![]() NOTE: The main part I added was (again, adapted from JD's answer) : obj. Which results in something like the following: Type Size PrettySize Length/Rows Columnsįactor.AgeGender factanal 12888 12.6 Kb 12 NA Names(out) <- c("Type", "Size", "PrettySize", "Length/Rows", "Columns") Out <- ame(obj.type, obj.size, obj.prettysize, obj.dim) This forces the SSD to actually delete data that should have been deleted when TRIM commands were originally sent. In this video I'm going to show you how to make you PC faster by optimizing the RAM usage in Windows. Windows will send the retrim command on the schedule you configure. Obj.prettysize <- napply(names, function(x) ) On Windows 8 and 10, the Optimize Drives application will attempt to optimize your SSDs even further. Saw this on a twitter post and think it's an awesome function by Dirk! Following on from JD Long's answer, I would do this for user friendly reading: # improved list of objects ls.objects(., ="Size", decreasing=TRUE, head=TRUE, n=n) Vec <- is.na(obj.dim) & (obj.type != "function") Obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class) Obj.class <- napply(names, function(x) as.character(class(x))) Names <- ls(pos = pos, pattern = pattern) Napply <- function(names, fn) sapply(names, function(x) ls.objects <- function (pos = 1, pattern, , to run under 64-bit Linux with ample memory.Īny other nice tricks folks want to share? One per post, please. ![]() But by far the most effective solution was. What tricks do people use to manage the available memory of an interactive R session? I use the functions below to list (and/or sort) the largest objects and to occassionally rm() some of them. ![]()
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