plot_novelty {novelqualcodes}

Plot novelty of interviews over time

Description

Novel codes are information that has not been previously mentioned by other interviewees. The trend of 'novel' interview codes provides insight into the richness of qualitative information.

This plot shows the trend of novel code generation; in the most basic way, the higher the number, the richer the information that has been generated in the study. By showing novel codes in context with any refinements to the questions, it also shows how that trend may have been affected by those refinements, and whether novel code generation is plateauing.

This chart alone should not be used to decide on a stopping point because it does not show the richness of individual interviews; some interviews are richer than others, therefore consider also using plot_richness() to look at the richness of each interview in terms of novel and duplicate codes.

Usage

plot_novelty(
  score_df,
  refinements = integer(0),
  col = list(stroke = "black", fill_ref = "black", fill = "grey80")
)

Arguments

score_df

(Dataframe) A dataframe of scored codes, as generated by score_codes().

refinements

Either a list object generated by import_field_notes(), or an Integer vector that lists when (in terms of interview sequence) refinements were made to the interview questions. For example, c(10, 15) means that interview questions were revised twice: First before the 10th interview, and then again before the 15th interview.

col

(List) A List containing named Character vectors. Accepted names are:

• stroke is the colour of point outlines as well as the line linking points together.

• fill_ref is the colour of points after a refinement.

• fill is the fill colour of points were no refinements were made.

Value

A ggplot object.

See Also

score_codes(), import_field_notes(), plot_richness(), save_last_plot()

Examples

# Field notes and coding matrices included with the package
path_to_notes    <- system.file("insect_study/records/refinements.xlsx", package = "novelqualcodes")
path_to_matrices <- system.file("insect_study/matrices/", package = "novelqualcodes")

# Import the data
my_refinements <- import_field_notes(path_to_notes)
my_matrices    <- import_coding_matrices(path_to_matrices)

# Score novel and duplicate codes
my_scores <- score_codes(my_matrices)

# Generate a plot with no refinements
plot_novelty(score_df = my_scores)

# Generate a plot using scored codes and imported refinements
plot_novelty(score_df = my_scores, refinements = my_refinements)

# Generate a plot using scored codes and a vector of refinement times
plot_novelty(score_df = my_scores, refinements = c(4, 8, 10))