inst/doc/reading-data.R

In bibnets: Importing, Constructing, and Exporting Bibliometric Networks

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(bibnets)

## ----generic-call, eval = FALSE-----------------------------------------------
# data <- read_biblio(
#   "my_data.csv",
#   id       = "doc_id",
#   authors  = "Authors",
#   keywords = "Keywords",
#   sep      = ";"
# )

## ----generic-demo-------------------------------------------------------------
f <- system.file("extdata", "openalex_works.csv", package = "bibnets")
generic <- read_biblio(
  f,
  id       = "id",
  authors  = "authorships.author.display_name",
  keywords = "primary_topic.display_name",
  sep      = "|"
)
generic$authors[[1]]
generic$keywords[[1]]

## ----custom-columns-----------------------------------------------------------
papers <- data.frame(
  id            = 1:4,
  `Author Names`= c("Smith J, Doe A, Lee K", "Smith J, Lee K",
                    "Doe A, Lee K", "Smith J, Doe A"),
  Tags          = c("ml, ai", "ml, nlp", "ai, nlp", "ml, ai"),
  check.names   = FALSE,
  stringsAsFactors = FALSE
)

# Point the builder at the column and give it the delimiter — no renaming.
author_network(papers, authors = "Author Names", sep = ",")
keyword_network(papers, keywords = "Tags", sep = ",")

## ----custom-id----------------------------------------------------------------
papers2 <- data.frame(
  paper_id = c("P1", "P2", "P3"),
  authors  = c("Alice, Bob", "Alice, Carol", "Bob, Carol"),
  stringsAsFactors = FALSE
)
author_network(papers2, authors = "authors", sep = ",", id = "paper_id")

## ----custom-sep---------------------------------------------------------------
bib <- data.frame(
  id      = 1:3,
  creators = c("Alice and Bob", "Alice and Carol", "Bob and Carol"),
  stringsAsFactors = FALSE
)
author_network(bib, authors = "creators", sep = " and ")

## ----references-sep-----------------------------------------------------------
d <- data.frame(
  id         = c("P1", "P2", "P3"),
  auth       = c("Alice, Bob", "Alice, Carol", "Bob, Carol"),
  references = c("R1, R2", "R1, R3", "R2, R3"),
  stringsAsFactors = FALSE
)
author_network(d, "coupling", authors = "auth", sep = ",",
               references_sep = ",")

## ----strip-quotes-------------------------------------------------------------
q <- data.frame(
  id      = 1:3,
  authors = c('"Alice"; "Bob"', '"Alice"; "Carol"', '"Bob"; "Carol"'),
  stringsAsFactors = FALSE
)
author_network(q)                       # quotes stripped -> ALICE, BOB, CAROL

## ----wrong-sep, warning = TRUE------------------------------------------------
bad <- data.frame(
  id      = 1:3,
  authors = c("Smith J| Doe A", "Smith J| Lee K", "Doe A| Lee K"),
  stringsAsFactors = FALSE
)
invisible(author_network(bad))          # warns: values contain "|"

## ----read-biblio-signature, eval = FALSE--------------------------------------
# data <- read_biblio("export.csv")          # auto-detect format
# data <- read_biblio("scopus_dir/")         # entire directory, rbind'd
# data <- read_biblio(c("a.csv", "b.csv"))   # multiple files, rbind'd
# data <- read_biblio("file.csv", format = "scopus")   # force a format

## ----scopus-call, eval = FALSE------------------------------------------------
# sc <- read_scopus("scopus.csv")

## ----wos-call, eval = FALSE---------------------------------------------------
# wos1 <- read_wos("savedrecs.txt")                       # plaintext (default)
# wos2 <- read_wos("savedrecs.tsv", format = "tab")       # tab-delimited

## ----dimensions-call, eval = FALSE--------------------------------------------
# dm <- read_dimensions("dimensions_export.csv")

## ----lens-call, eval = FALSE--------------------------------------------------
# ln <- read_lens("lens_export.csv")

## ----bibtex-ris-call, eval = FALSE--------------------------------------------
# bt <- read_bibtex("library.bib")
# ri <- read_ris("savedrecs.ris")

## ----crossref-call, eval = FALSE----------------------------------------------
# library(rcrossref)
# raw  <- cr_works(query = "graph neural networks", limit = 100)
# data <- read_crossref(raw$data)

## ----openalex-csv-demo--------------------------------------------------------
f <- system.file("extdata", "openalex_works.csv", package = "bibnets")
oa <- read_openalex_csv(f)
str(oa, max.level = 1)
head(oa[, c("id", "title", "year", "journal", "type")], 5)

## ----openalex-csv-lists-------------------------------------------------------
oa$authors[[1]]
oa$affiliations[[1]]
oa$countries[[1]]

## ----openalex-csv-networks----------------------------------------------------
co <- country_network(oa, counting = "fractional")
head(co, 5)

## ----openalex-fetch, eval = FALSE---------------------------------------------
# library(openalexR)
# raw  <- oa_fetch(entity = "works", search = "learning analytics", per_page = 200)
# data <- read_openalex(raw)

## ----manual-build-------------------------------------------------------------
df <- data.frame(
  id    = c("p1", "p2", "p3"),
  title = c("Paper A", "Paper B", "Paper C"),
  year  = c(2020L, 2021L, 2022L),
  stringsAsFactors = FALSE
)
df$authors <- list(
  c("ALICE", "BOB"),
  c("BOB", "CAROL"),
  c("ALICE", "CAROL", "DAVE")
)
df$references <- list(
  c("R1", "R2"),
  c("R1", "R3"),
  c("R2", "R3", "R4")
)
df$keywords <- list(
  c("graph", "network"),
  c("network", "embedding"),
  c("graph", "embedding", "neural")
)

author_network(df, "collaboration")
keyword_network(df)
reference_network(df)

## ----split-field-demo---------------------------------------------------------
split_field(c("Alice; Bob; Carol", "Dave; Eve"))
split_field(c("a|b|c", "d|e"), sep = "|")

## ----combine-sources----------------------------------------------------------
common <- c("id", "title", "year", "journal", "doi", "cited_by_count",
            "abstract", "type", "authors", "references", "keywords")

data(biblio_data)
b1 <- biblio_data
b2 <- biblio_data
b2$id <- paste0(b2$id, "_dup")

cols <- intersect(common, names(b1))
combined <- rbind(b1[, cols], b2[, cols])
nrow(combined)

## ----sanity-check-------------------------------------------------------------
data(scopus_quantum_cloud)
sc <- scopus_quantum_cloud

range(lengths(sc$authors))
range(lengths(sc$references))
range(lengths(sc$keywords))

head(sort(table(sc$journal), decreasing = TRUE), 5)
range(sc$year, na.rm = TRUE)
table(sc$type)