plot_score_animals: Plot frequency of animal model scores in abstracts
In JulFriedrich/miRetrieve: miRNA Text Mining in Abstracts

Description Usage Arguments Details Value See Also

Plot frequency of animal model scores in abstracts.

plot_score_animals(
  df,
  keywords = animal_keywords,
  case = FALSE,
  bins = NULL,
  colour = "steelblue3",
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

`df`	Data frame containing abstracts.
`keywords`	Character vector. Vector containing keywords. The animal model score is calculated based on these keywords. How much weight a keyword in `keywords` carries is determined how often it is present in `keywords`, e.g. if a keyword is mentioned twice in `keywords` and it is mentioned only once in an abstract, it adds 2 points to the score.
`case`	Boolean. If `case = TRUE`, terms contained in `keywords` are case sensitive. If `case = FALSE`, terms contained in `keywords` are case insensitive.
`bins`	Integer. Specifies how many bins are used to plot the distribution. If `bins = NULL`, bins are calculated over the whole range of scores, with one bin per score.
`colour`	String. Colour of histogram.
`col.abstract`	Symbol. Column containing abstracts.
`col.pmid`	Symbol. Column containing PubMed-IDs.
`title`	String. Plot title.

Plots a frequency distribution of animal model scores in abstracts of a data frame. The animal model score is influenced by the choice of terms in keywords. Plotting the distribution can help deciding if the terms are well-chosen, or in choosing the right threshold to decide which abstracts are considered to contain animal models.

Histogram displaying the distribution of animal scores in abstracts.

calculate_score_animals()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_biomarker(), calculate_score_patients(), calculate_score_topic(), plot_score_biomarker(), plot_score_patients(), plot_score_topic()