Link to read_settings() (#2270)

* Cosmetic tweaks

* Link to doc

* Roxygenize

R/lint.R

@@ -19,8 +19,8 @@
 #' - [lint()] (in case of `lint_dir()` and `lint_package()`; e.g. `linters` or `cache`)
 #' @param cache When logical, toggle caching of lint results. If passed a character string, store the cache in this
 #' directory.
-#' @param parse_settings Logical, default `TRUE`. Whether to try and parse the settings;
-#' otherwise, the [default_settings()] are used.
+#' @param parse_settings Logical, default `TRUE`. Whether to try and parse the [settings][read_settings]. Otherwise,
+#' the [default_settings()] are used.
 #' @param text Optional argument for supplying a string or lines directly, e.g. if the file is already in memory or
 #' linting is being done ad hoc.
 #'

R/settings.R

@@ -11,7 +11,7 @@
 #' or the environment variable `R_LINTR_LINTER_FILE`
 #' This file is a DCF file, see [base::read.dcf()] for details.
 #' Experimentally, we also support keeping the config in a plain R file. By default we look for
-#' a file named '.lintr.R' (in the same directories where we search for '.lintr').
+#' a file named `.lintr.R` (in the same directories where we search for `.lintr`).
 #' We are still deciding the future of config support in lintr, so user feedback is welcome.
 #' The advantage of R is that it maps more closely to how the configs are actually stored,
 #' whereas the DCF approach requires somewhat awkward formatting of parseable R code within
@@ -20,7 +20,7 @@
 #" otherwise "abusing" the ability to evaluate generic R code. Other recursive key-value stores
 #' like YAML could work, but require new dependencies and are harder to parse
 #' both programmatically and visually.
-#' @param filename source file to be linted
+#' @param filename Source file to be linted.
 read_settings <- function(filename) {
  reset_settings()