library(forestly)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) :
#> object 'type_sum.accel' not found
library(metalite)Overview
The forestly package controls forest plot layouts in a simple and
effective approach. To illustrate the idea, we use ADSL
(forestly_adsl_3grp) and ADAE
(forestly_adae_3grp) datasets in the forestly package that
contain three treatment groups.
The first step is to create the required metadata using
meta_forestly(). Then we use
prepare_ae_forestly() to prepare the input datasets for an
interactive forest plot. The outdata contains all the
parameters required to generate the interactive forest plot.
metadata <- meta_forestly(
dataset_adsl = forestly_adsl_3grp,
dataset_adae = forestly_adae_3grp,
parameter_term = "any;rel;ser",
population_subset = SAFFL == "Y",
observation_subset = SAFFL == "Y"
)
outdata <- metadata |> prepare_ae_forestly()
outdata
#> List of 18
#> $ meta :List of 7
#> $ population : chr "apat"
#> $ observation : chr "safety"
#> $ parameter : chr "any;rel;ser"
#> $ n :'data.frame': 361 obs. of 4 variables:
#> $ order : num [1:361] 1030 1031 1032 1033 1034 ...
#> $ group : chr [1:4] "Placebo" "Low Dose" "High Dose" "Total"
#> $ reference_group: num 1
#> $ parameter_order: Factor w/ 3 levels "any","rel","ser": 1 1 1 1 1 1 1 1 1 1 ...
#> $ prop :'data.frame': 361 obs. of 4 variables:
#> $ diff :'data.frame': 361 obs. of 2 variables:
#> $ n_pop :'data.frame': 1 obs. of 4 variables:
#> $ name : chr [1:361] "Atrial fibrillation" "Atrial flutter" "Atrial hypertrophy" "Atrioventricular block first degree" ...
#> $ soc_name : chr [1:361] "CARDIAC DISORDERS" "CARDIAC DISORDERS" "CARDIAC DISORDERS" "CARDIAC DISORDERS" ...
#> $ ci_lower :'data.frame': 361 obs. of 2 variables:
#> $ ci_upper :'data.frame': 361 obs. of 2 variables:
#> $ p :'data.frame': 361 obs. of 2 variables:
#> $ ae_listing :'data.frame': 1898 obs. of 15 variables:The interactive forest plot with default style can be generated.
outdata |>
format_ae_forestly() |>
ae_forestly()Maximum page options
By default, it will display the counts that round up to the nearest
hundred displayed in the interactive forest plot table. This can be
adjusted by using the max_page argument in
ae_forestly().
outdata |>
format_ae_forestly() |>
ae_forestly(max_page = 280)Change color
By default, forestly is using teal for treatment group and plum for
control group. If user wants to change the color, the color
argument in the format_ae_forestly() function can be used.
Here is an example for an interactive forest plot using black and
grey.
outdata |>
format_ae_forestly(color = c("black", "grey60", "grey40")) |>
ae_forestly()Display different columns
By using the display argument in format_ae_forestly(), we can display the total column.
outdata |>
format_ae_forestly(display = c("n", "prop", "fig_prop", "fig_diff", "total")) |>
ae_forestly()We can also display risk difference columns in a similar approach.
outdata |>
format_ae_forestly(display = c("n", "prop", "fig_prop", "fig_diff", "diff")) |>
ae_forestly()Control column width
We can control column width to customize the layout.
outdata |>
format_ae_forestly(
width_fig = 230,
footer_space = 110
) |>
ae_forestly(width = 1000)Horizontal scrolling
Set ae_forestly(..., width = NULL) to make the element
scrollable horizontally. This is particularly useful when embedding the
plot in pages with responsive design, using frameworks such as
Bootstrap.
Change variable listed in drill-down table
Users can explore AE listing by clicking
of each row after we specify column names in
ae_listing_display.
listing_var <- c(
"SEX", "RACE", "AGE",
"SITEID", "AESEV", "STUDYID",
"AESER", "AEREL", "ASTDT", "AENDT"
)
metadata |>
prepare_ae_forestly(
ae_listing_display = listing_var
) |>
format_ae_forestly() |>
ae_forestly()