Forest plot

Create Forest plot

generate_fig_rft(
  data,
  xlabel = NULL,
  ylabel = NULL,
  fact_subset = "Benefit",
  type_subset = "Binary",
  type_scale = "Fixed",
  scale_x = "Identity",
  x_scale_n1_p1 = "N",
  select_nnx
)

Arguments

data

(character) Data for figure

xlabel

(character) Label for x-axis

ylabel

(character) Label for y-axis

fact_subset

(character) filter data

type_subset

(character) Selected subset "Binary" or "Continuous"

type_scale

(character) selected scale display type

scale_x

(character) Type of scale for x-axis "Identity" or "log10"

x_scale_n1_p1

(character) fix x-axis scale between -1 and 1

select_nnx

(character) show NNT/NNH

Examples

dot_plot_src <- subset(effects_table, !is.na(Prop1))
bdin <- subset(dot_plot_src, Factor == "Benefit")
rdin <- subset(dot_plot_src, Factor == "Risk")

fplot_data <- prepare_dot_forest_plot_data(
  data = dot_plot_src,
  drug = unique(dot_plot_src$Trt1),
  benefit = unique(bdin$Outcome),
  risk = unique(rdin$Outcome),
  filters = "None",
  category = "All",
  type_graph = "Absolute risk",
  type_risk = "Crude proportions",
  ci_method = "Calculated"
)
#> [2024-07-17 15:32:14] > Prepare Dot plot data for binary outcomes
#> [2024-07-17 15:32:14] > trigger analysis based on type
#> [2024-07-17 15:32:14] >
#> absolute risk CI for binary outcomes is calculated and saved
#> [2024-07-17 15:32:14] > Prepare Forest plot data for absolute risk
#> [2024-07-17 15:32:14] > Prepare Dot plot data for binary outcomes
#> [2024-07-17 15:32:14] > trigger analysis based on type
#> [2024-07-17 15:32:14] >
#> absolute risk CI for binary outcomes is calculated and saved
#> [2024-07-17 15:32:14] > Prepare Forest plot data for absolute risk
#> [2024-07-17 15:32:14] > Dataout object from the create_order_label_der function is created
#> [2024-07-17 15:32:14] > Prepare data for Dot and Forest plots
forest_plot_data <- subset(
  fplot_data$forest_plot_data,
  factor == "Benefit" & type == "Binary"
)
generate_fig_rft(
  data = forest_plot_data,
  fact_subset = "Benefit",
  type_subset = "Binary",
  xlabel = "Treatment Difference with 95% CI\n",
  select_nnx = "No"
)
#> Warning: Unknown or uninitialised column: `Trt2`.
#> [2024-07-17 15:32:14] > Create Forest plot