‘SciViews::R’ - Base Functions

Subsettable functions

The $ operator is not suitable for functions in base R. It is not meaningful in that context. Yet, it may be convenient to use it in certain conditions. From the example of ?subsettable:

foo <- structure(function(x, type = c("histogram", "boxplot"), ...) {
  type <- match.arg(type, c("histogram", "boxplot"))
  switch(type,
    histogram = hist(x, ...),
    boxplot = boxplot(x, ...),
    stop("unknow type")
  )
}, class = c("function", "subsettable_type"))
foo
#> function (x, type = c("histogram", "boxplot"), ...) 
#> {
#>     type <- match.arg(type, c("histogram", "boxplot"))
#>     switch(type, histogram = hist(x, ...), boxplot = boxplot(x, 
#>         ...), stop("unknow type"))
#> }
#> attr(,"class")
#> [1] "function"         "subsettable_type"

# This function can be used as usual:
foo(rnorm(50), type = "histogram")

# ... but also this way:
foo$histogram(rnorm(50))

foo$boxplot(rnorm(50))

Object aliasing with aka()

The aka() function allows you to create an alias for an existing object (function, data set, etc.) with its own help page. This is useful when you want to provide a more user-friendly name for a function. The SciViews::R dialect uses it to provide snake_case versions of several R functions.

Data frame conversions with as_dtx() and related functions

The {svBase} package provides a set of functions to convert between different data frame types in R, including base R’s data.frame, data.table, tibble’s tbl_df, and SciViews’ data.trame. The main function for this purpose is as_dtx(), which converts an object to the preferred data frame type as specified by the user. The preferred data frame type can be set using the SciViews.as_dtx option.

# Create a regular base R data.frame
dtf <- dtf(
  x = 1:5,
  y = rnorm(5)
)
# Convert it into the preferred version
dtx <- as_dtx(dtf)
class(dtx)
#> [1] "data.trame" "data.frame"

Alternate assignment operator %<-%

The alternate assignment operator %<-% (or %->%) allows for multiple assignment (also known as destructuring assignment) and ensures that the assigned value is collected and converted to the preferred data frame type. This is particularly useful when working with {dplyr} pipelines and extensions like {dtplyr} or {dbplyr}.

Labels and units

The {svBase} package provides functions to manage labels and units for R objects. The label() function allows you to set or get a label for an object, while the labelise() function allows you to set bot the label and the units for an object. These functions are useful for adding metadata to your data frames and variables.

Enhanced messages

The {svBase} package includes functions for enhanced messaging in R. The stop_(),and warning_() functions provide a consistent way to display error messages, and warnings, respectively. These functions enhance the {cli} equivalents cli_abort() and cli_warn(). They benefit from their enhancements (better context, better formatting of the message…), and they they also allow for message translation where it is not done easily/automatically with {cli} functions.

Regarding message translation, the gettext_(), gettextf_(), a,d ngettext_() functions are wrappers around the base R gettext(), gettextf(), and ngettext() functions, also allowing to get translations in a different language than the current one for the R session. This is useful when you have to produce a report in a different language, or in a multi-lingual context. Hence, sentences for tables, plots, or other material can be translated independently of the R error messages.

The “data-dot” mechanism

The {svBase} package introduces the “data-dot” mechanism, which allows for easier management of data frames in R functions. It ensure that a first data= or .data= argument contains a data frame, or it injects . as first argument. This is particularly useful when working with {dplyr} pipelines.

Formula-masking and formula-select

The {svBase} package provides functions for formula-masking and formula-select, which allow for easier use of non-standard evaluations using formulas. They are similar to the data-masking and tidy-select mechanisms in the tidyverse, but offer several advantages.

Sections

Sections allow to group a series of related functions into sets that are easily discoverable.