metrics: hack fix missing funder and link columns #183

R/cr_md_funder.R

@@ -4,20 +4,32 @@
 #' @family transform
 #' @export
 cr_funder_df <- function(cr) {
-  if (!"funder" %in% colnames(cr)) {
-    out <- NULL
-  } else {
-    out <- cr %>%
+  empty_funder(cr) %>%
       dplyr::select(
-        doi,
-        container_title = container.title,
-        publisher,
-        issued,
-        issued_year,
-        funder
+        .data$doi,
+        container_title = .data$container.title,
+        .data$publisher,
+        .data$issued,
+        .data$issued_year,
+        .data$funder
       ) %>%
-      tidyr::unnest(c(funder), keep_empty = TRUE) %>%
-      dplyr::rename(fundref_doi = DOI, doi_asserted_by = doi.asserted.by)
+      tidyr::unnest("funder", keep_empty = TRUE) %>%
+      dplyr::rename(fundref_doi = .data$DOI, doi_asserted_by = .data$doi.asserted.by)
+}
+
+#' dirty hack to prevent for missing funder json nodes
+#' https://github.com/subugoe/metacheck/issues/183
+#'
+#' @noRd
+empty_funder <- function(cr) {
+  if (!"funder" %in% colnames(cr)) {
+    funder <- list(tibble::tibble(
+      DOI = as.character(NA), name = as.character(NA), doi.asserted.by = as.character(NA)
+    ))
+    out <- mutate(cr, funder = funder)
+  } else {
+    out <- cr
   }
   out
 }
+

R/cr_md_overview.R

@@ -11,8 +11,10 @@ cr_compliance_overview <- function(cr) {
 
   tdm_df <- cr_tdm_df(cr)
   compliant_tdm <- filter(tdm_df,
-                          is_tdm_compliant == TRUE)
+                          .data$is_tdm_compliant == TRUE)
   funder_df <- cr_funder_df(cr)
+  has_funder <- funder_df %>%
+    filter(!is.na(.data$name))
   orcid_df <- cr_has_orcid(cr)
 
   cr_overview <- cr %>%
@@ -22,9 +24,7 @@ cr_compliance_overview <- function(cr) {
       has_compliant_cc = .data$doi %in%
         filter(cc_df, .data$check_result == "All fine!")$doi,
       has_tdm_links = .data$doi %in% compliant_tdm$doi,
-      has_funder_info = unlist(across(
-        any_of("funder"), ~ sapply(.x, empty_list)
-      )),
+      has_funder_info = .data$doi %in% has_funder$doi,
       has_orcid = .data$doi %in% orcid_df$doi,
       has_open_abstract = unlist(across(any_of("abstract"), ~ !is.na(.x))),
       has_open_refs = unlist(across(

R/cr_md_tdm.R

@@ -6,27 +6,41 @@
 #' @family transform
 #' @export
 cr_tdm_df <- function(cr) {
-  if ("link" %in% colnames(cr)) {
-    out <- cr %>%
-      select(
+  empty_tdm(cr) %>%
+      dplyr::select(
         .data$doi,
         container_title = .data$container.title,
         .data$publisher,
         .data$issued,
         .data$issued_year,
         .data$link
       ) %>%
-      unnest(cols = "link", keep_empty = TRUE) %>%
-      mutate(
+      tidyr::unnest(cols = "link", keep_empty = TRUE) %>%
+      dplyr::mutate(
         is_tdm_compliant = ifelse(
           .data$content.version == "vor" &
             .data$intended.application == "text-mining",
           TRUE,
           FALSE
         )
       )
+}
+
+#' dirty hack to prevent for missing TDM json nodes
+#' https://github.com/subugoe/metacheck/issues/183
+#'
+#' @noRd
+empty_tdm <- function(cr) {
+  if (!"link" %in% colnames(cr)) {
+    link <- list(tibble::tibble(
+      URL = as.character(NA),
+      content.type = as.character(NA),
+      content.version = as.character(NA),
+      intended.application = as.character(NA)
+    ))
+    out <- dplyr::mutate(cr, link = link)
   } else {
-    out <- NULL
+    out <- cr
   }
   out
 }

R/metrics_funder.R

@@ -29,29 +29,27 @@
 funder_metrics <- function(funder_info = NULL) {
   is_cr_funder_df(funder_info)
 
-
-  if(length(unique(funder_info$name)) > 5) {
+  if(nrow(funder_info[all(!is.na("name")),]) == nrow(funder_info)) {
+    out <- dplyr::mutate(funder_info, name = "No funding info")
+  } else if (length(unique(funder_info$name)) > 5) {
     out <- funder_info %>%
     mutate(name = ifelse(is.na(.data$name), "No funding info", .data$name)) %>%
     mutate(name = forcats::fct_lump_n(.data$name, 5, other_level = "Other funders")) %>%
     mutate(name = forcats::fct_infreq(.data$name)) %>%
     mutate(name = forcats::fct_relevel(.data$name, "Other funders", after = Inf)) %>%
-    mutate(name = forcats::fct_relevel(.data$name, "No funding info", after = Inf)) %>%
-    group_by(indicator = .data$name) %>%
-    summarise(value = n_distinct(.data$doi)) %>%
-    dplyr::ungroup() %>%
-    mutate(prop = .data$value / length(unique(funder_info$doi)) * 100)
+    mutate(name = forcats::fct_relevel(.data$name, "No funding info", after = Inf))
   } else {
     out <-funder_info %>%
       mutate(name = ifelse(is.na(.data$name), "No funding info", .data$name)) %>%
       mutate(name = forcats::fct_infreq(.data$name)) %>%
-      mutate(name = forcats::fct_relevel(.data$name, "No funding info", after = Inf)) %>%
-      group_by(indicator = .data$name) %>%
-      summarise(value = n_distinct(.data$doi)) %>%
-      dplyr::ungroup() %>%
-      mutate(prop = .data$value / length(unique(funder_info$doi)) * 100)
+      mutate(name = forcats::fct_relevel(.data$name, "No funding info", after = Inf))
   }
-  return(out)
+  ind <- out %>%
+    group_by(indicator = .data$name) %>%
+    summarise(value = n_distinct(.data$doi)) %>%
+    dplyr::ungroup() %>%
+    mutate(prop = .data$value / length(unique(funder_info$doi)) * 100)
+  return(ind)
 }
 
 #' Check if funder compliance data is provided

R/metrics_tdm.R

@@ -31,7 +31,7 @@
 tdm_metrics <- function(tdm = NULL) {
   is_cr_tdm_df(tdm)
   compliant_tdm <- tdm %>%
-    filter(is_tdm_compliant == TRUE)
+    filter(.data$is_tdm_compliant == TRUE)
 
   non_compliant_tdm <- tdm[!tdm$doi %in% compliant_tdm$doi,]