R - 更有效 left_join
R - more effective left_join
我有两个数据框——一个包含名称和限制范围（只有几百行，最多 1000 行），需要分配给一个“测量”数据框，它可以包含数百万行（或千万行）。
目前我正在做 left_join 和过滤值以获得分配给每个测量的特定限制。然而，这是非常低效的并且耗费了大量资源。对于更大的数据帧，代码甚至无法 运行.
任何关于更有效解决方案的想法都会有所帮助。
library(dplyr)
## this one has got only few houndreds rows
df_limits <- read.table(text="Title station_id limit_from limit_to
Level_3_Low 1 0 70
Level_2_Low 1 70 90
Level_1_Low 1 90 100
Optimal 1 100 110
Level_1_High 1 110 130
Level_2_High 1 130 150
Level_3_High 1 150 180
Level_3_Low 2 0 70
Level_2_Low 2 70 90
Level_1_Low 2 90 100
Optimal 2 100 110
Level_1_High 2 110 130
Level_2_High 2 130 150
Level_3_High 2 150 180
Level_3_Low 3 0 70
Level_2_Low 3 70 90
Level_1_Low 3 90 100
Optimal 3 100 110
Level_1_High 3 110 130
Level_2_High 3 130 150
Level_3_High 3 150 180
",header = TRUE, stringsAsFactors = TRUE)
# this DF has got millions of rows
df_measurements <- read.table(text="measurement_id station_id value
12121534 1 172
12121618 1 87
12121703 1 9
12121709 2 80
12121760 2 80
12121813 2 115
12121881 3 67
12121907 3 100
12121920 3 108
12121979 1 102
12121995 1 53
12122022 1 77
12122065 2 158
12122107 2 144
12122113 2 5
12122135 3 100
12122187 3 136
12122267 3 130
12122359 1 105
12122366 1 126
12122398 1 143
",header = TRUE, stringsAsFactors = TRUE)
df_results <- left_join(df_measurements,df_limits, by = "station_id") %>%
filter ((value >= limit_from & value < limit_to) | is.na(Title)) %>%
select(names(df_measurements), Title)
这是一种方法。有问题的部分是条件 value < limit_to。 foverlaps 检查导致双重匹配的条件 value <= limit_to，因此我们在重叠连接之后调用过滤条件，然后 select 所需的列。请注意，结果与使用 dplyr 生成的 df_results 的顺序不同。
library(data.table)
dt_limits <- as.data.table(df_limits)
dt_measurements <- as.data.table(df_measurements)
setkey(dt_limits, station_id, limit_from, limit_to)
dt_results <- foverlaps(dt_measurements[, value2 := value],
dt_limits,
by.x = c("station_id", "value", "value2"),
type = "within",
)[value < limit_to,
.(measurement_id , station_id, value, Title)]
dt_results[]
#> measurement_id station_id value Title
#> 1: 12121534 1 172 Level_3_High
#> 2: 12121618 1 87 Level_2_Low
#> 3: 12121703 1 9 Level_3_Low
#> 4: 12121709 2 80 Level_2_Low
#> 5: 12121760 2 80 Level_2_Low
#> 6: 12121813 2 115 Level_1_High
#> 7: 12121881 3 67 Level_3_Low
#> 8: 12121907 3 100 Optimal
#> 9: 12121920 3 108 Optimal
#> 10: 12121979 1 102 Optimal
#> 11: 12121995 1 53 Level_3_Low
#> 12: 12122022 1 77 Level_2_Low
#> 13: 12122065 2 158 Level_3_High
#> 14: 12122107 2 144 Level_2_High
#> 15: 12122113 2 5 Level_3_Low
#> 16: 12122135 3 100 Optimal
#> 17: 12122187 3 136 Level_2_High
#> 18: 12122267 3 130 Level_2_High
#> 19: 12122359 1 105 Optimal
#> 20: 12122366 1 126 Level_1_High
#> 21: 12122398 1 143 Level_2_High
#> measurement_id station_id value Title
由 reprex package (v0.3.0)
于 2021-08-09 创建
另一个 data.table 解决方案使用 non-equijoins:
library(data.table)
setDT(df_measurements)
setDT(df_limits)
df_limits[df_measurements, .(station_id, measurement_id, value, Title),
on=.(station_id = station_id, limit_from < value, limit_to >= value)]
station_id measurement_id value Title
1: 1 12121534 172 Level_3_High
2: 1 12121618 87 Level_2_Low
3: 1 12121703 9 Level_3_Low
4: 2 12121709 80 Level_2_Low
5: 2 12121760 80 Level_2_Low
6: 2 12121813 115 Level_1_High
7: 3 12121881 67 Level_3_Low
8: 3 12121907 100 Level_1_Low
9: 3 12121920 108 Optimal
10: 1 12121979 102 Optimal
11: 1 12121995 53 Level_3_Low
12: 1 12122022 77 Level_2_Low
13: 2 12122065 158 Level_3_High
14: 2 12122107 144 Level_2_High
15: 2 12122113 5 Level_3_Low
16: 3 12122135 100 Level_1_Low
17: 3 12122187 136 Level_2_High
18: 3 12122267 130 Level_1_High
19: 1 12122359 105 Optimal
20: 1 12122366 126 Level_1_High
21: 1 12122398 143 Level_2_High
使用 subset + merge
的简单基础 R（不需要额外的包）选项
subset(
merge(
df_measurements,
df_limits,
all = TRUE
),
limit_from < value & limit_to >= value
)
给予
station_id measurement_id value Title limit_from limit_to
7 1 12121534 172 Level_3_High 150 180
9 1 12121618 87 Level_2_Low 70 90
15 1 12121703 9 Level_3_Low 0 70
23 1 12122022 77 Level_2_Low 70 90
34 1 12122398 143 Level_2_High 130 150
39 1 12121979 102 Optimal 100 110
43 1 12121995 53 Level_3_Low 0 70
54 1 12122366 126 Level_1_High 110 130
60 1 12122359 105 Optimal 100 110
65 2 12121760 80 Level_2_Low 70 90
75 2 12121813 115 Level_1_High 110 130
79 2 12121709 80 Level_2_Low 70 90
91 2 12122065 158 Level_3_High 150 180
97 2 12122107 144 Level_2_High 130 150
99 2 12122113 5 Level_3_Low 0 70
108 3 12121907 100 Level_1_Low 90 100
116 3 12121920 108 Optimal 100 110
124 3 12122267 130 Level_1_High 110 130
127 3 12121881 67 Level_3_Low 0 70
136 3 12122135 100 Level_1_Low 90 100
146 3 12122187 136 Level_2_High 130 150
另一种选择是使用 dplyr
df_measurements %>%
group_by(station_id) %>%
mutate(Title = with(
df_limits,
Title[
findInterval(
value,
unique(unlist(cbind(limit_from, limit_to)[station_id == first(.$station_id)])),
left.open = TRUE
)
]
)) %>%
ungroup()
这给出了
# A tibble: 21 x 4
measurement_id station_id value Title
<int> <int> <int> <fct>
1 12121534 1 172 Level_3_High
2 12121618 1 87 Level_2_Low
3 12121703 1 9 Level_3_Low
4 12121709 2 80 Level_2_Low
5 12121760 2 80 Level_2_Low
6 12121813 2 115 Level_1_High
7 12121881 3 67 Level_3_Low
8 12121907 3 100 Level_1_Low
9 12121920 3 108 Optimal
10 12121979 1 102 Optimal
# ... with 11 more rows
基准测试
f_TIC1 <- function() {
subset(
merge(
df_measurements,
df_limits,
all = TRUE
),
limit_from < value & limit_to >= value
)
}
f_TIC2 <- function() {
df_measurements %>%
group_by(station_id) %>%
mutate(Title = with(
df_limits,
Title[
findInterval(
value,
unique(unlist(cbind(limit_from, limit_to)[station_id == first(station_id)])),
left.open = TRUE
)
]
)) %>%
ungroup()
}
dt_limits <- as.data.table(df_limits)
dt_measurements <- as.data.table(df_measurements)
f_Waldi <- function() {
dt_limits[
dt_measurements,
.(station_id, measurement_id, value, Title),
on = .(station_id, limit_from < value, limit_to >= value)
]
}
f_TimTeaFan <- function() {
setkey(dt_limits, station_id, limit_from, limit_to)
foverlaps(dt_measurements[, value2 := value],
dt_limits,
by.x = c("station_id", "value", "value2"),
type = "within",
)[
value < limit_to,
.(measurement_id, station_id, value, Title)
]
}
你会看到
Unit: relative
expr min lq mean median uq max neval
f_TIC1() 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 100
f_TIC2() 4.848639 4.909985 4.895588 4.942616 5.124704 2.580819 100
f_Waldi() 3.182027 3.010615 3.069916 3.114160 3.397845 1.698386 100
f_TimTeaFan() 5.523778 5.112872 5.226145 5.112407 5.745671 2.446987 100
我有两个数据框——一个包含名称和限制范围（只有几百行，最多 1000 行），需要分配给一个“测量”数据框，它可以包含数百万行（或千万行）。
目前我正在做 left_join 和过滤值以获得分配给每个测量的特定限制。然而，这是非常低效的并且耗费了大量资源。对于更大的数据帧，代码甚至无法 运行.
任何关于更有效解决方案的想法都会有所帮助。
library(dplyr)
## this one has got only few houndreds rows
df_limits <- read.table(text="Title station_id limit_from limit_to
Level_3_Low 1 0 70
Level_2_Low 1 70 90
Level_1_Low 1 90 100
Optimal 1 100 110
Level_1_High 1 110 130
Level_2_High 1 130 150
Level_3_High 1 150 180
Level_3_Low 2 0 70
Level_2_Low 2 70 90
Level_1_Low 2 90 100
Optimal 2 100 110
Level_1_High 2 110 130
Level_2_High 2 130 150
Level_3_High 2 150 180
Level_3_Low 3 0 70
Level_2_Low 3 70 90
Level_1_Low 3 90 100
Optimal 3 100 110
Level_1_High 3 110 130
Level_2_High 3 130 150
Level_3_High 3 150 180
",header = TRUE, stringsAsFactors = TRUE)
# this DF has got millions of rows
df_measurements <- read.table(text="measurement_id station_id value
12121534 1 172
12121618 1 87
12121703 1 9
12121709 2 80
12121760 2 80
12121813 2 115
12121881 3 67
12121907 3 100
12121920 3 108
12121979 1 102
12121995 1 53
12122022 1 77
12122065 2 158
12122107 2 144
12122113 2 5
12122135 3 100
12122187 3 136
12122267 3 130
12122359 1 105
12122366 1 126
12122398 1 143
",header = TRUE, stringsAsFactors = TRUE)
df_results <- left_join(df_measurements,df_limits, by = "station_id") %>%
filter ((value >= limit_from & value < limit_to) | is.na(Title)) %>%
select(names(df_measurements), Title)
这是一种方法。有问题的部分是条件 value < limit_to。 foverlaps 检查导致双重匹配的条件 value <= limit_to，因此我们在重叠连接之后调用过滤条件，然后 select 所需的列。请注意，结果与使用 dplyr 生成的 df_results 的顺序不同。
library(data.table)
dt_limits <- as.data.table(df_limits)
dt_measurements <- as.data.table(df_measurements)
setkey(dt_limits, station_id, limit_from, limit_to)
dt_results <- foverlaps(dt_measurements[, value2 := value],
dt_limits,
by.x = c("station_id", "value", "value2"),
type = "within",
)[value < limit_to,
.(measurement_id , station_id, value, Title)]
dt_results[]
#> measurement_id station_id value Title
#> 1: 12121534 1 172 Level_3_High
#> 2: 12121618 1 87 Level_2_Low
#> 3: 12121703 1 9 Level_3_Low
#> 4: 12121709 2 80 Level_2_Low
#> 5: 12121760 2 80 Level_2_Low
#> 6: 12121813 2 115 Level_1_High
#> 7: 12121881 3 67 Level_3_Low
#> 8: 12121907 3 100 Optimal
#> 9: 12121920 3 108 Optimal
#> 10: 12121979 1 102 Optimal
#> 11: 12121995 1 53 Level_3_Low
#> 12: 12122022 1 77 Level_2_Low
#> 13: 12122065 2 158 Level_3_High
#> 14: 12122107 2 144 Level_2_High
#> 15: 12122113 2 5 Level_3_Low
#> 16: 12122135 3 100 Optimal
#> 17: 12122187 3 136 Level_2_High
#> 18: 12122267 3 130 Level_2_High
#> 19: 12122359 1 105 Optimal
#> 20: 12122366 1 126 Level_1_High
#> 21: 12122398 1 143 Level_2_High
#> measurement_id station_id value Title
由 reprex package (v0.3.0)
于 2021-08-09 创建另一个 data.table 解决方案使用 non-equijoins:
library(data.table)
setDT(df_measurements)
setDT(df_limits)
df_limits[df_measurements, .(station_id, measurement_id, value, Title),
on=.(station_id = station_id, limit_from < value, limit_to >= value)]
station_id measurement_id value Title
1: 1 12121534 172 Level_3_High
2: 1 12121618 87 Level_2_Low
3: 1 12121703 9 Level_3_Low
4: 2 12121709 80 Level_2_Low
5: 2 12121760 80 Level_2_Low
6: 2 12121813 115 Level_1_High
7: 3 12121881 67 Level_3_Low
8: 3 12121907 100 Level_1_Low
9: 3 12121920 108 Optimal
10: 1 12121979 102 Optimal
11: 1 12121995 53 Level_3_Low
12: 1 12122022 77 Level_2_Low
13: 2 12122065 158 Level_3_High
14: 2 12122107 144 Level_2_High
15: 2 12122113 5 Level_3_Low
16: 3 12122135 100 Level_1_Low
17: 3 12122187 136 Level_2_High
18: 3 12122267 130 Level_1_High
19: 1 12122359 105 Optimal
20: 1 12122366 126 Level_1_High
21: 1 12122398 143 Level_2_High
使用 subset + merge
subset(
merge(
df_measurements,
df_limits,
all = TRUE
),
limit_from < value & limit_to >= value
)
给予
station_id measurement_id value Title limit_from limit_to
7 1 12121534 172 Level_3_High 150 180
9 1 12121618 87 Level_2_Low 70 90
15 1 12121703 9 Level_3_Low 0 70
23 1 12122022 77 Level_2_Low 70 90
34 1 12122398 143 Level_2_High 130 150
39 1 12121979 102 Optimal 100 110
43 1 12121995 53 Level_3_Low 0 70
54 1 12122366 126 Level_1_High 110 130
60 1 12122359 105 Optimal 100 110
65 2 12121760 80 Level_2_Low 70 90
75 2 12121813 115 Level_1_High 110 130
79 2 12121709 80 Level_2_Low 70 90
91 2 12122065 158 Level_3_High 150 180
97 2 12122107 144 Level_2_High 130 150
99 2 12122113 5 Level_3_Low 0 70
108 3 12121907 100 Level_1_Low 90 100
116 3 12121920 108 Optimal 100 110
124 3 12122267 130 Level_1_High 110 130
127 3 12121881 67 Level_3_Low 0 70
136 3 12122135 100 Level_1_Low 90 100
146 3 12122187 136 Level_2_High 130 150
另一种选择是使用 dplyr
df_measurements %>%
group_by(station_id) %>%
mutate(Title = with(
df_limits,
Title[
findInterval(
value,
unique(unlist(cbind(limit_from, limit_to)[station_id == first(.$station_id)])),
left.open = TRUE
)
]
)) %>%
ungroup()
这给出了
# A tibble: 21 x 4
measurement_id station_id value Title
<int> <int> <int> <fct>
1 12121534 1 172 Level_3_High
2 12121618 1 87 Level_2_Low
3 12121703 1 9 Level_3_Low
4 12121709 2 80 Level_2_Low
5 12121760 2 80 Level_2_Low
6 12121813 2 115 Level_1_High
7 12121881 3 67 Level_3_Low
8 12121907 3 100 Level_1_Low
9 12121920 3 108 Optimal
10 12121979 1 102 Optimal
# ... with 11 more rows
基准测试
f_TIC1 <- function() {
subset(
merge(
df_measurements,
df_limits,
all = TRUE
),
limit_from < value & limit_to >= value
)
}
f_TIC2 <- function() {
df_measurements %>%
group_by(station_id) %>%
mutate(Title = with(
df_limits,
Title[
findInterval(
value,
unique(unlist(cbind(limit_from, limit_to)[station_id == first(station_id)])),
left.open = TRUE
)
]
)) %>%
ungroup()
}
dt_limits <- as.data.table(df_limits)
dt_measurements <- as.data.table(df_measurements)
f_Waldi <- function() {
dt_limits[
dt_measurements,
.(station_id, measurement_id, value, Title),
on = .(station_id, limit_from < value, limit_to >= value)
]
}
f_TimTeaFan <- function() {
setkey(dt_limits, station_id, limit_from, limit_to)
foverlaps(dt_measurements[, value2 := value],
dt_limits,
by.x = c("station_id", "value", "value2"),
type = "within",
)[
value < limit_to,
.(measurement_id, station_id, value, Title)
]
}
你会看到
Unit: relative
expr min lq mean median uq max neval
f_TIC1() 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 100
f_TIC2() 4.848639 4.909985 4.895588 4.942616 5.124704 2.580819 100
f_Waldi() 3.182027 3.010615 3.069916 3.114160 3.397845 1.698386 100
f_TimTeaFan() 5.523778 5.112872 5.226145 5.112407 5.745671 2.446987 100