R批处理模式下的回波时间戳

Nic*_*len 6 performance r

在批处理模式下运行时,我想更好地理解R脚本中语句的执行持续时间.有没有办法做到这一点?

我有一个想法,我喜欢看到这样做.在批处理中执行时,源将回显到指定的日志文件.有没有办法在这个日志文件中回显源代码旁边的时间戳?

> R CMD BATCH script.R script.Rout
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这是我今天看到的输出.

> tail -f script.Rout
...
> # features related to the date
> trandateN <- as.integer(trandate)
> dayOfWeek <- as.integer(wday(trandate))
> holiday <- mapply(isHoliday, trandate)
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我想看到类似......

> tail -f script.Rout
...
2013-06-27 11:18:01 > # features related to the date
2013-06-27 11:18:01 > trandateN <- as.integer(trandate)
2013-06-27 11:18:05 > dayOfWeek <- as.integer(wday(trandate))
2013-06-27 11:19:02 > holiday <- mapply(isHoliday, trandate)
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Mat*_*rde 5

您可以使用addTaskCallback以下方法创建每个顶级执行的日志.

.log <- data.frame(time=character(0), expr=character(0))
.logger <- function(expr, value, ok, visible) { # formals described in ?addTaskCallback
    time <- as.character(Sys.time())
    expr <- deparse(expr)
    .log <<- rbind(.log, data.frame(time, expr))
    return(TRUE) # required of task callback functions
}
.save.log <- function() {
    if (exists('.logger')) write.csv(.log, 'log.csv')
}
addTaskCallback(.logger)

x <- 1:10
y <- mean(x)

.save.log()
.log
#                      time                     expr
# 1 2013-06-27 12:01:45.837 addTaskCallback(.logger)
# 2 2013-06-27 12:01:45.866                x <- 1:10
# 3 2013-06-27 12:01:45.876             y <- mean(x)
# 4 2013-06-27 12:01:45.900              .save.log()
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当然data.frame,正如我在这里所做的那样,不是承担逐行增长的主要罪行,而是可以保持连接打开并直接写入文件,关闭连接on.exit.

如果你想要整理它,你可以很好地将日志记录设置打包到一个函数中.

.log <- function() {
    .logger <<- local({
        log <- data.frame(time=character(0), expr=character(0))
        function(expr, value, ok, visible) {
            time <- as.character(Sys.time())
            expr <- deparse(expr)
            log <<- rbind(log, data.frame(time, expr))
            return(TRUE)
        }
    })
    invisible(addTaskCallback(.logger))
}

.save.log <- function() {
    if (exists('.logger'))
        write.csv(environment(.logger)$log, 'log.csv')
}

.log()
x <- 1:10
y <- mean(x)
.save.log()
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