R - 替换满足数据帧之间某些条件的列值

时间：2017-11-14 08:37:47

标签： r dataframe grouping

我有两个数据框，如下所示：

> df_X
            X1 X2 X3 X5
1   2017-05-02 21  1 NA
2   2017-05-02 10  1 NA
3   2017-05-02  5  1 NA
4   2017-05-02  5  1 NA
5   2017-05-02 25  1 NA
6   2017-05-02 20  1 NA
7   2017-05-04 25  1 NA
8   2017-05-08 25  1 NA
9   2017-05-10 20  1 NA
10  2017-05-13 30  1 NA
11  2017-05-17  5  1 NA
12  2017-05-20 25  1 NA
13  2017-05-23 15  1 NA
14  2017-05-25 20  1 NA
15  2017-05-27 35  1 NA
16  2017-05-30 30  1 NA
17  2017-06-01 10  1 NA
18  2017-06-03 10  1 NA
19  2017-06-05  5  1 NA
20  2017-06-08 10  1 NA

和

> df_Y
   Y1        Y2         Y3         Y4
1   1 0.6656641 2017-08-01 2017-08-02
2   1 1.0285626 2017-08-17 2017-08-18
3   1 1.1905584 2017-08-21 2017-08-22
4   1 1.1026008 2017-08-24 2017-08-25
5   1 1.1570583 2017-08-28 2017-08-29
6   1 1.0676820 2017-08-31 2017-09-01
7   2 1.1003816 2017-08-31 2017-09-01
8   2 1.1901956 2017-09-04 2017-09-05
9   1 1.1636756 2017-09-05 2017-09-05
10  1 1.1724968 2017-09-07 2017-09-07
11  2 1.1335978 2017-09-08 2017-09-09
12  1 1.0873912 2017-09-08 2017-09-08
13  1 1.1238168 2017-09-11 2017-09-11
14  2 1.1564612 2017-09-12 2017-09-13
15  2 1.1688628 2017-09-14 2017-09-15
16  1 1.2326152 2017-09-14 2017-09-15
17  2 1.2014378 2017-09-16 2017-09-16
18  2 1.1911753 2017-09-19 2017-09-19
19  1 1.1445341 2017-09-19 2017-09-20
20  1 1.1299295 2017-09-21 2017-09-22

我想将X5的值替换为Y2的值，其中X1介于Y3和Y4之间，{{1}匹配X3。

使用循环很容易实现：

Y1

完成整个数据帧的循环需要很长时间，所以我想知道是否有一个矢量化解决方案，可能使用for (i in 1:NROW(df_Y)) { df_X$X5[(df_X$X1 >= df_Y$Y3[i]) & (df_X$X1 <= df_Y$Y4[i]) & (df_X$X3 == df_Y$Y1[i])] <- df_Y$Y2[i] } > df_X_result X1 X2 X3 X5 1 2017-05-02 21 1 NA ... 26 2017-08-13 11 1 NA 27 2017-08-18 10 1 1.0285626 28 2017-08-21 5 1 1.1905584 29 2017-08-23 5 1 NA 30 2017-08-25 20 1 1.1026008 31 2017-08-28 10 1 1.1570583 32 2017-08-30 3 1 NA 33 2017-08-31 10 1 1.0676820 34 2017-09-04 18 1 NA 35 2017-09-05 25 1 1.1636756 36 2017-09-07 30 1 1.1724968 37 2017-09-08 20 1 1.0873912 38 2017-09-11 20 1 1.1238168 39 2017-09-13 5 1 NA 40 2017-09-14 25 1 1.2326152 41 2017-09-15 30 1 1.2326152 ？

group_by()

df_X <- structure(list(X1 = c("2017-05-02", "2017-05-02", "2017-05-02", 
"2017-05-02", "2017-05-02", "2017-05-02", "2017-05-04", "2017-05-08", 
"2017-05-10", "2017-05-13", "2017-05-17", "2017-05-20", "2017-05-23", 
"2017-05-25", "2017-05-27", "2017-05-30", "2017-06-01", "2017-06-03", 
"2017-06-05", "2017-06-08", "2017-06-10", "2017-06-14", "2017-06-16", 
"2017-07-15", "2017-08-09", "2017-08-13", "2017-08-18", "2017-08-21", 
"2017-08-23", "2017-08-25", "2017-08-28", "2017-08-30", "2017-08-31", 
"2017-09-04", "2017-09-05", "2017-09-07", "2017-09-08", "2017-09-11", 
"2017-09-13", "2017-09-14", "2017-09-15", "2017-09-18", "2017-09-19", 
"2017-09-21", "2017-09-23", "2017-09-25", "2017-09-26", "2017-09-28", 
"2017-09-29", "2017-09-30", "2017-10-02", "2017-10-03", "2017-10-04", 
"2017-10-05", "2017-10-06", "2017-10-09", "2017-10-10", "2017-10-11", 
"2017-10-12", "2017-10-12", "2017-10-14", "2017-10-16", "2017-10-18", 
"2017-10-20", "2017-10-21", "2017-10-24", "2017-10-27", "2017-10-28", 
"2017-10-31", "2017-11-02", "2017-11-03", "2017-11-06", "2017-11-08", 
"2017-05-02", "2017-05-02", "2017-05-02", "2017-05-02", "2017-05-02", 
"2017-05-03", "2017-05-05", "2017-05-09", "2017-05-12", "2017-05-16", 
"2017-05-19", "2017-05-22", "2017-05-25", "2017-05-27", "2017-05-29", 
"2017-05-31", "2017-06-02", "2017-06-05", "2017-06-07", "2017-06-14", 
"2017-06-16", "2017-06-19", "2017-09-01", "2017-09-05", "2017-09-07", 
"2017-09-08", "2017-09-11", "2017-09-12", "2017-09-14", "2017-09-15", 
"2017-09-18", "2017-09-19", "2017-09-21", "2017-09-22", "2017-09-25", 
"2017-09-27", "2017-09-29", "2017-09-30", "2017-10-02", "2017-10-03", 
"2017-10-04", "2017-10-05", "2017-10-06", "2017-10-09", "2017-10-09", 
"2017-10-11", "2017-10-11", "2017-10-12", "2017-10-14", "2017-10-16", 
"2017-10-17", "2017-10-19", "2017-10-20", "2017-10-21", "2017-10-25", 
"2017-10-27", "2017-10-31", "2017-11-01", "2017-11-02", "2017-11-03", 
"2017-11-06", "2017-11-08"), X2 = c(21, 10, 5, 5, 25, 20, 25, 
25, 20, 30, 5, 25, 15, 20, 35, 30, 10, 10, 5, 10, 5, 10, 15, 
7, 23, 11, 10, 5, 5, 20, 10, 3, 10, 18, 25, 30, 20, 20, 5, 25, 
30, 15, 10, 20, 29, 25, 30, 10, 10, 5, 5, 30, 15, 25, 25, 5, 
30, 5, 10, 3, 20, 55, 10, 15, 30, 20, 30, 10, 20, 25, 25, 15, 
20, 35, 20, 10, 15, 10, 15, 5, 5, 5, 25, 20, 5, 15, 30, 20, 20, 
10, 20, 5, 20, 15, 5, 5, 5, 25, 25, 10, 10, 5, 5, 30, 25, 10, 
5, 20, 10, 30, 20, 25, 10, 20, 15, 10, 5, 30, 5, 10, 30, 3, 10, 
10, 40, 20, 24, 30, 40, 15, 10, 10, 5, 15, 30), X3 = c(1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2), X5 = c(NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("X1", 
"X2", "X3", "X5"), row.names = c(NA, 135L), class = "data.frame")

1 个答案:

答案 0 :(得分：1)

使用data.table的非equi连接功能时这很简单：

library(data.table)

# convert to data.table
setDT(df_X)
setDT(df_Y)

# data preparation:
df_X[, X1 := as.IDate(X1)]
df_Y[, c("Y3", "Y4") := lapply(.SD, as.IDate), .SDcols = c("Y3", "Y4")]
df_Y[, Y1 := as.numeric(Y1)]
df_X[, X5 := as.numeric(X5)]

# non-equi join + update by reference
df_X[df_Y, on = .(X3 = Y1, X1 >= Y3, X1 <= Y4), X5 := i.Y2]  #  <-- the main part

# convert back to data.frame
setDF(df_X)
setDF(df_Y)
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