Data retriveal from PubMed
+
+
-
-
-
-
-
-
-
-Data retriveal from PubMed using EDirect
Articles are downloaded as xml.
Journal Names are extracted from xml.
- - + + + + + + + + + + + + + + + - +myTerm <- rstudioapi::terminalCreate(show = FALSE)
rstudioapi::terminalSend(
myTerm,
-"xtract -input data/Turkey_all.xml -pattern PubmedArticle -element MedlineCitation/PMID PubDate/Year Journal/ISSN ISOAbbreviation > data/TurkeyAll.csv \n"
+"xtract -input data/Turkey_all.xml -pattern PubmedArticle -sep ' ' -def 'na' -element MedlineCitation/PMID Journal/ISSN ISOAbbreviation PubDate/Year > data/TurkeyAll.csv \n"
)
Sys.sleep(1)
repeat {
@@ -1795,179 +1849,193 @@ Data retriveal from PubMed
}
}[1] "Code Executed"-
The retrieved information was compiled in a table.
- - - -library(readr)
-
-authorkeywords <- read_table2("data/authorkeywords.csv",
-col_names = c("frequency", "author key word")) %>%
-select('author key word', 'frequency') %>%
-head(n = 20)
-
-PathologyTurkeyMeSH <- read_table2("data/PathologyTurkeyMeSH.csv",
-col_names = c("frequency", "MeSH term")) %>%
-select('MeSH term', 'frequency') %>%
-head(n = 20)Most common 20 author supplied keywords are given below.
- - - - - - -
-
-
-Properties of Journals
+
+
Retrieval of Data of Properties of Journals
nlmcatalog_result_journals_pmc.xml
nlmcatalog_result_currentlyindexed.xml
Analysis
-
-
-Results
-
-
-Discussion
-
-
-
+
+
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+Old
-Articles per journals per country
-Methods:
- - + +library(tidyverse)
+library(readr)
+
+TurkeyAll <- read_delim("data/TurkeyAll.csv",
+ "\t", escape_double = FALSE, col_names = FALSE,
+ na = "na", trim_ws = TRUE)
+
+names(TurkeyAll) <- c("PMID", "ISSN", "JournalName", "Year")
+
+# dim(TurkeyAll)[1]
+
+# min(TurkeyAll[,4], na.rm = TRUE)
+
+# max(TurkeyAll[,4], na.rm = TRUE)
+
+# glimpse(TurkeyAll)Pathology Journal ISSN List was retrieved from In Cites Clarivate, and Journal Data Filtered as follows: JCR Year: 2016 Selected Editions: SCIE,SSCI Selected Categories: 'PATHOLOGY' Selected Category Scheme: WoS
# 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 ORuniqueJournals <- TurkeyAll %>%
+ select(JournalName, ISSN) %>%
+ unique()
+# dim(uniqueJournals)[1]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.
- -# 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]")TurkeyAll2 <- TurkeyAll %>%
+ mutate(Journal = paste(JournalName, ISSN, sep = " ISSN "))
+ArticlesByYear <- TurkeyAll2 %>%
+ group_by(Journal, Year) %>%
+ summarise(n = n())
+ArticlesByYear <- ArticlesByYear %>%
+ spread(key = Year, value = n)
+TurkeyAll2 <- TurkeyAll2 %>%
+ select(Journal, JournalName, ISSN) %>%
+ unique()
+
+ArticlesByYear <- left_join(ArticlesByYear, TurkeyAll2, by = "Journal")
+ArticlesByYear <- ArticlesByYear %>%
+ select(
+ Journal, JournalName, ISSN, everything()
+ )Articles from Japan, German and Turkey are retrieved limiting the search with pathology journals, affiliation and last 10 years.
- -# 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)-element MedlineTA NLMCatalogRecord/NlmUniqueID -def ‘na’ -sep ‘’ -block TitleAlternate/Title -element TitleAlternate/Title
+“xtract -input data/nlmcatalog_result_currentlyindexed.xml -pattern NCBICatalogRecord -element ISSNLinking -def ‘na’ -sep ’ ’ -block TitleAlternate/Title -if TitleAlternate/Title@Sort -equals N -element TitleAlternate/Title > data/nlmcatalog.csv ”
+-sep ‘’
+NLMCatalogRecord/NlmUniqueID ISSNLinking
+ + + +myTerm <- rstudioapi::terminalCreate(show = FALSE)
+rstudioapi::terminalSend(
+myTerm,
+"xtract -input data/nlmcatalog_result_currentlyindexed.xml -pattern NCBICatalogRecord -tab '|' -element NLMCatalogRecord/NlmUniqueID -block ISSNLinking -tab '|' -element ISSNLinking -block Title -if Title@Sort -equals N -def 'na' -tab '|' -element TitleAlternate/Title > data/nlmcatalog.csv \n"
+)
+Sys.sleep(1)
+repeat {
+Sys.sleep(0.1)
+if (rstudioapi::terminalBusy(myTerm) == FALSE) {
+print("Code Executed")
+break
+}
+}[1] "Code Executed"library(readr)
+nlmcatalog <- read_delim("data/nlmcatalog.csv",
+ delim = "|",
+ escape_double = FALSE,
+ col_names = FALSE,
+ trim_ws = TRUE)Parsed with column specification:
+cols(
+ X1 = col_integer(),
+ X2 = col_character(),
+ X3 = col_character(),
+ X4 = col_character(),
+ X5 = col_character(),
+ X6 = col_character(),
+ X7 = col_character(),
+ X8 = col_character()
+)
+number of columns of result is not a multiple of vector length (arg 1)5116 parsing failures.
+row [38;5;246m# A tibble: 5 x 5[39m col row col expected actual file expected [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m actual [38;5;250m1[39m 2 [31mNA[39m 8 columns 2 columns 'data/nlmcatalog.csv' file [38;5;250m2[39m 3 [31mNA[39m 8 columns 3 columns 'data/nlmcatalog.csv' row [38;5;250m3[39m 4 [31mNA[39m 8 columns 5 columns 'data/nlmcatalog.csv' col [38;5;250m4[39m 6 [31mNA[39m 8 columns 4 columns 'data/nlmcatalog.csv' expected [38;5;250m5[39m 7 [31mNA[39m 8 columns 5 columns 'data/nlmcatalog.csv'
+... ................................. ... ....................................................... ........ .................................................................................................................................................................................... ...... ................................................................................. .... ................................................................................. ... ................................................................................. ... ................................................................................. ........ .................................................................................
+See problems(...) for more details.The retrieved information was compiled in a table.
- -ISSNTR <- table(ISSN(fetchTurkey)) %>%
- as_tibble() %>%
- rename(Turkey = n, Journal = Var1)
-ISSNDE <- table(ISSN(fetchGermany)) %>%
- as_tibble() %>%
- rename(Germany = n, Journal = Var1)
-ISSNJP <- table(ISSN(fetchJapan)) %>%
- as_tibble() %>%
- rename(Japan = n, Journal = Var1)
-articles_per_journal <- list(
- ISSNTR,
- ISSNDE,
- ISSNJP
-) %>%
- reduce(left_join, by = "Journal", .id = "id") %>%
- gather(Country, n, 2:4)
-articles_per_journal$Country <- factor(articles_per_journal$Country,
- levels =c("Japan", "Germany", "Turkey")){xml_nodeset (0)}+
+
+
+Results
+-
+
PubMed’de 1939-2018 tarihleri arasında, Türkiye adresli 200229 adet yayın mevcuttur.
+PubMed’de 1939-2018 tarihleri arasında, Türkiye adresli yayınlar 8517 farklı dergide yayımlanmıştır.
+
+
+
+
+
-
-
Discussion
+türkiye adresli olup da pubmedde yer alan makaleler hangi dergilerde kaçar adet yayınlanmış
+The retrieved information was compiled in a table.
+Methods:
Result:
-In this graph x-axis is the list of journals with decreasing impact factor, and y-axis is the number of articles published in that journal. The colors and shapes are showing the country of affiliation. We see that in one journal articles from Japan is more than 800.
- -ggplot(data = articles_per_journal, aes(x = Journal, y = n, group = Country,
- colour = Country, shape = Country,
- levels = Country
-)) +
- geom_point() +
- labs(x = "Journals with decreasing impact factor", y = "Number of Articles") +
- ggtitle("Pathology Articles Per Journal") +
- theme(plot.title = element_text(hjust = 0.5),
- axis.text.x=element_blank())ggplot(data = articles_per_journal, aes(x = Journal, y = n, group = Country,
+ colour = Country, shape = Country,
+ levels = Country
+)) +
+ geom_point() +
+ labs(x = "Journals with decreasing impact factor", y = "Number of Articles") +
+ ggtitle("Pathology Articles Per Journal") +
+ theme(plot.title = element_text(hjust = 0.5),
+ axis.text.x=element_blank())
Comment:
-It is seen that one of the journals ISSN: 1440-1827 has more than 800 articles from Japan. This journal is also from Japan. Here we wonder if there is an editorial preference for articles from their home country.
-We sometimes observe this situation if there is a conference in that country, and the conference abstracts are indexed.
-This may also be a clue that if a country has a journal listed in indexes, than it is more easy for the researchers in that country to publish their results.
-Future Work:
-Whether this observation is a unique situation, or there is a tendency in the journals to publish article from their country of origin, merits further investigation.
Feedback
Serdar Balcı, MD, Pathologist would like to hear your feedback: https://goo.gl/forms/YjGZ5DHgtPlR1RnB3
-This document will be continiously updated and the last update was on 2018-08-18.
+This document will be continiously updated and the last update was on 2018-08-22.
---
title: "Bibliographic Studies"
subtitle: "Journals Published Articles From Turkey"
author: "Serdar Balcı, MD, Pathologist"
date: '`r format(Sys.Date())`'
output: 
  html_notebook: 
    code_folding: hide
    fig_caption: yes
    highlight: kate
    theme: cerulean
    toc_float: yes
  html_document: 
    code_folding: hide
    df_print: kable
    fig_caption: yes
    highlight: kate
    keep_md: yes
    theme: cerulean
    toc_float: yes
---

```{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'figure/', echo = FALSE, warning = FALSE, message = FALSE, error = FALSE, eval = TRUE, tidy = TRUE, comment = NA)
```

```{r library, include=FALSE}
library(tidyverse)
```


<!-- # Sponsored by -->

<!-- [![](images/modelistatistik_logo-3-300x73.png)](https://www.modelistatistik.com/) -->


# Journals Published Articles From Turkey {.tabset .tabset-fade .tabset-pills}

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.  
Select from the tabs below.


## Aim 

**Aim:**

Here we will look at the Journals in which articles from Turkey are published. 



## Data retriveal from PubMed 

Articles are downloaded as `xml`.

```{r Search PubMed write 2018 data as xml, eval=FALSE, include=FALSE}
myTerm <- rstudioapi::terminalCreate(show = FALSE)
rstudioapi::terminalSend(
    myTerm,
    "esearch -db pubmed -query \"Turkey[Affiliation]\" -datetype PDAT -mindate 2018 -maxdate 3000 | efetch -format xml > data/Turkey_2018.xml \n"
)
Sys.sleep(1)
repeat {
    Sys.sleep(0.1)
    if (rstudioapi::terminalBusy(myTerm) == FALSE) {
        print("Code Executed")
        break
    }
}
```

```{r Search PubMed write all data as xml, eval=FALSE, include=FALSE}
myTerm <- rstudioapi::terminalCreate(show = FALSE)
rstudioapi::terminalSend(
    myTerm,
    "esearch -db pubmed -query \"Turkey[Affiliation]\" -datetype PDAT -mindate 1800 -maxdate 3000 | efetch -format xml > data/Turkey_all.xml \n"
)
Sys.sleep(1)
repeat {
    Sys.sleep(0.1)
    if (rstudioapi::terminalBusy(myTerm) == FALSE) {
        print("Code Executed")
        break
    }
}
```





```{r Search PubMed get 2018 data on the fly, eval=FALSE, include=FALSE}
myTerm <- rstudioapi::terminalCreate(show = FALSE)
rstudioapi::terminalSend(
    myTerm,
    "esearch -db pubmed -query \"Turkey[Affiliation]\" -datetype PDAT -mindate 2018 -maxdate 3000 | efetch -format xml | xtract  -pattern PubmedArticle -element MedlineCitation/PMID PubDate/Year Journal/ISSN ISOAbbreviation> data/onthefly_Turkey_2018.csv \n"
)
Sys.sleep(1)
repeat {
    Sys.sleep(0.1)
    if (rstudioapi::terminalBusy(myTerm) == FALSE) {
        print("Code Executed")
        break
    }
}
```


```{r Search PubMed get all data on the fly, eval=FALSE, include=FALSE}
myTerm <- rstudioapi::terminalCreate(show = FALSE)
rstudioapi::terminalSend(
    myTerm,
    "esearch -db pubmed -query \"Turkey[Affiliation]\" -datetype PDAT -mindate 1800 -maxdate 3000 | efetch -format xml | xtract  -pattern PubmedArticle -element MedlineCitation/PMID PubDate/Year Journal/ISSN ISOAbbreviation> data/onthefly_Turkey_all.csv \n"
)
Sys.sleep(1)
repeat {
    Sys.sleep(0.1)
    if (rstudioapi::terminalBusy(myTerm) == FALSE) {
        print("Code Executed")
        break
    }
}
```



Journal Names are extracted from xml.

```{r extract journal names from xml, eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
myTerm <- rstudioapi::terminalCreate(show = FALSE)
rstudioapi::terminalSend(
myTerm,
"xtract -input data/Turkey_2018.xml -pattern PubmedArticle -element MedlineCitation/PMID PubDate/Year Journal/ISSN ISOAbbreviation > data/Turkey2018.csv \n"
)
Sys.sleep(1)
repeat {
Sys.sleep(0.1)
if (rstudioapi::terminalBusy(myTerm) == FALSE) {
print("Code Executed")
break
}
}
```


```{r extract journal names from all data xml, message=FALSE, warning=FALSE}
myTerm <- rstudioapi::terminalCreate(show = FALSE)
rstudioapi::terminalSend(
myTerm,
"xtract -input data/Turkey_all.xml -pattern PubmedArticle -element MedlineCitation/PMID PubDate/Year Journal/ISSN ISOAbbreviation > data/TurkeyAll.csv \n"
)
Sys.sleep(1)
repeat {
Sys.sleep(0.1)
if (rstudioapi::terminalBusy(myTerm) == FALSE) {
print("Code Executed")
break
}
}
```




----

































The retrieved information was compiled in a table.

```{r message=FALSE, warning=FALSE}
library(readr)

authorkeywords <- read_table2("data/authorkeywords.csv",
col_names = c("frequency", "author key word")) %>%
select('author key word', 'frequency') %>%
head(n = 20)

PathologyTurkeyMeSH <- read_table2("data/PathologyTurkeyMeSH.csv",
col_names = c("frequency", "MeSH term")) %>%
select('MeSH term', 'frequency') %>%
head(n = 20)
```



_**Most common 20 author supplied keywords are given below.**_

```{r results = 'asis'}
pander::pander(authorkeywords, justify = "left", caption = "Most common 20 author supplied keywords")
```


## Properties of Journals



[nlmcatalog_result_journals_pmc.xml](https://www.ncbi.nlm.nih.gov/portal/utils/file_backend.cgi?Db=nlmcatalog&HistoryId=NCID_1_69755278_130.14.18.97_5555_1534585934_3590606783_0MetA0_S_HStore&QueryKey=2&Sort=PubDate&Filter=all&CompleteResultCount=2559&Mode=file&View=xml&p$l=Email&portalSnapshot=%2Fprojects%2Fentrez%2Fpubmed%2FPubMedGroup@1.136&BaseUrl=&PortName=live&RootTag=NLMCatalogRecordSet&DocType=NLMCatalogRecordSet%20PUBLIC%20%22-%2F%2FNLM%2F%2FDTD%20NLMCatalogRecordSet,%201st%20June%202017%2F%2FEN%22%20%22https://www.nlm.nih.gov/databases/dtd/nlmcatalogrecordset_170601.dtd%22&FileName=&ContentType=xml)


[nlmcatalog_result_currentlyindexed.xml](https://www.ncbi.nlm.nih.gov/portal/utils/file_backend.cgi?Db=nlmcatalog&HistoryId=NCID_1_69755278_130.14.18.97_5555_1534585934_3590606783_0MetA0_S_HStore&QueryKey=1&Sort=PubDate&Filter=all&CompleteResultCount=5242&Mode=file&View=xml&p$l=Email&portalSnapshot=%2Fprojects%2Fentrez%2Fpubmed%2FPubMedGroup@1.136&BaseUrl=&PortName=live&RootTag=NLMCatalogRecordSet&DocType=NLMCatalogRecordSet%20PUBLIC%20%22-%2F%2FNLM%2F%2FDTD%20NLMCatalogRecordSet,%201st%20June%202017%2F%2FEN%22%20%22https://www.nlm.nih.gov/databases/dtd/nlmcatalogrecordset_170601.dtd%22&FileName=&ContentType=xml)


[scimagojr2017.csv](https://www.scimagojr.com/journalrank.php?out=xls)

[scimagojr2017-wos.csv](https://www.scimagojr.com/journalrank.php?wos=true&out=xls)


![](images/scidata.png)




## Analysis

## Results


## Discussion



## Old



Articles per journals per country




**Methods:**

```{r load required packages}
# 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}
# 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}
# 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]")
```

Articles from Japan, German and Turkey are retrieved limiting the search with pathology journals, affiliation and last 10 years.

```{r Search PubMed, Get and Fetch}
# 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)
```

The retrieved information was compiled in a table.

```{r}

ISSNTR <- table(ISSN(fetchTurkey)) %>% 
    as_tibble() %>% 
    rename(Turkey = n, Journal = Var1)

ISSNDE <- table(ISSN(fetchGermany)) %>% 
    as_tibble() %>% 
    rename(Germany = n, Journal = Var1)

ISSNJP <- table(ISSN(fetchJapan)) %>% 
    as_tibble() %>% 
    rename(Japan = n, Journal = Var1)

articles_per_journal <- list(
    ISSNTR,
    ISSNDE,
    ISSNJP
) %>%
    reduce(left_join, by = "Journal", .id = "id") %>% 
    gather(Country, n, 2:4)

articles_per_journal$Country <- factor(articles_per_journal$Country,
                                       levels =c("Japan", "Germany", "Turkey"))

```



**Result:**

In this graph x-axis is the list of journals with decreasing impact factor, and y-axis is the number of articles published in that journal. The colors and shapes are showing the country of affiliation. We see that in one journal articles from Japan is more than 800.  

```{r}
ggplot(data = articles_per_journal, aes(x = Journal, y = n, group = Country,
                                     colour = Country, shape = Country,
                                     levels = Country
)) +
    geom_point() +
    labs(x = "Journals with decreasing impact factor", y = "Number of Articles") +
    ggtitle("Pathology Articles Per Journal") + 
    theme(plot.title = element_text(hjust = 0.5),
          axis.text.x=element_blank())

```


**Comment:**

It is seen that one of the journals [ISSN: 1440-1827](https://onlinelibrary.wiley.com/page/journal/14401827/homepage/productinformation.html) has more than 800 articles from Japan. This journal is also from Japan. Here we wonder if there is an editorial preference for articles from their home country. 

We sometimes observe this situation if there is a conference in that country, and the conference abstracts are indexed. 

This may also be a clue that if a country has a journal listed in indexes, than it is more easy for the researchers in that country to publish their results.


**Future Work:**

Whether this observation is a unique situation, or there is a tendency in the journals to publish article from their country of origin, merits further investigation. 



---


## Feedback

[Serdar Balcı, MD, Pathologist](https://github.com/sbalci) would like to hear your feedback: https://goo.gl/forms/YjGZ5DHgtPlR1RnB3

This document will be continiously updated and the last update was on `r Sys.Date()`.

---

## Back to Main Menu

[Main Page for Bibliographic Analysis](https://sbalci.github.io/pubmed/BibliographicStudies.html)
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
diff --git a/SEERResearchPerCountries.Rmd b/SEERResearchPerCountries.Rmd
new file mode 100644
index 0000000..1435790
--- /dev/null
+++ b/SEERResearchPerCountries.Rmd
@@ -0,0 +1,682 @@
+---
+title: "Bibliographic Studies"
+subtitle: "SEER Research Per Countries / Who is doing SEER Research?"
+author: "Serdar Balcı, MD, Pathologist"
+date: '`r format(Sys.Date())`'
+output:
+ html_notebook:
+ code_folding: hide
+ fig_caption: yes
+ highlight: kate
+ theme: cerulean
+ toc_float: yes
+ html_document:
+ code_folding: hide
+ df_print: kable
+ fig_caption: yes
+ highlight: kate
+ keep_md: yes
+ theme: cerulean
+ toc_float: yes
+---
+
+
+
+```{r global_options, include=FALSE}
+knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'figure/', echo = FALSE, warning = FALSE, message = FALSE, error = FALSE, eval = TRUE, tidy = TRUE, comment = NA)
+```
+
+```{r library, include=FALSE}
+library(tidyverse)
+```
+
+
+```{r}
+state.name
+```
+
+
+```{r}
+# install.packages("maps")
+# library(maps)
+# x <- map("world", plot=FALSE)
+# glimpse(x)
+# x$names
+
+```
+
+```{r}
+install.packages("rworldmap")
+library(rworldmap)
+vignette('rworldmap')
+data(countryExData)
+countryExData
+```
+
+
+
+
+
+
+
+
+
+
+
+
+SEER China vs others
+
+https://www.rdocumentation.org/packages/bayesTFR/versions/6.1-2/topics/country.names
+
+
+https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/state.html
+
+
+
+
+# Who works on SEER {.tabset .tabset-fade .tabset-pills}
+
+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.
+Select from the tabs below.
+
+---
+
+## Aim
+
+**Aim:**
+
+
+
+---
+
+## Data retriveal from PubMed using EDirect
+
+Articles are downloaded as `xml`.
+
+```{r Search PubMed write all data as xml, eval=FALSE, include=FALSE}
+myTerm <- rstudioapi::terminalCreate(show = FALSE)
+rstudioapi::terminalSend(
+ myTerm,
+ "esearch -db pubmed -query \"'SEER Program'[Mesh]
+\" -datetype PDAT -mindate 1800 -maxdate 3000 | efetch -format xml > data/pubmed_result_SEER_MeSH.xml \n"
+)
+Sys.sleep(1)
+repeat {
+ Sys.sleep(0.1)
+ if (rstudioapi::terminalBusy(myTerm) == FALSE) {
+ print("Code Executed")
+ break
+ }
+}
+```
+
+
+
+```{r extract journal names from all data xml, message=FALSE, warning=FALSE}
+myTerm <- rstudioapi::terminalCreate(show = FALSE)
+rstudioapi::terminalSend(
+myTerm,
+"xtract -input data/pubmed_result_SEER_MeSH.xml -pattern PubmedArticle -sep ' ' -def 'na' -element MedlineCitation/PMID PubDate/Year Affiliation> data/SEER_countries.csv \n"
+)
+Sys.sleep(1)
+repeat {
+Sys.sleep(0.1)
+if (rstudioapi::terminalBusy(myTerm) == FALSE) {
+print("Code Executed")
+break
+}
+}
+```
+
+
+```{r}
+library(readr)
+SEER_countries <- read_delim("data/SEER_countries.csv",
+ "\t", escape_double = FALSE, col_names = c("PMID", "year", "Affiliations"),
+ na = "NA", trim_ws = TRUE)
+# View(SEER_countries)
+```
+
+```{r}
+countries <- read_delim("data/countries.txt", delim = "|", col_names = c("abb", "country"))
+
+country <- countries$country
+
+country <- c(country, state.name)
+
+country[80] <- "Georgia_"
+
+```
+
+
+
+```{r}
+# SEER_countries <- cbind(SEER_countries, setNames(lapply(country, function(x) x=NA), country))
+
+# names(SEER_countries)[254] <- "GeorgiaUSA"
+
+
+```
+
+```{r}
+# grepl(pattern = "China", x = SEER_countries$Affiliations)
+```
+
+
+```{r}
+# deneme1 <- grepl(pattern = country[44], x = SEER_countries$Affiliations)
+
+# deneme2 <- sapply(country, function(x) grepl(x, SEER_countries$Affiliations))
+
+# sum(deneme1 != deneme2[,44])
+
+```
+
+```{r}
+# deneme2 <- as.data.frame(deneme2)
+
+# sum(deneme2$Turkey)
+```
+
+```{r}
+SEER_countries <- cbind(SEER_countries, sapply(country, function(x) grepl(x, SEER_countries$Affiliations)))
+```
+
+
+```{r}
+dim(SEER_countries)[1]
+```
+
+At the time of the research the number of articles with 'SEER Program'[Mesh] formula is `r dim(SEER_countries)[1]`.
+
+```{r}
+# deneme <- colSums(SEER_countries[,-(1:3)])
+
+# deneme <- as.data.frame(deneme)
+
+# deneme <- rownames_to_column(deneme, var = "countries")
+
+# names(deneme) <- c("countries", "number")
+
+# deneme %>% arrange(desc(number))
+
+
+```
+
+```{r}
+SEER_countries[SEER_countries == FALSE] <- 0
+
+SEER_countries[SEER_countries == TRUE] <- 1
+
+```
+
+
+
+```{r}
+countryTotals <- SEER_countries %>%
+ select(-c(1:3)) %>%
+ summarise_all(funs(sum))
+
+countryTotals[which(countryTotals>0)]
+
+publisherCountries <- names(countryTotals[which(countryTotals>0)])
+
+SEER_countries <- SEER_countries %>%
+ select(c(1:3, publisherCountries))
+
+```
+
+
+
+```{r}
+deneme <- SEER_countries %>%
+ gather(key = "Country", value = "Number", -c(1:3)) %>%
+ group_by(Country, year) %>%
+ summarise(total = sum(Number))
+```
+
+
+```{r}
+deneme %>%
+ filter(year != "na") %>%
+ filter(year != "2017") %>%
+ filter(year != "2018") %>%
+ggplot() +
+ aes(y = total, x = year, group = Country, color = Country) +
+ geom_line() +
+ guides(fill=FALSE, color=FALSE) +
+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+
+
+```{r}
+USAnames <- names(SEER_countries) %in% state.name
+
+Others <- setdiff(names(SEER_countries[-c(1:3)]), c(USAnames,"United States", "China"))
+
+
+deneme2 <- SEER_countries %>%
+ mutate(
+ sumUSA = rowSums(
+ select(., one_of(USAnames), `United States`)
+ )
+ ) %>%
+mutate(
+ sumOthers = rowSums(
+ select(., one_of(Others))
+ )
+ ) %>%
+ select(PMID, year, China, USA = sumUSA, Others = sumOthers)
+
+```
+
+```{r}
+deneme3 <- deneme2 %>%
+ gather(key = "Country", value = "Number", -c(1:2)) %>%
+ group_by(PMID, Country, year) %>%
+ summarise(total = sum(Number)) %>%
+ filter(year != "na") %>%
+ filter(year != "2017") %>%
+ filter(year != "2018") %>%
+ filter(total != "0")
+```
+
+
+```{r}
+# which(duplicated(deneme3$PMID))
+# which(duplicated(deneme3$PMID))-1
+
+# deneme3[which(duplicated(deneme3$PMID)),]
+
+together <- bind_cols(
+First = deneme3$Country[which(duplicated(deneme3$PMID))],
+Second = deneme3$Country[which(duplicated(deneme3$PMID))-1]
+)
+
+table(together$First, together$Second) %>% addmargins()
+bind_cols(
+
+```
+
+
+
+```{r}
+deneme4 <- deneme2 %>%
+ gather(key = "Country", value = "Number", -c(1:2)) %>%
+ group_by(Country, year) %>%
+ summarise(total = sum(Number)) %>%
+ filter(year != "na") %>%
+ filter(year != "2017") %>%
+ filter(year != "2018") %>%
+ filter(total != "0")
+```
+
+
+
+```{r}
+deneme4 %>%
+
+ggplot() +
+ aes(y = total, x = year, group = Country, color = Country) +
+ geom_line() +
+ # guides(fill=FALSE, color=FALSE) +
+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+
+
+
+
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— Serdar Balcı (@serdarbalci) October 6, 2018
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+Bibliographic Studies
+SEER Research Per Countries / Who is doing SEER Research?
+Serdar Balcı, MD, Pathologist
+2018-10-06
+ + [1] "Alabama" "Alaska"
+ [3] "Arizona" "Arkansas"
+ [5] "California" "Colorado"
+ [7] "Connecticut" "Delaware"
+ [9] "Florida" "Georgia"
+[11] "Hawaii" "Idaho"
+[13] "Illinois" "Indiana"
+[15] "Iowa" "Kansas"
+[17] "Kentucky" "Louisiana"
+[19] "Maine" "Maryland"
+[21] "Massachusetts" "Michigan"
+[23] "Minnesota" "Mississippi"
+[25] "Missouri" "Montana"
+[27] "Nebraska" "Nevada"
+[29] "New Hampshire" "New Jersey"
+[31] "New Mexico" "New York"
+[33] "North Carolina" "North Dakota"
+[35] "Ohio" "Oklahoma"
+[37] "Oregon" "Pennsylvania"
+[39] "Rhode Island" "South Carolina"
+[41] "South Dakota" "Tennessee"
+[43] "Texas" "Utah"
+[45] "Vermont" "Virginia"
+[47] "Washington" "West Virginia"
+[49] "Wisconsin" "Wyoming" # install.packages("maps")
+# library(maps)
+# x <- map("world", plot=FALSE)
+# glimpse(x)
+# x$names
+
+
+
+
+
+SEER China vs others
+https://www.rdocumentation.org/packages/bayesTFR/versions/6.1-2/topics/country.names
+https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/state.html
+
+
+
Who works on SEER
+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.
+Select from the tabs below.
+
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+
+Aim
+Aim:
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Data retriveal from PubMed using EDirect
+Articles are downloaded as xml.
myTerm <- rstudioapi::terminalCreate(show = FALSE)
+rstudioapi::terminalSend(
+myTerm,
+"xtract -input data/pubmed_result_SEER_MeSH.xml -pattern PubmedArticle -sep ' ' -def 'na' -element MedlineCitation/PMID PubDate/Year Affiliation> data/SEER_countries.csv \n"
+)
+Sys.sleep(1)
+repeat {
+Sys.sleep(0.1)
+if (rstudioapi::terminalBusy(myTerm) == FALSE) {
+print("Code Executed")
+break
+}
+}library(readr)
+SEER_countries <- read_delim("data/SEER_countries.csv",
+ "\t", escape_double = FALSE, col_names = c("PMID", "year", "Affiliations"),
+ na = "NA", trim_ws = TRUE)Parsed with column specification:
+cols(
+ PMID = col_integer(),
+ year = col_character(),
+ Affiliations = col_character()
+)Parsed with column specification:
+cols(
+ abb = col_character(),
+ country = col_character()
+)
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+
+
+# deneme1 <- grepl(pattern = country[44], x = SEER_countries$Affiliations)
+
+# deneme2 <- sapply(country, function(x) grepl(x, SEER_countries$Affiliations))
+
+# sum(deneme1 != deneme2[,44])[1] 6240At the time of the research the number of articles with ‘SEER Program’[Mesh] formula is 6240.
+ + + +# deneme <- colSums(SEER_countries[,-(1:3)])
+
+# deneme <- as.data.frame(deneme)
+
+# deneme <- rownames_to_column(deneme, var = "countries")
+
+# names(deneme) <- c("countries", "number")
+
+# deneme %>% arrange(desc(number))
+