相关疑难解决方法(0)

人们用什么技巧来管理交互式R会话的可用内存？我使用下面的函数[根据Petr Pikal和David Hinds在2004年的r-help列表中的帖子]列出(和/或排序)最大的对象,偶尔列出rm()其中的一些.但到目前为止,最有效的解决方案是在具有充足内存的64位Linux下运行.

人们想分享其他任何好玩的伎俩吗？请发一个帖子.

# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)
    obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
    obj.size <- napply(names, object.size)
    obj.dim <- t(napply(names, function(x)
                        as.numeric(dim(x))[1:2]))
    vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
    obj.dim[vec, 1] <- napply(names, length)[vec]
    out <- data.frame(obj.type, …

假设您正在使用大型工作环境,并且您不善于跟上环境变量,或者您有一些自动生成批量对象的过程.有没有办法扫描你ls()以识别具有给定类的所有对象？考虑以下简单示例:

#Random objects in my environment
x <- rnorm(100)
y <- rnorm(100)
z <- rnorm(100)

#I estimate some linear models for fun.
lm1 <- lm(y ~ x)
lm2 <- lm(y ~ z)
lm3 <- lm(y ~ x + z)

#Is there a programmatic way to identify all objects in my environment 
#that are of the "lm" class? Or really, any arbitrary class?
outList <- list(lm1, lm2, lm3)

#I want to look at a bunch of plots for all …