R根据其参考列将特定列从一个数据帧合并到另一个数据帧

Question

我是 R 初学者。 我有两个巨大的数据框，我想在 hkdata.2 添加一个名为 Vaccine 的新列，该数据是根据 hkdata.2 上的 2 个参考列（hhID 和成员）从另一个 DF 依从性中获取的，有人可以帮我吗？

hkdata.2
hhID    member  T0  delta   X_hh    X_fm    ILI age
1          1    7      0    0       0        0  44
1          2    7      0    0       0        0  36
2          1    8      0    1       0        0  39
2          2    8      0    1       0        0  39

adherence
hhID member mask soap vaccine
1      0      1    0    1   
1      1      1    1    1
1      2      0    0    1
2      0      1    0    0
2      1      0    0    0
2      2      1    0    1

所以最后我可以得到这样的东西。 在 hkdata.2 中有一个称为疫苗的额外列

hkdata.2
hhID    member  T0  delta   X_hh    X_fm    ILI age vaccine
1          1    7      0    0       0        0  44    1
1          2    7      0    0       0        0  36    1
2          1    8      0    1       0        0  39    0
2          2    8      0    1       0        0  39    1

Answer 1

更新：将v1.9.6 用于on= 语法。查看旧代码的历史记录。

require(data.table) # v1.9.6+
setDT(hkdata.2)[setDT(adherence), vaccine := i.vaccine, on=c("hhID", "member")]
#    hhID member T0 delta X_hh X_fm ILI age vaccine
# 1:    1      1  7     0    0    0   0  44       1
# 2:    1      2  7     0    0    0   0  36       1
# 3:    2      1  8     0    1    0   0  39       0
# 4:    2      2  8     0    1    0   0  39       1

1. setDT通过引用将 data.frame 转换为 data.table。

2. 对on= 指定的列执行连接。您需要的Note that this join is both a) fast *and* b) memory efficient. a) *fast* because they're binary search based joins, and no copy is being made here at all. Thevaccinecolumn is directly added to yourhkdata.2data.table. b) *memory efficient* because only the columnvaccine` 用于连接，而不是其他列（这对于非常大的数据集来说特别好）。

---

这是一个基准，假设每个 hhID 中有 100,000 个 hhIDs 和 200 个 members：

require(data.table) # v1.9.6
require(dplyr) # 0.4.3.9000

set.seed(98192L)
N = 40e6 # 40 million rows
hkdata.2 = data.frame(hhID   = rep(1:1e5, each=200), 
                      member = 1:200,
                      T0     = sample(10),
                      delta  = sample(0:1), 
                      X_hh   = sample(0:1), 
                      X_fm   = sample(0:1), 
                      ILI    = sample(0:1),
                      age    = sample(30:100, N/2, TRUE))

# let's go with 100,000 hhIDs and 400 members here:
adherence = data.frame(hhID    = rep(1:1e5, each=400), 
                       member  = 1:400,
                       mask    = sample(0:1),
                       soap    = sample(0:1),
                       vaccine = sample(0:1))

## dplyr timing
system.time(ans1 <- left_join(hkdata.2, select(adherence, -soap, -mask)))
#   user  system elapsed
# 16.977   2.163  19.605
## data.table timing
system.time(setDT(hkdata.2)[setDT(adherence), vaccine := i.vaccine, on=c("hhID", "member")])
#   user  system elapsed
#  1.186   0.233   1.427

dplyr 的内存使用峰值为 4.7GB，耗时 19.6 秒，而data.table 耗时 1.4 秒，内存使用峰值为 2.2GB。

总结：data.table 的速度提高了约 14 倍，内存效率提高了约 2.1 倍。

Answer 2

您可以使用plyr 库来执行此操作。

library(plyr)
new_frame=join(hkdata.2,adherence,by=c('hhID','member'))

Answer 3

 library(dplyr)
 left_join(hkdata.2, adherence)
 #    Joining by: c("hhID", "member")
 #  hhID member T0 delta X_hh X_fm ILI age mask soap vaccine
 #1    1      1  7     0    0    0   0  44    1    1       1
 #2    1      2  7     0    0    0   0  36    0    0       1
 #3    2      1  8     0    1    0   0  39    0    0       0
 #4    2      2  8     0    1    0   0  39    1    0       1

如果你不需要mask，soap

  left_join(hkdata.2, adherence) %>% select(-soap, -mask)

或者

  left_join(hkdata.2, adherence[,c("hhID", "member", "vaccine")])