miRetrieve (version 1.3.4)

plot_score_topic: Plot frequency of self-chosen topic scores 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.

Value

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

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.

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()