说我有以下数据表：
# Seed random number generator
set.seed(33550336)
# Create data tables
dt1 <- data.table(ID = sample(LETTERS[1:5], 20, replace = TRUE),
loc = sample(1:50, 20, replace = TRUE),
a = runif(20),
b = runif(20),
c = runif(20),
d = runif(20))
dt2 <- data.table(ID = sample(LETTERS[1:5], 20, replace = TRUE),
loc = sample(1:50, 20, replace = TRUE),
e = runif(20),
f = runif(20),
g = runif(20),
h = runif(20))
我想这样加入他们（根据this answer）：
# Join on ID and nearest rolling join on loc
dt2[dt1,
on = .(ID, loc),
roll = "nearest"]
# ID loc e f g h a b c d
# 1: E 2 0.6080648 0.59558616 0.9680243 0.65885155 0.75533475 0.46796072 0.07874670 0.372224933
# 2: B 22 0.2900181 0.89395076 0.5012072 0.81403388 0.24129711 0.66914193 0.11941211 0.330982361
# 3: C 23 0.7753557 0.31772779 0.3302613 0.02004258 0.32252276 0.09341920 0.29665070 0.563954195
# 4: A 46 0.1193827 0.89183103 0.7142606 0.17231293 0.62979589 0.19621242 0.48943734 0.318145133
# 5: B 26 0.2900181 0.89395076 0.5012072 0.81403388 0.65672029 0.45106318 0.47421905 0.605327569
# 6: E 17 0.4417452 0.03226111 0.5975499 0.49336668 0.83821385 0.99078941 0.93356571 0.459227328
# 7: D 24 0.8974042 0.90725532 0.5008502 0.21681072 0.86831894 0.41260922 0.65389531 0.930843432
# 8: D 24 0.8974042 0.90725532 0.5008502 0.21681072 0.82042112 0.82906524 0.59829109 0.859362233
# 9: D 44 0.3958956 0.06361996 0.8068514 0.56349064 0.29823590 0.04765864 0.65412304 0.742808806
# 10: E 11 0.4417452 0.03226111 0.5975499 0.49336668 0.15013055 0.83683385 0.18847332 0.139363770
# 11: D 11 0.5967619 0.23497655 0.5429504 0.56322079 0.68644344 0.46995509 0.35128292 0.910443478
# 12: A 50 0.1193827 0.89183103 0.7142606 0.17231293 0.65811523 0.48901176 0.96854302 0.875838825
# 13: E 17 0.4417452 0.03226111 0.5975499 0.49336668 0.93484739 0.57810132 0.75250483 0.607710552
# 14: A 21 0.4491745 0.61724476 0.3283133 0.51406071 0.96610736 0.03222779 0.05768814 0.436536989
# 15: A 6 0.4491745 0.61724476 0.3283133 0.51406071 0.69975907 0.35564120 0.42206040 0.309386788
# 16: B 49 0.1152318 0.99716746 0.1440101 0.70734795 0.05138897 0.80463532 0.41856763 0.421029334
# 17: C 9 0.1204828 0.47622000 0.6802176 0.36385191 0.98509395 0.49711655 0.68159049 0.003570911
# 18: D 7 0.5967619 0.23497655 0.5429504 0.56322079 0.69862668 0.91597522 0.53630369 0.297000037
# 19: C 8 0.1204828 0.47622000 0.6802176 0.36385191 0.80761410 0.87051653 0.93177628 0.671692311
# 20: B 5 0.5652708 0.50866629 0.3992037 0.87643314 0.69493460 0.99878010 0.77953456 0.820925302
太棒了。仅缺少一件事：loc中的dt1和dt2之间的差异（即delta = abs(x.loc - i.loc)）。不过，剩下的唯一loc来自dt1，因此我目前无法进行此计算。
Jaap在回答我的上一个问题时，将每个列命名为要单独保留并同时执行计算，如下所示：
dt2[dt1,
on = c("ID", "loc"),
roll = "nearest",
.(ID, loc = i.loc, a, b, c, d, e, f, g, h, delta = abs(x.loc - i.loc))][]
# ID loc a b c d e f g h delta
# 1: E 2 0.75533475 0.46796072 0.07874670 0.372224933 0.6080648 0.59558616 0.9680243 0.65885155 1
# 2: B 22 0.24129711 0.66914193 0.11941211 0.330982361 0.2900181 0.89395076 0.5012072 0.81403388 5
# 3: C 23 0.32252276 0.09341920 0.29665070 0.563954195 0.7753557 0.31772779 0.3302613 0.02004258 6
# 4: A 46 0.62979589 0.19621242 0.48943734 0.318145133 0.1193827 0.89183103 0.7142606 0.17231293 0
# 5: B 26 0.65672029 0.45106318 0.47421905 0.605327569 0.2900181 0.89395076 0.5012072 0.81403388 1
# 6: E 17 0.83821385 0.99078941 0.93356571 0.459227328 0.4417452 0.03226111 0.5975499 0.49336668 2
# 7: D 24 0.86831894 0.41260922 0.65389531 0.930843432 0.8974042 0.90725532 0.5008502 0.21681072 14
# 8: D 24 0.82042112 0.82906524 0.59829109 0.859362233 0.8974042 0.90725532 0.5008502 0.21681072 14
# 9: D 44 0.29823590 0.04765864 0.65412304 0.742808806 0.3958956 0.06361996 0.8068514 0.56349064 1
# 10: E 11 0.15013055 0.83683385 0.18847332 0.139363770 0.4417452 0.03226111 0.5975499 0.49336668 4
# 11: D 11 0.68644344 0.46995509 0.35128292 0.910443478 0.5967619 0.23497655 0.5429504 0.56322079 8
# 12: A 50 0.65811523 0.48901176 0.96854302 0.875838825 0.1193827 0.89183103 0.7142606 0.17231293 4
# 13: E 17 0.93484739 0.57810132 0.75250483 0.607710552 0.4417452 0.03226111 0.5975499 0.49336668 2
# 14: A 21 0.96610736 0.03222779 0.05768814 0.436536989 0.4491745 0.61724476 0.3283133 0.51406071 4
# 15: A 6 0.69975907 0.35564120 0.42206040 0.309386788 0.4491745 0.61724476 0.3283133 0.51406071 19
# 16: B 49 0.05138897 0.80463532 0.41856763 0.421029334 0.1152318 0.99716746 0.1440101 0.70734795 6
# 17: C 9 0.98509395 0.49711655 0.68159049 0.003570911 0.1204828 0.47622000 0.6802176 0.36385191 3
# 18: D 7 0.69862668 0.91597522 0.53630369 0.297000037 0.5967619 0.23497655 0.5429504 0.56322079 4
# 19: C 8 0.80761410 0.87051653 0.93177628 0.671692311 0.1204828 0.47622000 0.6802176 0.36385191 2
# 20: B 5 0.69493460 0.99878010 0.77953456 0.820925302 0.5652708 0.50866629 0.3992037 0.87643314 1
这很完美，除了必须命名每一列。因此，作为一种解决方法，我保留了两个数据表中的所有列（使用mget），然后通过链接计算delta：
# Columns to select
cols2sel <- c(paste0("x.", names(dt2)), paste0("i.", names(dt1)))
dt2[dt1,
on = c("ID", "loc"),
roll = "nearest",
mget(cols2sel)][, delta := abs(x.loc - i.loc)][]
# x.ID x.loc x.e x.f x.g x.h i.ID i.loc i.a i.b i.c i.d delta
# 1: E 1 0.6080648 0.59558616 0.9680243 0.65885155 E 2 0.75533475 0.46796072 0.07874670 0.372224933 1
# 2: B 27 0.2900181 0.89395076 0.5012072 0.81403388 B 22 0.24129711 0.66914193 0.11941211 0.330982361 5
# 3: C 29 0.7753557 0.31772779 0.3302613 0.02004258 C 23 0.32252276 0.09341920 0.29665070 0.563954195 6
# 4: A 46 0.1193827 0.89183103 0.7142606 0.17231293 A 46 0.62979589 0.19621242 0.48943734 0.318145133 0
# 5: B 27 0.2900181 0.89395076 0.5012072 0.81403388 B 26 0.65672029 0.45106318 0.47421905 0.605327569 1
# 6: E 15 0.4417452 0.03226111 0.5975499 0.49336668 E 17 0.83821385 0.99078941 0.93356571 0.459227328 2
# 7: D 38 0.8974042 0.90725532 0.5008502 0.21681072 D 24 0.86831894 0.41260922 0.65389531 0.930843432 14
# 8: D 38 0.8974042 0.90725532 0.5008502 0.21681072 D 24 0.82042112 0.82906524 0.59829109 0.859362233 14
# 9: D 45 0.3958956 0.06361996 0.8068514 0.56349064 D 44 0.29823590 0.04765864 0.65412304 0.742808806 1
# 10: E 15 0.4417452 0.03226111 0.5975499 0.49336668 E 11 0.15013055 0.83683385 0.18847332 0.139363770 4
# 11: D 3 0.5967619 0.23497655 0.5429504 0.56322079 D 11 0.68644344 0.46995509 0.35128292 0.910443478 8
# 12: A 46 0.1193827 0.89183103 0.7142606 0.17231293 A 50 0.65811523 0.48901176 0.96854302 0.875838825 4
# 13: E 15 0.4417452 0.03226111 0.5975499 0.49336668 E 17 0.93484739 0.57810132 0.75250483 0.607710552 2
# 14: A 25 0.4491745 0.61724476 0.3283133 0.51406071 A 21 0.96610736 0.03222779 0.05768814 0.436536989 4
# 15: A 25 0.4491745 0.61724476 0.3283133 0.51406071 A 6 0.69975907 0.35564120 0.42206040 0.309386788 19
# 16: B 43 0.1152318 0.99716746 0.1440101 0.70734795 B 49 0.05138897 0.80463532 0.41856763 0.421029334 6
# 17: C 6 0.1204828 0.47622000 0.6802176 0.36385191 C 9 0.98509395 0.49711655 0.68159049 0.003570911 3
# 18: D 3 0.5967619 0.23497655 0.5429504 0.56322079 D 7 0.69862668 0.91597522 0.53630369 0.297000037 4
# 19: C 6 0.1204828 0.47622000 0.6802176 0.36385191 C 8 0.80761410 0.87051653 0.93177628 0.671692311 2
# 20: B 6 0.5652708 0.50866629 0.3992037 0.87643314 B 5 0.69493460 0.99878010 0.77953456 0.820925302 1
这几乎满足了我的需求，但是现在我不得不搞乱固定列名，删除重复的列（即ID），这与Jaap优雅的初始解决方案不同。但是，该解决方案需要命名所有列。
我的问题：有没有办法做到两全其美，而不必指定每一列，但是还可以得到一种干净的格式，就像上面的代码块＃3一样？
答案 0 :(得分：2)
在以上评论中，@ Henrik提供的链接上有post by sritchie73，这是一种解决方法。一种解决方案是在连接之前复制在连接中使用的变量，以便将其保留在结果中并可以在计算中使用。
# Copy loc variables
dt1[, loc1 := loc]
dt2[, loc2 := loc]
# Perform join, calculate delta, drop loc1 & loc2
dt2[dt1,
on = c("ID", "loc"),
roll = "nearest"][
, delta := abs(loc1 - loc2)][
, c("loc1", "loc2") := NULL][]
给出，
# ID loc e f g h a b c d delta
# 1: E 2 0.6080648 0.59558616 0.9680243 0.65885155 0.75533475 0.46796072 0.07874670 0.372224933 1
# 2: B 22 0.2900181 0.89395076 0.5012072 0.81403388 0.24129711 0.66914193 0.11941211 0.330982361 5
# 3: C 23 0.7753557 0.31772779 0.3302613 0.02004258 0.32252276 0.09341920 0.29665070 0.563954195 6
# 4: A 46 0.1193827 0.89183103 0.7142606 0.17231293 0.62979589 0.19621242 0.48943734 0.318145133 0
# 5: B 26 0.2900181 0.89395076 0.5012072 0.81403388 0.65672029 0.45106318 0.47421905 0.605327569 1
# 6: E 17 0.4417452 0.03226111 0.5975499 0.49336668 0.83821385 0.99078941 0.93356571 0.459227328 2
# 7: D 24 0.8974042 0.90725532 0.5008502 0.21681072 0.86831894 0.41260922 0.65389531 0.930843432 14
# 8: D 24 0.8974042 0.90725532 0.5008502 0.21681072 0.82042112 0.82906524 0.59829109 0.859362233 14
# 9: D 44 0.3958956 0.06361996 0.8068514 0.56349064 0.29823590 0.04765864 0.65412304 0.742808806 1
# 10: E 11 0.4417452 0.03226111 0.5975499 0.49336668 0.15013055 0.83683385 0.18847332 0.139363770 4
# 11: D 11 0.5967619 0.23497655 0.5429504 0.56322079 0.68644344 0.46995509 0.35128292 0.910443478 8
# 12: A 50 0.1193827 0.89183103 0.7142606 0.17231293 0.65811523 0.48901176 0.96854302 0.875838825 4
# 13: E 17 0.4417452 0.03226111 0.5975499 0.49336668 0.93484739 0.57810132 0.75250483 0.607710552 2
# 14: A 21 0.4491745 0.61724476 0.3283133 0.51406071 0.96610736 0.03222779 0.05768814 0.436536989 4
# 15: A 6 0.4491745 0.61724476 0.3283133 0.51406071 0.69975907 0.35564120 0.42206040 0.309386788 19
# 16: B 49 0.1152318 0.99716746 0.1440101 0.70734795 0.05138897 0.80463532 0.41856763 0.421029334 6
# 17: C 9 0.1204828 0.47622000 0.6802176 0.36385191 0.98509395 0.49711655 0.68159049 0.003570911 3
# 18: D 7 0.5967619 0.23497655 0.5429504 0.56322079 0.69862668 0.91597522 0.53630369 0.297000037 4
# 19: C 8 0.1204828 0.47622000 0.6802176 0.36385191 0.80761410 0.87051653 0.93177628 0.671692311 2
# 20: B 5 0.5652708 0.50866629 0.3992037 0.87643314 0.69493460 0.99878010 0.77953456 0.820925302 1