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_13000-CountryBasedComparison.Rmd
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_13000-CountryBasedComparison.Rmd
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# Country Based Comparison
## Analysis
### PubMed Indexed Peer Reviewed Articles in Pathology Journals: A country based comparison
**Aim:**
Here, we are going to compare 3 countries (German, Japan and Turkey), in terms of number of articles in pathology journals during the last decade.
**Methods:**
If you want to see the code used in the analysis please click the code button on the right upper corner or throughout the page.
```
{r , eval=FALSE, include=FALSE, echo=TRUE}
# load required packages
library(tidyverse)
library(RISmed)
```
Pathology Journal ISSN List was retrieved from [In Cites Clarivate](https://jcr.incites.thomsonreuters.com/), and Journal Data Filtered as follows: `JCR Year: 2016 Selected Editions: SCIE,SSCI Selected Categories: 'PATHOLOGY' Selected Category Scheme: WoS`
```
{r Get ISSN List from data downloaded from WoS 2, eval=FALSE, include=FALSE, echo=TRUE}
# Get ISSN List from data downloaded from WoS
ISSNList <- JournalHomeGrid <- read_csv( data/JournalHomeGrid.csv ,
skip = 1) %>%
select(ISSN) %>%
filter(!is.na(ISSN)) %>%
t() %>%
paste( OR , collapse = ) # add OR between ISSN List
ISSNList <- gsub( OR $ , ,ISSNList) # to remove last OR
```
Data is retrieved from PubMed via RISmed package.
PubMed collection from National Library of Medicine (https://www.ncbi.nlm.nih.gov/pubmed/), has the most comprehensive information about peer reviewed articles in medicine.
The API (https://dataguide.nlm.nih.gov/), and R packages are available for getting and fetching data from the server.
The search formula for PubMed is generated as ISSN List AND Country[Affiliation] like done in [advanced search of PubMed](https://www.ncbi.nlm.nih.gov/pubmed/advanced).
```
{r Generate Search Formula For Pathology Journals AND Countries 2, eval=FALSE, include=FALSE, echo=TRUE}
# Generate Search Formula For Pathology Journals AND Countries
searchformulaTR <- paste( ' ,ISSNList, ' , AND , Turkey[Affiliation] )
searchformulaDE <- paste( ' ,ISSNList, ' , AND , Germany[Affiliation] )
searchformulaJP <- paste( ' ,ISSNList, ' , AND , Japan[Affiliation] )
```
```
{r Search PubMed 2, eval=FALSE, include=FALSE, echo=TRUE}
# Search PubMed, Get and Fetch
TurkeyArticles <- EUtilsSummary(searchformulaTR, type = 'esearch', db = 'pubmed', mindate = 2007, maxdate = 2017, retmax = 10000)
fetchTurkey <- EUtilsGet(TurkeyArticles)
GermanyArticles <- EUtilsSummary(searchformulaDE, type = 'esearch', db = 'pubmed', mindate = 2007, maxdate = 2017, retmax = 10000)
fetchGermany <- EUtilsGet(GermanyArticles)
JapanArticles <- EUtilsSummary(searchformulaJP, type = 'esearch', db = 'pubmed', mindate = 2007, maxdate = 2017, retmax = 10000)
fetchJapan <- EUtilsGet(JapanArticles)
```
From the fetched data the year of articles are grouped and counted by country.
```
{r Articles per countries per year 2, eval=FALSE, include=FALSE, echo=TRUE}
# Articles per countries per year
tableTR <- table(YearPubmed(fetchTurkey)) %>%
as_tibble() %>%
rename(Turkey = n, Year = Var1)
tableDE <- table(YearPubmed(fetchGermany)) %>%
as_tibble() %>%
rename(Germany = n, Year = Var1)
tableJP <- table(YearPubmed(fetchJapan)) %>%
as_tibble() %>%
rename(Japan = n, Year = Var1)
# Join Tables
articles_per_year_table <- list(
tableTR,
tableDE,
tableJP
) %>%
reduce(left_join, by = Year , .id = id )
```
```
{r Prepare table for output 2, eval=FALSE, include=FALSE, echo=TRUE}
# Prepare table for output
articles_per_year <- articles_per_year_table %>%
gather(Country, n, 2:4)
articles_per_year$Country <- factor(articles_per_year$Country,
levels =c( Japan , Germany , Turkey ))
```
**Result:**
In the below table we see the number of articles per country in the last decade.
```
{r Print the Table of Articles per year per country 2, eval=FALSE, include=FALSE, echo=TRUE}
# Print the Table of Articles per year, per country
knitr::kable(articles_per_year_table, caption = Table of Articles per year, per country )
```
And the figure below shows this data in a line graph.
```
{r Graph of Table of Articles per year per country 2, eval=FALSE, fig.align= center , include=FALSE}
ggplot(data = articles_per_year, aes(x = Year, y = n, group = Country,
colour = Country, shape = Country,
levels = Country
)) +
geom_line() +
geom_point() +
labs(x = Year , y = Number of Articles ) +
ggtitle( Pathology Articles Per Year ) +
theme(plot.title = element_text(hjust = 0.4),
text = element_text(size = 9))
```
**Comment:**
We see that Japan has much more articles than German and Turkey.
Turkey has a small increase in number of articles.
**Future Work:**
* Indentify why Japan has too much articles.
* Compare Japan with other countries.
* Compare Turkey with neighbours, EU, OECD & Middle East countries.
* Analyse multinational studies.
* Analyse adding journal impact as a factor.