In KTH-Library/bibliomatrix: Provide Functionality for Bibliometric Analytics

```{css echo=FALSE} @font-face { font-family: 'Figtree'; font-style: normal; font-weight: 400; src: url('fonts/figtree-v5-latin_latin-ext-regular.woff2') format('woff2'); }

body { font-family: 'Figtree'; }

```r
# unit_codes for testing:
# 
# u1g9umtq
# u1jr9fll 
# u1ygqmuy
# u13bp6vd
# u18qe64m
# u1o2ujjd

library(flexdashboard)
library(DT)
library(readr)
library(purrr)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(bibliomatrix)
library(tidyr)
library(scales)
#library(plotly)
library(kableExtra)
library(glue)
library(extrafont)
library(kthapi)
library(DBI)
library(ktheme)
#library(tidyquant)
library(zoo)

colors_vb <- palette_kth_neo(17)

# Retrieve result tables to use in tabs and key indicators

data <- data.frame()

if (params$embed_data || !params$use_package_data) {
  con <- con_bib()
  data <- abm_data(
    con = con,
    unit_code = params$unit_code,
    pub_year = abm_config()$start_year:abm_config()$stop_year,
    analysisId = 0
    ) %>% collect()
  dbDisconnect(con)
}

if (params$use_package_data != TRUE) {
  df_diva <- abm_table1(data)
  df_diva_full <- abm_table1_full(data)
  df_cit3y <- abm_table2(data)
  df_cf <- abm_table3(data)
  df_jcf <- abm_table4(data)
  df_copub <- abm_table5(data)
  df_oa <- abm_table6(data)
  df_scop_cit <- abm_table_scop_cit(data)
  df_scop_normcit <- abm_table_scop_normcit(data)
  df_scop_snip <- abm_table_scop_snip(data)
  df_scop_copub <- abm_table_scop_copub(data)
  df_coverage <- abm_coverage(data)
  con <- con_bib()
  unit_meta <- unit_info(con = con) %>% filter(unit_code == params$unit_code)
  df_copub_countries <- abm_copub_countries(con = con,
                                            unit_level = ifelse(isTRUE(unit_meta$org_level >= 0),
                                                                unit_meta$org_level, 3),
                                            unit_code = params$unit_code,
                                            analysisId = abm_config()$analysis_id,
                                            analysis_start = abm_config()$start_year,
                                            analysis_stop = abm_config()$stop_year)
  df_copub_orgs <- abm_copub_orgs(con = con,
                                  unit_level = ifelse(isTRUE(unit_meta$org_level >= 0),
                                                      unit_meta$org_level, 3),
                                  unit_code = params$unit_code,
                                  analysisId = abm_config()$analysis_id,
                                  analysis_start = abm_config()$start_year,
                                  analysis_stop = abm_config()$stop_year)
  df_sdg_year <- abm_sdg_year(con = con,
                              data = data,
                              analysisId = abm_config()$analysis_id,
                              analysis_start = abm_config()$start_year,
                              analysis_stop = abm_config()$stop_year)
  df_sdg_table <- abm_sdg_table(con = con,
                                data = data,
                                analysisId = abm_config()$analysis_id,
                                analysis_start = abm_config()$start_year,
                                analysis_stop = abm_config()$stop_year)
  dbDisconnect(con)
} else {
  df_diva <- pluck(abm_public_kth$units, params$unit_code, "diva")
  df_diva_full <- pluck(abm_public_kth$units, params$unit_code, "diva_full")
  df_cit3y <- pluck(abm_public_kth$units, params$unit_code, "wos_cit3y")
  df_cf <- pluck(abm_public_kth$units, params$unit_code, "wos_cf")
  df_jcf <- pluck(abm_public_kth$units, params$unit_code, "wos_jcf")
  df_copub <- pluck(abm_public_kth$units, params$unit_code, "wos_copub")
  df_copub_countries <- pluck(abm_public_kth$units, params$unit_code, "wos_copub_countries")
  df_copub_orgs <- pluck(abm_public_kth$units, params$unit_code, "wos_copub_orgs")
  df_oa <- pluck(abm_public_kth$units, params$unit_code, "oa")
  df_scop_cit <- pluck(abm_public_kth$units, params$unit_code, "scop_cit")
  df_scop_normcit <- pluck(abm_public_kth$units, params$unit_code, "scop_normcit")
  df_scop_snip <- pluck(abm_public_kth$units, params$unit_code, "scop_snip")
  df_scop_copub <- pluck(abm_public_kth$units, params$unit_code, "scop_copub")
  df_coverage <- pluck(abm_public_kth$units, params$unit_code, "coverage")
  df_sdg_year <- pluck(abm_public_kth$units, params$unit_code, "scop_sdg_year")
  df_sdg_table <- pluck(abm_public_kth$units, params$unit_code, "scop_sdg_table")
  unit_meta <- filter(abm_public_kth$meta, unit_code == params$unit_code)
}

unit_level <- unit_meta %>% pull(org_level)

if(isTRUE(unit_level >= 0)){

  unit_label <- 
    abm_public_kth$meta %>% 
    filter(unit_code == params$unit_code) %>%
    pull(unit_long_en)

} else {

  unit_label <- 
    kth_displayname(user = params$unit_code, "kthid")
}

#unit_label <- paste0("Annual Bibliometric Monitoring: ", unit_label)



current_date <- format(Sys.Date(), "%Y%m%d")

abm_unit_title <- paste0("ABM: ", unit_label)

STATIC <- "https://kth-library.github.io/abm"

Overview

r unit_label

Row {data-height=240}

  yrfields <- grep("^[1-2][0-9]{3}$", names(df_diva), value = TRUE)
  firstyear <- min(yrfields)
  lastyear <- max(yrfields)

  as_range <- function(x, delim = "-") {
    y <- as.integer(x)
    paste(collapse = delim, na.omit(dplyr::case_when(
      is.finite(y) ~ y
    )))
  }

Publications in DiVA r as_range(lastyear)
(fractional counts)

abm_ui_valuebox_publications(df_diva, lastyear, colors_vb[1], unit_label)

WoS coverage r as_range(c(firstyear, lastyear))
(peer reviewed)

abm_ui_valuebox_coverage(df_coverage, colors_vb[1], "wos", unit_label)

Scopus coverage r as_range(c(firstyear, lastyear))
(peer reviewed)

abm_ui_valuebox_coverage(df_coverage, colors_vb[1], "scopus", unit_label)

has_rows <- df_cf %>% filter(!is.na(P_frac)) %>% nrow() > 0
last_interval <- ifelse(has_rows, paste((as.numeric(nth(df_cf$Publication_Year, -2))-2),nth(df_cf$Publication_Year, -2), sep = "-"), "")

Citation impact r last_interval (WoS) {.no-padding .no-mobile}

abm_ui_bullet_citations(df_cf)

Citation impact r last_interval (WoS) {.no-padding .mobile}

abm_ui_bullet_citations(df_cf)

has_rows <- df_jcf %>% filter(!is.na(P_frac)) %>% nrow() > 0
last_interval <- ifelse(has_rows, paste((as.numeric(nth(df_jcf$Publication_Year, -2))-2),nth(df_jcf$Publication_Year, -2), sep = "-"), "")

Journal impact r last_interval (WoS) {.no-padding .no-mobile}

abm_ui_bullet_journal(df_jcf)

Journal impact r last_interval (WoS) {.no-padding .mobile}

abm_ui_bullet_journal(df_jcf)

has_rows <- df_copub %>% filter(!is.na(P_full)) %>% nrow > 0
last_interval <- ifelse(has_rows, paste((as.numeric(nth(df_copub$Publication_Year, -2))-2),nth(df_copub$Publication_Year, -2), sep = "-"), "")

Co‑publishing r last_interval (WoS) {.no-padding .no-mobile}

abm_ui_waffle_copub(df_copub)

Co‑publishing r last_interval (WoS) {.no-padding .mobile}

abm_ui_waffle_copub(df_copub)

Row

Notes more...{target="_blank" style="position:relative;float:right;"}

woscov <- df_coverage %>% 
    filter(Publication_Type == "Peer reviewed") %>% 
    summarise(woscov = sum(sumwos_frac) / sum(p_frac)) %>% 
    pull(woscov)

cat("<p>Indicators in the Citation impact, Journal impact and Co&#8209;publishing tabs are based on Web of Science publications only. Corresponding Scopus indicators can be found under the Scopus tab.</p>")

if(isTRUE(unit_level >= 0)){

  #<span title='mytitle' onclick=\"window.location.href='https://KTH-Library.github.io/abm/ABM_guide.pdf';\" style='cursor: pointer;></span>'
  glue("<p><span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Overall Web of Science coverage for peer reviewed publications in this unit is ",
       "{coveragetext(woscov)}, {100*round(woscov, 3)}%.</span></p>")

} else {

  cat("<p><b>Bibliometric results for individual researchers should always be interpreted with caution. </b></br/>
      Bibliometric indicators work best at an aggregated level, and at the individual level 
      the publication profile of researchers, their collaboration networks and their subject specialization may 
      interact with e.g. the field-normalization of citation indicators. The relatively low publication volume of 
      individuals also makes indicator averages very unreliable. </p>")

}

cat("<p>Note that the coverage numbers for Web of Science and Scopus apply to publications in the KTH bibliometric database Bibmet. If a WoS-id or Scoupus-id from DiVA can not be matched in Bibmet, it does not count as covered.</p>")

Row

Background

The bibliometric indicators referred are based on publications registered in DiVA and published r abm_config()$start_year to r abm_config()$stop_year. Only publications which have been affiliated to KTH are included. This means that publications written by a researcher before she/he was employed at KTH, and that are not affiliated to KTH, will not be included in the statistics.

Statistics regarding citations and co‑publishing are based on the subset of publications in DiVA that are registered in Web of Science (Citation impact, Journal impact and Co‑publishing tabs) or Scopus (Scopus tab).

Row

Publication data

# download button for publication list
unit_file_label <- params$unit_code
if (isTRUE(unit_level == 1)){
  unit_file_label <- 
    unit_info() %>% 
    filter(unit_code == params$unit_code) %>% 
    pull(unit_short)
}

abm_ui_button_publist(
  data = data,
  is_loggedin = params$embed_data,
  unit_label = unit_label,
  unit_code = params$unit_code,
  unit_file_label = unit_file_label,
  is_authorbased = FALSE
)

# DiVA Dream Portal button
if (params$is_employee == TRUE) {
  abm_ui_button_diva()
}

Publication summary

r unit_label

Row {.tabset data-height=700}

Fractional count

Publication volume, fractionalized {.no-padding .no-mobile}

abm_ui_datatable_diva(
  df_diva,
  unit_file_label = unit_file_label,
  unit_title = abm_unit_title
)

Publication volume, fractionalized {.no-padding .mobile}

abm_ui_kable_diva(df_diva)

Full count

Publication volume, full count {.no-padding .no-mobile}

abm_ui_datatable_diva_full(
  df_diva_full, 
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Publication volume, full count {.no-padding .mobile}

abm_ui_kable_diva_full(df_diva_full)

Row

Publication volume {.no-padding .no-mobile}

if (nrow(df_diva) > 0) {
  p <- abm_graph_diva(df_diva)
  p
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Publication volume {.no-padding .mobile}

if (nrow(df_diva) > 0) {
  p
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Web of Science coverage by publication type {.no-padding .no-mobile}

if (nrow(df_diva %>% left_join(get_pubtype_order(), by = c("Publication_Type_DiVA" = "diva_publication_type")) %>% filter(WoS_coverage != 0)) > 0) {
  p <- abm_graph_wos_coverage(df_diva)
  p
} else {
  shiny::HTML("<p><i>There are no data available for this graph</i></p>")
}

Web of Science coverage by publication type {.no-padding .mobile}

if (nrow(df_diva) > 0) {
  p
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Citation impact

r unit_label

Row

Average field normalized citations (Cf)
r timegraph_header_legend()

if (df_cf %>% filter(!is.na(P_frac)) %>% nrow() > 0){
  time_graph(df_cf, indicator = "cf", ma = TRUE, weight = "P_frac", refline = 1, ylabel = "Average Cf")
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Share of publications among Top 10% cited
r timegraph_header_legend()

if (df_cf %>% filter(!is.na(P_frac)) %>% nrow() > 0){
  time_graph(df_cf, indicator = "top10_share", ma = TRUE, weight = "P_frac", refline = .1, percent = TRUE, ylabel = "Share Top 10%")
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Row {data-height=550}

Citations 3-year window, fractional counts {.no-mobile}

abm_ui_datatable_city3y(
  df_cit3y, 
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Citations 3-year window, fractional counts {.mobile}

abm_ui_kable_cit3y(df_cit3y)

Field normalized citations, fractionalized {.no-mobile}

abm_ui_datatable_cf(
  df_cf,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Field normalized citations, fractionalized {.no-padding .mobile}

abm_ui_kable_cf(df_cf)

Row

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("This table is based on Web of Science publication types Article, Proceedings paper, Review, Letter and Editorial.<br>")

if(isTRUE(unit_level >= 0)){

  years <- df_cit3y %>% filter(substr(Publication_Year_ch, 1, 1) == "2") %>% pull(Publication_Year_ch) %>% as.integer()
  mincov <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(woscov_frac) %>% min()
  minpubs <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(sumwos_full) %>% min()

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Web of Science coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication types Article, peer review and Conference paper, peer review)<br></span>"))

  if(minpubs < 50)
    cat("<b>Indicators based on < 50 publications are considered unreliable</b>")

} else {

  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")

}

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("This table is based on Web of Science publication types Article and Review.<br>")

if(isTRUE(unit_level >= 0)){

  woscov_cf <- df_coverage %>%
    filter(Publication_Type == "Article, peer review", Publication_Year %in% df_cf$Publication_Year)

  mincov <- min(woscov_cf$woscov_frac)
  minpubs <- min(woscov_cf$sumwos_full)

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Web of Science coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication type Article, peer review)<br></span>"))

  if(minpubs < 50)
    cat("<b>Indicators based on < 50 publications are considered unreliable</b>")

} else {

  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")

}

Journal impact

r unit_label

Row

Field normalized journal impact (JCf)
r timegraph_header_legend()

if(df_jcf %>% filter(!is.na(P_frac)) %>% nrow() > 0) {
  time_graph(df_jcf, indicator = "jcf", ma = TRUE, weight = "P_frac", refline = 1, "Average Journal Cf")
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Share of publications in Top 20% journals
r timegraph_header_legend()

if(df_cf %>% filter(!is.na(P_frac)) %>% nrow() > 0) {
  time_graph(df_jcf, indicator = "top20_share", ma = TRUE, weight = "P_frac", refline = .2, "Share Journal Top 20%", percent = TRUE)
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Row {data-height=550}

Journal impact, fractionalized {.no-mobile}

abm_ui_datatable_jcf(
  df_jcf,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Journal impact, fractionalized {.no-padding .mobile}

abm_ui_kable_jcf(df_jcf)

Row

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("This table is based on Web of Science publication types Article and Review.<br>")

if(isTRUE(unit_level >= 0)){
  woscov_jcf <- df_coverage %>%
    filter(Publication_Type == "Article, peer review", Publication_Year %in% df_jcf$Publication_Year)

  mincov <- min(woscov_jcf$woscov_frac)
  minpubs <- min(woscov_jcf$sumwos_full)

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Web of Science coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication type Article, peer review)<br></span>"))

  if(minpubs < 50)
    cat("<b>Indicators based on < 50 publications are considered unreliable</b>")

} else {

  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")

}

Co‑publishing {data-orientation=rows}

r unit_label

Row {.tabset data-height=1500}

Summary

##### **International and Swedish non-university co‑publication** wzxhzdk:37

International and Swedish non-university co‑publications (full counts) {.no-mobile}

abm_ui_datatable_copub(
  df_copub,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

International and Swedish non-university co‑publications (full counts) {.no-padding .mobile}

abm_ui_kable_copub(df_copub)

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("<hr>")
cat("This table is based on Web of Science publication types Article and Review.<br>")

if(isTRUE(unit_level >= 0)){
  woscov_copub <- df_coverage %>%
    filter(Publication_Type == "Article, peer review", Publication_Year %in% df_copub$Publication_Year)

  mincov <- min(woscov_copub$woscov_full)
  minpubs <- min(df_copub$P_full)

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Web of Science coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication type Article, peer review)<br></span>"))

  if(minpubs < 50)
    cat("<b>Indicators based on < 50 publications are considered unreliable</b>")

} else {

  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")

}
cat("<hr>")

Countries and organizations

Co‑publication countries {.no-mobile}

abm_ui_datatable_copub_countries(
  df_copub_countries,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
) 

Co‑publication countries {.no-padding .mobile}

abm_ui_kable_copub_countries(df_copub_countries) 

Co‑publication organizations {.no-mobile}

abm_ui_datatable_copub_orgs(
  df_copub_orgs,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
) 

Co‑publication countries {.no-padding .mobile}

abm_ui_kable_copub_orgs(df_copub_orgs) 

Open Access

r unit_label

Row

Overview of Open Access publication types

if (df_oa %>% filter(!is.na(P_tot)) %>% nrow() > 0) {
  abm_graph_oadata_piechart(df_oa, type = "ggplot")
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Open Access publication types across time

if (df_oa %>% filter(!is.na(P_tot)) %>% nrow() > 0) {
  abm_graph_oadata_stackedarea(df_oa)
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Row {data-height=550}

Open Access publications, full counts {.no-mobile}

abm_ui_datatable_oa(
  df_oa,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Open Access publications, full counts {.no-padding .mobile}

abm_ui_kable_oa(df_oa)

Row

Notes more...{target="_blank" style="position:relative;float:right;"}

The Open Access type of the publications was fetched from the Unpaywall REST API. The method to determine the OA type is presented here.

In summary, the different OA types can be described as follows:

• Diamond: The full text has been found on a publisher website, there is no article publication cost.

• Gold: The full text has been found on a publisher website, in a journal that is fully OA.

• Hybrid: The full text has been found on a publisher website, in a journal that is not fully OA.

• Green: The full text has been found in an institutional repository, such as DiVA.

N.B.: This table is based on peer-reviewed publications for which Unpaywall could determine the OA type (necessary condition: have a DOI number). Unpaywall's method is empirical and does not strictly equate to copyright license. As a consequence, the OA status of a given publication may vary with time --- for example turning from "Not OA" to "Green" when a full text is added to DiVA.

Scopus

r unit_label

Row {.tabset data-height=1800}

Citation Impact

##### **Average field weighted citation impact (FWCI)**
`r timegraph_header_legend()` wzxhzdk:49

##### **Share of publications among Top 10% cited**
`r timegraph_header_legend()` wzxhzdk:50

Citations, open window {.no-mobile}

abm_ui_datatable_scop_cit(
  df_scop_cit,
  unit_file_label = unit_file_label,
  unit_title = abm_unit_title
)

Citations, open window {.mobile}

abm_ui_kable_scop_cit(df_scop_cit)

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("<hr>")
cat("This table is based on Scopus publication types Article, Conference Paper, Review, Letter and Editorial.<br>")
if(isTRUE(unit_level >= 0)){
  years <- df_scop_cit %>% filter(substr(Publication_Year_ch, 1, 1) == "2") %>% pull(Publication_Year_ch) %>% as.integer()
  mincov <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(scopcov_frac) %>% min()
  minpubs <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(sumscop_full) %>% min()
  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Scopus coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication types Article, peer review and Conference paper, peer review)<br></span>"))
  if(minpubs < 50)
    cat("<b>Indicators based on < 50 publications are considered unreliable</b>")
} else {
  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")
}
cat("<hr>")

Field weighted citation impact (FWCI) {.no-mobile}

abm_ui_datatable_scop_normcit(
  df_scop_normcit,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Field weighted citation impact (FWCI) {.mobile}

abm_ui_kable_scop_normcit(df_scop_normcit)

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("<hr>")
cat("This table is based on Scopus publication types Article, Review and Conference Paper.<br>")
if(isTRUE(unit_level >= 0)){
  scopcov_normcit <- df_coverage %>%
    filter(Publication_Type == "Peer reviewed", Publication_Year %in% df_scop_normcit$Publication_Year)

  mincov <- min(scopcov_normcit$scopcov_frac)
  minpubs <- min(scopcov_normcit$sumscop_full)

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Scopus coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication types Article, peer review and Conference paper, peer review)<br></span>"))
  if(minpubs < 50)
    cat("<b>Indicators based on < 50 publications are considered unreliable</b>")
} else {
  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")
}
cat("<hr>")

Journal Impact

##### **Field normalized journal impact (SNIP)**
`r timegraph_header_legend()` wzxhzdk:57

##### **Share of publications in Top 20% journals**
`r timegraph_header_legend()` wzxhzdk:58

##### **Journal impact, fractionalized** {.no-mobile} wzxhzdk:59 ##### **Journal impact, fractionalized** {.mobile} wzxhzdk:60 ##### **Notes** [ more...](`r STATIC`/ABM_guide.html#scopus){target="_blank" style="position:relative;float:right;"} wzxhzdk:61

Co‑publishing

##### **International and corporate co‑publication** wzxhzdk:62

International and corporate co‑publications (full counts) {.no-mobile}

abm_ui_datatable_scop_copub(
  df_scop_copub,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

International and corporate co‑publications (full counts) {.mobile}

abm_ui_kable_scop_copub(df_scop_copub)

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("<hr>")
cat("This table is based on Scopus publication types Article, Review and Conference Paper.<br>")

if(isTRUE(unit_level >= 0)){
  scopcov_copub <- df_coverage %>%
    filter(Publication_Type == "Peer reviewed", Publication_Year %in% df_scop_copub$Publication_Year)

  mincov <- min(scopcov_copub$scopcov_full)
  minpubs <- min(df_scop_copub$P_full)

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Scopus coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication types Article, peer review and Conference paper, peer review)<br></span>"))

  if(minpubs < 50)
    cat("<b>Indicators based on < 50 publications are considered unreliable</b>")

} else {

  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")

}
cat("<hr>")

SDG

r unit_label

Row {data-height=650}

Sustainable Development Goals (fractional counts)

if(df_sdg_table %>% filter(SDG_Displayname != 'None') %>% nrow() > 0) {
  abm_graph_sdg(df_sdg_table)
} else {
  shiny::HTML("<p><i>There are no publications available for this graph</i></p>")
}

Row {data-height=800}

Publications with any Sustainable Development Goal (fractional counts) {.no-mobile}

abm_ui_datatable_sdg_year(
  df_sdg_year,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Publications with any Sustainable Development Goal (fractional counts) {.mobile}

abm_ui_kable_sdg_year(df_sdg_year)

Sustainable Development Goals (fractional counts) {.no-mobile}

abm_ui_datatable_sdg_table(
  df_sdg_table,
  unit_file_label = unit_file_label, 
  unit_title = abm_unit_title
)

Sustainable Development Goals (fractional counts) {.mobile}

abm_ui_kable_sdg_table(df_sdg_table)

Row

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("This table is based on Scopus publication types Article, Review and Conference Paper.<br>")

if(isTRUE(unit_level >= 0)){

  years <- df_scop_cit %>% filter(substr(Publication_Year_ch, 1, 1) == "2") %>% pull(Publication_Year_ch) %>% as.integer()
  mincov <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(scopcov_frac) %>% min()
  minpubs <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(sumscop_full) %>% min()

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Scopus coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication types Article, peer review and Conference paper, peer review)<br></span>"))

} else {

  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")

}

Notes more...{target="_blank" style="position:relative;float:right;"}

cat("This table is based on Scopus publication types Article, Review and Conference Paper.<br>")

if(isTRUE(unit_level >= 0)){

  years <- df_scop_cit %>% filter(substr(Publication_Year_ch, 1, 1) == "2") %>% pull(Publication_Year_ch) %>% as.integer()
  mincov <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(scopcov_frac) %>% min()
  minpubs <- df_coverage %>% filter(Publication_Type == "Peer reviewed" & Publication_Year %in% years) %>% pull(sumscop_full) %>% min()

  cat(glue("<span title='Legend: 75% or above is good, 60% or above is moderate while lower than 60% is poor'>Rows are based on at least <b>{minpubs}</b> (full counted) publications with ",
           "<b>{coveragetext(mincov)}</b> Scopus coverage (at least <b>{round(100*mincov, 1)}%</b>).<br>",
           "(DiVA publication types Article, peer review and Conference paper, peer review)<br></span>"))

} else {

  cat("<b>Bibliometric results for individual researchers should always be interpreted with caution.</b>")

}

About

Row {data-height=1024}

KTH-Library/bibliomatrix documentation built on Feb. 29, 2024, 5:54 a.m.