data.table:如何将字符向量传递给函数get data.table以将其内容视为列名? [英] data.table: How do I pass a character vector to a function get data.table to treat its contents as column names?
问题描述
这是一个数据表:
library(data.table)
DT <-数据。 table(airquality)
此示例生成我想要的输出:
DT [,`:=`(New_Ozone = log(Ozone),New_Wind = log(Wind))]
如何编写函数 log_those_columns
,使以下代码段输出相同的结果?
old_names<-c( Ozone, Wind)
new_names<--c( New_Ozone, New_Wind)
log_those_columns(DT,old_names,new_names)
注意我需要 old_names
和 new_names
足够灵活以包含任意数量的列。
(我从与此主题相关的类似StackOverflow问题中看到,答案可能涉及 .SD
的某种组合, with = F
, parse()
, eval()
,和/或 substitute()
,但我似乎无法确定要使用哪个以及在哪里使用。
拾取 MichaelChirico的评论,函数定义可以写为:
log_those_columns<-函数(DT,cols_in,cols_new){
DT [,(cols_new):= lapply(.SD,log),.SDcols = cols_in]
}
返回:
log_those_columns(DT ,old_names,new_names)
DT
Ozone Solar.R风温月日New_Ozone New_Wind
1:41190 7.4 67 5 1 3.713572 2.001480
2:36 118 8.0 72 5 2 3.583519 2.079442
3:12 149 12.6 74 5 3 2.484907 2.533697
4:18313 11.5 62 5 4 2.890372 2.442347
5:不适用不适用14.3 56 5 5不适用2.660260
---
149:30193 6.9 70 9 26 3.401197 1.931521
150:不适用145 13.2 77 9 27 NA 2.580217
151:14 191 14.3 75 9 28 2.639057 2.660260
152:18 131 8.0 76 9 29 2.890372 2.079442
153:20223 11.5 68 9 30 2.995732 2.442347
。
更灵活的方法
用于转换数据的函数也可以作为参数传递:
fct_those_columns<-函数(DT,cols_in,cols_new,fct ){
DT [,(cols_new):= lapply(.SD,fct),.SDcols = col s_in]
}
通话:
fct_those_columns(DT,old_names,new_names,log)
head(DT)
预期:
Ozone Solar.R风温度月日New_Ozone New_Wind
1:41190 7.4 67 5 1 3.713572 2.001480
2:36 118 8.0 72 5 2 3.583519 2.079442
3:12 149 12.6 74 5 3 2.484907 2.533697
4:18313 11.5 62 5 4 2.890372 2.442347
5 :不适用不适用14.3 56 5 5不适用2.660260
6:28不适用14.9 66 5 6 3.332205 2.701361
函数名称可以作为字符传递:
fct_those_columns(DT,old_names,new_names, sqrt)
head(DT)
臭氧太阳能。 R风温月日New_Ozone New_Wind
1:41190 7.4 67 5 1 6.403124 2.72 0294
2:36118 8.0 72 5 2 6.000000 2.828427
3:12149 12.6 74 5 3 3.464102 3.549648
4:18313 11.5 62 5 4 4.242641 3.391165
5:不适用不适用14.3 56 5 5不适用3.781534
6:28不适用14.9 66 5 6 5.291503 3.860052
或作为匿名函数:
fct_those_columns(DT,old_names,new_names,function(x)x ^(1/2))
head(DT)
臭氧Solar.R风温月日New_Ozone New_Wind
1:41190 7.4 67 5 1 6.403124 2.720294
2:36118 8.0 72 5 2 6.000000 2.828427
3:12 149 12.6 74 5 3 3.464102 3.549648
4:18313 11.5 62 5 4 4.242641 3.391165
5:不适用不适用14.3 56 5 5不适用3.781534
6:28不适用14.9 66 5 6 5.291503 3.860052
一种更加灵活的方法
下面的函数通过在输入列的名称前自动添加该函数的名称来导出新列的名称:
fct_those_columns<-函数(DT,cols_in,fct){
fct_name<-替代(fct)
cols_new <-paste(if(class(fct_name)== name)fct_name else fct_name [3],cols_in,sep = _)
DT [,(cols_new):= lapply(.SD ,fct),.SDcols = cols_in]
}
DT<-data.table(airquality)
fct_those_columns(DT,old_names,sqrt)
fct_those_columns( DT,old_names,data.table :: as.IDate)
fct_those_columns(DT,old_names,function(x)x ^(1/2))
DT
Ozone Solar.R风温度月份Day sqrt_Ozone sqrt_Wind as.IDate_Ozone as.IDate_Wind x ^( 1/2)_臭氧x ^(1/2)_风
1:41190 7.4 67 5 1 6.403124 2.720294 1970-02-11 1970-01-08 6.40312 4 2.720294
2:36118 8.0 72 5 2 6.000000 2.828427 1970-02-06 1970-01-09 6.000000 2.828427
3:12 149 12.6 74 5 3 3.464102 3.549648 1970-01-13 1970-01 -13 3.464102 3.549648
4:18 313 11.5 62 5 4 4.242641 3.391165 1970-01-19 1970-01-12 4.242641 3.391165
5:不适用不适用14.3 56 5 5不适用3.781534< NA> 1970-01-15 NA 3.781534
---
149:30193 6.9 70 9 26 5.477226 2.626785 1970-01-31 1970-01-07 5.477226 2.626785
150:NA 145 13.2 77 9 27 NA 3.633180< NA> 1970-01-14不适用3.633180
151:14 191 14.3 75 9 28 3.741657 3.781534 1970-01-15 1970-01-15 3.741657 3.781534
152:18131 8.0 76 9 29 4.242641 2.828427 1970-01 -19 1970-01-09 4.242641 2.828427
153:20 223 11.5 68 9 30 4.472136 3.391165 1970-01-21 1970-01-12 4.472136 3.391165
请注意, x ^(1/2)_Ozone
在R和需要放在反引号中:
DT $`x ^(1/2)_Ozone`
Here is a data.table:
library(data.table)
DT <- data.table(airquality)
This example produces the output I want:
DT[, `:=`(New_Ozone= log(Ozone), New_Wind=log(Wind))]
How can I write a function log_those_columns
such that the following code snippet outputs the same result?
old_names <- c("Ozone", "Wind")
new_names <- c("New_Ozone", "New_Wind")
log_those_columns(DT, old_names, new_names)
Note that I need old_names
and new_names
to be flexible enough to contain any number of columns.
(I see from the similar StackOverflow questions on this topic that the answer probably involves some combination of .SD
, with=F
, parse()
, eval()
, and/or substitute()
, but I can't seem to nail which of those to use and where).
Picking up MichaelChirico's comment, the function definition can be written as:
log_those_columns <- function(DT, cols_in, cols_new) {
DT[, (cols_new) := lapply(.SD, log), .SDcols = cols_in]
}
which returns:
log_those_columns(DT, old_names, new_names)
DT
Ozone Solar.R Wind Temp Month Day New_Ozone New_Wind 1: 41 190 7.4 67 5 1 3.713572 2.001480 2: 36 118 8.0 72 5 2 3.583519 2.079442 3: 12 149 12.6 74 5 3 2.484907 2.533697 4: 18 313 11.5 62 5 4 2.890372 2.442347 5: NA NA 14.3 56 5 5 NA 2.660260 --- 149: 30 193 6.9 70 9 26 3.401197 1.931521 150: NA 145 13.2 77 9 27 NA 2.580217 151: 14 191 14.3 75 9 28 2.639057 2.660260 152: 18 131 8.0 76 9 29 2.890372 2.079442 153: 20 223 11.5 68 9 30 2.995732 2.442347
as expected.
A more flexible approach
The function used to transform the data can be passed as a parameter as well:
fct_those_columns <- function(DT, cols_in, cols_new, fct) {
DT[, (cols_new) := lapply(.SD, fct), .SDcols = cols_in]
}
The call:
fct_those_columns(DT, old_names, new_names, log)
head(DT)
works as expected:
Ozone Solar.R Wind Temp Month Day New_Ozone New_Wind 1: 41 190 7.4 67 5 1 3.713572 2.001480 2: 36 118 8.0 72 5 2 3.583519 2.079442 3: 12 149 12.6 74 5 3 2.484907 2.533697 4: 18 313 11.5 62 5 4 2.890372 2.442347 5: NA NA 14.3 56 5 5 NA 2.660260 6: 28 NA 14.9 66 5 6 3.332205 2.701361
The function name can be passed as character:
fct_those_columns(DT, old_names, new_names, "sqrt")
head(DT)
Ozone Solar.R Wind Temp Month Day New_Ozone New_Wind 1: 41 190 7.4 67 5 1 6.403124 2.720294 2: 36 118 8.0 72 5 2 6.000000 2.828427 3: 12 149 12.6 74 5 3 3.464102 3.549648 4: 18 313 11.5 62 5 4 4.242641 3.391165 5: NA NA 14.3 56 5 5 NA 3.781534 6: 28 NA 14.9 66 5 6 5.291503 3.860052
or as an anonymous function:
fct_those_columns(DT, old_names, new_names, function(x) x^(1/2))
head(DT)
Ozone Solar.R Wind Temp Month Day New_Ozone New_Wind 1: 41 190 7.4 67 5 1 6.403124 2.720294 2: 36 118 8.0 72 5 2 6.000000 2.828427 3: 12 149 12.6 74 5 3 3.464102 3.549648 4: 18 313 11.5 62 5 4 4.242641 3.391165 5: NA NA 14.3 56 5 5 NA 3.781534 6: 28 NA 14.9 66 5 6 5.291503 3.860052
An even more flexible approach
The function below derives the names of the new columns by prepending the names of the input columns with the name of the function automatically:
fct_those_columns <- function(DT, cols_in, fct) {
fct_name <- substitute(fct)
cols_new <- paste(if (class(fct_name) == "name") fct_name else fct_name[3], cols_in, sep = "_")
DT[, (cols_new) := lapply(.SD, fct), .SDcols = cols_in]
}
DT <- data.table(airquality)
fct_those_columns(DT, old_names, sqrt)
fct_those_columns(DT, old_names, data.table::as.IDate)
fct_those_columns(DT, old_names, function(x) x^(1/2))
DT
Ozone Solar.R Wind Temp Month Day sqrt_Ozone sqrt_Wind as.IDate_Ozone as.IDate_Wind x^(1/2)_Ozone x^(1/2)_Wind 1: 41 190 7.4 67 5 1 6.403124 2.720294 1970-02-11 1970-01-08 6.403124 2.720294 2: 36 118 8.0 72 5 2 6.000000 2.828427 1970-02-06 1970-01-09 6.000000 2.828427 3: 12 149 12.6 74 5 3 3.464102 3.549648 1970-01-13 1970-01-13 3.464102 3.549648 4: 18 313 11.5 62 5 4 4.242641 3.391165 1970-01-19 1970-01-12 4.242641 3.391165 5: NA NA 14.3 56 5 5 NA 3.781534 <NA> 1970-01-15 NA 3.781534 --- 149: 30 193 6.9 70 9 26 5.477226 2.626785 1970-01-31 1970-01-07 5.477226 2.626785 150: NA 145 13.2 77 9 27 NA 3.633180 <NA> 1970-01-14 NA 3.633180 151: 14 191 14.3 75 9 28 3.741657 3.781534 1970-01-15 1970-01-15 3.741657 3.781534 152: 18 131 8.0 76 9 29 4.242641 2.828427 1970-01-19 1970-01-09 4.242641 2.828427 153: 20 223 11.5 68 9 30 4.472136 3.391165 1970-01-21 1970-01-12 4.472136 3.391165
Note that x^(1/2)_Ozone
is not a syntactically valid name in R and needs to be put in backquotes:
DT$`x^(1/2)_Ozone`
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