Resolve CRAN errors

Missing scare-quotes around "dataspice" in DESCRIPTION
Non-exported functions can't have \dontrun examples
Other weird stuff

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: dataspice
 Version: 1.0.0
 Title: Create Lightweight Schema.org Descriptions of Data
-Description: The goal of dataspice is to make it easier for researchers to 
-  create basic, lightweight, and concise metadata files for their datasets. 
-  These basic files can then be used to make useful information available during 
-  analysis, create a helpful dataset "README" webpage, and produce more complex 
-  metadata formats to aid dataset discovery. Metadata fields are based on 
+Description: The goal of 'dataspice' is to make it easier for researchers to
+  create basic, lightweight, and concise metadata files for their datasets.
+  These basic files can then be used to make useful information available during
+  analysis, create a helpful dataset "README" webpage, and produce more complex
+  metadata formats to aid dataset discovery. Metadata fields are based on
   the 'Schema.org' and 'Ecological Metadata Language' standards.
 Authors@R: c(
     person("Carl", "Boettiger", role = c("aut"), comment = "https://github.com/cboettig"),

R/eml_crosswalk.R

@@ -247,8 +247,7 @@ crosswalk <- function(doc, term) {
 #' # Now fill in the attributes data.frame. See `EML::set_attributes`.
 #'
 #' # And last, set the attributes on our EML document
-#'
-#'   eml_doc$dataset$dataTable[[1]]$attributeList <- EML::set_attributes(attributes)
+#' eml_doc$dataset$dataTable[[1]]$attributeList <- EML::set_attributes(attributes)
 #' }
 crosswalk_variables <- function(spice) {
   if (is.character(spice)) {

R/eml_to_spice.R

@@ -33,15 +33,6 @@ get_access_spice <- function(x){
 #'
 #' @param eml (emld) an EML object
 #' @param path (character) folder path for saving the table to disk
-#'
-#' @examples
-#' \dontrun{
-#' library(EML)
-#'
-#' eml_path <- system.file("example-dataset/broodTable_metadata.xml", package = "dataspice")
-#' eml <- read_eml(eml_path)
-#' es_access(eml)
-#' }
 es_access <- function(eml, path = NULL) {
     entities <- get_entities(eml)
     access_entities <- lapply(entities, get_access_spice)
@@ -119,13 +110,6 @@ get_attributes_spice <- function(x) {
 #'
 #' @import dplyr
 #' @importFrom readr write_csv
-#'
-#' @examples
-#' \dontrun{
-#' eml_path <- system.file("example-dataset/broodTable_metadata.xml", package = "dataspice")
-#' eml <- read_eml(eml_path)
-#' es_attributes(eml)
-#' }
 es_attributes <- function(eml, path = NULL) {
   entities <- get_entities(eml)
   attrTables <- lapply(entities, get_attributes_spice)
@@ -149,13 +133,6 @@ es_attributes <- function(eml, path = NULL) {
 #'
 #' @param eml (emld) an EML object
 #' @param path (character) folder path for saving the table to disk
-#'
-#' @examples
-#' \dontrun{
-#' eml_path <- system.file("example-dataset/broodTable_metadata.xml", package = "dataspice")
-#' eml <- read_eml(eml_path)
-#' es_biblio(eml)
-#' }
 es_biblio <- function(eml, path = NULL) {
   biblio_eml <- eml %>%
     unlist() %>%
@@ -208,13 +185,6 @@ es_biblio <- function(eml, path = NULL) {
 #' @importFrom purrr discard
 #' @importFrom tibble enframe
 #' @importFrom tidyr spread
-#'
-#' @examples
-#' \dontrun{
-#' eml_path <- system.file("example-dataset/broodTable_metadata.xml", package = "dataspice")
-#' eml <- read_eml(eml_path)
-#' es_creators(eml)
-#' }
 es_creators <- function(eml, path = NULL) {
   people <- get_entities(eml,
                          entities = c("creator", "contact", "associatedParty", "metadataProvider"),

R/jsonld_to_mustache.R

@@ -56,13 +56,6 @@ parse_GeoShape_points <- function(points) {
 #' @param path (character) Path to file on disk to convert
 #'
 #' @return (list) Mustache-appropriate list
-#'
-#' @examples
-#' \dontrun{
-#' json_path <- "myjson.json"
-#' data <- jsonld_to_mustache(json_path)
-#' whisker::whisker_render("{{ keywords }}", data)
-#' }
 jsonld_to_mustache <- function(path) {
   json <- jsonlite::read_json(path)
 

R/prep.R

@@ -13,7 +13,6 @@
 #' @param ... parameters passed to `list.files()`. For example, use `recursive = TRUE`
 #' to list files in a folder recursively or use `pattern` to filter files for patterns.
 #' @return `prep_attributes()` updates the `attributes.csv` and writes to `attributes_path`.
-#' `validate_file_paths()` returns a vector of valid file_paths detected from `data_path`.
 #' @export
 #' @examples
 #' \dontrun{
@@ -31,8 +30,6 @@
 #' # extract from a folder using folder path
 #' data_path <- system.file("example-dataset", package = "dataspice")
 #' prep_attributes(data_path)
-#' # get vector of valid (existing) file paths
-#' validate_file_paths(data_path)
 #' }
 prep_attributes <- function(data_path = "data",
                             attributes_path = "data/metadata/attributes.csv",