One more solution, which works for both numeric & character/factor data: Mode <- function(x) { ux <- unique(x) ux[which.max(tabulate(match(x, ux)))] } On my dinky little machine, that can generate & find the mode of a 10M-integer vector in about half a second. If your data set might have multiple modes, the above solution takes the … Read more
Using aggregate: aggregate(x$Frequency, by=list(Category=x$Category), FUN=sum) Category x 1 First 30 2 Second 5 3 Third 34 In the example above, multiple dimensions can be specified in the list. Multiple aggregated metrics of the same data type can be incorporated via cbind: aggregate(cbind(x$Frequency, x$Metric2, x$Metric3) … (embedding @thelatemail comment), aggregate has a formula interface too aggregate(Frequency … Read more
Well then: welcome to the R world 😉 Here you go Setting up the code urls <- c( “http://stat.ethz.ch/R-manual/R-devel/library/base/html/connections.html”, “http://en.wikipedia.org/wiki/Xz”, “xxxxx” ) readUrl <- function(url) { out <- tryCatch( { # Just to highlight: if you want to use more than one # R expression in the “try” part then you’ll have to # use … Read more
Use showWarnings = FALSE: dir.create(file.path(mainDir, subDir), showWarnings = FALSE) setwd(file.path(mainDir, subDir)) dir.create() does not crash if the directory already exists, it just prints out a warning. So if you can live with seeing warnings, there is no problem with just doing this: dir.create(file.path(mainDir, subDir)) setwd(file.path(mainDir, subDir))
You can use sessionInfo() to accomplish that. > sessionInfo() R version 2.15.0 (2012-03-30) Platform: x86_64-pc-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=C LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] graphics grDevices utils datasets stats grid methods base other attached packages: [1] ggplot2_0.9.0 reshape2_1.2.1 plyr_1.7.1 loaded via a namespace … Read more
Ensure you record your work in a reproducible script. From time-to-time, reopen R, then source() your script. You’ll clean out anything you’re no longer using, and as an added benefit will have tested your code.
An update, several years later This answer is old, and R has moved on. Tweaking read.table to run a bit faster has precious little benefit. Your options are: Using vroom from the tidyverse package vroom for importing data from csv/tab-delimited files directly into an R tibble. See Hector’s answer. Using fread in data.table for importing … Read more
Just initialize it with empty vectors: df <- data.frame(Date=as.Date(character()), File=character(), User=character(), stringsAsFactors=FALSE) Here’s an other example with different column types : df <- data.frame(Doubles=double(), Ints=integer(), Factors=factor(), Logicals=logical(), Characters=character(), stringsAsFactors=FALSE) str(df) > str(df) ‘data.frame’: 0 obs. of 5 variables: $ Doubles : num $ Ints : int $ Factors : Factor w/ 0 levels: $ Logicals … Read more
Since R version 2.12, there’s a droplevels() function. levels(droplevels(subdf$letters))
If you want the output to print to the terminal it is best to use Rscript Rscript a.R Note that when using R CMD BATCH a.R that instead of redirecting output to standard out and displaying on the terminal a new file called a.Rout will be created. R CMD BATCH a.R # Check the output … Read more