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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# pubmedDashboard: Creating PubMed Data Visualization Dashboards <img src='man/figures/logo.png' align="right" height="139" style="float:right; height:200px;" />
<!-- badges: start -->
[](https://github.com/rempsyc/pubmedDashboard/actions)
[](https://rempsyc.r-universe.dev/ui/#package:pubmedDashboard)
[](https://CRAN.R-project.org/package=pubmedDashboard)
[](https://github.com/rempsyc/pubmedDashboard/commits/main)
[](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[](https://github.com/sponsors/rempsyc)
[](https://github.com/rempsyc?tab=followers)
[](https://github.com/rempsyc/pubmedDashboard/stargazers)
<!-- badges: end -->
**This package is now deprecated in favour of the [`pubDashboard`](https://rempsyc.github.io/pubDashboard/) package.**
The goal of `pubmedDashboard` is to facilitate the creation of pretty data visualization dashboards using the `flexdashboard` and `easyPubMed` packages.
## Installation
You can install the development version of `pubmedDashboard` like so:
``` r
# If `remotes` isn't installed, use `install.packages("remotes")`
remotes::install_github("rempsyc/pubmedDashboard")
```
## Basic Examples
`pubmedDashboard` helps parse the address to identify department and university of affiliation, as well as country.
```{r, warning=FALSE}
library(pubmedDashboard)
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."
)
get_affiliation(address, "department")
get_affiliation(address, "university")
get_country(address)
```
## Mega Function
One simple function allows to download the paper data from PubMed, convert the XLM data to a dataframe, extract affiliations, match universities to countries, identify countries and continents, and save the file to disk for later reuse.
```{r example, warning=FALSE}
save_process_pubmed_batch(
pubmed_query_string = "passion [Title/Abstract]",
journal = c("Journal of Personality and Social Psychology", "Health Psychology"),
year_low = 2023,
year_high = 2030
)
```
## Table formatting functions
Various functions allow to prepare DT tables to display specific information, such as journal paper percentages by continent or country.
```{r read_bind_all, message=FALSE}
data <- read_bind_all_data()
```
```{r table_continent, eval = FALSE}
table_continent(data)
```
## Figure formatting functions
```{r waffle_country, warning=FALSE, message=FALSE}
waffle_country(data)
```
## Example Dashboards
The full source-code for these dashboards are available on the corresponding button at the top-right of each dashboard.
### Neglected 95% Dashboard
[](https://remi-theriault.com/dashboards/neglected_95)
### Passion Dashboard
[](https://remi-theriault.com/dashboards/passion)