version submitted to CRAN

Author: njahn82

README.md

@@ -305,6 +305,7 @@ To display how many full-text links were found and which sources were used in a
 
 
 ```r
+if(!is.null(oa_df))
 oa_df %>%
   group_by(is_oa) %>%
   summarise(Articles = n()) %>%
@@ -317,13 +318,14 @@ oa_df %>%
 
 |is_oa | Articles| Proportion|
 |:-----|--------:|----------:|
-|FALSE |       35|        0.7|
-|TRUE  |       15|        0.3|
+|FALSE |       33|       0.66|
+|TRUE  |       17|       0.34|
 
 How did Unpaywall find those Open Access full-texts, which were characterized as best matches, and how are these OA types distributed over publication types?
 
 
 ```r
+if(!is.null(oa_df))
 oa_df %>%
   filter(is_oa == TRUE) %>%
   select(best_oa_location, genre) %>%
@@ -336,17 +338,14 @@ oa_df %>%
 
 
 
-|evidence                                                 |genre           | Articles|
-|:--------------------------------------------------------|:---------------|--------:|
-|open (via page says license)                             |journal-article |        4|
-|open (via free pdf)                                      |journal-article |        3|
-|oa repository (via OAI-PMH title and first author match) |journal-article |        2|
-|oa journal (via doaj)                                    |journal-article |        1|
-|oa journal (via observed oa rate)                        |journal-article |        1|
-|oa repository (via OAI-PMH title match)                  |report          |        1|
-|open (via crossref license)                              |journal-article |        1|
-|open (via free pdf)                                      |book-chapter    |        1|
-|open (via free pdf)                                      |other           |        1|
+|evidence                                                 |genre               | Articles|
+|:--------------------------------------------------------|:-------------------|--------:|
+|open (via free pdf)                                      |journal-article     |        7|
+|open (via page says license)                             |journal-article     |        6|
+|oa repository (via OAI-PMH doi match)                    |journal-article     |        1|
+|oa repository (via OAI-PMH title and first author match) |journal-article     |        1|
+|open (via free pdf)                                      |book-chapter        |        1|
+|open (via page says license)                             |proceedings-article |        1|
 
 #### More examples
 

codemeta.json

@@ -1,13 +1,16 @@
 {
-  "@context": ["https://doi.org/10.5063/schema/codemeta-2.0", "http://schema.org"],
+  "@context": [
+    "https://doi.org/10.5063/schema/codemeta-2.0",
+    "http://schema.org"
+  ],
   "@type": "SoftwareSourceCode",
   "identifier": "roadoi",
   "description": "This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly\n    oaDOI, a service finding free full-texts of academic papers by linking DOIs with \n    open access journals and repositories. It provides unified access to various data sources \n    for open access full-text links including Crossref and the Directory of Open Access \n    Journals (DOAJ). API usage is free and no registration is required.",
   "name": "roadoi: Find Free Versions of Scholarly Publications via Unpaywall",
   "codeRepository": "https://github.com/ropensci/roadoi",
   "issueTracker": "https://github.com/ropensci/roadoi/issues",
   "license": "https://spdx.org/licenses/MIT",
-  "version": "0.5.2",
+  "version": "0.6",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -102,18 +105,6 @@
       },
       "sameAs": "https://CRAN.R-project.org/package=rmarkdown"
     },
-    {
-      "@type": "SoftwareApplication",
-      "identifier": "lubridate",
-      "name": "lubridate",
-      "provider": {
-        "@id": "https://cran.r-project.org",
-        "@type": "Organization",
-        "name": "Comprehensive R Archive Network (CRAN)",
-        "url": "https://cran.r-project.org"
-      },
-      "sameAs": "https://CRAN.R-project.org/package=lubridate"
-    },
     {
       "@type": "SoftwareApplication",
       "identifier": "rcrossref",
@@ -252,11 +243,25 @@
   ],
   "releaseNotes": "https://github.com/ropensci/roadoi/blob/master/NEWS",
   "readme": "https://github.com/ropensci/roadoi/blob/master/README.md",
-  "contIntegration": ["https://travis-ci.org/ropensci/roadoi", "https://ci.appveyor.com/project/ropensci/roadoi", "https://codecov.io/github/ropensci/roadoi?branch=master"],
+  "contIntegration": [
+    "https://travis-ci.org/ropensci/roadoi",
+    "https://ci.appveyor.com/project/ropensci/roadoi",
+    "https://codecov.io/github/ropensci/roadoi?branch=master"
+  ],
   "review": {
     "@type": "Review",
     "url": "https://github.com/ropensci/onboarding/issues/115",
     "provider": "http://ropensci.org"
   },
-  "keywords": ["open-access", "oadoi", "r", "webclient", "code4lib", "altmetrics", "rstats", "r-package", "peer-reviewed"]
+  "keywords": [
+    "open-access",
+    "oadoi",
+    "r",
+    "webclient",
+    "code4lib",
+    "altmetrics",
+    "rstats",
+    "r-package",
+    "peer-reviewed"
+  ]
 }

cran-comments.md

@@ -20,7 +20,7 @@ I have run R CMD check on downstream dependencies using revdepcheck::revdep_chec
 Dear CRAN maintainers, 
 
 This submission fixes the problems shown on
-<https://cran.r-project.org/web/checks/check_results_roadoi.html>.
+<https://cran.r-project.org/web/checks/check_results_roadoi.html>, and problems encountered in the last submission.
 
 Thanks for alerting me!
 

vignettes/intro.Rmd

@@ -149,7 +149,6 @@ my_data <- purrr::map(random_dois,
 purrr::map_df(my_data, "result")
 #show errors
 purrr::map(my_data, "error")
-
 ```
 
 ### Use Case: Studying the compliance with open access policies
@@ -186,6 +185,7 @@ After obtaining the data, reporting with R is straightforward. You can even gene
 To display how many full-text links were found and which sources were used in a nicely formatted markdown-table using the [`knitr`](https://yihui.name/knitr/)-package:
 
 ```{r, results='asis'}
+if(!is.null(oa_df))
 oa_df %>%
   group_by(is_oa) %>%
   summarise(Articles = n()) %>%
@@ -197,6 +197,7 @@ oa_df %>%
 How did Unpaywall find those Open Access full-texts, which were characterized as best matches, and how are these OA types distributed over publication types?
 
 ```{r, results='asis'}
+if(!is.null(oa_df))
 oa_df %>%
   filter(is_oa == TRUE) %>%
   select(best_oa_location, genre) %>%