data.frame"), mean_shap_score = structure(list(
. = c(0.400683706247415, 0.160881078211374, 0.114046945856654,
0.0954511656763976, 0.0916842353508965, 0.0821915370408051
)), row.names = c("wind", "temp", "month_12", "text_mostly_cloudy",
"month_10", "barometer"), class = "data.frame"), BIAS0 = structure(list(
BIAS = 0.00201910734176636), row.names = c(NA, -1L), class = "data.frame")),
`2016-01-02` = list(shap_score = structure(list(holiday = c(-0.0269235819578171,
-0.0375330299139023, -0.150185480713844, -0.180965185165405,
-0.0315828360617161, -0.0701441094279289), temp = c(0.243527591228485,
0.371963232755661, 0.0472849868237972, -0.0628702938556671,
0.0950800850987434, 0.0399793609976768), wind = c(0.312333345413208,
1.05714356899261, 0.438581466674805, 0.249333217740059,
-0.289588332176208, -0.241006031632423), humidity = c(0.122070841491222,
0.0394690744578838, 0.30509290099144, 0.145370110869408,
0.127359732985497, 0.120208732783794), barometer = c(-0.00119671260472387,
-0.141092225909233, 0.165500268340111, 0.272881299257278,
0.180314168334007, 0.366155594587326), weekday = c(0.0754338949918747,
0.102761223912239, 0.0114814033731818, 0.0844534561038017,
0.0735206827521324, 0.0815589874982834), weekend = c(0,
0, 0, 0, 0, 0), workday_on_holiday = c(-0.00934421829879284,
-0.0113556934520602, -0.0479315407574177, -0.0799224302172661,
-0.00402316637337208, -0.00376265193335712), weekend_on_holiday = c(0,
0, 0, 0, 0, 0), protocol_active = c(0, 0, 0, 0, 0, 0),
text_fog = c(0.121573232114315, 0.142543613910675,
0.0842383652925491, 0.0666860789060593, 0.118839487433434,
0.0962422415614128), text_light_rain = c(-0.00105138635262847,
-0.00085716979810968, -0.00249776896089315, -0.00258254911750555,
-0.00207408331334591, -0.00207408331334591), text_mostly_cloudy = c(-0.102763526141644,
-0.145860776305199, -0.17062683403492, -0.107765935361385,
-0.0928198248147964, -0.0847341865301132), text_passing_clouds = c(0.00650645792484283,
-0.0119929825887084, 0.00129361264407635, 0.00169141637161374,
0.00034726966987364, 0.000147873506648466), text_rain = c(0.0432413183152676,
0.0620547793805599, 0.0612813010811806, 0.0676166415214539,
0.067610040307045, 0.0667802914977074), text_scattered_clouds = c(0.00680965557694435,
-0.0301695875823498, -0.0163288377225399, 0.00500796362757683,
-0.013679507188499, -0.0105399489402771), text_sunny = c(0.00175180716905743,
0.0044323937036097, 0.00130739971064031, -0.000148897292092443,
0.000350211892509833, 0.00125334970653057), month_1 = c(0.13130210340023,
0.0630867704749107, 0.140457063913345, 0.141706764698029,
0.0985265597701073, 0.0603985860943794), month_2 = c(-0.00919519551098347,
0.0138415843248367, 0.0109194563701749, 0.0105534996837378,
0.0103707788512111, 0.0100245578214526), month_3 = c(0.0568007789552212,
-0.0135478051379323, 0.000756092253141105, 0.00378916412591934,
-0.0378580540418625, -0.0227362904697657), month_4 = c(-0.0758921280503273,
-0.0510909892618656, -0.0998639911413193, -0.0716328546404839,
-0.0660951882600784, -0.0549110621213913), month_5 = c(0.000941433012485504,
0.000817757972981781, 0.0000343952524417546, 0.0000343952524417546,
0.000308761722408235, 0.000308761722408235), month_6 = c(-0.0190888661891222,
-0.0180853568017483, -0.0180853568017483, -0.0131239648908377,
-0.0131239648908377, -0.0192493200302124), month_7 = c(-0.0366223938763142,
-0.033847738057375, -0.033847738057375, -0.0919496417045593,
-0.0880158543586731, -0.0880158543586731), month_8 = c(0.00105724367313087,
0.0109531516209245, -0.00258957524783909, -0.0119656454771757,
-0.0273083839565516, -0.0315780155360699), month_9 = c(0.040503092110157,
0.0272863991558552, 0.0342562571167946, 0.0368664264678955,
0.0334650687873363, 0.0334650687873363), month_10 = c(0.0210105869919062,
0.0340395793318748, 0.0246942117810249, 0.0683412477374077,
0.0773100107908249, 0.0828942507505417), month_11 = c(0.0134705044329166,
0.0149277104064822, 0.0455087646842003, 0.0467537902295589,
0.0175258759409189, 0.00985155906528234), month_12 = c(0.127066493034363,
0.0923486724495888, 0.200634613633156, 0.169035747647285,
0.19908843934536, 0.209459736943245)), row.names = c(NA,
-6L), class = "data.frame"), mean_shap_score = structure(list(
. = c(0.420971721993227, 0.164329549071548, 0.129385712326475,
0.114136796096405, 0.110441525715531, 0.09215682157331
)), row.names = c("wind", "temp", "month_10", "humidity",
"holiday", "month_3"), class = "data.frame"), BIAS0 = structure(list(
BIAS = 0.00194627046585083), row.names = c(NA, -1L
), class = "data.frame"))))
答案 0 :(得分：2)
这是一个选项，其中我们遍历list，从嵌套组件中提取“ BIAS0”元素，并在每个list元素的“ shap_score”中创建一列，然后返回完整的list
library(purrr)
library(dplyr)
map(lst, ~ map(.x, ~
{
.x$shap_score <- mutate(.x$shap_score, BIAS0 = .x$BIAS0[[1]])
.x
}
))
答案 1 :(得分：0)
首先，我不得不说，这些嵌套列表的类型很快变得非常混乱。
以下是您列表中一个示例的选项：
tmp <- as.data.frame(lst$`6`$`2016-01-02`$shap_score)
tmp$BIAS0 <- unlist(lst$`6`$`2016-01-02`$BIAS0)