根据每个组的另一个查找 table 有条件地为一个数据帧插入值？
Conditionally interpolate values for one data frame based on another lookup table per group?
这类似于下面的 。不过，我还需要再做几步：
• 按列分组 ID 和 order
• 对于df_dat中的每个val，在df_lookup table中查找相应的ratio，条件如下：
o If val < min(df_lookup$val), set new_ratio = min(df_lookup$ratio)
o If val > max(df_lookup$val), set new_ratio = max(df_lookup$ratio)
o If val falls within df_lookup$val range, do a simple linear interpolation
我的数据：
library(dplyr)
df_lookup <- tribble(
~ID, ~order, ~pct, ~val, ~ratio,
"batch1", 1, 1, 1, 0.2,
"batch1", 1, 10, 8, 0.5,
"batch1", 1, 25, 25, 1.2,
"batch2", 2, 1, 2, 0.1,
"batch2", 2, 10, 15, 0.75,
"batch2", 2, 25, 33, 1.5,
"batch2", 2, 50, 55, 3.2,
)
df_lookup
#> # A tibble: 7 x 5
#> ID order pct val ratio
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 batch1 1 1 1 0.2
#> 2 batch1 1 10 8 0.5
#> 3 batch1 1 25 25 1.2
#> 4 batch2 2 1 2 0.1
#> 5 batch2 2 10 15 0.75
#> 6 batch2 2 25 33 1.5
#> 7 batch2 2 50 55 3.2
df_dat <- tribble(
~order, ~ID, ~val,
1, "batch1", 0.1,
1, "batch1", 30,
1, "batch1", 2,
1, "batch1", 12,
2, "batch1", 45,
2, "batch2", 1.5,
2, "batch2", 30,
2, "batch2", 13,
2, "batch2", 60,
)
df_dat
#> # A tibble: 9 x 3
#> order ID val
#> <dbl> <chr> <dbl>
#> 1 1 batch1 0.1
#> 2 1 batch1 30
#> 3 1 batch1 2
#> 4 1 batch1 12
#> 5 2 batch1 45
#> 6 2 batch2 1.5
#> 7 2 batch2 30
#> 8 2 batch2 13
#> 9 2 batch2 60
之前的解决方案不尊重生成错误结果的分组。
示例：
对于 order = 2 和 ID = batch1，new_ratio 应该是 NA，因为这些条件不在查找 table 中。
对于 order = 1、ID = batch2 和 val = 30，new_ratio 不应高于 1.2（最大 ratio 值）。
对于order = 1、ID = batch1和val = 2，new_ratio = 0.243是0.2和0.5之间的插值ratio。
感谢任何帮助！
#error
df_dat %>%
group_by(ID, order) %>%
mutate(new_ratio = with(df_lookup, approx(val, ratio, val))$y)
#> Error: Column `new_ratio` must be length 4 (the group size) or one, not 7
#wrong output
df_dat %>%
group_by(ID, order) %>%
mutate(val1 = val) %>%
mutate(new_ratio = with(df_lookup, approx(val, ratio, val1))$y)
#> # A tibble: 9 x 5
#> # Groups: ID, order [3]
#> order ID val val1 new_ratio
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1 batch1 0.1 0.1 NA
#> 2 1 batch1 30 30 1.39
#> 3 1 batch1 2 2 0.1
#> 4 1 batch1 12 12 0.643
#> 5 2 batch1 45 45 2.43
#> 6 2 batch2 1.5 1.5 0.15
#> 7 2 batch2 30 30 1.39
#> 8 2 batch2 13 13 0.679
#> 9 2 batch2 60 60 NA
预期输出
# A tibble: 9 x 4
order ID val new_ratio
<dbl> <chr> <dbl> <dbl>
1 1 batch1 0.1 0.2
2 1 batch1 30 1.2
3 1 batch1 2 0.243
4 1 batch1 12 0.643
5 2 batch1 45 NA
6 2 batch2 1.5 0.1
7 2 batch2 30 1.38
8 2 batch2 13 0.65
9 2 batch2 60 3.2
library(dplyr)
df_dat %>%
left_join(df_lookup, by=c('ID','order'), suffix = c(".dat", ".lkp")) %>%
group_by(ID, order, val.dat) %>%
mutate(ratio_new = case_when(val.dat < min(val.lkp) ~ min(ratio),
val.dat > max(val.lkp) ~ max(ratio),
#Add ifelse to handle the scenarios where val.lkp and ratio are NAs as approx will fail in these scenarios
between(val.dat, min(val.lkp), max(val.lkp)) ~ ifelse(all(is.na(ratio)), NA_real_, approx(x=val.lkp, y=ratio, xout=val.dat)$y),
TRUE ~ NA_real_)) %>%
slice(1)
# A tibble: 9 x 7
# Groups: ID, order, val.dat [9]
order ID val.dat pct val.lkp ratio ratio_new
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 batch1 0.1 1 1 0.2 0.2
2 1 batch1 2 1 1 0.2 0.243
3 1 batch1 12 1 1 0.2 0.665
4 1 batch1 30 1 1 0.2 1.2
5 2 batch1 45 NA NA NA NA
6 2 batch2 1.5 1 2 0.1 0.1
7 2 batch2 13 1 2 0.1 0.65
8 2 batch2 30 1 2 0.1 1.38
9 2 batch2 60 1 2 0.1 3.2
在data.table中使用roll和rollends的选项：
df_lookup[, m := (ratio - shift(ratio, -1L)) / (val - shift(val, -1L))]
df_dat[, new_ratio :=
df_lookup[.SD, on=.(order, ID, val), roll=Inf, rollends=c(FALSE, FALSE),
x.m * (i.val - x.val) + x.ratio]
]
#for val in df_dat that are more than those in df_lookup
df_dat[is.na(new_ratio), new_ratio :=
df_lookup[copy(.SD), on=.(order, ID, val), roll=Inf, x.ratio]]
#for val in df_dat that are less than those in df_lookup
df_dat[is.na(new_ratio), new_ratio :=
df_lookup[copy(.SD), on=.(order, ID, val), roll=-Inf, x.ratio]]
输出：
order ID val new_ratio
1: 1 batch1 0.1 0.2000000
2: 1 batch1 30.0 1.2000000
3: 1 batch1 2.0 0.2428571
4: 1 batch1 12.0 0.6647059
5: 2 batch1 45.0 NA
6: 2 batch2 1.5 0.1000000
7: 2 batch2 30.0 1.3750000
8: 2 batch2 13.0 0.6500000
9: 2 batch2 60.0 3.2000000
数据：
library(data.table)
df_lookup <- fread('ID, order, pct, val, ratio
"batch1", 1, 1, 1, 0.2
"batch1", 1, 10, 8, 0.5
"batch1", 1, 25, 25, 1.2
"batch2", 2, 1, 2, 0.1
"batch2", 2, 10, 15, 0.75
"batch2", 2, 25, 33, 1.5
"batch2", 2, 50, 55, 3.2')
df_dat <- fread('order, ID, val
1, "batch1", 0.1
1, "batch1", 30
1, "batch1", 2
1, "batch1", 12
2, "batch1", 45
2, "batch2", 1.5
2, "batch2", 30
2, "batch2", 13
2, "batch2", 60')
最后两行代码也可以用非equi连接代替：
df_dat[is.na(new_ratio), new_ratio:=
df_lookup[copy(.SD), on=.(order, ID, val<val), x.ratio, mult="last"]]
df_dat[is.na(new_ratio), new_ratio:=
df_lookup[copy(.SD), on=.(order, ID, val>val), x.ratio, mult="first"]]
df_dat
这是我解决你的问题的方法，使用 data.table
我用了很多中间步骤，所以你可以检查结果和操作每个步骤，看看发生了什么/所以代码可以缩短很多。
library(data.table)
#set data to data.tables
setDT(df_dat); setDT(df_lookup)
#set range df_lookup values by ID and order combination
df_lookup[, `:=`( val2 = shift( val, type = "lead" ),
ratio2 = shift( ratio, type = "lead" ) ),
by = .( ID, order ) ][]
#join non-equi
df_dat[ df_lookup,
`:=`( val_start = i.val,
val_end = i.val2,
ratio_start = i.ratio,
ratio_end = i.ratio2 ),
on = .( ID, order, val > val, val < val2) ][]
#interpolatie new_ratio for values that fall within a range of dt_lookup
df_dat[, new_ratio := ratio_start + ( (val - val_start) * (ratio_end - ratio_start) / (val_end - val_start) )][]
#create data.table with ratio-value for minimum- and maximum value in df_lookup
df_lookup_min_max <- df_lookup[, .( val_min = min( val ), val_max = max( val ),
ratio_min = min( ratio ), ratio_max = max( ratio ) ),
by = .(ID, order) ]
df_lookup_min_max_melt <- melt( df_lookup_min_max,
id.vars = c( "ID", "order" ),
measure.vars = patterns( val = "^val",
ratio = "^ratio" ) )
df_dat[ is.na( new_ratio ),
new_ratio := df_lookup_min_max_melt[ df_dat[ is.na( new_ratio ), ],
ratio,
on = .(ID, order, val ),
roll = "nearest" ] ][]
df_dat[, `:=`(val_start = NULL, val_end = NULL, ratio_start = NULL, ratio_end = NULL)][]
最终输出
# order ID val new_ratio
# 1: 1 batch1 0.1 0.2000000
# 2: 1 batch1 30.0 1.2000000
# 3: 1 batch1 2.0 0.2428571
# 4: 1 batch1 12.0 0.6647059
# 5: 2 batch1 45.0 NA
# 6: 2 batch2 1.5 0.1000000
# 7: 2 batch2 30.0 1.3750000
# 8: 2 batch2 13.0 0.6500000
# 9: 2 batch2 60.0 3.2000000
编辑
行 5: 2 batch1 45.0 NA 在这里是因为在您的 df_lookup...
中没有 order == 2 & ID == batch1 组合
也许这是一个错字？
尽管如此：代码似乎处理得很好 ;-)
这类似于下面的
• 按列分组 ID 和 order
• 对于df_dat中的每个val，在df_lookup table中查找相应的ratio，条件如下：
o If val < min(df_lookup$val), set new_ratio = min(df_lookup$ratio)
o If val > max(df_lookup$val), set new_ratio = max(df_lookup$ratio)
o If val falls within df_lookup$val range, do a simple linear interpolation
我的数据：
library(dplyr)
df_lookup <- tribble(
~ID, ~order, ~pct, ~val, ~ratio,
"batch1", 1, 1, 1, 0.2,
"batch1", 1, 10, 8, 0.5,
"batch1", 1, 25, 25, 1.2,
"batch2", 2, 1, 2, 0.1,
"batch2", 2, 10, 15, 0.75,
"batch2", 2, 25, 33, 1.5,
"batch2", 2, 50, 55, 3.2,
)
df_lookup
#> # A tibble: 7 x 5
#> ID order pct val ratio
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 batch1 1 1 1 0.2
#> 2 batch1 1 10 8 0.5
#> 3 batch1 1 25 25 1.2
#> 4 batch2 2 1 2 0.1
#> 5 batch2 2 10 15 0.75
#> 6 batch2 2 25 33 1.5
#> 7 batch2 2 50 55 3.2
df_dat <- tribble(
~order, ~ID, ~val,
1, "batch1", 0.1,
1, "batch1", 30,
1, "batch1", 2,
1, "batch1", 12,
2, "batch1", 45,
2, "batch2", 1.5,
2, "batch2", 30,
2, "batch2", 13,
2, "batch2", 60,
)
df_dat
#> # A tibble: 9 x 3
#> order ID val
#> <dbl> <chr> <dbl>
#> 1 1 batch1 0.1
#> 2 1 batch1 30
#> 3 1 batch1 2
#> 4 1 batch1 12
#> 5 2 batch1 45
#> 6 2 batch2 1.5
#> 7 2 batch2 30
#> 8 2 batch2 13
#> 9 2 batch2 60
之前的解决方案不尊重生成错误结果的分组。
示例：
对于 order = 2 和 ID = batch1，new_ratio 应该是 NA，因为这些条件不在查找 table 中。
对于 order = 1、ID = batch2 和 val = 30，new_ratio 不应高于 1.2（最大 ratio 值）。
对于order = 1、ID = batch1和val = 2，new_ratio = 0.243是0.2和0.5之间的插值ratio。
感谢任何帮助！
#error
df_dat %>%
group_by(ID, order) %>%
mutate(new_ratio = with(df_lookup, approx(val, ratio, val))$y)
#> Error: Column `new_ratio` must be length 4 (the group size) or one, not 7
#wrong output
df_dat %>%
group_by(ID, order) %>%
mutate(val1 = val) %>%
mutate(new_ratio = with(df_lookup, approx(val, ratio, val1))$y)
#> # A tibble: 9 x 5
#> # Groups: ID, order [3]
#> order ID val val1 new_ratio
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 1 batch1 0.1 0.1 NA
#> 2 1 batch1 30 30 1.39
#> 3 1 batch1 2 2 0.1
#> 4 1 batch1 12 12 0.643
#> 5 2 batch1 45 45 2.43
#> 6 2 batch2 1.5 1.5 0.15
#> 7 2 batch2 30 30 1.39
#> 8 2 batch2 13 13 0.679
#> 9 2 batch2 60 60 NA
预期输出
# A tibble: 9 x 4
order ID val new_ratio
<dbl> <chr> <dbl> <dbl>
1 1 batch1 0.1 0.2
2 1 batch1 30 1.2
3 1 batch1 2 0.243
4 1 batch1 12 0.643
5 2 batch1 45 NA
6 2 batch2 1.5 0.1
7 2 batch2 30 1.38
8 2 batch2 13 0.65
9 2 batch2 60 3.2
library(dplyr)
df_dat %>%
left_join(df_lookup, by=c('ID','order'), suffix = c(".dat", ".lkp")) %>%
group_by(ID, order, val.dat) %>%
mutate(ratio_new = case_when(val.dat < min(val.lkp) ~ min(ratio),
val.dat > max(val.lkp) ~ max(ratio),
#Add ifelse to handle the scenarios where val.lkp and ratio are NAs as approx will fail in these scenarios
between(val.dat, min(val.lkp), max(val.lkp)) ~ ifelse(all(is.na(ratio)), NA_real_, approx(x=val.lkp, y=ratio, xout=val.dat)$y),
TRUE ~ NA_real_)) %>%
slice(1)
# A tibble: 9 x 7
# Groups: ID, order, val.dat [9]
order ID val.dat pct val.lkp ratio ratio_new
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 batch1 0.1 1 1 0.2 0.2
2 1 batch1 2 1 1 0.2 0.243
3 1 batch1 12 1 1 0.2 0.665
4 1 batch1 30 1 1 0.2 1.2
5 2 batch1 45 NA NA NA NA
6 2 batch2 1.5 1 2 0.1 0.1
7 2 batch2 13 1 2 0.1 0.65
8 2 batch2 30 1 2 0.1 1.38
9 2 batch2 60 1 2 0.1 3.2
在data.table中使用roll和rollends的选项：
df_lookup[, m := (ratio - shift(ratio, -1L)) / (val - shift(val, -1L))]
df_dat[, new_ratio :=
df_lookup[.SD, on=.(order, ID, val), roll=Inf, rollends=c(FALSE, FALSE),
x.m * (i.val - x.val) + x.ratio]
]
#for val in df_dat that are more than those in df_lookup
df_dat[is.na(new_ratio), new_ratio :=
df_lookup[copy(.SD), on=.(order, ID, val), roll=Inf, x.ratio]]
#for val in df_dat that are less than those in df_lookup
df_dat[is.na(new_ratio), new_ratio :=
df_lookup[copy(.SD), on=.(order, ID, val), roll=-Inf, x.ratio]]
输出：
order ID val new_ratio
1: 1 batch1 0.1 0.2000000
2: 1 batch1 30.0 1.2000000
3: 1 batch1 2.0 0.2428571
4: 1 batch1 12.0 0.6647059
5: 2 batch1 45.0 NA
6: 2 batch2 1.5 0.1000000
7: 2 batch2 30.0 1.3750000
8: 2 batch2 13.0 0.6500000
9: 2 batch2 60.0 3.2000000
数据：
library(data.table)
df_lookup <- fread('ID, order, pct, val, ratio
"batch1", 1, 1, 1, 0.2
"batch1", 1, 10, 8, 0.5
"batch1", 1, 25, 25, 1.2
"batch2", 2, 1, 2, 0.1
"batch2", 2, 10, 15, 0.75
"batch2", 2, 25, 33, 1.5
"batch2", 2, 50, 55, 3.2')
df_dat <- fread('order, ID, val
1, "batch1", 0.1
1, "batch1", 30
1, "batch1", 2
1, "batch1", 12
2, "batch1", 45
2, "batch2", 1.5
2, "batch2", 30
2, "batch2", 13
2, "batch2", 60')
最后两行代码也可以用非equi连接代替：
df_dat[is.na(new_ratio), new_ratio:=
df_lookup[copy(.SD), on=.(order, ID, val<val), x.ratio, mult="last"]]
df_dat[is.na(new_ratio), new_ratio:=
df_lookup[copy(.SD), on=.(order, ID, val>val), x.ratio, mult="first"]]
df_dat
这是我解决你的问题的方法，使用 data.table
我用了很多中间步骤，所以你可以检查结果和操作每个步骤，看看发生了什么/所以代码可以缩短很多。
library(data.table)
#set data to data.tables
setDT(df_dat); setDT(df_lookup)
#set range df_lookup values by ID and order combination
df_lookup[, `:=`( val2 = shift( val, type = "lead" ),
ratio2 = shift( ratio, type = "lead" ) ),
by = .( ID, order ) ][]
#join non-equi
df_dat[ df_lookup,
`:=`( val_start = i.val,
val_end = i.val2,
ratio_start = i.ratio,
ratio_end = i.ratio2 ),
on = .( ID, order, val > val, val < val2) ][]
#interpolatie new_ratio for values that fall within a range of dt_lookup
df_dat[, new_ratio := ratio_start + ( (val - val_start) * (ratio_end - ratio_start) / (val_end - val_start) )][]
#create data.table with ratio-value for minimum- and maximum value in df_lookup
df_lookup_min_max <- df_lookup[, .( val_min = min( val ), val_max = max( val ),
ratio_min = min( ratio ), ratio_max = max( ratio ) ),
by = .(ID, order) ]
df_lookup_min_max_melt <- melt( df_lookup_min_max,
id.vars = c( "ID", "order" ),
measure.vars = patterns( val = "^val",
ratio = "^ratio" ) )
df_dat[ is.na( new_ratio ),
new_ratio := df_lookup_min_max_melt[ df_dat[ is.na( new_ratio ), ],
ratio,
on = .(ID, order, val ),
roll = "nearest" ] ][]
df_dat[, `:=`(val_start = NULL, val_end = NULL, ratio_start = NULL, ratio_end = NULL)][]
最终输出
# order ID val new_ratio
# 1: 1 batch1 0.1 0.2000000
# 2: 1 batch1 30.0 1.2000000
# 3: 1 batch1 2.0 0.2428571
# 4: 1 batch1 12.0 0.6647059
# 5: 2 batch1 45.0 NA
# 6: 2 batch2 1.5 0.1000000
# 7: 2 batch2 30.0 1.3750000
# 8: 2 batch2 13.0 0.6500000
# 9: 2 batch2 60.0 3.2000000
编辑
行 5: 2 batch1 45.0 NA 在这里是因为在您的 df_lookup...
中没有 order == 2 & ID == batch1 组合
也许这是一个错字？
尽管如此：代码似乎处理得很好 ;-)