plot_score_topic: Plot frequency of self-chosen topic scores in abstracts

In JulFriedrich/miRetrieve: miRNA Text Mining in Abstracts

Description

Plot frequency of self-chosen topic scores in abstracts.

Usage

plot_score_topic(
  df,
  keywords,
  case = FALSE,
  name.topic = "TOPIC",
  bins = NULL,
  colour = "steelblue3",
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

Arguments

df	Data frame containing abstracts.
keywords	Character vector. Vector containing keywords. How much weight a keyword in keywords carries is determined by 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.
name.topic	String. Name of the topic.
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.

Details

Plots a frequency distribution of self-chosen topic scores in abstracts of a data frame. The topic score is influenced by the choice of terms in keywords. Plotting the distribution can help in choosing the right threshold to decide which abstracts correspond to the self-chosen topic.

Value

Histogram displaying the distribution of self-chosen topic scores in abstracts.

See Also

calculate_score_topic(), assign_topic()

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

JulFriedrich/miRetrieve documentation built on Sept. 20, 2021, 11:37 p.m.