Update readme to fix http URL

Author: amoeba

README.Rmd

@@ -28,7 +28,7 @@ The goal of `dataspice` is to make it easier for researchers to create basic, li
 - Create a helpful dataset README webpage for your data similar to how [pkgdown](https://pkgdown.r-lib.org/) creates websites for R packages.
 - Produce more complex metadata formats for richer description of your datasets and to aid dataset discovery.
 
-Metadata fields are based on [Schema.org/Dataset](http://schema.org/Dataset) and other [metadata standards](https://github.com/ropenscilabs/dataspice#resources) and represent a lowest common denominator which means converting between formats should be relatively straightforward.
+Metadata fields are based on [Schema.org/Dataset](https://schema.org/Dataset) and other [metadata standards](https://github.com/ropenscilabs/dataspice#resources) and represent a lowest common denominator which means converting between formats should be relatively straightforward.
 
 ## Example
 

README.md

@@ -20,7 +20,7 @@ then be used to:
     datasets and to aid dataset discovery.
 
 Metadata fields are based on
-[Schema.org/Dataset](http://schema.org/Dataset) and other [metadata
+[Schema.org/Dataset](https://schema.org/Dataset) and other [metadata
 standards](https://github.com/ropenscilabs/dataspice#resources) and
 represent a lowest common denominator which means converting between
 formats should be relatively straightforward.
@@ -40,16 +40,20 @@ Installation
 You can install the development version from
 [GitHub](https://github.com/) with:
 
-    # install.packages("devtools")
-    devtools::install_github("ropenscilabs/dataspice")
+``` r
+# install.packages("devtools")
+devtools::install_github("ropenscilabs/dataspice")
+```
 
 Workflow
 --------
 
-    create_spice()
-    # Then fill in template CSV files, more on this below
-    write_spice()
-    build_site() # Optional
+``` r
+create_spice()
+# Then fill in template CSV files, more on this below
+write_spice()
+build_site() # Optional
+```
 
 ![worfklowdiagram](man/figures/dataspice_workflow.png)
 
@@ -87,21 +91,27 @@ the associated helper function and/or
 To see an example of how `prep_attributes()` works, load the data files
 that ship with the package:
 
-    data_files <- list.files(system.file("example-dataset/", package = "dataspice"),
-                             pattern = ".csv",
-                             full.names = TRUE)
+``` r
+data_files <- list.files(system.file("example-dataset/", package = "dataspice"),
+                         pattern = ".csv",
+                         full.names = TRUE)
+```
 
 This function assumes that the metadata templates are in a folder called
 `metadata` within a `data` folder.
 
-    attributes_path <- file.path("data", "metadata", "attributes.csv")
+``` r
+attributes_path <- file.path("data", "metadata", "attributes.csv")
+```
 
 Using `purrr::map()`, this function can be applied over multiple files
 to populate the header names
 
-    data_files %>%
-      purrr::map(~ prep_attributes(.x, attributes_path),
-                 attributes_path = attributes_path)
+``` r
+data_files %>%
+  purrr::map(~ prep_attributes(.x, attributes_path),
+             attributes_path = attributes_path)
+```
 
 The output of `prep_attributes()` has the first two columns filled out:
 
@@ -260,10 +270,10 @@ temporal coverage
 
 `creators.csv` has one row for each of the dataset authors
 
-| id  | name           | affiliation                                           | email                                                                          |
-|:----|:---------------|:------------------------------------------------------|:-------------------------------------------------------------------------------|
-| NA  | Jeanette Clark | National Center for Ecological Analysis and Synthesis | <a href="mailto:[email protected]" class="email">[email protected]</a> |
-| NA  | Rich,Brenner   | Alaska Department of Fish and Game                    | richard.brenner.alaska.gov                                                     |
+| id  | name           | affiliation                                           | email                      |
+|:----|:---------------|:------------------------------------------------------|:---------------------------|
+| NA  | Jeanette Clark | National Center for Ecological Analysis and Synthesis | <[email protected]>    |
+| NA  | Rich,Brenner   | Alaska Department of Fish and Game                    | richard.brenner.alaska.gov |
 
 ### Save JSON-LD file
 
@@ -297,34 +307,38 @@ compatibility with terms from [Schema.org](https://schema.org). However,
 `dataspice` will do its best to convert your `dataspice` metadata to
 EML:
 
-    library(dataspice)
+``` r
+library(dataspice)
 
-    # Load an example dataspice JSON that comes installed with the package
-    spice <- system.file(
-      "examples", "annual-escapement.json",
-      package = "dataspice")
+# Load an example dataspice JSON that comes installed with the package
+spice <- system.file(
+  "examples", "annual-escapement.json",
+  package = "dataspice")
 
-    # Convert it to EML
-    eml_doc <- spice_to_eml(spice)
-    #> Warning: variableMeasured not crosswalked to EML because we don't have enough
-    #> information. Use `crosswalk_variables` to create the start of an EML attributes
-    #> table. See ?crosswalk_variables for help.
-    #> You might want to run EML::eml_validate on the result at this point and fix what validations errors are produced.You will commonly need to set `packageId`, `system`, and provide `attributeList` elements for each `dataTable`.
+# Convert it to EML
+eml_doc <- spice_to_eml(spice)
+#> Warning: variableMeasured not crosswalked to EML because we don't have enough
+#> information. Use `crosswalk_variables` to create the start of an EML attributes
+#> table. See ?crosswalk_variables for help.
+#> You might want to run EML::eml_validate on the result at this point and fix what validations errors are produced.You will commonly need to set `packageId`, `system`, and provide `attributeList` elements for each `dataTable`.
+```
 
 You may receive warnings depending on which `dataspice` fields you
 filled in and this process will very likely produce an invalid EML
 record which is totally fine:
 
-    library(EML)
+``` r
+library(EML)
 
-    eml_validate(eml_doc)
-    #> [1] FALSE
-    #> attr(,"errors")
-    #> [1] "Element '{https://eml.ecoinformatics.org/eml-2.2.0}eml': The attribute 'packageId' is required but missing."                                  
-    #> [2] "Element '{https://eml.ecoinformatics.org/eml-2.2.0}eml': The attribute 'system' is required but missing."                                     
-    #> [3] "Element 'dataTable': Missing child element(s). Expected is one of ( physical, coverage, methods, additionalInfo, annotation, attributeList )."
-    #> [4] "Element 'dataTable': Missing child element(s). Expected is one of ( physical, coverage, methods, additionalInfo, annotation, attributeList )."
-    #> [5] "Element 'dataTable': Missing child element(s). Expected is one of ( physical, coverage, methods, additionalInfo, annotation, attributeList )."
+eml_validate(eml_doc)
+#> [1] FALSE
+#> attr(,"errors")
+#> [1] "Element '{https://eml.ecoinformatics.org/eml-2.2.0}eml': The attribute 'packageId' is required but missing."                                  
+#> [2] "Element '{https://eml.ecoinformatics.org/eml-2.2.0}eml': The attribute 'system' is required but missing."                                     
+#> [3] "Element 'dataTable': Missing child element(s). Expected is one of ( physical, coverage, methods, additionalInfo, annotation, attributeList )."
+#> [4] "Element 'dataTable': Missing child element(s). Expected is one of ( physical, coverage, methods, additionalInfo, annotation, attributeList )."
+#> [5] "Element 'dataTable': Missing child element(s). Expected is one of ( physical, coverage, methods, additionalInfo, annotation, attributeList )."
+```
 
 This is because some fields in `dataspice` store information in
 different structures and because EML requires many fields that
@@ -336,23 +350,29 @@ package](https://github.com/ropensci/eml).
 
 Once you’re done, you can write out an EML XML file:
 
-    out_path <- tempfile()
-    write_eml(eml_doc, out_path)
-    #> NULL
+``` r
+out_path <- tempfile()
+write_eml(eml_doc, out_path)
+#> NULL
+```
 
 ### Convert from EML
 
 Like converting `dataspice` to EML, we can convert an existing EML
 record to a set of `dataspice` metadata tables which we can then work
 from within `dataspice`:
 
-    library(EML)
+``` r
+library(EML)
 
-    eml_path <- system.file("example-dataset/broodTable_metadata.xml", package = "dataspice")
-    eml <- read_eml(eml_path)
+eml_path <- system.file("example-dataset/broodTable_metadata.xml", package = "dataspice")
+eml <- read_eml(eml_path)
+```
 
-    # Creates four CSVs files in the `data/metadata` directory
-    my_spice <- eml_to_spice(eml, "data/metadata")
+``` r
+# Creates four CSVs files in the `data/metadata` directory
+my_spice <- eml_to_spice(eml, "data/metadata")
+```
 
 Resources
 ---------