Regenerate vars_dict.rda and vars_dict docs based on changes to CSV

R/data.R

@@ -181,16 +181,8 @@
 #' to their human-readable value (ROOF_CNST = 1
 #' becomes ROOF_CNST = Shingle/Asphalt).
 #'
-#' @format A data frame with 496 rows and 19 variables:
+#' @format A data frame with 488 rows and 11 variables:
 #' \describe{
-#'   \item{var_from_source}{Name of the raw data source that the variable
-#'   was extracted from}
-#'   \item{var_from_table}{Name Athena table containing the variable, if
-#'   applicable}
-#'   \item{var_from_ctas}{Name of the CREATE TABLE AS SELECT statement that
-#'   created the variable, if applicable}
-#'   \item{var_from_view}{Name of the Athena view that contains the variable.
-#'   This is typically the main data source for end users}
 #'   \item{var_name_hie}{Column name of variable when stored in the legacy
 #'   ADDCHARS SQL table}
 #'   \item{var_name_iasworld}{Column name for variable as stored in the system
@@ -207,17 +199,11 @@
 #'   For example, ind_ variables are always indicators (booleans), while char_
 #'   variables are always property characteristics}
 #'   \item{var_data_type}{R data type variable values should be stored as}
-#'   \item{var_model_type}{Model type that uses this variable}
-#'   \item{var_is_published}{Logical value indicating whether to publish in
-#'   open data}
-#'   \item{var_is_predictor}{Logical value indicating whether to use variable
-#'   in modeling on the right-hand side. Left-hand side is always sale price}
 #'   \item{var_code}{Factor value for categorical variable. These are the values
 #'   stored in the system of record}
 #'   \item{var_value}{Human-readable translation of factor value}
 #'   \item{var_value_short}{Human-readable translation of factor value, but as
 #'   short as possible}
-#'   \item{var_notes}{Field descriptions and caveats}
 #' }
 "vars_dict"