Package index • 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
`comp_outcome`	Example composite outcome data used for Figure 12
`control_fonts()`	Control Fonts
`corr`	Example correlogram data used for Figure 10
`create_correlogram()`	Create a correlogram from a given dataframe
`create_dot_forest_plot()`	Dot and Forest plots
`create_order_label_der()`	Dot Plot order label
`cumexcess`	Example cumulative excess plot data used for Figure 13
`data_bands`	Example bands data used for Figure 3
`data_lines`	stage: stage each line represents xstart: x-axis starting point of each line xend: x-axis ending point of each line y: y-axis position of each line col: color of each line xpos: x-axis position of each annotation (relative to xstart) ypos: y-axis position of each annotation (relative to y)
`demography`	Example demography data used for Figure 2
`effects_table`	Example effects table data used for Figure 6 and Figure 7
`generate_fig_lft()`	Dot plot
`generate_fig_rft()`	Forest plot
`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()`	Create a Grouped Bar Chart
`labs_bold()`	Create expression
`line_chart()`	Line Chart
`populated_effects_table()`	Populated effects table
`prepare_absolute_risk_data()`	Forest plot data for absolute risk
`prepare_absolute_risk_exp_adj()`	Forest plot data for exposure-adjusted rates
`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_cont_benefits_data()`	Forest plot data for continuous outcomes
`prepare_dot_data_b()`	Dot plot data for binary outcomes
`prepare_dot_data_exp_adj()`	Dot plot data for exposure-adjusted rates
`pre_proc()` `prepare_dot_forest_plot_data()`	Dot and Forest plots data
`prepare_odds_ratio_data()`	Forest plot data for odds ratio
`prepare_relative_risk_data()`	Forest plot data for relative risk
`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
`stacked_barchart()`	Stacked Bar Chart
`supplied_br_forest()`	Function to trigger analysis based on type
`value_tree()`	Create Value Tree

Reference

All functions