IPBES_TCA_Ch1_evidence_causes
@@ -3189,8 +3189,8 @@The BuidNo is automatically increased by one each time the report is rendered. It is used to indicate different renderings when the version stays the same.
#|
count$concepts_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(concepts_1_st),
+ title_and_abstract.search = compact(paste0("(", nature_st, ") AND (", concepts_1_st, ")")),
count_only = TRUE,
output = "list",
verbose = TRUE
)$countAssessing the broader biodiversity concepts by searching for concepts_1 corpus AND concepts_2 corpus
#|
-count$concepts_1_2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", concepts_1_st, ") AND (", concepts_2_st, ")")),
+count$concepts_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", nature_st, ") AND (", concepts_2_st, ")")),
count_only = TRUE,
output = "list",
verbose = TRUE
)$countTo be discussed
-Here the term narrowed_tca is used to narrow down the TCA corpus.
+Assessing the broader biodiversity concepts by searching for concepts_1 corpus AND concepts_2 corpus
#|
-count$narrowed_tca <- openalexR::oa_fetch(
- title_and_abstract.search = compact(narrowed_tca_st),
+count$concepts_1_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", nature_st, ") AND (", concepts_1_st, ") AND (", concepts_2_st, ")")),
count_only = TRUE,
output = "list",
verbose = TRUE
-)$count
-
-count$narrowed_tca_tca <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", tca_corpus_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-##########
-
-set.seed(13)
-data <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", tca_corpus_st, ")")),
- options = list(
- select = c(
- "id",
- "doi",
- "authorships",
- "publication_year",
- "display_name",
- "abstract_inverted_index"
- ),
- sample = 250,
- seed = 13
- ),
- count_only = FALSE,
- output = "tibble",
- verbose = TRUE
-) |> dplyr::mutate(
- ab = substr(ab, 1, 5000)
-)
-
-data$author_abbr <- IPBES.R::abbreviate_authors(data)
-
-data |>
- dplyr::select(
- id,
- doi,
- author = author_abbr,
- title = display_name,
- abstract = ab
- ) |>
- writexl::write_xlsx(path = file.path("data", "samples_4.2_narrowed_tca.xlsx"))
-rm(data)
-
-##########
-
-
-count$narrowed_tca_nature <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", nature_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$countHere the terms about criteria are used: - criteria_1 - criteria_2 - criteria_3 - criteria_4
+To be discussed
+Here the term narrowed_tca is used to narrow down the TCA corpus.
#|
-count$narrowed_tca_c1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_1_st, ")")),
+count$narrowed_tca <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(narrowed_tca_st),
count_only = TRUE,
output = "list",
verbose = TRUE
)$count
-##########
-
-set.seed(13)
-data <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_1_st, ")")),
- options = list(
- select = c(
- "id",
- "doi",
- "authorships",
- "publication_year",
- "display_name",
- "abstract_inverted_index"
- ),
- sample = 250,
- seed = 13
- ),
- count_only = FALSE,
- output = "tibble",
- verbose = TRUE
-) |> dplyr::mutate(
- ab = substr(ab, 1, 5000)
-)
-
-data$author_abbr <- IPBES.R::abbreviate_authors(data)
-
-data |>
- dplyr::select(
- id,
- doi,
- author = author_abbr,
- title = display_name,
- abstract = ab
- ) |>
- writexl::write_xlsx(path = file.path("data", "samples_5.1_narrowed_criteria1.xlsx"))
-rm(data)
-
-##########
-
-count$narrowed_tca_c2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_2_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
+count$narrowed_tca_tca <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", tca_corpus_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+##########
+
+set.seed(13)
+data <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", tca_corpus_st, ")")),
+ options = list(
+ select = c(
+ "id",
+ "doi",
+ "authorships",
+ "publication_year",
+ "display_name",
+ "abstract_inverted_index"
+ ),
+ sample = 250,
+ seed = 13
+ ),
+ count_only = FALSE,
+ output = "tibble",
+ verbose = TRUE
+) |> dplyr::mutate(
+ ab = substr(ab, 1, 5000)
+)
+
+data$author_abbr <- IPBES.R::abbreviate_authors(data)
+
+data |>
+ dplyr::select(
+ id,
+ doi,
+ author = author_abbr,
+ title = display_name,
+ abstract = ab
+ ) |>
+ writexl::write_xlsx(path = file.path("data", "samples_4.2_narrowed_tca.xlsx"))
+rm(data)
+
+##########
-##########
-
-set.seed(13)
-data <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_2_st, ")")),
- options = list(
- select = c(
- "id",
- "doi",
- "authorships",
- "publication_year",
- "display_name",
- "abstract_inverted_index"
- ),
- sample = 250,
- seed = 13
- ),
- count_only = FALSE,
- output = "tibble",
- verbose = TRUE
-) |> dplyr::mutate(
- ab = substr(ab, 1, 5000)
-)
-
-data$author_abbr <- IPBES.R::abbreviate_authors(data)
-
-data |>
- dplyr::select(
- id,
- doi,
- author = author_abbr,
- title = display_name,
- abstract = ab
- ) |>
- writexl::write_xlsx(path = file.path("data", "samples_5.2_narrowed_criteria2.xlsx"))
-rm(data)
-
-##########
-
-count$narrowed_tca_c3 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_3_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-##########
-
-set.seed(13)
-data <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_3_st, ")")),
- options = list(
- select = c(
- "id",
- "doi",
- "authorships",
- "publication_year",
- "display_name",
- "abstract_inverted_index"
- ),
- sample = 250,
- seed = 13
- ),
- count_only = FALSE,
- output = "tibble",
- verbose = TRUE
-) |> dplyr::mutate(
- ab = substr(ab, 1, 5000)
-)
-
-data$author_abbr <- IPBES.R::abbreviate_authors(data)
-
-data |>
- dplyr::select(
- id,
- doi,
- author = author_abbr,
- title = display_name,
- abstract = ab
- ) |>
- writexl::write_xlsx(path = file.path("data", "samples_5.3_narrowed_criteria3.xlsx"))
-rm(data)
-
-##########
-
-count$narrowed_tca_c4 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_4_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-##########
-
-set.seed(13)
-data <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_4_st, ")")),
- options = list(
- select = c(
- "id",
- "doi",
- "authorships",
- "publication_year",
- "display_name",
- "abstract_inverted_index"
- ),
- sample = 250,
- seed = 13
- ),
- count_only = FALSE,
- output = "tibble",
- verbose = TRUE
-) |> dplyr::mutate(
- ab = substr(ab, 1, 5000)
-)
-
-data$author_abbr <- IPBES.R::abbreviate_authors(data)
-
-data |>
- dplyr::select(
- id,
- doi,
- author = author_abbr,
- title = display_name,
- abstract = ab
- ) |>
- writexl::write_xlsx(path = file.path("data", "samples_5.4_narrowed_criteria4.xlsx"))
-rm(data)
-
-##########Here the terms about criteria are used: - criteria_1 - criteria_2 - criteria_3 - criteria_4
+#|
+
+count$narrowed_tca_c1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_1_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+##########
+
+set.seed(13)
+data <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_1_st, ")")),
+ options = list(
+ select = c(
+ "id",
+ "doi",
+ "authorships",
+ "publication_year",
+ "display_name",
+ "abstract_inverted_index"
+ ),
+ sample = 250,
+ seed = 13
+ ),
+ count_only = FALSE,
+ output = "tibble",
+ verbose = TRUE
+) |> dplyr::mutate(
+ ab = substr(ab, 1, 5000)
+)
+
+data$author_abbr <- IPBES.R::abbreviate_authors(data)
+
+data |>
+ dplyr::select(
+ id,
+ doi,
+ author = author_abbr,
+ title = display_name,
+ abstract = ab
+ ) |>
+ writexl::write_xlsx(path = file.path("data", "samples_5.1_narrowed_criteria1.xlsx"))
+rm(data)
+
+##########
+
+count$narrowed_tca_c2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_2_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+##########
+
+set.seed(13)
+data <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_2_st, ")")),
+ options = list(
+ select = c(
+ "id",
+ "doi",
+ "authorships",
+ "publication_year",
+ "display_name",
+ "abstract_inverted_index"
+ ),
+ sample = 250,
+ seed = 13
+ ),
+ count_only = FALSE,
+ output = "tibble",
+ verbose = TRUE
+) |> dplyr::mutate(
+ ab = substr(ab, 1, 5000)
+)
+
+data$author_abbr <- IPBES.R::abbreviate_authors(data)
+
+data |>
+ dplyr::select(
+ id,
+ doi,
+ author = author_abbr,
+ title = display_name,
+ abstract = ab
+ ) |>
+ writexl::write_xlsx(path = file.path("data", "samples_5.2_narrowed_criteria2.xlsx"))
+rm(data)
+
+##########
+
+count$narrowed_tca_c3 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_3_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+##########
+
+set.seed(13)
+data <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_3_st, ")")),
+ options = list(
+ select = c(
+ "id",
+ "doi",
+ "authorships",
+ "publication_year",
+ "display_name",
+ "abstract_inverted_index"
+ ),
+ sample = 250,
+ seed = 13
+ ),
+ count_only = FALSE,
+ output = "tibble",
+ verbose = TRUE
+) |> dplyr::mutate(
+ ab = substr(ab, 1, 5000)
+)
+
+data$author_abbr <- IPBES.R::abbreviate_authors(data)
+
+data |>
+ dplyr::select(
+ id,
+ doi,
+ author = author_abbr,
+ title = display_name,
+ abstract = ab
+ ) |>
+ writexl::write_xlsx(path = file.path("data", "samples_5.3_narrowed_criteria3.xlsx"))
+rm(data)
+
+##########
+
+count$narrowed_tca_c4 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_4_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+##########
+
+set.seed(13)
+data <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", narrowed_tca_st, ") AND (", criteria_4_st, ")")),
+ options = list(
+ select = c(
+ "id",
+ "doi",
+ "authorships",
+ "publication_year",
+ "display_name",
+ "abstract_inverted_index"
+ ),
+ sample = 250,
+ seed = 13
+ ),
+ count_only = FALSE,
+ output = "tibble",
+ verbose = TRUE
+) |> dplyr::mutate(
+ ab = substr(ab, 1, 5000)
+)
+
+data$author_abbr <- IPBES.R::abbreviate_authors(data)
+
+data |>
+ dplyr::select(
+ id,
+ doi,
+ author = author_abbr,
+ title = display_name,
+ abstract = ab
+ ) |>
+ writexl::write_xlsx(path = file.path("data", "samples_5.4_narrowed_criteria4.xlsx"))
+rm(data)
+
+###########|
-
-
-count$st_1_nat <- openalexR::oa_fetch(
- title_and_abstract.search = st_1_nat,
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-count$st_2_tca <- openalexR::oa_fetch(
- title_and_abstract.search = st_2_tca,
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-count$st_3_nat <- openalexR::oa_fetch(
- title_and_abstract.search = st_3_nat,
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-
-
-count$biodiv_loss_nat_s_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_1_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_s_2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_2_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_s_3 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_3_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_s_4 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_4_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_s_5 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_5_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-
-
-count$biodiv_loss_narrowed_s_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_1_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_narrowed_s_2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_2_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_narrowed_s_3 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_3_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_narrowed_s_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_4_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_narrowed_s_5 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_5_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-
-
-count$biodiv_loss_nat_narrowed_s_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_1_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_narrowed_s_2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_2_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_narrowed_s_3 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_3_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_narrowed_s_4 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_4_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count
-count$biodiv_loss_nat_narrowed_s_5 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_5_st, ")")),
- count_only = TRUE,
- output = "list",
- verbose = TRUE
-)$count#|
+
+
+count$st_1_nat <- openalexR::oa_fetch(
+ title_and_abstract.search = st_1_nat,
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+count$st_2_tca <- openalexR::oa_fetch(
+ title_and_abstract.search = st_2_tca,
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+count$st_3_nat <- openalexR::oa_fetch(
+ title_and_abstract.search = st_3_nat,
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+
+
+count$biodiv_loss_nat_s_1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_1_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_s_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_2_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_s_3 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_3_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_s_4 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_4_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_s_5 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_5_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+
+
+count$biodiv_loss_narrowed_s_1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_1_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_narrowed_s_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_2_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_narrowed_s_3 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_3_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_narrowed_s_1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_4_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_narrowed_s_5 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_5_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+
+
+count$biodiv_loss_nat_narrowed_s_1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_1_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_narrowed_s_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_2_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_narrowed_s_3 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_3_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_narrowed_s_4 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_4_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count
+count$biodiv_loss_nat_narrowed_s_5 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_5_st, ")")),
+ count_only = TRUE,
+ output = "list",
+ verbose = TRUE
+)$count#|
-
-if (!file.exists(params$fn_corpus_6)) {
- corpus_6$st_1_nat <- openalexR::oa_fetch(
- title_and_abstract.search = st_1_nat,
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
-
- corpus_6$st_2_tca <- openalexR::oa_fetch(
- title_and_abstract.search = st_2_tca,
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
-
- corpus_6$st_3_nat <- openalexR::oa_fetch(
- title_and_abstract.search = st_3_nat,
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
-
- corpus_6$biodiv_loss_nat_s_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_1_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_s_2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_2_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_s_3 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_3_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_s_4 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_4_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_s_5 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_5_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
-
- corpus_6$biodiv_loss_narrowed_s_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_1_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_narrowed_s_2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_2_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_narrowed_s_3 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_3_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_narrowed_s_4 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_4_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_narrowed_s_5 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_5_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
-
- corpus_6$biodiv_loss_nat_narrowed_s_1 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_1_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_narrowed_s_2 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_2_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_narrowed_s_3 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_3_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_narrowed_s_4 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_4_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
- corpus_6$biodiv_loss_nat_narrowed_s_5 <- openalexR::oa_fetch(
- title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_5_st, ")")),
- output = "dataframe",
- verbose = TRUE
- ) |>
- dplyr::distinct(id, .keep_all = TRUE)
-
- saveRDS(corpus_6, file = params$fn_corpus_6)
-}#|
+
+if (!file.exists(params$fn_corpus_6)) {
+ corpus_6$st_1_nat <- openalexR::oa_fetch(
+ title_and_abstract.search = st_1_nat,
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+
+ corpus_6$st_2_tca <- openalexR::oa_fetch(
+ title_and_abstract.search = st_2_tca,
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+
+ corpus_6$st_3_nat <- openalexR::oa_fetch(
+ title_and_abstract.search = st_3_nat,
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+
+ corpus_6$biodiv_loss_nat_s_1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_1_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_s_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_2_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_s_3 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_3_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_s_4 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_4_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_s_5 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_1_nat, ") AND (", biodiv_loss_3_5_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+
+ corpus_6$biodiv_loss_narrowed_s_1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_1_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_narrowed_s_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_2_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_narrowed_s_3 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_3_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_narrowed_s_4 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_4_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_narrowed_s_5 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_2_tca, ") AND (", biodiv_loss_3_5_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+
+ corpus_6$biodiv_loss_nat_narrowed_s_1 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_1_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_narrowed_s_2 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_2_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_narrowed_s_3 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_3_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_narrowed_s_4 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_4_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+ corpus_6$biodiv_loss_nat_narrowed_s_5 <- openalexR::oa_fetch(
+ title_and_abstract.search = compact(paste0("(", st_3_nat, ") AND (", biodiv_loss_3_5_st, ")")),
+ output = "dataframe",
+ verbose = TRUE
+ ) |>
+ dplyr::distinct(id, .keep_all = TRUE)
+
+ saveRDS(corpus_6, file = params$fn_corpus_6)
+}#|
-
-saveRDS(count, params$fn_count)#|
+
+saveRDS(count, params$fn_count)if (!file.exists(params$fn_corpus_6_xlsx)) {
- corpus_6 <- readRDS(params$fn_corpus_6)[-1]
-
- for (nm in names(corpus_6)) {
- corpus_6[[nm]] <- corpus_6[[nm]] |>
- dplyr::select(
- id,
- doi,
- title = display_name,
- abstract = ab,
- author,
- publication_year
- ) |>
- dplyr::mutate(
- abstract = substr(abstract, 1, 5000),
- set = nm
- )
- corpus_6[[nm]]$author_abbr <- IPBES.R::abbreviate_authors(corpus_6[[nm]])
- corpus_6[[nm]] <- corpus_6[[nm]] |>
- dplyr::select(
- id,
- doi,
- author_abbr,
- title,
- abstract,
- set
- )
- }
-
- all <- do.call(rbind, corpus_6) |>
- dplyr::mutate(
- set = NULL
- ) |>
- dplyr::distinct(
- id,
- .keep_all = TRUE
- )
-
- for (nm in names(corpus_6)) {
- corpus_6[[nm]] <- corpus_6[[nm]] |>
- dplyr::select(
- id
- ) |>
- dplyr::mutate(
- set = "X"
- )
- names(corpus_6[[nm]])[2] <- nm
- }
-
- all <- all |>
- dplyr::full_join(
- corpus_6$st_1_nat,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$st_2_tca,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$st_3_nat,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_s_1,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_s_2,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_s_3,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_s_4,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_s_5,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_narrowed_s_1,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_narrowed_s_2,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_narrowed_s_3,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_narrowed_s_4,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_narrowed_s_5,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_narrowed_s_1,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_narrowed_s_2,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_narrowed_s_3,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_narrowed_s_4,
- by = "id"
- ) |>
- dplyr::full_join(
- corpus_6$biodiv_loss_nat_narrowed_s_5,
- by = "id"
- )
-
- names(all)[-(1:5)] <- c(
- "6.1", "6.2", "6.3",
- "6.1.1", "6.1.2", "6.1.3", "6.1.4", "6.1.5",
- "6.2.1", "6.2.2", "6.2.3", "6.2.4", "6.2.5",
- "6.3.1", "6.3.2", "6.3.3", "6.3.4", "6.3.5"
- )
-
- writexl::write_xlsx(all, path = params$fn_corpus_6_xlsx)
- rm(all, corpus_6)
-}if (!file.exists(params$fn_corpus_6_xlsx)) {
+ corpus_6 <- readRDS(params$fn_corpus_6)[-1]
+
+ for (nm in names(corpus_6)) {
+ corpus_6[[nm]] <- corpus_6[[nm]] |>
+ dplyr::select(
+ id,
+ doi,
+ title = display_name,
+ abstract = ab,
+ author,
+ publication_year
+ ) |>
+ dplyr::mutate(
+ abstract = substr(abstract, 1, 5000),
+ set = nm
+ )
+ corpus_6[[nm]]$author_abbr <- IPBES.R::abbreviate_authors(corpus_6[[nm]])
+ corpus_6[[nm]] <- corpus_6[[nm]] |>
+ dplyr::select(
+ id,
+ doi,
+ author_abbr,
+ title,
+ abstract,
+ set
+ )
+ }
+
+ all <- do.call(rbind, corpus_6) |>
+ dplyr::mutate(
+ set = NULL
+ ) |>
+ dplyr::distinct(
+ id,
+ .keep_all = TRUE
+ )
+
+ for (nm in names(corpus_6)) {
+ corpus_6[[nm]] <- corpus_6[[nm]] |>
+ dplyr::select(
+ id
+ ) |>
+ dplyr::mutate(
+ set = "X"
+ )
+ names(corpus_6[[nm]])[2] <- nm
+ }
+
+ all <- all |>
+ dplyr::full_join(
+ corpus_6$st_1_nat,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$st_2_tca,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$st_3_nat,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_s_1,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_s_2,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_s_3,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_s_4,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_s_5,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_narrowed_s_1,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_narrowed_s_2,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_narrowed_s_3,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_narrowed_s_4,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_narrowed_s_5,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_narrowed_s_1,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_narrowed_s_2,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_narrowed_s_3,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_narrowed_s_4,
+ by = "id"
+ ) |>
+ dplyr::full_join(
+ corpus_6$biodiv_loss_nat_narrowed_s_5,
+ by = "id"
+ )
+
+ names(all)[-(1:5)] <- c(
+ "6.1", "6.2", "6.3",
+ "6.1.1", "6.1.2", "6.1.3", "6.1.4", "6.1.5",
+ "6.2.1", "6.2.2", "6.2.3", "6.2.4", "6.2.5",
+ "6.3.1", "6.3.2", "6.3.3", "6.3.4", "6.3.5"
+ )
+
+ writexl::write_xlsx(all, path = params$fn_corpus_6_xlsx)
+ rm(all, corpus_6)
+}#|
-
-basename <- file.path("figures", "Ch1_evidence_causes")
-
-nf <- list.files(
- path = dirname(basename),
- pattern = basename(basename)
-) |>
- length()
-
-if (nf < 3) {
- puml <- readLines(file.path("input", "Ch1_evidence_causes.plantuml"))
-
- ## 0.[1:2]]
- puml <- gsub(
- pattern = "%$%0.1%$%",
- replacement = paste0("**n = ", count$nature_corpus |> format(big.mark = ","), "**\\n---\\nnature_corpus"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%0.2%$%",
- replacement = paste0("**n = ", count$tca_corpus |> format(big.mark = ","), "**\\n---\\ntca_corpus"),
- x = puml,
- fixed = TRUE
- )
-
- ## 1
- puml <- gsub(
- pattern = "%$%1%$%",
- replacement = paste0("**n = ", count$ethical |> format(big.mark = ","), "**\\n---\\nethical"),
- x = puml,
- fixed = TRUE
- )
-
- ## 2.[1:2]
- puml <- gsub(
- pattern = "%$%2.1%$%",
- replacement = paste0("**n = ", count$concepts_1 |> format(big.mark = ","), "**\\n---\\nconcepts_1"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%2.2%$%",
- replacement = paste0("**n = ", count$concepts_1_2 |> format(big.mark = ","), "**\\n---\\nconcepts_1_2"),
- x = puml,
- fixed = TRUE
- )
-
- ## 3 OUTSTANDING
-
- ## 4.[1:2]
- puml <- gsub(
- pattern = "%$%4.1%$%",
- replacement = paste0("**n = ", count$narrowed_tca_tca |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_tca"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%4.2%$%",
- replacement = paste0("**n = ", count$narrowed_tca |> format(big.mark = ","), "**\\n---\\nnarrowed_tca"),
- x = puml,
- fixed = TRUE
- )
-
- ## 5.[1:4]
- puml <- gsub(
- pattern = "%$%5.1%$%",
- replacement = paste0("**n = ", count$narrowed_tca_c1 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c1"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%5.2%$%",
- replacement = paste0("**n = ", count$narrowed_tca_c2 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c2"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%5.3%$%",
- replacement = paste0("**n = ", count$narrowed_tca_c3 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c3"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%5.4%$%",
- replacement = paste0("**n = ", count$narrowed_tca_c4 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c4"),
- x = puml,
- fixed = TRUE
- )
-
- ## 6.[1:3]
- puml <- gsub(
- pattern = "%$%6.1%$%",
- replacement = paste0("**n = ", count$st_1_nat |> format(big.mark = ","), "**\\n---\\nst_1_nat"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.2%$%",
- replacement = paste0("**n = ", count$st_2_tca |> format(big.mark = ","), "**\\n---\\nst_2_tca"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.3%$%",
- replacement = paste0("**n = ", count$st_3_nat |> format(big.mark = ","), "**\\n---\\nst_3_nat"),
- x = puml,
- fixed = TRUE
- )
-
- ## 6.1.[1:5]
- puml <- gsub(
- pattern = "%$%6.1.1%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_s_1 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_1"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.1.2%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_s_2 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_2"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.1.3%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_s_3 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_3"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.1.4%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_s_4 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_4"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.1.5%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_s_5 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_5"),
- x = puml,
- fixed = TRUE
- )
-
- ## 6.2.[1:5]
- puml <- gsub(
- pattern = "%$%6.2.1%$%",
- replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_1 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_1"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.2.2%$%",
- replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_2 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_2"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.2.3%$%",
- replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_3 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_3"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.2.4%$%",
- replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_4 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_4"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.2.5%$%",
- replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_5 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_5"),
- x = puml,
- fixed = TRUE
- )
-
- ## 6.3.[1:5]
- puml <- gsub(
- pattern = "%$%6.3.1%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_1 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_1"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.3.2%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_2 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_2"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.3.3%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_3 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_3"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.3.4%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_4 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_4"),
- x = puml,
- fixed = TRUE
- )
- puml <- gsub(
- pattern = "%$%6.3.5%$%",
- replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_5 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_5"),
- x = puml,
- fixed = TRUE
- )
-
- puml |>
- paste(collapse = "\n") |>
- plantuml::plantuml() |>
- plantuml::get_graph(
- file = file.path("figures", "Ch1_evidence_causes.pdf")
- )
-
- puml |>
- paste(collapse = "\n") |>
- plantuml::plantuml() |>
- plantuml::get_graph(
- file = file.path("figures", "Ch1_evidence_causes.svg")
- )
-
- puml |>
- paste(collapse = "\n") |>
- plantuml::plantuml() |>
- plantuml::get_graph(
- file = file.path("figures", "Ch1_evidence_causes.png")
- )
-}#|
+
+basename <- file.path("figures", "Ch1_evidence_causes")
+
+nf <- list.files(
+ path = dirname(basename),
+ pattern = basename(basename)
+) |>
+ length()
+
+if (nf < 3) {
+ puml <- readLines(file.path("input", "Ch1_evidence_causes.plantuml"))
+
+ ## 0.[1:2]]
+ puml <- gsub(
+ pattern = "%$%0.1%$%",
+ replacement = paste0("**n = ", count$nature_corpus |> format(big.mark = ","), "**\\n---\\nnature_corpus"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%0.2%$%",
+ replacement = paste0("**n = ", count$tca_corpus |> format(big.mark = ","), "**\\n---\\ntca_corpus"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 1
+ puml <- gsub(
+ pattern = "%$%1%$%",
+ replacement = paste0("**n = ", count$ethical |> format(big.mark = ","), "**\\n---\\nethical"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 2.[1:2]
+ puml <- gsub(
+ pattern = "%$%2.1%$%",
+ replacement = paste0("**n = ", count$concepts_1 |> format(big.mark = ","), "**\\n---\\nconcepts_1"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%2.2%$%",
+ replacement = paste0("**n = ", count$concepts_2 |> format(big.mark = ","), "**\\n---\\nconcepts_2"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%2.3%$%",
+ replacement = paste0("**n = ", count$concepts_1_2 |> format(big.mark = ","), "**\\n---\\nconcepts_1_2"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 3 OUTSTANDING
+
+ ## 4.[1:2]
+ puml <- gsub(
+ pattern = "%$%4.1%$%",
+ replacement = paste0("**n = ", count$narrowed_tca_tca |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_tca"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%4.2%$%",
+ replacement = paste0("**n = ", count$narrowed_tca |> format(big.mark = ","), "**\\n---\\nnarrowed_tca"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 5.[1:4]
+ puml <- gsub(
+ pattern = "%$%5.1%$%",
+ replacement = paste0("**n = ", count$narrowed_tca_c1 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c1"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%5.2%$%",
+ replacement = paste0("**n = ", count$narrowed_tca_c2 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c2"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%5.3%$%",
+ replacement = paste0("**n = ", count$narrowed_tca_c3 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c3"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%5.4%$%",
+ replacement = paste0("**n = ", count$narrowed_tca_c4 |> format(big.mark = ","), "**\\n---\\nnarrowed_tca_c4"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 6.[1:3]
+ puml <- gsub(
+ pattern = "%$%6.1%$%",
+ replacement = paste0("**n = ", count$st_1_nat |> format(big.mark = ","), "**\\n---\\nst_1_nat"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.2%$%",
+ replacement = paste0("**n = ", count$st_2_tca |> format(big.mark = ","), "**\\n---\\nst_2_tca"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.3%$%",
+ replacement = paste0("**n = ", count$st_3_nat |> format(big.mark = ","), "**\\n---\\nst_3_nat"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 6.1.[1:5]
+ puml <- gsub(
+ pattern = "%$%6.1.1%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_s_1 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_1"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.1.2%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_s_2 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_2"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.1.3%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_s_3 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_3"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.1.4%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_s_4 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_4"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.1.5%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_s_5 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_s_5"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 6.2.[1:5]
+ puml <- gsub(
+ pattern = "%$%6.2.1%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_1 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_1"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.2.2%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_2 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_2"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.2.3%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_3 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_3"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.2.4%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_4 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_4"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.2.5%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_narrowed_s_5 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_narrowed_s_5"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ ## 6.3.[1:5]
+ puml <- gsub(
+ pattern = "%$%6.3.1%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_1 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_1"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.3.2%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_2 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_2"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.3.3%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_3 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_3"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.3.4%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_4 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_4"),
+ x = puml,
+ fixed = TRUE
+ )
+ puml <- gsub(
+ pattern = "%$%6.3.5%$%",
+ replacement = paste0("**n = ", count$biodiv_loss_nat_narrowed_s_5 |> format(big.mark = ","), "**\\n---\\nbiodiv_loss_nat_narrowed_s_5"),
+ x = puml,
+ fixed = TRUE
+ )
+
+ puml |>
+ paste(collapse = "\n") |>
+ plantuml::plantuml() |>
+ plantuml::get_graph(
+ file = file.path("figures", "Ch1_evidence_causes.pdf")
+ )
+
+ puml |>
+ paste(collapse = "\n") |>
+ plantuml::plantuml() |>
+ plantuml::get_graph(
+ file = file.path("figures", "Ch1_evidence_causes.svg")
+ )
+
+ puml |>
+ paste(collapse = "\n") |>
+ plantuml::plantuml() |>
+ plantuml::get_graph(
+ file = file.path("figures", "Ch1_evidence_causes.png")
+ )
+}[1] "figures/Ch1_evidence_causes.png"Subset of TCA Corpus
How many, what percentage, and what papers in the full TC corpus?
-The Ethics Corpus consists of 134669 works, which is 2.85% of the TCA Corpus
+The Ethics Corpus consists of 135025 works, which is 2.85% of the TCA Corpus
Subset of OpenAlex Corpus
-The Concepts 1 Corpus (talking about biodiversity in relation to conservation, management, policy, or governance ) consists of 241972 works, which is 0.93% of the Nature Corpus. Of these, 63093 (% of Nature Corpus and % of the Concept 1 Corpus) works also talk about broader than non-human species and ecosystems.
+The Concepts 1 Corpus (talking about biodiversity in relation to conservation, management, policy, or governance ) consists of 242186 works, which is 0.93% of the Nature Corpus. Of these, 63160 (% of Nature Corpus and % of the Concept 1 Corpus) works also talk about broader than non-human species and ecosystems.
Subset of TCA Corpus
-The Narrowed TFC Corpus consists of 1057955 works, which is 22.39% of the TCA Corpus.
+The Narrowed TFC Corpus consists of 1059666 works, which is 22.4% of the TCA Corpus.
Subset of Nature Corpus
-The Narrowed Nature Corpus consists of 1173555 works, which is 4.49% of the Nature Corpus.
+The Narrowed Nature Corpus consists of 1175388 works, which is 4.49% of the Nature Corpus.
283628 works in the narrowed TCA Corpus, which is 26.81% of the Narrowed TCA Corpus.
+284036 works in the narrowed TCA Corpus, which is 26.8% of the Narrowed TCA Corpus.
409871 works in the narrowed TCA Corpus, which is 38.74% of the Narrowed TCA Corpus.
+410758 works in the narrowed TCA Corpus, which is 38.76% of the Narrowed TCA Corpus.
574600 works in the narrowed TCA Corpus, which is 54.31% of the Narrowed TCA Corpus.
+575643 works in the narrowed TCA Corpus, which is 54.32% of the Narrowed TCA Corpus.
865334 works in the narrowed TCA Corpus, which is 81.79% of the Narrowed TCA Corpus.
+866756 works in the narrowed TCA Corpus, which is 81.8% of the Narrowed TCA Corpus.
Step 1 AND Step 2There are the following numbers of wotks in the Nature Corpus:
Topic 1, which is 0%Topic 2, which is 0%Topic 3, which is 0%Topic 4, which is 0%Topic 5, which is 0%Topic 3, which is 0%Topic 4, which is 0%Topic 5, which is 0%Step 1 AND Step 2There are the following numbers of wotks in the Narrowed TCA Corpus:
Topic 1, which is 0.01%Topic 1, which is 0.01%Topic 2, which is 0%Topic 3, which is 0%Topic 4, which is %Topic 5, which is 0.01%Topic 5, which is 0.01%Step 1 AND Step 2There are the following numbers of wotks in the Nature Corpus AND Narrowed TCA Corpus:
Topic 1, which is 0.01%Topic 2, which is 0%Topic 3, which is 0%Topic 4, which is 0.01%Topic 5, which is 0.01%Topic 4, which is 0.01%Topic 5, which is 0.01%