Animal nutrition journal report: December 2017

A report is published for a personal interesting of the recent animal nutrition. The papers about “animal nutrition” which published at some SCI(E) journals were selected and analyzed. HTML scraping, data visualization, English-Korean translation, and report generation are automate process except sorting of the papers. Codes are available at https://github.com/YoungjunNa/journal_scraping

1. List of journals

journal_list <- read.csv("journal_list.txt", header=TRUE)
kable(journal_list)
journals
journal of animal science
journal of dairy science
animal
poultry science
animal feed science and technology
livestock science
journal of animal science and biotechnology
revista brasileira de zootecnia
asian-australasian journal of animal science

2. List of papers

2.1. Review

results <- read.csv("journal_result_2017_12.txt")
results <- results[,c(7,10,8,5,4,1,2,3,6)]
filter(results, type=="review")

2.2. Monogastric

filter(results, type=="original article" & class=="monogastric")

2.3. Ruminant

filter(results, type=="original article" & class=="ruminant")

3. Wordcloud analysis

3.1. Monogastric

wc <- read.csv("journal_result_2017_12.txt", stringsAsFactors = FALSE)
#wc <- filter(wc, class == "ruminant")
wc <- filter(wc, class == "monogastric")
#wc <- filter(wc, str_detect(wc$subject,"supplement")==TRUE)

#WORDCLOUD
wc <- Corpus(VectorSource(wc$subject))

wc_data<-tm_map(wc,stripWhitespace)
wc_data<-tm_map(wc_data, removeWords, c("and"))

wc_data<-tm_map(wc_data, tolower)
wc_data<-tm_map(wc_data,removeNumbers)
wc_data<-tm_map(wc_data, removePunctuation)
wc_data<-tm_map(wc_data, removeWords, stopwords("english"))
wc_data<-tm_map(wc_data, removeWords, c("affect","effect","effects","and","the","our","that","for","are","also","more","has","must","have","should","this","with"))

tdm_wc<-TermDocumentMatrix(wc_data) #Creates a TDM
TDM1<-as.matrix(tdm_wc) #Convert this into a matrix format
v = sort(rowSums(TDM1), decreasing = TRUE) #Gives you the frequencies for every word

wordcloud(wc_data, max.words = Inf, min.freq = 1, random.order = FALSE, rot.per = 0.1, colors = brewer.pal(8, "Dark2"))