I am trying to perform a search on specific authors
so I can look up but I don't know how to extract citation, or plot journals that he or she published papers in
library(RISmed)
#now let's look up this author
res <- EUtilsSummary('Gene Myers', type='esearch', db='pubmed')
summary(res)
The first thing to notice is that what you already produced contains the PubMed IDs
for the papers that match your query.
res#PMID
[1] "30481296" "29335514" "26102528" "25333104" "23541733" "22743769"
[7] "21685076" "20937014" "20122179" "19447790" "12804086" "12061009"
Knowing the IDs, you can retrieve detailed information on all of them
using EUtilsGet
res2 = EUtilsGet(res#PMID)
Now we can get the items required for a citation from res2.
ArticleTitle(res2) ## Article Titles
Title(res2) ## Publication Names
YearPubmed(res2) ## Year of publication
Volume(res2) ## Volume
Issue(res2) ## Issue number
Author(res2) ## Lists of Authors
There is much more information embedded in the res2 object.
If you look at the help page ?Medline, you can get a good idea
of the other information.
When you retrieve the detailed information of the selected articles using EUtilsGet, the journal information is stored as ISO abbreviated term.
library(RISmed)
#now let's look up this author
res <- EUtilsSummary('Gene Myers', type='esearch', db='pubmed')
summary(res)
res2 = EUtilsGet(res, db = "pubmed")
sort(table(res2#ISOAbbreviation), decreasing = T)[1:5] ##Top 5 journals
Gigascience Bioinformatics J Comput Biol BMC Bioinformatics Curr Biol
3 2 2 1 1
Related
I'm a professor from the University College in Ghent (Belgium) and we are brainstorming about the organization of our exams, Research Techniques (quite a bit of statistics). We are thinking of generating individual exams for all our students, but we want to make the grading as easy as possible.
We were thinking of generating n exams, using R-exams and allowing the students to answer using google forms / OneDrive forms or whatever platform to generate an excel with al the answers from the students. The content of each answer vector would be different, although the type of answer for each question would be the same.
If would be awesome if we could generate an answer excel sheet with all the answers per generated exam: this way we only need to div the answer provided by the students with the answers generated by R-exams. Is such a functionality available or possible?
With kind regards
Jens Buysse
The functionality you are looking for is not readily available in R/exams but it is not too hard to write a little bit of code that puts it together.
All exams2xyz() interfaces return a list of exams, containing a list of exercises, containing (among other things) the meta-information for each question. You can extract this and put it into an Excel sheet.
Also you can use the exams_metainfo() extractor to display the information within R.
As a simple example consider:
library("exams")
set.seed(0)
exm <- exams2html(c("swisscapital.Rmd", "deriv.Rmd"), n = 3)
Now exm is a list of n = 3 exams, each containing 2 exercises, for which then the metainformation can be extracted. For example for the first exercise in the first exam:
exm[[1]][[1]]$metainfo$name
## [1] "Swiss Capital"
exm[[1]][[1]]$metainfo$solution
## [1] FALSE FALSE FALSE TRUE FALSE
exm[[1]][[1]]$metainfo$string
## [1] "Swiss Capital: 4"
To display this information in R:
exams_metainfo(exm)
## exam1
## 1. Swiss Capital: 4
## 2. derivative exp: 55.25 (55.24--55.26)
##
## exam2
## 1. Swiss Capital: 2
## 2. derivative exp: 1.79 (1.78--1.8)
##
## exam3
## 1. Swiss Capital: 4
## 2. derivative exp: 46.73 (46.72--46.74)
You can also get just one exam via the print() method:
print(exams_metainfo(exm), 2)
## exam2
## 1. Swiss Capital: 2
## 2. derivative exp: 1.79 (1.78--1.8)
Trying to figure something out for a pet project and is awfully stuck :(
The project centers around order of Tweet occurrences. I collected Tweets on 3 different topics for 2 actors. I want to determine which actor's tweet on each particular topic occurred earlier overall. A friend recommended me look into the package "survival", but I couldn't see how it could work. Any suggestion would be welcome! Thanks so much!
EDIT: Additional information
created_at name
1544469754 chicagotribune
1541550304 chicagotribune
party type topic
M 1 trade
M 1 trade
The variable represents in following information
-created at: the time the tweet was sent out
-name: Twitter account name
-party: classification variable of political leaning
-type: binary indicator (1 = media type A, 0 = media type B)
-topic: the topic the tweet belongs to (3 topics total)
I don't think this is a survival analysis problem, you just need to find the earliest timestamp within each topic. I think something like this should work:
# Read in example data
df = readr::read_table("created_at name party type topic
1544469754 chicagotribune M 1 trade
1541550304 chicagotribune M 1 trade")
df %>%
group_by(topic) %>%
summarise(first_tweeter = name[which.min(created_at)])
Good day
I am a newbie to Stackoverflow:)
I am trying my hand with programming with R and found this platform a great source of help.
I have developed some code leveraging stackoverflow, but now I am failing to read the metadata from this htm file
Please direct download this file before using in R
setwd("~/NLP")
library(tm)
library(rvest)
library(tm.plugin.factiva)
file <-read_html("facts.htm")
source <- FactivaSource(file)
corpus <- Corpus(source, readerControl = list(language = NA))
# See the contents of the documents
inspect(corpus)
head(corpus)
<<VCorpus>>
Metadata: corpus specific: 0, document level (indexed): 0
Content: documents: 3
See meta-data associated with first article
meta(corpus[[3]])
meta(corpus[[3]])
author : character(0)
datetimestamp: 2017-08-31
description : character(0)
heading : Rain, Rain, Rain
id : TIMEUK-170830-e
language : en
origin : thetimes.co.uk
edition : character(0)
section : Comment
subject : c("Hurricanes/Typhoons", "Storms", "Political/General News", "Disasters/Accidents", "Natural Disasters/Catastrophes", "Risk News", "Weather")
coverage : c("United States", "North America")
company : character(0)
industry : character(0)
infocode : character(0)
infodesc : character(0)
wordcount : 333
publisher : News UK & Ireland Limited
rights : © Times Newspapers Limited 2017
How can I save each metadata (SE, HD, AU, ..PUB, AU) - all 18 metadata elements column-wise in a dataframe or write to excel for each document in corpus?
Example of output:
SE HD AU ...
Doc 1
2
3
Thank you for your help
The simplest way I know of to do it is:
Make a data frame from each of the three lists in your corpus:
one<-data.frame(unlist(meta(corpus[[1]])))
two<-data.frame(unlist(meta(corpus[[2]])))
three<-data.frame(unlist(meta(corpus[[3]])))
Then you will want to merge them into a single data frame. For the first two, this is easy to do, as using "row.names" will cause them to merge on the NON VARIABLE row names. But the second merge, you need to merge based on the column now named "Row.Names" So you need to create and rename the first column of the third file with the row names, using setDT allows you to do this without adding another full set of information, just redirecting R to see the row names as the first column
setDT(three, keep.rownames = TRUE)[]
colnames(three)[1] <- "Row.names"
then you simply merge the first and second data frame into variable named meta, and then merge meta with three using "Row.names" (the new name of the first column now).
meta <- merge(one, two, by="row.names", all=TRUE)
meta <- merge(meta, three, by = "Row.names", all=TRUE)
Your data will look like this:
Row.names unlist.meta.corpus..1.... unlist.meta.corpus..2.... unlist.meta.corpus..3....
1 author Jenni Russell <NA> <NA>
2 coverage1 United States North Korea United States
3 coverage2 North America United States North America
4 coverage3 <NA> Japan <NA>
5 coverage4 <NA> Pyongyang <NA>
6 coverage5 <NA> Asia Pacific <NA>
Those NA values are there because not all of the sub-lists had values for all of the observations.
By using the all=TRUE on both merges, you preserve all of the fields, with and without data, which makes it easy to work with moving forward.
If you look at this PDF from CRAN on page two the section Details shows you how to access the content and metadata. From there is is simply about unlisting to move them into data frames.
If you get lost, send a comment and I will do what I can to help you out!
EDIT BY REQUEST:
To write this to Excel is not super difficult because the data is already "square" in a uniform data frame. You would just install xlsx package and xlxsjars then use the following function:
write.xlsx(meta, file, sheetName="Sheet1",
col.names=TRUE, row.names=TRUE, append=FALSE, showNA=TRUE)
You can find information about the package here: page 38 gives more detail.
And if you want to save the content, you can change meta to content in the files which extract the data from corpus and make the initial dataframes. The entire process will be the same otherwise
I have long text file using help of R language I want to summarize text in at least 10 to 20 line or in small sentences.
How to summarize text in at least 10 line with R language ?
You may try this (from the LSAfun package):
genericSummary(D,k=1)
whereby 'D' specifies your text document and 'k' the number of sentences to be used in the summary. (Further modifications are shown in the package documentation).
For more information:
http://search.r-project.org/library/LSAfun/html/genericSummary.html
There's a package called lexRankr that summarizes text in the same way that Reddit's /u/autotldr bot summarizes articles. This article has a full walkthrough on how to use it but just as a quick example so you can test it yourself in R:
#load needed packages
library(xml2)
library(rvest)
library(lexRankr)
#url to scrape
monsanto_url = "https://www.theguardian.com/environment/2017/sep/28/monsanto-banned-from-european-parliament"
#read page html
page = xml2::read_html(monsanto_url)
#extract text from page html using selector
page_text = rvest::html_text(rvest::html_nodes(page, ".js-article__body p"))
#perform lexrank for top 3 sentences
top_3 = lexRankr::lexRank(page_text,
#only 1 article; repeat same docid for all of input vector
docId = rep(1, length(page_text)),
#return 3 sentences to mimick /u/autotldr's output
n = 3,
continuous = TRUE)
#reorder the top 3 sentences to be in order of appearance in article
order_of_appearance = order(as.integer(gsub("_","",top_3$sentenceId)))
#extract sentences in order of appearance
ordered_top_3 = top_3[order_of_appearance, "sentence"]
> ordered_top_3
[1] "Monsanto lobbyists have been banned from entering the European parliament after the multinational refused to attend a parliamentary hearing into allegations of regulatory interference."
[2] "Monsanto officials will now be unable to meet MEPs, attend committee meetings or use digital resources on parliament premises in Brussels or Strasbourg."
[3] "A Monsanto letter to MEPs seen by the Guardian said that the European parliament was not “an appropriate forum” for discussion on the issues involved."
I am very new with the GO analysis and I am a bit confuse how to do it my list of genes.
I have a list of genes (n=10):
gene_list
SYMBOL ENTREZID GENENAME
1 AFAP1 60312 actin filament associated protein 1
2 ANAPC11 51529 anaphase promoting complex subunit 11
3 ANAPC5 51433 anaphase promoting complex subunit 5
4 ATL2 64225 atlastin GTPase 2
5 AURKA 6790 aurora kinase A
6 CCNB2 9133 cyclin B2
7 CCND2 894 cyclin D2
8 CDCA2 157313 cell division cycle associated 2
9 CDCA7 83879 cell division cycle associated 7
10 CDCA7L 55536 cell division cycle associated 7-like
and I simply want to find their function and I've been suggested to use GO analysis tools.
I am not sure if it's a correct way to do so.
here is my solution:
x <- org.Hs.egGO
# Get the entrez gene identifiers that are mapped to a GO ID
xx<- as.list(x[gene_list$ENTREZID])
So, I've got a list with EntrezID that are assigned to several GO terms for each genes.
for example:
> xx$`60312`
$`GO:0009966`
$`GO:0009966`$GOID
[1] "GO:0009966"
$`GO:0009966`$Evidence
[1] "IEA"
$`GO:0009966`$Ontology
[1] "BP"
$`GO:0051493`
$`GO:0051493`$GOID
[1] "GO:0051493"
$`GO:0051493`$Evidence
[1] "IEA"
$`GO:0051493`$Ontology
[1] "BP"
My question is :
how can I find the function for each of these genes in a simpler way and I also wondered if I am doing it right or?
because I want to add the function to the gene_list as a function/GO column.
Thanks in advance,
EDIT: There is a new Bioinformatics SE (currently in beta mode).
I hope I get what you are aiming here.
BTW, for bioinformatics related topics, you can also have a look at biostar which have the same purpose as SO but for bioinformatics
If you just want to have a list of each function related to the gene, you can query database such ENSEMBl through the biomaRt bioconductor package which is an API for querying biomart database.
You will need internet though to do the query.
Bioconductor proposes packages for bioinformatics studies and these packages come generally along with good vignettes which get you through the different steps of the analysis (and even highlight how you should design your data or which would be then some of the pitfalls).
In your case, directly from biomaRt vignette - task 2 in particular:
Note: there are slightly quicker way that the one I reported below:
# load the library
library("biomaRt")
# I prefer ensembl so that the one I will query, but you can
# query other bases, try out: listMarts()
ensembl=useMart("ensembl")
# as it seems that you are looking for human genes:
ensembl = useDataset("hsapiens_gene_ensembl",mart=ensembl)
# if you want other model organisms have a look at:
#listDatasets(ensembl)
You need to create your query (your list of ENTREZ ids). To see which filters you can query:
filters = listFilters(ensembl)
And then you want to retrieve attributes : your GO number and description. To see the list of available attributes
attributes = listAttributes(ensembl)
For you, the query would look like something as:
goids = getBM(
#you want entrezgene so you know which is what, the GO ID and
# name_1006 is actually the identifier of 'Go term name'
attributes=c('entrezgene','go_id', 'name_1006'),
filters='entrezgene',
values=gene_list$ENTREZID,
mart=ensembl)
The query itself can take a while.
Then you can always collapse the information in two columns (but I won't recommend it for anything else that reporting purposes).
Go.collapsed<-Reduce(rbind,lapply(gene_list$ENTREZID,function(x)
tempo<-goids[goids$entrezgene==x,]
return(
data.frame('ENTREZGENE'= x,
'Go.ID'= paste(tempo$go_id,collapse=' ; '),
'GO.term'=paste(tempo$name_1006,collapse=' ; '))
)
Edit:
If you want to query a past version of the ensembl database:
ens82<-useMart(host='sep2015.archive.ensembl.org',
biomart='ENSEMBL_MART_ENSEMBL',
dataset='hsapiens_gene_ensembl')
and then the query would be:
goids = getBM(attributes=c('entrezgene','go_id', 'name_1006'),
filters='entrezgene',values=gene_list$ENTREZID,
mart=ens82)
However, if you had in mind to do a GO enrichment analysis, your list of genes is too short.