TSQCA: Threshold Sweep Extensions for Qualitative Comparative Analysis

Description

Provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep-Single (CTS-S), Condition Threshold Sweep-Multiple (CTS-M), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an exploratory tool for discovering causal structures. Built on top of the 'QCA' package by Dusa (2019) doi:10.1007/978-3-319-75668-4, with function arguments following 'QCA' conventions. Based on set-theoretic methods by Ragin (2008) doi:10.7208/chicago/9780226702797.001.0001 and established robustness protocols by Rubinson et al. (2019) doi:10.1177/00491241211036158.

Author(s)

Maintainer: Yuki Toyoda yuki.toyoda.ds@hosei.ac.jp

Other contributors:

• Japan Society for the Promotion of Science (KAKENHI Grant Number JP20K01998) [funder]

See Also

Useful links:

• https://github.com/im-research-yt/TSQCA

• doi:10.5281/zenodo.17899390

• Report bugs at https://github.com/im-research-yt/TSQCA/issues

Symbol sets for configuration charts

Description

Symbol sets for configuration charts

Usage

SYMBOL_SETS

Format

An object of class list of length 3.

Add metrics rows to markdown table

Description

Add metrics rows to markdown table

Usage

add_metrics_rows(table_str, metrics, labels)

Build condition-path matrix for configuration chart

Description

Build condition-path matrix for configuration chart

Usage

build_config_matrix(paths, conditions = NULL, symbols)

Arguments

Value

Matrix with conditions as rows, paths as columns

Build configuration chart for a single solution

Description

Build configuration chart for a single solution

Usage

build_single_chart(
  paths,
  sol,
  symbols,
  labels,
  include_metrics,
  condition_order = NULL,
  solution_index = 1
)

Generate configuration chart from paths (simple interface)

Description

A simpler interface for generating configuration charts when you have paths directly (without a full QCA solution object).

Usage

config_chart_from_paths(
  paths,
  symbol_set = c("unicode", "ascii", "latex"),
  language = c("en", "ja"),
  condition_order = NULL,
  n_sol = 1L,
  solution_note = TRUE,
  solution_note_style = c("simple", "detailed"),
  epi_list = NULL
)

Arguments

Value

Character string containing Markdown-formatted table.

Examples

# Simple usage with paths
paths <- c("A*B", "A*C*~D", "B*E")
chart <- config_chart_from_paths(paths)
cat(chart)

# With ASCII symbols
chart <- config_chart_from_paths(paths, symbol_set = "ascii")
cat(chart)

# With multiple solution note
chart <- config_chart_from_paths(paths, n_sol = 2)
cat(chart)

# With detailed note including EPIs
chart <- config_chart_from_paths(
  paths, n_sol = 2,
  solution_note_style = "detailed",
  epi_list = c("A*B")
)
cat(chart)

Generate configuration chart for multiple solutions (simple interface)

Description

Generates separate configuration charts for multiple solutions.

Usage

config_chart_multi_solutions(
  solutions,
  symbol_set = c("unicode", "ascii", "latex"),
  language = c("en", "ja"),
  condition_order = NULL,
  show_epi = FALSE
)

Arguments

Value

Character string containing Markdown-formatted tables.

Examples

# Multiple solutions
solutions <- list(
  c("A*B", "C"),
  c("A*B", "D"),
  c("A*C")
)
chart <- config_chart_multi_solutions(solutions)
cat(chart)

# With EPI identification
chart <- config_chart_multi_solutions(solutions, show_epi = TRUE)
cat(chart)

Convert configuration matrix to Markdown table

Description

Convert configuration matrix to Markdown table

Usage

config_matrix_to_md(mat, row_header = "Condition", center_align = TRUE)

Arguments

Value

Character string of Markdown table

MCTS–QCA: Multi-condition threshold sweep

Description

Performs a grid search over thresholds of multiple X variables. For each combination of thresholds in sweep_list, the outcome Y and all X variables are binarized, and a crisp-set QCA is executed.

Usage

ctSweepM(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_list,
  thrY,
  dir.exp = NULL,
  include = "?",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

Value

If return_details = FALSE, a data frame with columns:

• combo_id — index of the threshold combination

• threshold — character string summarizing thresholds, e.g. "X1=6, X2=7, X3=7"

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — per-combination list of combo_id, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# Quick demonstration with 2 conditions (< 5 seconds)
# This explores 2^2 = 4 threshold combinations
sweep_list <- list(
  X1 = 6:7,  # Reduced from 6:8 to 6:7
  X2 = 6:7   # Reduced from 6:8 to 6:7
)

# Run multiple condition threshold sweep with reduced parameters (standard)
result_quick <- ctSweepM(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),  # Reduced from 3 to 2 conditions
  sweep_list = sweep_list,
  thrY = 7
)
head(result_quick$summary)

# Run with negated outcome (~Y)
result_neg <- ctSweepM(
  dat = sample_data,
  outcome = "~Y",
  conditions = c("X1", "X2"),
  sweep_list = sweep_list,
  thrY = 7
)
head(result_neg$summary)


# Full multi-condition analysis with 3 conditions
# This explores 3^3 = 27 threshold combinations (takes ~5-8 seconds)
sweep_list_full <- list(
  X1 = 6:8,
  X2 = 6:8,
  X3 = 6:8
)

result_full <- ctSweepM(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_list = sweep_list_full,
  thrY = 7
)

# Visualize threshold-dependent solution paths
head(result_full$summary)

CTS–QCA: Single-condition threshold sweep

Description

Performs a threshold sweep for one focal condition X. For each threshold in sweep_range, the outcome Y and all X variables are binarized using user-specified thresholds, and a crisp-set QCA is executed.

Usage

ctSweepS(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_var,
  sweep_range,
  thrY,
  thrX_default = 7,
  dir.exp = NULL,
  include = "?",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

Value

If return_details = FALSE, a data frame with columns:

• threshold — swept threshold for sweep_var

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — per-threshold list of threshold, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# Run single condition threshold sweep on X3 (standard)
result <- ctSweepS(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_var = "X3",
  sweep_range = 6:8,
  thrY = 7,
  thrX_default = 7
)
head(result$summary)

# Run with negated outcome (~Y)
result_neg <- ctSweepS(
  dat = sample_data,
  outcome = "~Y",
  conditions = c("X1", "X2", "X3"),
  sweep_var = "X3",
  sweep_range = 6:8,
  thrY = 7,
  thrX_default = 7
)
head(result_neg$summary)

Convert data frame to Markdown table

Description

Convert data frame to Markdown table

Usage

df_to_md_table(df, digits = 3)

Arguments

Value

Character string of Markdown table.

DTS–QCA: Two-dimensional X–Y threshold sweep

Description

Sweeps thresholds for multiple X variables and the outcome Y jointly. For each combination of X thresholds and each candidate Y threshold, the data are binarized and a crisp-set QCA is executed.

Usage

dtSweep(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_list_X,
  sweep_range_Y,
  dir.exp = NULL,
  include = "?",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

Value

If return_details = FALSE, a data frame with columns:

• combo_id — index of threshold combination

• thrY — threshold for Y

• thrX — character summary of X thresholds

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — list of runs with combo_id, thrY, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# Quick demonstration with reduced complexity (< 5 seconds)
# Using 2 conditions and 2 threshold levels
sweep_list_X <- list(
  X1 = 6:7,  # Reduced from 6:8 to 6:7
  X2 = 6:7   # Reduced from 6:8 to 6:7
)

sweep_range_Y <- 6:7  # Reduced from 6:8 to 6:7

# Run dual threshold sweep with reduced parameters
# This explores 2 × 2^2 = 8 threshold combinations
result_quick <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),  # Reduced from 3 to 2 conditions
  sweep_list_X = sweep_list_X,
  sweep_range_Y = sweep_range_Y
)
head(result_quick$summary)


# Full analysis with all conditions and thresholds
# This explores 3 × 3^3 = 81 threshold combinations (takes ~10-15 seconds)
sweep_list_X_full <- list(
  X1 = 6:8,
  X2 = 6:8,
  X3 = 6:8
)

sweep_range_Y_full <- 6:8

result_full <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_list_X = sweep_list_X_full,
  sweep_range_Y = sweep_range_Y_full
)

# Analyze threshold-dependent causal structures
head(result_full$summary)

Escape special characters for Markdown

Description

Escapes asterisks and other special characters that have special meaning in Markdown syntax.

Usage

escape_md(text)

Arguments

Value

Character. Text with special characters escaped.

Extract all metrics from QCA solution object

Description

Safely extracts solution-level and term-level metrics from various QCA solution object structures. Handles cases where multiple solutions exist and sol.incl.cov may be NULL.

Usage

extract_all_metrics(IC, sol_obj = NULL)

Arguments

Value

List with elements:

• sol_inclS — solution consistency (or NA)

• sol_PRI — solution PRI (or NA)

• sol_covS — solution coverage (or NA)

• term_df — data frame of per-term metrics (or NULL)

Extract all unique conditions from paths

Description

Extract all unique conditions from paths

Usage

extract_conditions_from_paths(paths)

Arguments

Value

Character vector of unique condition names (without ~)

Extract per-path metrics for configuration chart

Description

Extract per-path metrics for configuration chart

Usage

extract_path_metrics_for_chart(sol, solution_index = 1)

Extract paths from a single solution

Description

Extract paths from a single solution

Usage

extract_paths_from_solution(solution)

Extract solution list from QCA object

Description

Extract solution list from QCA object

Usage

extract_solution_list(sol)

Extract solution-level metrics for configuration chart

Description

Extract solution-level metrics for configuration chart

Usage

extract_solution_metrics_for_chart(sol, solution_index = 1)

Extract and format terms from solutions

Description

Extracts individual terms from solution expressions and returns formatted unique terms.

Usage

extract_terms(solutions, var_names, use_tilde = TRUE)

Arguments

Value

List with:

• all_terms — all terms (with duplicates)

• unique_terms — unique terms

• n_total — total term count

• n_unique — unique term count

Examples

var_names <- c("X1", "X2", "X3")
solutions <- c("X1*X2 + X3", "X1*X2 + X1*X3")
extract_terms(solutions, var_names)

Format a QCA solution expression

Description

Formats a complete solution expression (multiple terms joined by +).

Usage

format_qca_solution(solution, var_names, use_tilde = TRUE)

Arguments

Value

Character. The formatted solution expression.

Examples

var_names <- c("KSP", "KPR", "PRD", "RVT", "RCM")
format_qca_solution("KSPRVT + ~KPRPRD + RCM", var_names)
# Returns: "KSP*RVT + ~KPR*PRD + RCM"

Format multiple QCA solutions

Description

Formats a vector of solution expressions.

Usage

format_qca_solutions(solutions, var_names, use_tilde = TRUE)

Arguments

Value

Character vector. Formatted solution expressions.

Examples

var_names <- c("KSP", "KPR", "PRD", "RVT", "RCM")
solutions <- c("KSPRVT + RCM", "~KPRPRD")
format_qca_solutions(solutions, var_names)

Format a single QCA term

Description

Inserts * between variables in a term where it may have been omitted.

Usage

format_qca_term(term, var_names, use_tilde = TRUE)

Arguments

Value

Character. The formatted term with * between all variables.

Examples

var_names <- c("KSP", "KPR", "PRD", "RVT", "RCM")
format_qca_term("KSPRVTRCM", var_names)
# Returns: "KSP*RVT*RCM"

format_qca_term("~KPRPRD", var_names)
# Returns: "~KPR*PRD"

Generate Configuration Chart from QCA Solution

Description

Creates a Markdown-formatted configuration chart (Fiss-style table) from QCA minimization results. Supports single solution with multiple paths, and multiple solutions (displayed as separate tables).

Usage

generate_config_chart(
  sol,
  symbol_set = c("unicode", "ascii", "latex"),
  include_metrics = TRUE,
  language = c("en", "ja"),
  condition_order = NULL
)

Arguments

Value

Character string containing Markdown-formatted table(s).

Examples

## Not run: 
# After running QCA::minimize()
library(QCA)
tt <- truthTable(data, outcome = "Y", conditions = c("A", "B", "C"))
sol <- minimize(tt, include = "?", details = TRUE)

# Generate configuration chart
chart <- generate_config_chart(sol)
cat(chart)

# For LaTeX/PDF output (e.g., rticles)
chart <- generate_config_chart(sol, symbol_set = "latex")

# ASCII for maximum compatibility
chart <- generate_config_chart(sol, symbol_set = "ascii")

# Japanese labels
chart <- generate_config_chart(sol, language = "ja")

## End(Not run)

Generate cross-threshold configuration chart from sweep results

Description

Creates a configuration chart from threshold sweep results. Supports two levels of aggregation: solution-term level (Fiss-style, default) and threshold-level summary.

Usage

generate_cross_threshold_chart(
  result,
  conditions = NULL,
  symbol_set = c("unicode", "ascii", "latex"),
  chart_level = c("term", "summary"),
  language = c("en", "ja")
)

Arguments

Value

Character string containing Markdown-formatted table.

Examples

## Not run: 
data(sample_data)
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

# Solution-term level, Fiss-style (default)
chart <- generate_cross_threshold_chart(result, c("X1", "X2", "X3"))
cat(chart)

# Threshold-level summary
chart <- generate_cross_threshold_chart(result, c("X1", "X2", "X3"),
                                         chart_level = "summary")
cat(chart)

## End(Not run)

Generate Markdown Report for QCA Analysis

Description

Creates a markdown report from QCA analysis results. Supports two formats: "full" (comprehensive) and "simple" (for manuscripts).

Usage

generate_report(
  result,
  output_file = "qca_report.md",
  format = c("full", "simple"),
  title = "QCA Analysis Report",
  dat = NULL,
  desc_vars = NULL,
  include_chart = TRUE,
  chart_symbol_set = c("unicode", "ascii", "latex"),
  chart_level = c("term", "summary"),
  solution_note = TRUE,
  solution_note_style = c("simple", "detailed"),
  solution_note_lang = c("en", "ja")
)

Arguments

Value

Invisibly returns the path to the generated report.

Examples

## Not run: 
data(sample_data)
thrX <- c(X1 = 7, X2 = 7, X3 = 7)

result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = thrX,
  return_details = TRUE
)

# With descriptive statistics and configuration charts
generate_report(result, "my_report.md", format = "full", 
                dat = sample_data, include_chart = TRUE)

# Without configuration charts
generate_report(result, "my_report.md", format = "simple",
                include_chart = FALSE)

# With Fiss-style term-level charts (default, recommended for publications)
generate_report(result, "my_report.md", format = "full")

# With threshold-level summary charts
generate_report(result, "my_report.md", format = "full",
                chart_level = "summary")

# With detailed solution notes (including EPIs)
generate_report(result, "my_report.md", format = "full",
                solution_note_style = "detailed")

## End(Not run)

Generate solution note for multiple solutions

Description

Creates a note explaining that multiple equivalent solutions exist and that the displayed configuration is based on M1.

Usage

generate_solution_note(
  n_sol,
  epi_list = NULL,
  style = c("simple", "detailed"),
  language = c("en", "ja"),
  format = c("markdown", "latex")
)

Arguments

Value

Character string of the note, or empty string if n_sol <= 1.

Examples

# Simple note
generate_solution_note(2, style = "simple")

# Detailed note with EPIs
generate_solution_note(3, epi_list = c("A*B", "C"), style = "detailed")

# Japanese
generate_solution_note(2, style = "simple", language = "ja")

Generate solution-term level chart (Fiss-style)

Description

Creates a configuration chart where each column represents a single prime implicant (configuration), following Fiss (2011) notation.

Usage

generate_term_level_chart(sum_df, conditions, symbols, language = "en")

Arguments

Value

Character string containing Markdown-formatted table.

Generate threshold-level summary chart

Description

Creates a configuration chart where each column represents one threshold, showing all conditions that appear in any configuration at that threshold.

Usage

generate_threshold_level_chart(sum_df, conditions, symbols, language = "en")

Arguments

Value

Character string containing Markdown-formatted table.

Get all unique terms from multiple solutions

Description

Get all unique terms from multiple solutions

Usage

get_all_terms(sol_list)

Arguments

Value

List with:

• all_terms — all terms (with duplicates)

• unique_terms — unique terms

• term_counts — table of term frequencies

Determine condition status in a term

Description

Checks whether a condition is present, absent (negated), or don't care in a given term.

Usage

get_condition_status(term, condition)

Arguments

Details

Uses word boundary matching to avoid false positives when condition names are substrings of each other (e.g., X1 vs X10).

Value

Character. One of "present", "absent", or "dontcare".

Get labels for configuration chart based on language

Description

Get labels for configuration chart based on language

Usage

get_config_labels(language)

Get the number of intermediate solutions

Description

Get the number of intermediate solutions

Usage

get_n_solutions(sol)

Arguments

Value

Integer. Number of intermediate solutions, or 0 if none.

Identify Essential Prime Implicants from multiple solutions

Description

Finds terms that appear in ALL solutions (EPIs) versus terms that appear in only some solutions (SPIs).

Usage

identify_epi(solutions)

Arguments

Value

List with:

• epi — Essential prime implicants (in all solutions)

• spi — Selective prime implicants (in some solutions)

• n_solutions — Number of solutions

Examples

solutions <- list(
  c("A*B", "C", "D"),
  c("A*B", "C", "E"),
  c("A*B", "C", "F")
)
result <- identify_epi(solutions)
# result$epi = c("A*B", "C")
# result$spi = c("D", "E", "F")

OTS–QCA: Outcome threshold sweep

Description

Sweeps the threshold of the outcome Y while keeping the thresholds of all X conditions fixed.

Usage

otSweep(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_range,
  thrX,
  dir.exp = NULL,
  include = "?",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

Value

If return_details = FALSE, a data frame with columns:

• thrY — threshold for Y

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — per-Y-threshold list of thrY, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# Set fixed thresholds for conditions
thrX <- c(X1 = 7, X2 = 7, X3 = 7)

# Run outcome threshold sweep (standard)
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:9,
  thrX = thrX
)
head(result$summary)

# Run with negated outcome (~Y)
# Analyzes conditions for Y < threshold
result_neg <- otSweep(
  dat = sample_data,
  outcome = "~Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:9,
  thrX = thrX
)
head(result_neg$summary)

Parse a single path/term into conditions

Description

Parse a single path/term into conditions

Usage

parse_path_conditions(path)

Arguments

Value

List with 'present' and 'absent' condition names

Parse solution expression into individual terms

Description

Splits a solution expression (ORed terms) into individual prime implicants.

Usage

parse_solution_terms(expr)

Arguments

Value

Character vector of terms (e.g., c("X3", "X1*X2")), or NULL if no valid expression.

Print method for TSQCA results

Description

Displays a concise overview of TSQCA analysis results.

Usage

## S3 method for class 'tsqca_result'
print(x, ...)

## S3 method for class 'otSweep_result'
print(x, ...)

## S3 method for class 'dtSweep_result'
print(x, ...)

## S3 method for class 'ctSweepS_result'
print(x, ...)

## S3 method for class 'ctSweepM_result'
print(x, ...)

Arguments

Value

Invisibly returns x.

Examples

data(sample_data)
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)
print(result)

Binary calibration helper for TSQCA

Description

Converts a numeric vector into a crisp set (0/1) based on a threshold.

Usage

qca_bin(x, thr)

Arguments

Value

Integer vector of 0/1 with the same length as x.

Extract solution information from a QCA minimization result

Description

Internal helper to obtain the solution expression, consistency (inclS) and coverage (covS) from an object returned by QCA::minimize().

Usage

qca_extract(sol, extract_mode = c("first", "all", "essential"))

Arguments

Value

A list with elements depending on extract_mode.

For "first": expression, inclS, covS.

For "all": adds n_solutions.

For "essential": adds selective_terms, unique_terms, n_solutions.

If extraction fails, returns "No solution" and NA_real_ for numeric values.

Sample dataset for TSQCA examples

Description

A small artificial dataset with variables:

Y

Outcome (numeric)

X1

Condition 1

X2

Condition 2

X3

Condition 3

Usage

sample_data

Format

A data frame with 80 rows and 4 variables.

Split solution expression into terms

Description

Split solution expression into terms

Usage

split_solution_terms(solution)

Arguments

Value

Character vector of terms.

Summary method for TSQCA results

Description

Displays detailed results table with solution formulas and fit measures.

Usage

## S3 method for class 'tsqca_result'
summary(object, ...)

## S3 method for class 'otSweep_result'
summary(object, ...)

## S3 method for class 'dtSweep_result'
summary(object, ...)

## S3 method for class 'ctSweepS_result'
summary(object, ...)

## S3 method for class 'ctSweepM_result'
summary(object, ...)

Arguments

Value

Invisibly returns object.

Examples

data(sample_data)
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)
summary(result)

Write full report content

Description

Write full report content

Usage

write_full_report(
  result,
  con,
  dat = NULL,
  desc_vars = NULL,
  include_chart = TRUE,
  chart_symbol_set = "unicode",
  chart_level = "term",
  solution_note = TRUE,
  solution_note_style = "simple",
  solution_note_lang = "en"
)

Write simple report content

Description

Write simple report content

Usage

write_simple_report(
  result,
  con,
  include_chart = TRUE,
  chart_symbol_set = "unicode",
  chart_level = "term",
  solution_note = TRUE,
  solution_note_style = "simple",
  solution_note_lang = "en"
)