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I have a list of data frame with 40802 gene names and I have data frame with 14000 article information. The article information contains Article, Abstract, Day, Month, Year.

I have transformed the date into normal format, and the abstract as character.

I want to have a plot of X in time, and the frequency of the gene names appears in the abstract. EG

| Date       | Gene Name | Frequency |
|------------|-----------|-----------|
| 2017-03-20 | GAPDH     | 5         |
| 2017-03-21 | AKT       | 6         |

Basically, I want to know the gene names most frequently published in the last 100 days and have a timeline to see the evolution of said genenames. Something like a trend.

library(RISmed)

##Research the query - can be anything relevant to protein expression.
##Multiple research not tested yet

search_topic <- 'protein expression'

##Evaluate the query with reldate = days before today, retmax = maximun number of returned results

search_query <- EUtilsSummary(search_topic, retmax=15000, reldate = 100)
##explore the outcome

summary(search_query)

##get the ids for tall the queries to get the articles

QueryId(search_query)
##get all the records associated with the ID - THIS TAKES LOOONG TIME

records<- EUtilsGet(search_query)

##Analyze the structure
str(records)

summary(records)

##Create a data frame with article/abstract/date

pubmed_data <- data.frame('Title'=ArticleTitle(records),'Abstract'=AbstractText(records),
                             "Day"=DayPubmed(records), "Month" = MonthPubmed(records), "Year"=YearPubmed(records))
##explore the data
head(pubmed_data,1)
##gene names
genename <- read.csv("genename.csv", header = T, stringsAsFactors = F)

##remove any NA tittles

pubmed <-pubmed_data[-which(is.na(pubmed_data$Title)), ]
##Coerce the date to YYYY-MM-DD

pubmed$Date <- as.Date( paste( pubmed$Day , pubmed$Month , sep = "." )  , format = "%d.%m" )

I've read a lot and cannot figure out how to find genemane[1,1] inside pubmed$Abstract, and count the times it appeared by time. Making a plot where X is the last 100 days and the line prot would be the frequency of the genenames, And the legend would be the genename. So a trend can be observed.

I would really appreciate any ideas how this can be done.

I have tried tm, and have tried a lot of different things, but still hitting a wall. Is my concept wrong?

lmo
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Filipe Rigueiro
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1 Answers1

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# from: https://stackoverflow.com/questions/45485701/count-frequency-of-words-in-text-and-create-plot
# get some text
txt <- c("I have a list of data frame with 40802 gene names and I have data frame with 14000 article information. 
The article information contains Article, Abstract, Day, Month, Year.I have transformed the date into normal format, 
and the abstract as character. I want to have a plot of X in time, and the frequency of the gene names appears in the abstract.
Basically, I want to know the gene names most frequently published in the last 100 days and have a timeline to see the evolution of said genenames. 
Something like a trend.")

# cut to ngramms for dataframe example
txt <- strwrap(x = txt,width = 20)
# create some data frame
pubmed_data <- data.frame(Title=abbreviate(names.arg = txt,minlength = 5,method = "left.kept",named = F),Abstract=txt,stringsAsFactors = F)
pubmed_data

# tm package
library(tm)
wrds <- termFreq(doc = pubmed_data$Abstract,control = list(tolower=TRUE,removePunctuation=TRUE,removeNumbers=TRUE))
wrds <- sort(unclass(wrds),decreasing = T)
wrds <- data.frame(tokens=names(wrds),n=as.integer(wrds))
wrds$tokens <- reorder(wrds$tokens,wrds$n)

library(ggplot2)
ggplot(data = wrds,aes(x = tokens,y = n,fill=n))+geom_bar(stat="identity")+scale_y_continuous(breaks = 1:max(wrds$n))+
  coord_flip()


# tidy packages 
library(tidytext)
library(dplyr)
wrds2 <- pubmed_data %>% select(-Title) %>% unnest_tokens(input = "Abstract",output = "tokens",to_lower = T) %>% 
  filter(grepl(pattern="\\D+",x=.$tokens)) %>% group_by(tokens) %>%
  count %>% ungroup %>% mutate(tokens=reorder(tokens,n))

ggplot(data = wrds2,aes(x = tokens,y = n,fill=n))+geom_bar(stat="identity")+scale_y_continuous(breaks = 1:max(wrds$n))+
  coord_flip()
Edvardoss
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