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

  1. stage: stage each line represents

  2. xstart: x-axis starting point of each line

  3. xend: x-axis ending point of each line

  4. y: y-axis position of each line

  5. col: color of each line

  6. xpos: x-axis position of each annotation (relative to xstart)

  7. 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