Package 'spiderorchid'

ABDC Journal Quality List

Description

This is a dataset that contains the quality list of rankings of the Australian Business Deans Council (ABDC) from 2025. You can read more about this list here.

Usage

data(abdc)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2651 rows and 7 columns.

Value

A data frame with 2651 observations on the following 7 variables:

title:

Title of the journal

publisher:

Publishing house

issn:

International Standard Serial Number

issn_online:

ISSN Online - as ISSN, but for the online, rather than print version

year_inception:

Year the journal started

field_of_research:

Field of Research Code as provided by the Australian Bureau of Statistics

rank:

In order of best to lowest rank: A*, A, B, or C

Source

https://abdc.edu.au/abdc-journal-quality-list/

Examples

library(dplyr)
abdc |>
  filter(field_of_research == "4905") |>
  arrange(rank) |>
  select(title, rank)

---

CORE (Computing Research and Education) lists of conference and journal rankings

Description

Two datasets are provided: core and core_journals, which contains lists of conference and journal rankings respectively, according to the CORE executive committee. The details of the CORE organisation, and its procedure for ranking are provided below.

Usage

core

core_journals

Format

An object of class tbl_df (inherits from tbl, data.frame) with 982 rows and 2 columns.

An object of class tbl_df (inherits from tbl, data.frame) with 639 rows and 4 columns.

Details

CORE is an association of university departments of computer science in Australia and New Zealand. Prior to 2004 it was known as the Computer Science Association, CSA.

The CORE Conference Ranking provides assessments of major conferences in the computing disciplines. The rankings are managed by the CORE Executive Committee, with periodic rounds for submission of requests for addition or reranking of conferences. Decisions are made by academic committees based on objective data requested as part of the submission process. Conference rankings are determined by a mix of indicators, including citation rates, paper submission and acceptance rates, and the visibility and research track record of the key people hosting the conference and managing its technical program. A more detailed statement categorizing the ranks A*, A, B, and C can be found here.

Value

core is a data frame with 982 observations and two variables:

⁠title:⁠

Title of the conference

⁠rank:⁠

Conferences are assigned to one of the following categories:

• A*: flagship conference, a leading venue in a discipline area

• A: excellent conference, and highly respected in a discipline area

• B: good conference, and well regarded in a discipline area

• C: other ranked conference venues that meet minimum standards

core_journals is a data frame with 639 observations and 4 variables:

⁠title:⁠

Title of the journal

field_of_research:

Field of Research Code as provided by the Australian Bureau of Statistics

issn:

International Standard Serial Number

rank:

In order of best to lowest rank: A*, A, B, or C

Source

https://www.core.edu.au/conference-portal

Examples

core
core_journals

---

Monash EBS PURE publications data

Description

This dataset contains publications since 2018, downloaded from PURE on 16 January 2025. Additional data can be updated using the fetch_pure() function.

Usage

ebs_pure

Format

A data frame with 7 variables:

pure_id

character. The unique identifier for the publication in PURE.

year

integer. The year of publication.

authors

character. The authors of the publication.

title

character. The title of the publication.

journal

character. The journal where the publication appeared.

subtype

character. The subtype of the publication.

bib

character. A bibliographic citation in the Harvard format.

doi

character. The DOI of the publication where available.

Source

https://research.monash.edu/en/organisations/econometrics-business-statistics/publications/

---

ERA2010 Journal List

Description

This is a dataset that contains the list of journal rankings from the ARC Excellence in Research for Australia 2010 round.

Usage

data(era2010)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 20712 rows and 5 columns.

Value

A data frame with 20712 rows and the following 5 variables:

eraid:

ERA ID of the journal

title:

Title of the journal

issn:

International Standard Serial Number

field_of_research:

Field of Research Code as provided by the Australian Bureau of Statistics at the time. Note that the codes have since changed.

rank:

In order of best to lowest rank: A*, A, B, or C

Source

https://www.righttoknow.org.au/request/journal_list_relating_to_the_201

Examples

library(dplyr)
era2010 |>
  filter(field_of_research == "0104") |>
  arrange(rank)

---

Fetch packages from CRAN

Description

This function searches for CRAN packages by author names. Note that some authors may use different name variations on CRAN (e.g., "Di Cook" and "Dianne Cook"), so it may be necessary to call the function with several variations.

Usage

fetch_cran(
  author_names = NULL,
  package_names = NULL,
  downloads_from = "2000-01-01",
  downloads_to = Sys.Date()
)

Arguments

author_names	A character vector containing the authors' names in the form used on CRAN.
package_names	A character vector of package names. Ignored if author_names is provided.
downloads_from	A date or character string in the format "YYYY-MM-DD" specifying the date from which to start counting downloads. Default is "2000-01-01".
downloads_to	A date or character string in the format "YYYY-MM-DD" specifying the last date for counting downloads. Default is current date.

Value

A data frame returning meta data about a package including total downloads between downloads_from and downloads_to.

Examples

## Not run: 
cran2024 <- fetch_cran(
   author_names = c("Michael Lydeamore", "Di Cook", "Dianne Cook", "Hyndman"),
   downloads_from = "2024-01-01",
   downloads_to = "2024-12-31"
)

## End(Not run)

---

Fetch article information given a DOI

Description

Retrieves publications for a given list of DOIs using the DOI API and formats them into a structured tibble.

Usage

fetch_doi(doi)

Arguments

doi	A character vector of DOIs.

Value

A tibble containing the article information.

Examples

fetch_doi("10.1016/j.ijforecast.2023.10.010")

---

Fetch publications from ORCID

Description

Retrieves publications for given ORCID IDs, and returns them as a tibble. Only publications with DOIs are returned. The function uses the ORCID API to fetch the DOIs, and then uses the DOI API to fetch the publication details for each DOI.

Usage

fetch_orcid(orcid_ids)

Arguments

orcid_ids

A character vector of ORCID IDs.

Details

This function requires authentication on ORCID. If you have not previously authenticated, it will prompt you to do so when first run. If you just follow the prompts, you will be authenticated, but only for downloading your own papers. If you want to download papers from other ORCID IDs, you will need to authenticate with a 2-legged OAuth. Follow the instructions at https://info.orcid.org/register-a-client-application-production-member-api/. To avoid having to do this in each session, store the token obtained from orcid_auth() in your .Renviron file by running usethis::edit_r_environ(). It should be of the form ORCID_TOKEN=<your token>.

Value

A tibble containing all publications for the specified ORCID IDs.

Examples

## Not run: 
fetch_orcid(c("0000-0003-2531-9408", "0000-0001-5738-1471"))

## End(Not run)

---

Fetch publications from PURE

Description

To download data from PURE, it is necessary to have access to the API via Simon Angus and the Astro team https://astro.monash.edu/. The API key is stored in the environment variable PURE_API_KEY. You can add it to your environment using edit_r_environ(). This is end-point restricted to Monash IP addresses. So either use it on campus or invoke the VPN before using it off campus.

Usage

fetch_pure(years)

Arguments

years

A numeric vector of publication years. All publications between the minimum year and the maximum year are returned.

Value

A data frame containing the data fetched from the PURE API covering the specified publication years.

See Also

ebs_pure

---

Fetch publications from Google Scholar

Description

Retrieves publications for given Google Scholar IDs, and returns them as a tibble. This function retrieves publications for a given Google Scholar ID and formats them into a structured tibble.

Usage

fetch_scholar(scholar_id)

Arguments

scholar_id

A character vector of Google Scholar IDs.

Value

A tibble containing all publications for the specified Google Scholar IDs.

Examples

## Not run: 
fetch_scholar("vamErfkAAAAJ")

## End(Not run)

---

Find rankings of journals from the Monash Business School, ABDC, CORE, SCImago or ERA2010.

Description

Given a list of journal titles, this function will return their ranking from various lists. Data sets used are:

monash:

Monash Business School

abdc:

Australian Business Deans' Council

era2010:

ERA 2010

core:

CORE

scimago:

SCImago

This function is used in the Journal rankings shiny app.

Usage

journal_ranking(
  title,
  source = c("monash", "abdc", "era2010", "core", "scimago"),
  fuzzy = TRUE,
  only_best = length(title) > 1,
  ...
)

Arguments

title	A character vector containing (partial) journal names.
source	A character string indicating which ranking data base to use. Default "monash".
fuzzy	Should fuzzy matching be used. If FALSE, partial exact matching is used. Otherwise, full fuzzy matching is used.
only_best	If TRUE, only the best matching journal is returned.
...	Other arguments are passed to agrepl (if fuzzy is TRUE), or grepl otherwise.

Value

A data frame containing the journal title, rank and source for each matching journal.

Author(s)

Rob J Hyndman

Examples

# Return ranking for individual journals or conferences
journal_ranking("Annals of Statistics")
journal_ranking("Annals of Statistics", "abdc")
journal_ranking("International Conference on Machine Learning")
journal_ranking("International Conference on Machine Learning", "core")
journal_ranking("R Journal", "scimago", only_best = TRUE)

---

Monash Business School Journal Quality List

Description

This is a dataset that contains the list of quality journal rankings from the Monash Business School. In most cases, it follows ABDC with A* equal to Group 1 and A equal to Group 2. The "Group 1+" category contains a small set of the highest rank journals. The data set is updated from time to time when journals not on the ABDC list are classified. See https://www.intranet.monash/business/research-services/research-standards for the latest information.

Usage

data(monash)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 4489 rows and 2 columns.

Value

A data frame with 4489 observations on the following 2 variables:

title:

Title of the journal

rank:

In order of best to lowest rank: Group 1+, Group 1, Group 2

Source

Monash Business School

Examples

library(dplyr)
library(stringr)
monash |>
  filter(str_detect(title, "Statist")) |>
  arrange(rank)

---

SCImago Journal Rank for all journals indexed by Scopus

Description

This data was taken from https://www.scimagojr.com/journalrank.php

Usage

data(scimago)

Format

An object of class tbl_df (inherits from tbl, data.frame) with 32193 rows and 29 columns.

Value

A tibble with 32193 rows and 29 variables:

year

Year of SCImago Journal Ranking calculation.

rank

Rank of the journal among all journals.

sourceid

Database ID of the journal.

title

Jounal's title.

type

Type: "journal", "book series", "trade journal", or "conference and proceedings"

issn

ISSN journal identifier.

sjr

SCImago Journal Rank indicator. It expresses the average number of weighted citations received in the selected year by the documents published in the selected journal in the three previous years, –i.e. weighted citations received in year X to documents published in the journal in years X-1, X-2 and X-3. See detailed description of SJR (PDF).

sjr_best_quartile

Highest quartile of the journal among all categories it belongs to.

h_index

Hirsch index of the journal. The h index expresses the journal's number of articles (h) that have received at least h citations. It quantifies both journal scientific productivity and scientific impact and it is also applicable to scientists, countries, etc. (see H-index wikipedia definition).

total_docs_year

Total number of published documents within a specific year. All types of documents are considered, including citable and non citable documents.

total_docs_3years

Published documents in the three previous years (selected year documents are excluded), i.e.when the year X is selected, then X-1, X-2 and X-3 published documents are retrieved. All types of documents are considered, including citable and non citable documents.

total_refs

Total number of citations received by a journal to the documents published within a specific year.

total_cites_3years

Number of citations received in the seleted year by a journal to the documents published in the three previous years, –i.e. citations received in year X to documents published in years X-1, X-2 and X-3. All types of documents are considered.

citable_docs_3years

Number of citable documents published by a journal in the three previous years (selected year documents are excluded). Exclusively articles, reviews and conference papers are considered..

cites_doc_2years

Average citations per document in a 2 year period. It is computed considering the number of citations received by a journal in the current year to the documents published in the two previous years, –i.e. citations received in year X to documents published in years X-1 and X-2. Comparable to Journal Impact Factor.

ref_doc

Average number of references per document in the selected year..

country

Country of the publisher.

publisher

Publisher of the journal.

categories

Categories the jounal belongs to.

highest_category

Category in which the journal ranks highest by percentile.

highest_rank

Rank of journal in highest_category.

highest_percentile

Highest percentile of journal in any category.

Author(s)

Rob Hyndman

Source

SCImago Journal & Country Rank. Retrieved from https://www.scimagojr.com/journalrank.php

---

Monash EBS academic research staff IDs

Description

This dataset contains the mappings between researcher names and their respective ORCID and Google Scholar IDs. It is useful for identifying and linking academic profiles across different platforms.

Usage

staff_ids

Format

A data frame with 4 variables:

first_name

character. The first name of the individual.

last_name

character. The last name of the individual.

orcid_id

character. The ORCID identifier.

scholar_id

character. The Google Scholar user ID.

Source

https://www.monash.edu/business/ebs/our-people/staff-directory

---