improve package index, remove mention of dplyr::data_frame, improve r…

…eference index, link vignette from docs (#169)

* improve package index, remove mention of dplyr::data_frame, improve reference index, link vignette from docs

* fix lint + link

R/lookfor.R

@@ -25,6 +25,7 @@
 #' @param x a tibble returned by `look_for()`
 #' @return a tibble data frame featuring the variable position, name and
 #' description (if it exists) in the original data frame
+#' @seealso `vignette("look_for")`
 #' @details The function looks into the variable names for matches to the
 #' keywords. If available, variable labels are included in the search scope.
 #' Variable labels of data.frame imported with \pkg{foreign} or

R/recode.R

@@ -5,9 +5,9 @@
 #'
 #' @importFrom dplyr recode
 #' @inheritParams dplyr::recode
-#' @param .keep_value_labels If TRUE, keep original value labels.
-#'   If FALSE, remove value labels.
-#' @param .combine_value_labels If TRUE, will combine original value labels
+#' @param .keep_value_labels If `TRUE`, keep original value labels.
+#'   If `FALSE`, remove value labels.
+#' @param .combine_value_labels If `TRUE`, will combine original value labels
 #'   to generate new value labels. Note that unexpected results could be
 #'   obtained if a same old value is recoded into several different new values.
 #' @param .sep Separator to be used when combining value labels.

R/to_labelled.R

@@ -206,8 +206,8 @@ memisc_to_labelled <- function(x) {
 #'   codes
 #' @details
 #' If you convert a labelled vector into a factor with prefix, i.e. by using
-#' [to_factor(levels = "prefixed")][to_factor()], `to_labelled.factor()` is able
-#' to reconvert it to a labelled vector with same values and labels.
+#' [`to_factor(levels = "prefixed")`][to_factor()], `to_labelled.factor()` is
+#' able to reconvert it to a labelled vector with same values and labels.
 #' @export
 #' @examples
 #' # Converting factors to labelled vectors

_pkgdown.yml

@@ -24,7 +24,7 @@ reference:
 - title: Manipulating value labels
   desc:  Functions to set, manipulate and remove value labels
   contents:
-  - labelled
+  - haven::labelled
   - val_label
   - remove_var_label
   - sort_val_labels
@@ -34,19 +34,19 @@ reference:
   - copy_labels
   - update_variable_labels_with
 - title: Data dictionary
-  desc:  Functions to look for keywords variable names / labels and create a data dictionary
+  desc:  Functions to look for keywords variable names / labels and create a data dictionary.
   contents:
   - look_for
 - title: Manipulating SPSS style missing values
   desc:  Functions to set, manipulate and remove SPSS style missing values
   contents:
-  - labelled_spss
+  - haven::labelled_spss
   - na_values
   - copy_labels
   - remove_user_na
 - title: Tagged missing values
   contents:
-  - tagged_na
+  - haven::tagged_na
   - unique_tagged_na
   - tagged_na_to_user_na
 - title: Converting

vignettes/labelled.Rmd

@@ -491,7 +491,7 @@ If you are using the `%>%` operator, you can use the functions `set_variable_lab
 ```{r}
 library(dplyr)
 
-df <- data_frame(s1 = c("M", "M", "F"), s2 = c(1, 1, 2)) %>%
+df <- tibble(s1 = c("M", "M", "F"), s2 = c(1, 1, 2)) %>%
   set_variable_labels(s1 = "Sex", s2 = "Question") %>%
   set_value_labels(s1 = c(Male = "M", Female = "F"), s2 = c(Yes = 1, No = 2))
 df$s2