查找R中有图案的列的最大/最小值

我在开发data.table时遇到问题，该表基于共享名称模式的多个列为我提供最大/最小值

这是一个简化的表格：

int <- seq(as.POSIXct("2016-04-08"), as.POSIXct("2016-04-10"), by="6 h")
df <- data.frame(date = int, x_01 = runif(9), x_02 = runif(9), x_10 = runif(9), b_31 = runif(9))
df$date <- format(as.POSIXct(df$date), format = "%Y-%m-%dM")

理想情况下，汇总表的列名应包含原始表中的名称。我的实际数据集由多个数据帧组成，这些数据帧具有与模式匹配的不同列数。随着我收集更多的数据，将添加新的变量，因此能够基于

colname

模式执行函数非常重要

谢谢你的帮助

您可以尝试以下代码：

## building the data.table
int <- seq(as.POSIXct("2016-04-08"), as.POSIXct("2016-04-10"), by="6 h")
df <- data.frame(date = int, x_01 = runif(9), x_02 = runif(9), x_10 = runif(9), b_31 = runif(9))
df$date <- format(as.POSIXct(df$date), format = "%Y-%m-%dM")

## actual work begins here
library(data.table)
setDT(df)

indices <- grep("x_",colnames(df))

col_names <- colnames(df)[indices]

query_min <- paste0(col_names,'min=min(',col_names,')')

query_max <- paste0(col_names,'max=max(',col_names,')')

query_1 <- paste(c(query_min,query_max),collapse=',')

eval(parse(text=paste0("df[,.(",query_1,"),by=date]")))

##          date    x_01min     x_02min   x_10min   x_01max     x_02max   x_10max
##1: 2016-04-08M 0.07527176 0.026276086 0.3315467 0.9404001 0.906662120 0.7069425
##2: 2016-04-09M 0.34796983 0.065390319 0.2437374 0.8130796 0.739978420 0.6760062
##3: 2016-04-10M 0.45671821 0.003374905 0.7245515 0.4567182 0.003374905 0.7245515

##构建data.table
int

set.seed（1L）；
int
cols是我选择的最佳答案！bgoldst，感谢您提供如此优雅的解决方案，感谢您对其他解决方案的解释和基准测试。我将其应用于我的一个数据帧，其中包含20个变量的35136 obs，速度极快@Kunal我的方法与你的方法最为相似，因此你的解决方案可以帮助我了解我的不足之处。我会在代码解决方案中加入
setDT（df）
。没有它，代码就无法运行。我在仔细阅读解释后发现了这一点，但其他人可能会忽略这一关键步骤。
sum <- setDT(df)[, list(x_01min=min(x_01), x_01max=max(x_01),
                    x_02min=min(x_02), x_02max=max(x_02),
                    x_10min=min(x_10), x_10max=max(x_10)), by=list(date)]

## building the data.table
int <- seq(as.POSIXct("2016-04-08"), as.POSIXct("2016-04-10"), by="6 h")
df <- data.frame(date = int, x_01 = runif(9), x_02 = runif(9), x_10 = runif(9), b_31 = runif(9))
df$date <- format(as.POSIXct(df$date), format = "%Y-%m-%dM")

## actual work begins here
library(data.table)
setDT(df)

indices <- grep("x_",colnames(df))

col_names <- colnames(df)[indices]

query_min <- paste0(col_names,'min=min(',col_names,')')

query_max <- paste0(col_names,'max=max(',col_names,')')

query_1 <- paste(c(query_min,query_max),collapse=',')

eval(parse(text=paste0("df[,.(",query_1,"),by=date]")))

##          date    x_01min     x_02min   x_10min   x_01max     x_02max   x_10max
##1: 2016-04-08M 0.07527176 0.026276086 0.3315467 0.9404001 0.906662120 0.7069425
##2: 2016-04-09M 0.34796983 0.065390319 0.2437374 0.8130796 0.739978420 0.6760062
##3: 2016-04-10M 0.45671821 0.003374905 0.7245515 0.4567182 0.003374905 0.7245515

library(data.table);
setDT(df); ## ensure df is a data.table

cns <- grep(value=T,'^x_',names(df));
df[,do.call(c,lapply(cns,function(cn) { x <- get(cn); setNames(nm=paste0(cn,c('min','max')),.(min(x),max(x))); })),.(date)];
##           date   x_01min   x_01max    x_02min   x_02max    x_10min    x_10max
## 1: 2016-04-08M 0.2655087 0.9082078 0.06178627 0.6870228 0.21214252 0.93470523
## 2: 2016-04-09M 0.2016819 0.9446753 0.38410372 0.7698414 0.12555510 0.65167377
## 3: 2016-04-10M 0.6291140 0.6291140 0.99190609 0.9919061 0.01339033 0.01339033

library(data.table);
library(microbenchmark);

bgoldst <- function(df) { cns <- grep(value=T,'^x_',names(df)); df[,do.call(c,lapply(cns,function(cn) { x <- get(cn); setNames(nm=paste0(cn,c('min','max')),.(min(x),max(x))); })),.(date)]; };
kunal <- function(df) { indices <- grep('x_',colnames(df)); col_names <- colnames(df)[indices]; query_min <- paste0(col_names,'min=min(',col_names,')'); query_max <- paste0(col_names,'max=max(',col_names,')'); query_1 <- paste(c(query_min,query_max),collapse=','); eval(parse(text=paste0('df[,.(',query_1,'),by=date]'))); };
psidom <- function(df) { cols <- names(df)[grepl('x_',names(df))]; newCols <- paste0(rep(cols,each=2),c('max','min')); sumFun <- function(col) list(max(col),min(col)); df[,c(newCols):=unlist(lapply(.SD,sumFun),recursive=F),.(date),.SDcols=cols]; unique(df[,.SD,.SDcols=c('date',newCols)]); };

set.seed(1L);
int <- seq(as.POSIXct('2016-04-08'),as.POSIXct('2016-04-10'),by='6 h');
df <- data.frame(date=int,x_01=runif(9L),x_02=runif(9L),x_10=runif(9L),b_31=runif(9L));
df$date <- format(as.POSIXct(df$date),format='%Y-%m-%dM');
setDT(df);

expected <- bgoldst(copy(df)); co <- names(expected);
identical(expected,kunal(copy(df))[,co,with=F]);
## [1] TRUE
identical(expected,psidom(copy(df))[,co,with=F]);
## [1] TRUE

microbenchmark(bgoldst(copy(df)),kunal(copy(df)),psidom(copy(df)));
## Unit: milliseconds
##               expr      min       lq     mean   median       uq      max neval
##  bgoldst(copy(df)) 1.397569 1.445893 1.522512 1.490369 1.538908 2.749805   100
##    kunal(copy(df)) 1.318453 1.362287 1.483356 1.403555 1.443968 4.733684   100
##   psidom(copy(df)) 1.451881 1.532920 1.625494 1.573120 1.624010 3.097487   100

set.seed(1L);
NR <- 500L; NC <- 100L;
df <- data.frame(
    date=seq(as.POSIXct('2016-04-08'),by='6 h',len=NR),
    setNames(nm=paste0('x_',seq_len(NC)),as.data.frame(replicate(NC,runif(NR)))),
    b_31=runif(NR)
);
df$date <- format(as.POSIXct(df$date),format='%Y-%m-%dM');
setDT(df);

expected <- bgoldst(copy(df)); co <- names(expected);
identical(expected,kunal(copy(df))[,co,with=F]);
## [1] TRUE
identical(expected,psidom(copy(df))[,co,with=F]);
## [1] TRUE

microbenchmark(bgoldst(copy(df)),kunal(copy(df)),psidom(copy(df)));
## Unit: milliseconds
##               expr      min        lq      mean    median        uq       max neval
##  bgoldst(copy(df)) 94.75322 100.94627 106.61343 102.37655 105.89292 164.58885   100
##    kunal(copy(df)) 21.38946  23.04383  24.60639  24.20192  25.18723  69.29593   100
##   psidom(copy(df)) 45.32431  48.76798  50.63476  49.60532  51.03667  92.41567   100

cols <- names(df)[grepl("x_", names(df))]
newCols <- paste0(rep(cols, each = 2), c("max", "min"))
sumFun <- function(col) list(max(col), min(col))
setDT(df)[, c(newCols) := unlist(lapply(.SD, sumFun), recursive = F), .(date), .SDcols = cols]
sum <- unique(df[, .SD, .SDcols = c("date", newCols)])
> sum
          date   x_01max   x_01min    x_02max     x_02min   x_10max   x_10min
1: 2016-04-08M 0.8770486 0.1828969 0.99869872 0.159936264 0.8983131 0.3767007
2: 2016-04-09M 0.6475017 0.1429131 0.57890510 0.007439883 0.9242098 0.1077233
3: 2016-04-10M 0.9176341 0.9176341 0.05900942 0.059009423 0.2717861 0.2717861