Package 'pubmedDashboard'

Title: Creating PubMed Data Visualization Dashboards
Description: Package to facilitate the creation of data visualization dashboards through the flexdashboard and easyPubMed packages. This package is now deprecated in favour of the pubDashboard package.
Authors: Rémi Thériault [aut, cre]
Maintainer: Rémi Thériault <[email protected]>
License: GPL (>= 3)
Version: 0.0.3
Built: 2024-09-27 04:45:20 UTC
Source: https://github.com/rempsyc/pubmedDashboard

Help Index


Add affiliations to pubmedDashboard dataframe

Description

Add affiliations to pubmedDashboard dataframe

Usage

add_affiliation(data)

Arguments

data

The dataframe on which to add affiliations (department and university).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]",
  "AND ('2023/01/01' [Date - Publication] : '2023/12/31' [Date - Publication])"
)

d.fls <- batch_pubmed_download2(
  pubmed_query_string = pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
articles.df <- all_articles_to_df(d.fls)
articles.df2 <- add_affiliation(articles.df)
articles.df2[5, ]

## End(Not run)

Add regions to pubmedDashboard dataframe

Description

Add regions to pubmedDashboard dataframe

Usage

add_region(data)

Arguments

data

The dataframe on which to add region.

Examples

## Not run: 
d.fls <- batch_pubmed_download2(
  pubmed_query_string = paste(
    "passion [Title/Abstract]",
    "AND Dualistic Model of Passion [Text Word]",
    "AND ('2023/01/01' [Date - Publication] : '2023/12/31' [Date - Publication])"
  ),
  year_low = 2023,
  year_high = 2023
)
articles.df <- all_articles_to_df(d.fls)
articles.df2 <- add_affiliation(articles.df)
articles.df3 <- match_university(articles.df2)
articles.df4 <- add_region(articles.df3)
articles.df4[2, ]

## End(Not run)

Convert list of PubMed XLM files to dataframe

Description

Convert list of PubMed XLM files to dataframe

Usage

all_articles_to_df(d.fls)

Arguments

d.fls

The list of XLM PubMed data.

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]",
  "AND ('2023/01/01' [Date - Publication] : '2023/12/31' [Date - Publication])"
)
d.fls <- batch_pubmed_download2(
  pubmed_query_string = pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
articles.df <- all_articles_to_df(d.fls)
articles.df[5, ]

## End(Not run)

Convert PubMed XLM files to dataframe

Description

Convert PubMed XLM files to dataframe

Usage

article_to_df2(
  pubmedArticle,
  autofill = FALSE,
  max_chars = 500,
  getKeywords = FALSE,
  getAuthors = TRUE
)

Arguments

pubmedArticle

The PubMed data, in XLM form.

autofill

Autofill the affiliation address when missing.

max_chars

Maximum number of characters for the address.

getKeywords

Whether to extract keywords as well.

getAuthors

Whether to extract the author information.

Examples

## Not run: 
dami_query <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]",
  "AND ('2023/01/01' [Date - Publication] : '2023/12/31' [Date - Publication])"
)
dami_on_pubmed <- easyPubMed::get_pubmed_ids(dami_query)
dami_abstracts_xml <- easyPubMed::fetch_pubmed_data(dami_on_pubmed)
dami_abstracts_list <- easyPubMed::articles_to_list(dami_abstracts_xml)
article_to_df2(pubmedArticle = dami_abstracts_list[[2]], autofill = FALSE)[1, ]

## End(Not run)

Download PubMed data with query string

Description

Download PubMed data with query string

Usage

batch_pubmed_download2(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2030,
  data_folder = "data",
  batch_size = 5000,
  api_key = NULL
)

Arguments

pubmed_query_string

The PubMed query string.

year_low

The year the data should start.

year_high

The year the data should end.

data_folder

Where to save the data.

batch_size

The download batch size.

api_key

The api key for faster processing (optional).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

batch_pubmed_download2(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)

## End(Not run)

Check whether the PubMed API token is of the correct length and valid

Description

Check whether the PubMed API token is of the correct length and valid

Usage

check_pubmed_api_token(API_TOKEN_PUBMED)

Arguments

API_TOKEN_PUBMED

The PubMed API token

Examples

API_TOKEN_PUBMED <- c(
  "GNZuCGNZuCGNZuCGNZuCGNZuCtwed3twed32"
)

check_pubmed_api_token(API_TOKEN_PUBMED)

Clean dataframe, for names of journals and continents

Description

Clean dataframe, for names of journals and continents

Usage

clean_journals_continents(data)

Arguments

data

The processed dataframe of data


Convert a Hex string to regular text

Description

Convert a Hex string to regular text

Usage

convert_hex_to_char(hex_string)

Arguments

hex_string

The address containg hex characters to convert.

Examples

address <- c(
  "D&#xe9;partement de Psychologie, Universit&#xe9; du Qu&#xe9;bec &#xe0; Montr&#xe9;al",
  "Department of Behavioural and Cognitive Sciences, University of Luxembourg"
)
convert_hex_to_char(address)

List of countries taken from the package countrycode

Description

List of countries taken from the package countrycode

Usage

countries

Format

A vector of class character containing country names.


Detect missing journals

Description

Detect missing journals

Usage

detect_missing_journals(data)

Arguments

data

The processed dataframe of data


Generate a dygraph of journal paper percentages, by country and year

Description

Generate a dygraph of journal paper percentages, by country and year

Usage

dygraph_year(data, level = "continent")

Arguments

data

The processed dataframe of data

level

Level of analysis, either country or continent

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2021,
  year_high = 2023
)
data <- read_bind_all_data()
dygraph_year(data)
dygraph_year(data, "country")

## End(Not run)

Extracts correct component from a splitted affiliation address

Description

Extracts correct component from a splitted affiliation address

Usage

extract_split_address(splitted.address, string)

Arguments

splitted.address

The address splitted with stringr::str_split.

string

The keyword to determine which string component to keep.

Examples

address <- c(
  "Department of Psychology",
  " Cornell University",
  " Ithaca",
  " New York 14853-7601."
)

extract_split_address(address, "University")
extract_split_address(address, "Department")

Get affiliations

Description

Get affiliations

Usage

get_affiliation(address, info = "university")

Arguments

address

The address to parse.

info

The information to extract, one of c("university", "department").

Examples

address <- c(
  "Department of Psychology, Cornell University, Ithaca, New York 14853-7601.",
  "Dipartimento di Psicologia Generale, Università di Padova, Italy.",
  "Universität Mannheim, Federal Republic of Germany.",
  "Département de psychologie, Universit&#xe9; du Québec à Montréal, Canada."
)

get_affiliation(address, "department")
get_affiliation(address, "university")

Get country

Description

Get country

Usage

get_country(address)

Arguments

address

The address to parse.

Examples

## Not run: 
d.fls <- batch_pubmed_download2(
  pubmed_query_string = paste(
    "passion [Title/Abstract]",
    "AND Dualistic Model of Passion [Text Word]",
    "AND ('2023/01/01' [Date - Publication] : '2023/12/31' [Date - Publication])"
  ),
  year_low = 2023,
  year_high = 2023
)
articles.df <- all_articles_to_df(d.fls)
articles.df2 <- add_affiliation(articles.df)
articles.df3 <- match_university(articles.df2)
get_country(articles.df3$address)

## End(Not run)

List of academic journals and corresponding fields

Description

List of academic journals and corresponding fields

Usage

journal_field

Format

A data frame with 25 rows and 3 variables:

journal

academic journal

journal_short

short name for the journal

field

the field of research

original_journal

Whether it is part of one of the six original journals

...


Match list of universities to countries in a dataframe

Description

Match list of universities to countries in a dataframe

Usage

match_university(data)

Arguments

data

The dataframe to use for matching.

Examples

## Not run: 
d.fls <- batch_pubmed_download2(
  pubmed_query_string = paste(
    "passion [Title/Abstract]",
    "AND Dualistic Model of Passion [Text Word]",
    "AND ('2023/01/01' [Date - Publication] : '2023/12/31' [Date - Publication])"
  )
)
articles.df <- all_articles_to_df(d.fls)
articles.df2 <- add_affiliation(articles.df)
articles.df3 <- match_university(articles.df2)
articles.df3[5, ]

## End(Not run)

Like Excel vlookup/grep, both both ways

Description

Like Excel vlookup/grep, both both ways

Usage

partial_vlookup(pattern, lookup_vector)

Arguments

pattern

The pattern to compare.

lookup_vector

The dictionnary in which to look for the pattern.

Examples

address <- c(
  "Department of Psychology, Cornell University, Ithaca, New York 14853-7601.",
  "Dipartimento di Psicologia Generale, Università di Padova, Italy.",
  "Universität Mannheim, Federal Republic of Germany.",
  "Département de psychologie, Université du Québec à Montréal, Canada."
)
partial_vlookup(address, universities$university)
uni <- c(
  "Cornell University", "Università di Padova",
  "Universität Mannheim", "Université du Québec à Montréal"
)
partial_vlookup(uni, universities$university)

Read local pubmedDashboard data files and bind them in a single dataframe

Description

Read local pubmedDashboard data files and bind them in a single dataframe

Usage

read_bind_all_data(data_folder = "data")

Arguments

data_folder

The folder in which the data lives


Render complete pubmedDashboard dashboard

Description

Render complete pubmedDashboard dashboard

Usage

render_dashboard(
  file_name = "dashboard",
  title = "title",
  author = "author",
  pubmed_query_string = "",
  journal = NULL,
  year_low = 2023,
  year_high = 2023,
  month_low = "01",
  month_high = 12,
  day_low = "01",
  day_high = 31,
  data_folder = "data",
  batch_size = 5000,
  api_key = NULL,
  verbose = TRUE,
  query_pubmed = FALSE,
  tab_continent = TRUE,
  tab_continent_year = TRUE,
  tab_continent_journal = TRUE,
  tab_country = TRUE,
  tab_country_journal = TRUE,
  tab_psychology = FALSE,
  tab_economics = FALSE,
  tab_general = FALSE,
  tab_figure1 = FALSE,
  tab_missing = TRUE
)

Arguments

file_name

Desired file name.

title

Desired dashboard title.

author

Desired displayed dashboard author.

pubmed_query_string

The PubMed query string.

journal

The list of desired journals.

year_low

The year the data should start.

year_high

The year the data should end.

month_low

The year the data should start.

month_high

The year the data should end.

day_low

The year the data should start.

day_high

The year the data should end.

data_folder

Where to save the data.

batch_size

The download batch size.

api_key

The api key for faster processing (optional).

verbose

Whether to include progress messages.

query_pubmed

Whether to query pubmed. This must be set to TRUE explicitely to avoid long operations. When the data is already downloaded and available in the data folder, this step is unnecessary.

tab_continent

Whether to render the "Continent" tab.

tab_continent_year

Whether to render the "Continent by year" tab.

tab_continent_journal

Whether to render the "Continent by journal" tab.

tab_country

Whether to render the "Country" tab.

tab_country_journal

Whether to render the "Country by journal" tab.

tab_psychology

Whether to render the "Psychology" tab.

tab_economics

Whether to render the "Economics" tab.

tab_general

Whether to render the "General" tab.

tab_figure1

Whether to render the "Figure 1" tab.

tab_missing

Whether to render the "Missing" tab.

Examples

## Not run: 
render_dashboard(
  file_name = "my_dashboard",
  title = "Wonderful Dashboard",
  author = "Rémi Thériault",
  pubmed_query_string = "passion [Title/Abstract]",
  journal = c("Journal of Personality and Social Psychology", "Health Psychology"),
  year_low = 2023,
  year_high = 2023,
  query_pubmed = TRUE,
  tab_figure1 = TRUE
)

## End(Not run)

Mega function to process and save PubMed data

Description

Mega function to process and save PubMed data

Usage

save_process_pubmed_batch(
  pubmed_query_string = "",
  journal = NULL,
  year_low = 2024,
  year_high = 2024,
  month_low = "01",
  month_high = 12,
  day_low = "01",
  day_high = 31,
  data_folder = "data",
  suffix = "",
  batch_size = 5000,
  api_key = NULL,
  verbose = TRUE
)

Arguments

pubmed_query_string

The PubMed query string.

journal

The list of desired journals.

year_low

The year the data should start.

year_high

The year the data should end.

month_low

The year the data should start.

month_high

The year the data should end.

day_low

The year the data should start.

day_high

The year the data should end.

data_folder

Where to save the data.

suffix

What suffix to add to the name file.

batch_size

The download batch size.

api_key

The api key for faster processing (optional).

verbose

Whether to include progress messages.

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)

## End(Not run)

Generate table of journal paper percentages, by continent and year

Description

Generate table of journal paper percentages, by continent and year

Usage

scatter_continent_year(
  data,
  method = "lm",
  plotly = TRUE,
  citation = NULL,
  citation_size = 15,
  ...
)

Arguments

data

The processed dataframe of data

method

Which method to use for the regression line, either "lm" (default) or "loess".

plotly

Logical, whether to use plotly for dynamic data visualization.

citation

Optionally, a citation to add as a footer.

citation_size

Font size of the citation.

...

Further arguments passed to rempsyc::nice_scatter

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2022,
  year_high = 2023
)
data <- read_bind_all_data()
suppressWarnings(scatter_continent_year(data))

## End(Not run)

Generate table of journal paper percentages, by continent and year

Description

Generate table of journal paper percentages, by continent and year

Usage

scatter_country_year(
  data,
  method = "lm",
  plotly = TRUE,
  citation = NULL,
  citation_size = 15,
  ...
)

Arguments

data

The processed dataframe of data

method

Which method to use for the regression line, either "lm" (default) or "loess".

plotly

Logical, whether to use plotly for dynamic data visualization.

citation

Optionally, a citation to add as a footer.

citation_size

Font size of the citation.

...

Further arguments passed to rempsyc::nice_scatter

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2018,
  year_high = 2020
)
data <- read_bind_all_data()
suppressWarnings(scatter_country_year(data))

## End(Not run)

Generate table of journal paper percentages, by continent and year

Description

Generate table of journal paper percentages, by continent and year

Usage

scatter_figure1(data, method = "lm", original = TRUE, plotly = TRUE, ...)

Arguments

data

The processed dataframe of data

method

Which method to use for the regression line, either "lm" (default) or "loess".

original

Logical; if TRUE, attempts to mimic Arnett's (2008) Figure 1 in style.

plotly

Logical, whether to use plotly for dynamic data visualization.

...

Further arguments passed to rempsyc::nice_scatter

Examples

## Not run: 
pubmed_query_string <- paste(
  "Developmental Psychology [Journal]",
  "OR Journal of Personality and Social Psychology [Journal]",
  "OR Journal of Abnormal Psychology [Journal]",
  "OR Journal of Family Psychology [Journal]",
  "OR Health Psychology [Journal]",
  "OR Journal of Educational Psychology [Journal]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
scatter_figure1(data)

## End(Not run)

Generate table of journal paper percentages, by continent

Description

Generate table of journal paper percentages, by continent

Usage

table_continent(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
table_continent(data)

## End(Not run)

Generate table of journal paper percentages, by continent and journals

Description

Generate table of journal paper percentages, by continent and journals

Usage

table_continent_journal(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
table_continent_journal(data)

## End(Not run)

Generate table of journal paper percentages, by continent and year

Description

Generate table of journal paper percentages, by continent and year

Usage

table_continent_year(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2022,
  year_high = 2023
)
data <- read_bind_all_data()
table_continent_year(data)

## End(Not run)

Generate table of journal paper percentages, by country

Description

Generate table of journal paper percentages, by country

Usage

table_country(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
table_country(data)

## End(Not run)

Generate table of journal paper percentages, by continent and year

Description

Generate table of journal paper percentages, by continent and year

Usage

table_country_journal(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
table_country_journal(data)

## End(Not run)

Generate table of journal paper percentages, by country and year

Description

Generate table of journal paper percentages, by country and year

Usage

table_country_year(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
table_country_year(data)

## End(Not run)

Count number of papers per journal, with year range

Description

Count number of papers per journal, with year range

Usage

table_journal_count(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).


Generate table of journal paper percentages, by country

Description

Generate table of journal paper percentages, by country

Usage

table_missing_country(data, datatable = TRUE)

Arguments

data

The processed dataframe of data

datatable

Whether to output a DT::datatable HTML table widget instead of a regular dataframe (defaults to TRUE).

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
data[1, c(4, 6)] <- NA
table_missing_country(data)

## End(Not run)

A data frame of university and corresponding country

Description

Obtained from GitHub, and then modified with minor improvements and more universities.

Usage

universities

Format

A data frame with 9420 rows and 2 variables:

country_code

the country code

university

the university

...

Source

https://raw.githubusercontent.com/endSly/world-universities-csv/master/world-universities.csv


List of US states taken from the package countrycode

Description

List of US states taken from the package countrycode

Usage

us_states

Format

A data frame with 50 rows and 3 variables:

state.name

the name of the state

state.abb

the name of the abbreviation

state.regex

the regex for that state

...


Generate a waffle chart of journal paper percentages, by continent (each square = 1% of data)

Description

Generate a waffle chart of journal paper percentages, by continent (each square = 1% of data)

Usage

waffle_continent(data, citation = NULL, citation_size = NULL)

Arguments

data

The processed dataframe of data

citation

Optionally, a citation to add as a footer.

citation_size

Font size of the citation.

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
waffle_continent(data)

## End(Not run)

Generate a waffle chart of journal paper percentages, by continent (each square = 1% of data)

Description

Generate a waffle chart of journal paper percentages, by continent (each square = 1% of data)

Usage

waffle_continent_journal(
  data,
  citation,
  citation_size = NULL,
  journal_abbreviation = TRUE
)

Arguments

data

The processed dataframe of data

citation

Optionally, a citation to add as a footer.

citation_size

Font size of the citation.

journal_abbreviation

Logical, whether to use the journal abbreviation to fit the entire plot, otherwise some journal names can be quite long and accordingly be cropped.

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
waffle_continent_journal(data)

## End(Not run)

Generate a waffle plot made of country flags

Description

Generate a waffle plot made of country flags

Usage

waffle_country(data, citation, citation_size = NULL)

Arguments

data

The processed dataframe of data

citation

Optionally, a citation to add as a footer.

citation_size

Font size of the citation.

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
waffle_country(data)

## End(Not run)

Generate a waffle chart of journal paper percentages, by continent (each square = 1% of data)

Description

Generate a waffle chart of journal paper percentages, by continent (each square = 1% of data)

Usage

waffle_country_journal(
  data,
  citation,
  citation_size = NULL,
  journal_abbreviation = TRUE
)

Arguments

data

The processed dataframe of data

citation

Optionally, a citation to add as a footer.

citation_size

Font size of the citation.

journal_abbreviation

Logical, whether to use the journal abbreviation to fit the entire plot, otherwise some journal names can be quite long and accordingly be cropped.

Examples

## Not run: 
pubmed_query_string <- paste(
  "passion [Title/Abstract]",
  "AND Dualistic Model of Passion [Text Word]"
)

save_process_pubmed_batch(
  pubmed_query_string,
  year_low = 2023,
  year_high = 2023
)
data <- read_bind_all_data()
waffle_country_journal(data)

## End(Not run)

List of world capitals taken from the package maps

Description

List of world capitals taken from the package maps

Usage

world_capitals

Format

A data frame with 259 rows and 6 variables:

name

the name of the capital

country.etc

the country of the capital

pop

population of the capital

lat

latitude of the capital

long

longitude of the capital

capital

whether it is a capital

...