If you are not only using R in the interactive console for ad-hoc data analysis, but running eg batch jobs (for ETL, reporting, modeling, forecasting etc) as well, then logging the status(changes) of your script is a must so that later on you can review / debug what have happened.

For most cases, it’s enough to load the package and use the functions with the log prefix to log important and not so important messages, for example:

library(logger)
data(mtcars)
log_info('The dataset includes {nrow(mtcars)} rows')
if (max(mtcars$hp) < 1000) { log_warn('Oh, no! There are no cars with more than 1K horsepower in the dataset :/') log_debug('The most powerful car is {rownames(mtcars)[which.max(mtcars$hp)]} with {max(mtcars$hp)} hp') } Interestingly, the most powerful car was not being logged – because by default the logger prints messages with at least the INFO log level: log_threshold() #> [1] 400 #> attr(,"level") #> [1] "INFO" #> attr(,"class") #> [1] "loglevel" "integer" To change that, specify the new log level threshold, eg TRACE to log everything: log_threshold(TRACE) The rerunning the above code chunk: log_info('Loading data') data(mtcars) log_info('The dataset includes {nrow(mtcars)} rows') if (max(mtcars$hp) < 1000) {
log_warn('Oh, no! There are no cars with more than 1K horsepower in the dataset :/')
log_debug('The most powerful car is {rownames(mtcars)[which.max(mtcars$hp)]} with {max(mtcars$hp)} hp')
}

You may also find the ?log_eval function useful to log both an R expression and its result in the same log record:

f <- sqrt
g <- mean
x <- 1:31
log_eval(y <- f(g(x)), level = INFO)
str(y)
#>  num 4

For more details, check the function reference in the manual, or start with the The Anatomy of a Log Request and Customizing the Format and the Destination of a Log Record vignettes.