PubMedPackages.Rmd

---
title: "PubMed Packages"
output: 
  html_notebook: 
    highlight: tango
    number_sections: yes
    theme: spacelab
    toc: yes
---

# easyPubMed

```{r}
library(easyPubMed)
library(XML)
```


## easyPubMed: Search and Retrieve Scientific Publication Records from PubMed  


https://cran.r-project.org/web/packages/easyPubMed/index.html

https://cran.r-project.org/web/packages/easyPubMed/easyPubMed.pdf

https://cran.r-project.org/web/packages/easyPubMed/vignettes/easyPM_vignette_html.html

https://cran.r-project.org/web/packages/easyPubMed/vignettes/easyPM_vignette_pdf.pdf

```{r}
my_query <- "Damiano Fantini[AU]"
my_entrez_id <- get_pubmed_ids(my_query)
my_abstracts_txt <- fetch_pubmed_data(my_entrez_id, format = "abstract")
my_abstracts_txt[1:10]
```



```{r}
my_abstracts_xml <- fetch_pubmed_data(my_entrez_id)
class(my_abstracts_xml) 
```


```{r}
#
# apply "saveXML" to each //ArticleTitle tag via XML::xpathApply()
my_titles <- unlist(xpathApply(my_abstracts_xml, "//ArticleTitle", saveXML))
#
# use gsub to remove the tag, also trim long titles
my_titles <- gsub("(^.{5,10}Title>)|(<\\/.*$)", "", my_titles)
my_titles[nchar(my_titles)>75] <- paste(substr(my_titles[nchar(my_titles)>75], 1, 70), 
                                        "...", sep = "")
print(my_titles)
```




```{r}
new_query <- "(APE1[TI] OR OGG1[TI]) AND (2012[PDAT]:2016[PDAT])"
out.A <- batch_pubmed_download(pubmed_query_string = new_query, 
                               format = "xml", 
                               batch_size = 150,
                               dest_file_prefix = "easyPM_example")
```



```{r}
out.A # this variable stores the name of the output files
```

```{r}
my_PM_list <- articles_to_list(my_abstracts_xml)
class(my_PM_list[[4]])
```


```{r}
cat(substr(my_PM_list[[4]], 1, 984))
```


```{r}
curr_PM_record <- my_PM_list[[4]]
custom_grep(curr_PM_record, tag = "DateCompleted")
```


```{r}
custom_grep(curr_PM_record, tag = "LastName", format = "char")
```


```{r}
my.df <- article_to_df(curr_PM_record, max_chars = 18)
#
# Fields extracted from the PubMed record
colnames(my.df)
```


```{r}
#
# Trim long strings and then Display some content: each row corresponds to one author
my.df$title <- substr(my.df$title, 1, 15)
my.df$address <- substr(my.df$address, 1, 19)
my.df$jabbrv <- substr(my.df$jabbrv, 1, 10)
my.df[,c("pmid", "title", "jabbrv", "firstname", "address")]
```


```{r}
my.df2 <- article_to_df(curr_PM_record, autofill = TRUE)
my.df2$title <- substr(my.df2$title, 1, 15)
my.df2$jabbrv <- substr(my.df2$jabbrv, 1, 10)
my.df2$address <- substr(my.df2$address, 1, 19)
my.df2[,c("pmid", "title", "jabbrv", "firstname", "address")]
```


```{r}
new_PM_query <- "(APEX1[TI] OR OGG1[TI]) AND (2010[PDAT]:2013[PDAT])"
out.B <- batch_pubmed_download(pubmed_query_string = new_PM_query, dest_file_prefix = "apex1_sample")
```

```{r}
# Retrieve the full name of the XML file downloaded in the previous step
new_PM_file <- out.B[1]
new_PM_df <- table_articles_byAuth(pubmed_data = new_PM_file, included_authors = "first", max_chars = 0)
# Alternatively, the output of a fetch_pubmed_data() could have been used
#
# Printing a sample of the resulting data frame
new_PM_df$address <- substr(new_PM_df$address, 1, 28)
new_PM_df$jabbrv <- substr(new_PM_df$jabbrv, 1, 9)
print(new_PM_df[1:10, c("pmid", "year", "jabbrv", "lastname", "address")])  
```



## Querying PubMed via the easyPubMed package in R  

http://www.biotechworld.it/bioinf/2016/01/05/querying-pubmed-via-the-easypubmed-package-in-r/


## easyPubMed for business: scraping PubMed data in R for a targeting campaign  

http://www.biotechworld.it/bioinf/2016/01/21/scraping-pubmed-data-via-easypubmed-xml-and-regex-in-r-for-a-targeting-campaign/


# europepmc

europepmc: R Interface to the Europe PubMed Central RESTful Web Service

https://cran.r-project.org/web/packages/europepmc/index.html

https://cran.r-project.org/web/packages/europepmc/europepmc.pdf

## Making proper trend graphs

https://cran.r-project.org/web/packages/europepmc/vignettes/evergreenreviewgraphs.html


```{r}
library(europepmc)
```


```{r}
europepmc::epmc_hits_trend(query = "aspirin", period = 2010:2016)
```


```{r}
tt_oa <- europepmc::epmc_hits_trend("OPEN_ACCESS:Y", period = 1995:2017, synonym = FALSE)
tt_oa
```


```{r}
library(ggplot2)
ggplot(tt_oa, aes(year, query_hits / all_hits)) +
  geom_point() + 
  geom_line() +
  xlab("Year published") + 
  ylab("Proportion of OA full-texts in Europe PMC")
```


```{r}
dvcs <- c("code.google.com", "github.com", 
          "sourceforge.net", "bitbucket.org", "cran.r-project.org")
# make queries including reference section
dvcs_query <- paste0('REF:"', dvcs, '"')
```

```{r}
library(dplyr)
my_df <- purrr::map_df(dvcs_query, function(x) {
  # get number of publications with indexed reference lists
  refs_hits <- 
    europepmc::epmc_hits_trend("has_reflist:y", period = 2009:2016, synonym = FALSE)$query_hits
  # get hit count querying for code repositories 
  europepmc::epmc_hits_trend(x, period = 2009:2016, synonym = FALSE) %>% 
    dplyr::mutate(query_id = x) %>%
    dplyr::mutate(refs_hits = refs_hits) %>%
    dplyr::select(year, all_hits, refs_hits, query_hits, query_id)
}) 
my_df
```



```{r}
### total
hits_summary <- my_df %>% 
  group_by(query_id) %>% 
  summarise(all = sum(query_hits)) %>% 
  arrange(desc(all))
hits_summary
```


```{r}
library(ggplot2)
ggplot(my_df, aes(factor(year), query_hits / refs_hits, group = query_id, 
                  color = query_id)) +
  geom_line(size = 1, alpha = 0.8) +
  geom_point(size = 2) +
  scale_color_brewer(name = "Query", palette = "Set1")+
  xlab("Year published") +
  ylab("Proportion of articles in Europe PMC")
```





## Introducing europepmc, an R interface to Europe PMC RESTful API

https://cran.r-project.org/web/packages/europepmc/vignettes/introducing-europepmc.html


```{r}
library(europepmc)
europepmc::epmc_search('malaria')
```


```{r}
europepmc::epmc_search('malaria', synonym = FALSE)
```


```{r}
europepmc::epmc_search('"Human malaria parasites"')
```


```{r}
europepmc::epmc_search('"Human malaria parasites"', limit = 10)
```



```
sort = 'cited'
sort = 'date' 
```

```{r}
my_dois <- c(
  "10.1159/000479962",
  "10.1002/sctm.17-0081",
  "10.1161/strokeaha.117.018077",
  "10.1007/s12017-017-8447-9"
  )
  plyr::ldply(my_dois, function(x) {
  europepmc::epmc_search(paste0("DOI:", x))
  })
```

```
output = "id_list" 
output = "raw"
```

```{r}
europepmc::epmc_search('AUTH:"Salmon Maelle"')
```


```{r}
q <- 'AUTH:"PÜHLER Alfred" OR AUTH:"Pühler Alfred Prof. Dr." OR AUTH:"Puhler A"'
europepmc::epmc_search(q, limit = 1000)
```


```{r}
europepmc::epmc_search('AUTHORID:"0000-0002-7635-3473"', limit = 200, sort = "cited")
```


```{r}
europepmc::epmc_search('disease:meningitis')
```


```{r}
europepmc::epmc_tm(30242204)
```



```{r}
europepmc::epmc_search('(HAS_PDB:y) AND FIRST_PDATE:2016')
```



```{r}
europepmc::epmc_citations("9338777", limit = 500)
```




```{r}
europepmc::epmc_refs("28632490", limit = 200)
```




```{r}
europepmc::epmc_ftxt("PMC3257301")
```











# pubmed.mineR

pubmed.mineR: Text Mining of PubMed Abstracts


https://cran.r-project.org/web/packages/pubmed.mineR/index.html

https://cran.r-project.org/web/packages/pubmed.mineR/pubmed.mineR.pdf


# PubMedWordcloud

PubMedWordcloud: 'Pubmed' Word Clouds


https://cran.r-project.org/web/packages/PubMedWordcloud/index.html

https://cran.r-project.org/web/packages/PubMedWordcloud/PubMedWordcloud.pdf