将列表列表合并在一起，并根据其中一个列表中包含的信息创建新列
我正在尝试在2个单独的列表之一中创建一个新列。
df1的一个元素如下：
$`c(5, 19)`
$`c(5, 19)`[[1]]
Feature Gain Cover Frequency
1: plaza_eliptica 0.948578681145 0.53759794901 0.2794117647
2: wind 0.014083116347 0.10011187610 0.1343137255
3: temp 0.011637657812 0.08581378948 0.1460784314
4: year 0.006344014204 0.12430881478 0.1137254902
5: humidity 0.004941509318 0.03941622272 0.0862745098
6: barometer 0.003729098869 0.03750491037 0.0715686275
7: season 0.003482740507 0.02015837244 0.0254901961
8: month 0.003016223359 0.03462645560 0.0539215686
9: day 0.002926824939 0.00525381171 0.0578431373
10: weekday 0.000644114655 0.01335391670 0.0176470588
11: week_of_month 0.000587970927 0.00074890364 0.0117647059
12: workday_on_holiday 0.000025880557 0.00107281595 0.0009803922
13: holiday 0.000002167362 0.00003216151 0.0009803922
df2的第一个元素看起来像
[[1]]
[[1]][[1]]
date c_farolillo
[1,] "2016-01-01" "17"
[[1]][[2]]
date c_farolillo
[1,] "2016-01-02" "9"
[[1]][[3]]
date c_farolillo
[1,] "2016-01-03" "8"
[[1]][[4]]
date c_farolillo
[1,] "2016-01-04" "3"
[[1]][[5]]
date c_farolillo
[1,] "2016-01-05" "4"
[[1]][[6]]
date c_farolillo
[1,] "2016-01-06" "4"
我正在尝试使用df2的第一个元素，并在df1中创建一个新列。也就是说，我要考虑这个元素：
[[1]]
[[1]][[1]]
date c_farolillo
[1,] "2016-01-01" "17"
并将一列添加到df1的元素1中，例如：
$`c(5, 19)`
$`c(5, 19)`[[1]]
Feature Gain Cover Frequency date c_farolillo
1: plaza_eliptica 0.948578681145 0.53759794901 0.2794117647 2016-01-01 17
2: wind 0.014083116347 0.10011187610 0.1343137255 2016-01-01 17
3: temp 0.011637657812 0.08581378948 0.1460784314 2016-01-01 17
4: year 0.006344014204 0.12430881478 0.1137254902 2016-01-01 17
5: humidity 0.004941509318 0.03941622272 0.0862745098 2016-01-01 17
6: barometer 0.003729098869 0.03750491037 0.0715686275 2016-01-01 17
7: season 0.003482740507 0.02015837244 0.0254901961 2016-01-01 17
8: month 0.003016223359 0.03462645560 0.0539215686 2016-01-01 17
9: day 0.002926824939 0.00525381171 0.0578431373 2016-01-01 17
10: weekday 0.000644114655 0.01335391670 0.0176470588 2016-01-01 17
11: week_of_month 0.000587970927 0.00074890364 0.0117647059 2016-01-01 17
12: workday_on_holiday 0.000025880557 0.00107281595 0.0009803922 2016-01-01 17
13: holiday 0.000002167362 0.00003216151 0.0009803922 2016-01-01 17
然后取df2的元素2：
[[1]][[2]]
date c_farolillo
[1,] "2016-01-02" "9"
并执行相同的操作，但对于df1的元素2。
$`c(5, 19)`[[2]]
Feature Gain Cover Frequency date c_farolillo
1: plaza_eliptica 0.95025739085 0.5490795291 0.283433134 2016-01-02 9
2: temp 0.01236820897 0.0832973356 0.150698603 2016-01-02 9
3: wind 0.01196041617 0.0895609496 0.125748503 2016-01-02 9
4: year 0.00604315510 0.1158975396 0.112774451 2016-01-02 9
5: season 0.00511480982 0.0173938219 0.027944112 2016-01-02 9
6: humidity 0.00500999458 0.0578155014 0.086826347 2016-01-02 9
7: barometer 0.00325812831 0.0340156062 0.071856287 2016-01-02 9
8: month 0.00323898173 0.0354103288 0.062874251 2016-01-02 9
9: day 0.00220665600 0.0067323511 0.058882236 2016-01-02 9
10: weekday 0.00050300478 0.0103857430 0.014970060 2016-01-02 9
11: workday_on_holiday 0.00001964502 0.0002228799 0.000998004 2016-01-02 9
12: week_of_month 0.00001960867 0.0001884139 0.002994012 2016-01-02 9
两个主列表中的每个三个列表都包含合并的数据。也就是说，df1的元素3 list3将合并到gether：
[[2]][[3]]
date pza_del_carmen
[1,] "2016-01-03" "10"
$`c(7, 1, 2, 18)`[[3]]
Feature Gain Cover Frequency date pza_del_carmen
1: pza_de_espana 0.75620312440 0.2776437590 0.1729106628 2016-01-03 10
2: retiro 0.21115176179 0.2195341962 0.1498559078 2016-01-03 10
3: escuelas_aguirre 0.01304161322 0.0993235815 0.0970220941 2016-01-03 10
4: wind 0.00497255534 0.0963420148 0.1123919308 2016-01-03 10
5: temp 0.00490558802 0.1068475040 0.1585014409 2016-01-03 10
6: barometer 0.00356537931 0.0580338186 0.0787704131 2016-01-03 10
7: humidity 0.00201778550 0.0233865914 0.0672430355 2016-01-03 10
8: year 0.00177749645 0.0517034409 0.0547550432 2016-01-03 10
9: day 0.00086333491 0.0048338563 0.0509125841 2016-01-03 10
10: month 0.00047874430 0.0234141348 0.0211335255 2016-01-03 10
11: weekday 0.00040798584 0.0168542292 0.0144092219 2016-01-03 10
12: week_of_month 0.00032152928 0.0032202756 0.0105667627 2016-01-03 10
13: season 0.00026657228 0.0186674991 0.0105667627 2016-01-03 10
14: weekend_on_holiday 0.00002652936 0.0001950987 0.0009606148 2016-01-03 10
数据1：
编辑：
list2数据与此处的Info_assessment列表中的数据相同。
编辑：
新数据：
list1 <- list(`c(5, 19)` = list(structure(list(Feature = c("plaza_eliptica",
"wind", "temp", "year", "humidity", "barometer", "season", "month",
"day", "weekday", "week_of_month", "workday_on_holiday", "holiday"
), Gain = c(0.948578681144529, 0.0140831163472628, 0.0116376578118342,
0.00634401420383024, 0.0049415093180091, 0.00372909886882749,
0.00348274050673969, 0.00301622335931412, 0.00292682493887959,
0.000644114654618996, 0.000587970926885777, 0.0000258805573006903,
0.00000216736196828243), Cover = c(0.537597949014824, 0.100111876095501,
0.0858137894753769, 0.12430881477959, 0.0394162227230228, 0.0375049103727748,
0.0201583724440218, 0.034626455595298, 0.005253811712761, 0.0133539166971052,
0.000748903637236591, 0.00107281594659352, 0.000032161505893596
), Frequency = c(0.279411764705882, 0.134313725490196, 0.146078431372549,
0.113725490196078, 0.0862745098039216, 0.0715686274509804, 0.0254901960784314,
0.053921568627451, 0.057843137254902, 0.0176470588235294, 0.0117647058823529,
0.000980392156862745, 0.000980392156862745)), row.names = c(NA,
-13L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x560f12912cd0>),
structure(list(Feature = c("plaza_eliptica", "temp", "wind",
"year", "season", "humidity", "barometer", "month", "day",
"weekday", "workday_on_holiday", "week_of_month"), Gain = c(0.950257390847805,
0.0123682089682263, 0.0119604161685161, 0.00604315510198456,
0.00511480981946408, 0.00500999457778123, 0.00325812831159771,
0.00323898173138714, 0.00220665599964529, 0.000503004779421417,
0.0000196450237473069, 0.0000196086704235932), Cover = c(0.549079529057103,
0.0832973355514094, 0.0895609496061689, 0.115897539589901,
0.0173938218615296, 0.0578155014108067, 0.0340156061873294,
0.0354103287593173, 0.00673235113002399, 0.0103857430401735,
0.000222879883826733, 0.000188413922410228), Frequency = c(0.283433133732535,
0.150698602794411, 0.125748502994012, 0.112774451097804,
0.0279441117764471, 0.0868263473053892, 0.0718562874251497,
0.062874251497006, 0.0588822355289421, 0.0149700598802395,
0.000998003992015968, 0.0029940119760479)), row.names = c(NA,
-12L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x560f12912cd0>)),
`c(7, 1, 2, 18)` = list(structure(list(Feature = c("pza_de_espana",
"retiro", "escuelas_aguirre", "wind", "temp", "barometer",
"humidity", "year", "day", "month", "weekday", "week_of_month",
"season", "weekend_on_holiday"), Gain = c(0.762835844259031,
0.205459059740918, 0.0130315791677542, 0.0045078890564497,
0.00444974962904841, 0.00339293826829134, 0.00189508238873358,
0.00187978643588582, 0.00100750177875752, 0.000538521180289064,
0.000402209068457385, 0.000300268511436018, 0.0002522936065142,
0.0000472769084332351), Cover = c(0.293886140204331, 0.227015081557907,
0.0916711798129263, 0.0951455374927713, 0.102043766809557,
0.0520602895145079, 0.0284397058958519, 0.0521635564204478,
0.00571869176893915, 0.0177917404833809, 0.0133466738877007,
0.00350419034156103, 0.0168233263876777, 0.000390119422439669
), Frequency = c(0.175908221797323, 0.154875717017208, 0.101338432122371,
0.111854684512428, 0.136711281070746, 0.0736137667304015,
0.0678776290630975, 0.0583173996175908, 0.0506692160611855,
0.0248565965583174, 0.0181644359464627, 0.011472275334608,
0.0124282982791587, 0.00191204588910134)), row.names = c(NA,
-14L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x560f12912cd0>),
structure(list(Feature = c("pza_de_espana", "retiro",
"escuelas_aguirre", "wind", "temp", "barometer", "humidity",
"year", "day", "month", "weekday", "week_of_month", "season",
"weekend_on_holiday"), Gain = c(0.762803211914528, 0.205468329058334,
0.0130409115957786, 0.00452752500477332, 0.0044356364989903,
0.00339296767537418, 0.00189335858734865, 0.00188130563208465,
0.00100789616848372, 0.000544801039619805, 0.000402657864212878,
0.000301123014739554, 0.000252994653373448, 0.0000472812923590283
), Cover = c(0.293865486823143, 0.227012786737776, 0.0915793870076463,
0.0953268282831992, 0.101871655299658, 0.0520671739749038,
0.0284167576945319, 0.0521819149815037, 0.00571869176893915,
0.0179064814899808, 0.0133374946071727, 0.00349960070129703,
0.0168256212078097, 0.000390119422439669), Frequency = c(0.175908221797323,
0.154875717017208, 0.10038240917782, 0.112810707456979,
0.135755258126195, 0.0736137667304015, 0.0678776290630975,
0.0583173996175908, 0.0506692160611855, 0.0258126195028681,
0.0181644359464627, 0.011472275334608, 0.0124282982791587,
0.00191204588910134)), row.names = c(NA, -14L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x560f12912cd0>)))
新数据2：
list2 <- list(`c(5, 19)` = list(Info_assessment = list(structure(c("2016-01-01",
"17"), .Dim = 1:2, .Dimnames = list(NULL, c("date", "c_farolillo"
))), structure(c("2016-01-02", "9"), .Dim = 1:2, .Dimnames = list(
NULL, c("date", "c_farolillo")))), X_test = list(structure(c(1,
1, 2016, 1, 1, 1, 0, 1, 1, 1, 0, 1, 52.2692307692308, 5.46153846153846,
84.9615384615385, 30.1315384615385, 25), .Dim = c(1L, 17L), .Dimnames = list(
NULL, c("day", "month", "year", "quarter", "semester", "weekday",
"weekend", "season", "holiday", "workday_on_holiday", "weekend_on_holiday",
"week_of_month", "temp", "wind", "humidity", "barometer",
"plaza_eliptica"))), structure(c(2, 1, 2016, 1, 1, 0, 1,
1, 0, 0, 0, 1, 47.7307692307692, 10.4230769230769, 77.0769230769231,
30.1834615384615, 29), .Dim = c(1L, 17L), .Dimnames = list(NULL,
c("day", "month", "year", "quarter", "semester", "weekday",
"weekend", "season", "holiday", "workday_on_holiday", "weekend_on_holiday",
"week_of_month", "temp", "wind", "humidity", "barometer",
"plaza_eliptica")))), Y_test = list(structure(17, .Dim = c(1L,
1L), .Dimnames = list(NULL, "c_farolillo")), structure(9, .Dim = c(1L,
1L), .Dimnames = list(NULL, "c_farolillo")))), `c(7, 1, 2, 18)` = list(
Info_assessment = list(structure(c("2016-01-01", "12"), .Dim = 1:2, .Dimnames = list(
NULL, c("date", "pza_del_carmen"))), structure(c("2016-01-02",
"10"), .Dim = 1:2, .Dimnames = list(NULL, c("date", "pza_del_carmen"
)))), X_test = list(structure(c(1, 1, 2016, 1, 1, 1, 0, 1,
1, 1, 0, 1, 52.2692307692308, 5.46153846153846, 84.9615384615385,
30.1315384615385, 28, 17, 6), .Dim = c(1L, 19L), .Dimnames = list(
NULL, c("day", "month", "year", "quarter", "semester",
"weekday", "weekend", "season", "holiday", "workday_on_holiday",
"weekend_on_holiday", "week_of_month", "temp", "wind",
"humidity", "barometer", "pza_de_espana", "escuelas_aguirre",
"retiro"))), structure(c(2, 1, 2016, 1, 1, 0, 1, 1, 0,
0, 0, 1, 47.7307692307692, 10.4230769230769, 77.0769230769231,
30.1834615384615, 21, 24, 5), .Dim = c(1L, 19L), .Dimnames = list(
NULL, c("day", "month", "year", "quarter", "semester",
"weekday", "weekend", "season", "holiday", "workday_on_holiday",
"weekend_on_holiday", "week_of_month", "temp", "wind",
"humidity", "barometer", "pza_de_espana", "escuelas_aguirre",
"retiro")))), Y_test = list(structure(12, .Dim = c(1L,
1L), .Dimnames = list(NULL, "pza_del_carmen")), structure(10, .Dim = c(1L,
1L), .Dimnames = list(NULL, "pza_del_carmen")))))
1 个答案:
答案 0 :(得分：1)
它是list中的list，因此我们可以Map覆盖相应的list，然后在内部进行第二次Map
Map(function(lst1, lst2) Map(function(dat1, dat2) dat1[,
colnames(dat2) := .(dat2[1], dat2[2])][], lst1, lst2), list1,
lapply(list2, `[[`, "Info_assessment") )
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