Function reference • brcharts

All functions
`add_exprs()`	Partially bold a string
`br_charts_theme()`	BR charts theme
`brdata`	Example effects table
`calculate_diff_bin()`	CI for absolute risk for binary outcomes
`calculate_diff_con()`	CI for treatment difference in continuous outcomes
`calculate_diff_rates()`	CI for treatment difference in exposure-adjusted rates
`calculate_log_odds_ratio_bin()`	CI for log odds ratio for binary outcomes
`calculate_log_rel_risk_bin()`	CI for log relative risk for binary outcomes
`calculate_odds_ratio_bin()`	CI for odds ratio for binary outcomes
`calculate_rel_risk_bin()`	CI for relative risk for binary outcomes
`check_effects_table()`	Check if data contains required features to run a specific plot
`check_feature()`	intermediate function used to display log messages check if a specific feature exist in the data
`check_feature_string()`	intermediate function used to display log messages check if a specific feature exist in the data
`colfun()`	Function for colors
`comorbidities`	Example comorbidities data used for Figure 4
`control_fonts()`	Control Fonts
`cumexcess`	Example cumulative excess plot data used for Figure 13
`demography`	Example demography data used for Figure 2
`effects_table`	Example effects table data used for Figure 6 and Figure 7
`generate_tradeoff_plot()`	Trade-off plot
`gensurv()`	Simulate data (utilized for function tests)
`gensurv_combined()`	Combine the cumulative excess plot and corresponding table into one figure
`gensurv_plot()`	Create a cumulative excess plot from a given dataframe
`gensurv_table()`	Create a table that corresponds to the cumulative excess plot
`ggsave_custom()`	Wrapper to ggsave: Save a ggplot (or other grid object) with sensible defaults
`grouped_barchart()`	Grouped Bar Chart
`labs_bold()`	Create expression
`prepare_br_calculated_ci()`	Prepare data analysis for binary and continuous outcomes with Calculated interval confidence identifies whether the dataframe is for Benefit or Risk analysis
`prepare_br_supplied_ci()`	Prepare data analysis for binary and continuous outcomes with Supplied interval confidence identifies whether the dataframe is for Benefit or Risk analysis
`prepare_tradeoff_data()`	Prepare data for the tradeoff plot
`prepare_tradeoff_plot()`	Prepare trade-off plot
`pyramid_chart()`	Pyramid Chart
`relmax()`	Derive maximum boundary value for axis Derive boundary value to include all values
`relmin()`	Derive minimum boundary value for axis Derive boundary value to include all values
`save_mermaid()`	Save DiagrameR::mermaid object
`scatter_plot()`	Create a scatterplot from a given dataframe.
`scatterplot`	Example scatterplot data used for Figure 11
`value_tree()`	Create Value Tree

Reference

All functions