Business and Biopdiversity - Ch 4 - Snowball Pilot

Author

Rainer M Krug

Back to Readme

Setup

Code 
#|
if (!exists("params")) {
    params <- rmarkdown::yaml_front_matter("snowball.qmd")$params
}

library(IPBES.R)

library(openalexR)
Thank you for using openalexR!
To acknowledge our work, please cite the package by calling `citation("openalexR")`.
To suppress this message, add `openalexR.message = suppressed` to your .Renviron file.
Code 
library(tibble)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
Code 
library(knitr)

library(networkD3)

# list.files(
#     "R",
#     full.names = TRUE,
#     recursive = TRUE
# ) |>
#     sapply(
#         FUN <- source
#     )

kp_dois <- file.path("input", "key_papers", params$key_paper) |>
    readLines() |>
    grep(
        pattern = "doi = ",
        value = TRUE
    ) |>
    sub(
        pattern = ".*\\{",
        replacement = ""
    ) |>
    gsub(
        pattern = "\\}.$",
        replacement = ""
    )

Searches

Setup OpenAlex usage

Code 
#|

fn <- file.path("data", "key_works.rds")
if (file.exists(fn)) {
    key_works <- readRDS(fn)
} else {
    key_works <- oa_fetch(
        entity = "works",
        doi = kp_dois,
        verbose = FALSE
    )
    saveRDS(key_works, fn)
}

Do first snowball search (S1)

Code 
fn <- file.path(".", "data", "snowball_1.rds")
if (file.exists(fn)) {
    snowball_1 <- readRDS(fn)
} else {
    snowball_1 <- oa_snowball(
        identifier = openalexR:::shorten_oaid(key_works$id),
        verbose = FALSE
    )
    saveRDS(snowball_1, fn)
}

flat_snow_1 <- snowball2df(snowball_1) |>
    as_tibble()

The search resulted in 668 papers and 935 citations, including the key-papers.

Do second snowball search (S2)

For the second snowball search (S2) we use the corpus identified by S1 as the key-papers.

This needs to be enabled (eval = false needs to be changed to eval = true in the following code block) as the resulting data file is to large for github and running of the snowball search takes several hours.

Code 
fn <- file.path(".", "data", "snowball_2.rds")
if (file.exists(fn)) {
    snowball_2 <- readRDS(fn)
} else {
    snowball_2 <- oa_snowball(
        identifier = openalexR:::shorten_oaid(snowball_1$nodes$id),
        verbose = FALSE
    )
    saveRDS(snowball_2, fn)
}

flat_snow_2 <- snowball2df(snowball_1) |>
    as_tibble()

The search resulted in 167275 papers and 225735 citations, including the key-papers, i.e. papers in corpus S1.

Export S1 Corpus to Excel file

Only S1 can be exported to Excel due to the size of the S2 corpus.

Code 
#|

fn <- file.path(".", "data", "snowball_1.xlsx")
if (!file.exists(fn)) {
    to_xlsx(
        snowball = snowball_1,
        xls_filename = fn
    )
}

# fn <- file.path(".", "data", "snowball_2.xlsx")
# if (!file.exists(fn)) {
#     to_xlsx(
#         snowball = snowball_2,
#         xls_filename = fn
#     )
# }

To download Excel file click here

The column are: (the Concept columns are not that relevant at the moment)

  • id: internal id fromOpenAlex
  • author: authors of the paper
  • publication_year: publication year
  • title: title of the paper
  • doi: doi of the paper
  • no_referenced_works: number of references in the paper which are also in OpenAlex
  • cited_global: Number of times the paper has been cited
  • cited_global_per_year: standardised number of times cirted (cited_global / number of years published)
  • no_connections: number of connections in the rgaph, i.e. either cited or citing a paper in the snowball corpus
  • concepts_l0: Concept 0. level assigned by OpenAlex
  • concepts_l1: Concept 1. level assigned by OpenAlex
  • concepts_l2: Concept 2. level assigned by OpenAlex
  • concepts_l3: Concept 3. level assigned by OpenAlex
  • concepts_l4: Concept 4. level assigned by OpenAlex
  • concepts_l5: Concept 5. level assigned by OpenAlex
  • author_institute: Institute of the authors
  • institute_country: Country of the institute
  • abstract: the abstract of the paper

Graphs

Static Citation Network Graph of the S1 Corpus

Only S1 corpus of S1 can be graphed due to the size of the S2 corpus.

Code 
if ((length(list.files(path = "figures", pattern = "Snowball_1_.*", recursive = TRUE))) < 4) {
    plot_snowball(
        snowball_1,
        name = "Snowball_1"
    )
}

Right click - Download for (better quality)

Interactive Citation Network Graph of the S1 Corpus

Code 
nwg <- IPBES.R::plot_snowball_interactive(snowball_1)
saveNetwork(
    nwg, 
    file = file.path("figures", "S1_interactive_network.html"),
    selfcontained = TRUE
)
unlink(file.path("figures", "S1_interactive_network_files"), recursive = TRUE)
nwg

To open the interactive graph in a standalone window click here.

The following interactions are possible:

  • moving your mouse over a node, the title author and year of the paper is shown.
  • clicking on a node will open the paper in a new tab.
  • scrolling up and down with your scroll wheel zooms in and out
  • clicking on the canvas and move the mouse will move the network
  • clicking on a node and dragging it moves the node

Identification of references with more than one edge

This is the number of connections (connection_count) of the paper (id)

Only the corpus of S1 is shown here due to the size of the S2 corpus.

Code 
#|
mult_edge <- flat_snow_1 |>
    select(id, connection_count) |>
    filter(connection_count > 1) |>
    arrange(desc(connection_count))

links <- flat_snow_1 |>
    filter(id %in% mult_edge$id)

links |>
    select(id, display_name, publication_year, doi, connection_count) |>
    arrange(desc(connection_count)) |>
    knitr::kable()
id display_name publication_year doi connection_count
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