# Introduction to logger

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)
    log_info('Loading data')
    #> INFO [2024-03-05 15:10:41] Loading data
    data(mtcars)
    log_info('The dataset includes {nrow(mtcars)} rows')
    #> INFO [2024-03-05 15:10:41] The dataset includes 32 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')
    }
    #> WARN [2024-03-05 15:10:41] Oh, no! There are no cars with more than 1K horsepower in the dataset :/

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()
    #> Log level: INFO

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')
    #> INFO [2024-03-05 15:10:41] Loading data
    data(mtcars)
    log_info('The dataset includes {nrow(mtcars)} rows')
    #> INFO [2024-03-05 15:10:41] The dataset includes 32 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')
    }
    #> WARN [2024-03-05 15:10:41] Oh, no! There are no cars with more than 1K horsepower in the dataset :/
    #> DEBUG [2024-03-05 15:10:41] The most powerful car is Maserati Bora with 335 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)
    #> INFO [2024-03-05 15:10:41] 'y <- f(g(x))' => '4'
    str(y)
    #>  num 4

Sometimes, it may be reasonable to log R objects as markdown, e.g. a smallish `data.frame` or `data.table`, e.g. `mtcars` or `iris`. Calling the formatter using `pander` instead of `glue` can help:

    log_formatter(formatter_pander)
    log_info(head(iris))
    #> INFO [2024-03-05 15:10:41]
    #> INFO [2024-03-05 15:10:41]
    #> INFO [2024-03-05 15:10:41]  Sepal.Length   Sepal.Width   Petal.Length   Petal.Width   Species
    #> INFO [2024-03-05 15:10:41] -------------- ------------- -------------- ------------- ---------
    #> INFO [2024-03-05 15:10:41]      5.1            3.5           1.4            0.2       setosa
    #> INFO [2024-03-05 15:10:41]      4.9             3            1.4            0.2       setosa
    #> INFO [2024-03-05 15:10:41]      4.7            3.2           1.3            0.2       setosa
    #> INFO [2024-03-05 15:10:41]      4.6            3.1           1.5            0.2       setosa
    #> INFO [2024-03-05 15:10:41]       5             3.6           1.4            0.2       setosa
    #> INFO [2024-03-05 15:10:41]      5.4            3.9           1.7            0.4       setosa
    #> INFO [2024-03-05 15:10:41]

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.