In this vignette we show the general autoslider.core
workflow, how you can create functions that produce study-specific
outputs, and how you can integrate them into the
autoslider.core framework.
Of course, you need to have the autoslider.core package
installed, and you need to have data available. In this example I use
example data stored in the autoslider.core package.
The data needs to be stored in a named list where the names should correspond to ADaM data sets.
The folder structure could look something like:
├── programs
│ ├── run_script.R
│ ├── R
| | ├── helping_functions.R
| | ├── output_functions.R
├── outputs
├── specs.yml
├── filters.ymlThe autoslideR workflow would be implemented in the
run_script.R file. This workflow does not require the files
in R/. However, if custom output-creating functions are
implemented, R/ would be the place to put them.
The autoslideR workflow has four main aspects:
specs.ymlThis file contains the specifications of all outputs you would like to create.
For each output we define specific information, namely the program
name, the footnotes & titles, the paper (this indicates the
orientation, P for portrait and L for landscape, the number indicates
the font size), the suffix and args.
It could look something like that:
- program: t_ds_slide
titles: Patient Disposition ({filter_titles("adsl")})
footnotes: 't_ds footnotes'
paper: L6
suffix: ITT
- program: t_dm_slide
titles: Patient Demographics and Baseline Characteristics
footnotes: 't_dm_slide footnote'
paper: L6
suffix: ITT
args:
arm: "TRT01A"
vars: ["SEX", "AGE", "RACE", "ETHNIC", "COUNTRY"]
The program name refers to a function that produces an output. This
could be one of the _slide functions in
autoslider.core or a custom function.
Titles and footnotes are added once the outputs are created. We refer to that as decorating the outputs.
The suffix specifies the name of the filters that are applied to the
data, before the data is funneled into the function (program). The
filters themselves are specified in the filters.yml
file.
filters.ymlIn filters.yml we specify the names of the filters used
across the outputs. Each filter has a name (e.g. FAS), a
title (Full Analysis Set), and then the filtering condition
on a target dataset. The filter title may be appended to the output
title. For the t_ds_slides slide above all filter titles
that target the adsl dataset would be included in the brackets. We would
thus expect the title to read: “Patient Disposition (Full Analysis
Set)”
[what is the type?]
As you can see, we don’t just have population filters, but also
filters on serious adverse events. We can thus produce SAE tables by
just supplying the serious adverse events to the AE table function. This
concept generalizes also to PARAMCD values.
ITT:
title: Intent to Treat Population
condition: ITTFL =='Y'
target: adsl
type: slref
SAS:
title: Secondary Analysis Set
condition: SASFL == 'Y'
target: adsl
type: slref
SE:
title: Safety Evaluable Population
condition: SAFFL=='Y'
target: adsl
type: slref
SER:
title: Serious Adverse Events
condition: AESER == 'Y'
target: adae
type: anl
You can find an overview of all autoslider.core
functions here.
Note that all output-producing functions end with _slide
while the prefix (i.e. t_, l_,
g_) specify the type of output (i.e. table, listing, or
graph respectively). Custom functions are needed if the
autoslider.core functions do not cover the outputs you
need. More on that further down.
A typical workflow could look something like this:
library("dplyr")
# load all filters
filters::load_filters(filters, overwrite = TRUE)
# read data
data <- list(
"adsl" = eg_adsl %>%
mutate(
FASFL = SAFFL, # add FASFL for illustrative purpose for t_pop_slide
# DISTRTFL is needed for t_ds_slide but is missing in example data
DISTRTFL = sample(c("Y", "N"), size = length(TRT01A), replace = TRUE, prob = c(.1, .9))
) %>%
preprocess_t_ds(), # this preproccessing is required by one of the autoslider.core functions
"adae" = eg_adae,
"adtte" = eg_adtte,
"adrs" = eg_adrs,
"adlb" = eg_adlb
)
# create outputs based on the specs and the functions
outputs <- spec_file %>%
read_spec() %>%
# we can also filter for specific programs, if we don't want to create them all
filter_spec(., program %in% c(
"t_ds_slide",
"t_dm_slide"
)) %>%
# these filtered specs are now piped into the generate_outputs function.
# this function also requires the data
generate_outputs(datasets = data) %>%
# now we decorate based on the specs, i.e. add footnotes and titles
decorate_outputs(
version_label = NULL
)
#> ✔ 2/3 outputs matched the filter condition `program %in% c("t_ds_slide", "t_dm_slide")`.
#> ❯ Running program `t_ds_slide` with suffix 'ITT'.
#> Filter 'ITT' matched target ADSL.
#> 400/400 records matched the filter condition `ITTFL == 'Y'`.
#> ❯ Running program `t_dm_slide` with suffix 'ITT'.
#> Filter 'ITT' matched target ADSL.
#> 400/400 records matched the filter condition `ITTFL == 'Y'`.We can have a look at one of the outputs stored in the outputs file:
outputs$t_dm_slide_ITT
#> An object of class "dVTableTree"
#> Slot "tbl":
#> Patient Demographics and Baseline Characteristics, Intent to Treat Population
#>
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#> A: Drug X B: Placebo C: Combination All Patients
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#> Sex
#> F 79 (59%) 82 (61.2%) 70 (53%) 231 (57.8%)
#> M 55 (41%) 52 (38.8%) 62 (47%) 169 (42.2%)
#> Age
#> Median 33.00 35.00 35.00 34.00
#> Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0 20.0 - 69.0
#> Race
#> ASIAN 68 (50.7%) 67 (50%) 73 (55.3%) 208 (52%)
#> BLACK OR AFRICAN AMERICAN 31 (23.1%) 28 (20.9%) 32 (24.2%) 91 (22.8%)
#> WHITE 27 (20.1%) 26 (19.4%) 21 (15.9%) 74 (18.5%)
#> AMERICAN INDIAN OR ALASKA NATIVE 8 (6%) 11 (8.2%) 6 (4.5%) 25 (6.2%)
#> MULTIPLE 0 1 (0.7%) 0 1 (0.2%)
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 (0.7%) 0 1 (0.2%)
#> OTHER 0 0 0 0
#> UNKNOWN 0 0 0 0
#> Ethnicity
#> NOT REPORTED 6 (4.5%) 10 (7.5%) 11 (8.3%) 27 (6.8%)
#> HISPANIC OR LATINO 15 (11.2%) 18 (13.4%) 15 (11.4%) 48 (12%)
#> NOT HISPANIC OR LATINO 104 (77.6%) 103 (76.9%) 101 (76.5%) 308 (77%)
#> UNKNOWN 9 (6.7%) 3 (2.2%) 5 (3.8%) 17 (4.2%)
#> Country
#> CHN 74 (55.2%) 81 (60.4%) 64 (48.5%) 219 (54.8%)
#> USA 10 (7.5%) 13 (9.7%) 17 (12.9%) 40 (10%)
#> BRA 13 (9.7%) 7 (5.2%) 10 (7.6%) 30 (7.5%)
#> PAK 12 (9%) 9 (6.7%) 10 (7.6%) 31 (7.8%)
#> NGA 8 (6%) 7 (5.2%) 11 (8.3%) 26 (6.5%)
#> RUS 5 (3.7%) 8 (6%) 6 (4.5%) 19 (4.8%)
#> JPN 5 (3.7%) 4 (3%) 9 (6.8%) 18 (4.5%)
#> GBR 4 (3%) 3 (2.2%) 2 (1.5%) 9 (2.2%)
#> CAN 3 (2.2%) 2 (1.5%) 3 (2.3%) 8 (2%)
#> CHE 0 0 0 0
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#>
#> t_dm_slide footnote
#> Confidential and for internal use only
#> GitHub repository: https://github.com/insightsengineering/autoslider.core.git
#> Git hash: ce58a63d30d6386fc98113f72c42ef6a0f843336
#>
#> Slot "titles":
#> Patient Demographics and Baseline Characteristics, Intent to Treat Population
#>
#> Slot "footnotes":
#> [1] "t_dm_slide footnote"
#> [2] "Confidential and for internal use only"
#>
#> Slot "paper":
#> [1] "L6"
#>
#> Slot "width":
#> [1] 41 11 11 14 12Now we can save it to a slide. For this example I store the output in
a tempfile, you would likely store it in the outputs/
folder.
Unless your requirements are really specific, the most efficient way to write a study function is to base it off of a template function from the TLG catalogue.
The function you would want to create should take as input a list of
datasets and potentially additional arguments. Within the function, you
should not worry about filtering the data, as this should be taken care
of with the filters.yml file and the general workflow. To
work properly with autoslider.core your function should
return either:
ggplot2 object for graphsrtables object for tablesrlistings object for listingsThat’s it!
Now let’s see how this works in practice.
A function that works within the autoslideR workflow
should also work on its own. This makes it straightforward to develop
and test.
As an example, lets create a function corresponding to a TLG catalogue output.
We are going create a table based on LBT06 Laboratory Abnormalities by Visit and Baseline Status:
lbt06 <- function(datasets) {
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- datasets$adsl %>% tern::df_explicit_na()
adlb <- datasets$adlb %>% tern::df_explicit_na()
# Please note that df_explict_na has a na_level argument defaulting to "<Missing>",
# Please don't change the na_level to anything other than NA, empty string or the default "<Missing>".
adlb_f <- adlb %>%
dplyr::filter(ABLFL != "Y") %>%
dplyr::filter(!(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
dplyr::mutate(AVISIT = droplevels(AVISIT)) %>%
formatters::var_relabel(AVISIT = "Visit")
adlb_f_crp <- adlb_f %>% dplyr::filter(PARAMCD == "CRP")
# Define the split function
split_fun <- rtables::drop_split_levels
lyt <- rtables::basic_table(show_colcounts = TRUE) %>%
rtables::split_cols_by("ARM") %>%
rtables::split_rows_by("AVISIT",
split_fun = split_fun, label_pos = "topleft",
split_label = formatters::obj_label(adlb_f_crp$AVISIT)
) %>%
tern::count_abnormal_by_baseline(
"ANRIND",
abnormal = c(Low = "LOW", High = "HIGH"),
.indent_mods = 4L
) %>%
tern::append_varlabels(adlb_f_crp, "ANRIND", indent = 1L) %>%
rtables::append_topleft(" Baseline Status")
result <- rtables::build_table(
lyt = lyt,
df = adlb_f_crp,
alt_counts_df = adsl
) %>%
rtables::trim_rows()
result
}Let’s see if this works:
lbt06(data)
#> Visit
#> Analysis Reference Range Indicator A: Drug X B: Placebo C: Combination
#> Baseline Status (N=134) (N=134) (N=132)
#> ———————————————————————————————————————————————————————————————————————————————————————
#> WEEK 1 DAY 8
#> Low
#> Not low 16/119 (13.4%) 22/113 (19.5%) 24/112 (21.4%)
#> Low 2/15 (13.3%) 2/21 (9.5%) 7/20 (35%)
#> Total 18/134 (13.4%) 24/134 (17.9%) 31/132 (23.5%)
#> High
#> Not high 21/114 (18.4%) 20/112 (17.9%) 17/115 (14.8%)
#> High 2/20 (10%) 4/22 (18.2%) 3/17 (17.6%)
#> Total 23/134 (17.2%) 24/134 (17.9%) 20/132 (15.2%)
#> WEEK 2 DAY 15
#> Low
#> Not low 26/119 (21.8%) 20/113 (17.7%) 12/112 (10.7%)
#> Low 2/15 (13.3%) 3/21 (14.3%) 4/20 (20%)
#> Total 28/134 (20.9%) 23/134 (17.2%) 16/132 (12.1%)
#> High
#> Not high 15/114 (13.2%) 17/112 (15.2%) 15/115 (13%)
#> High 2/20 (10%) 4/22 (18.2%) 4/17 (23.5%)
#> Total 17/134 (12.7%) 21/134 (15.7%) 19/132 (14.4%)
#> WEEK 3 DAY 22
#> Low
#> Not low 15/119 (12.6%) 21/113 (18.6%) 18/112 (16.1%)
#> Low 0/15 3/21 (14.3%) 0/20
#> Total 15/134 (11.2%) 24/134 (17.9%) 18/132 (13.6%)
#> High
#> Not high 22/114 (19.3%) 18/112 (16.1%) 17/115 (14.8%)
#> High 2/20 (10%) 5/22 (22.7%) 1/17 (5.9%)
#> Total 24/134 (17.9%) 23/134 (17.2%) 18/132 (13.6%)
#> WEEK 4 DAY 29
#> Low
#> Not low 30/119 (25.2%) 13/113 (11.5%) 16/112 (14.3%)
#> Low 3/15 (20%) 2/21 (9.5%) 5/20 (25%)
#> Total 33/134 (24.6%) 15/134 (11.2%) 21/132 (15.9%)
#> High
#> Not high 17/114 (14.9%) 11/112 (9.8%) 16/115 (13.9%)
#> High 2/20 (10%) 6/22 (27.3%) 3/17 (17.6%)
#> Total 19/134 (14.2%) 17/134 (12.7%) 19/132 (14.4%)
#> WEEK 5 DAY 36
#> Low
#> Not low 17/119 (14.3%) 19/113 (16.8%) 16/112 (14.3%)
#> Low 2/15 (13.3%) 3/21 (14.3%) 5/20 (25%)
#> Total 19/134 (14.2%) 22/134 (16.4%) 21/132 (15.9%)
#> High
#> Not high 19/114 (16.7%) 17/112 (15.2%) 11/115 (9.6%)
#> High 4/20 (20%) 6/22 (27.3%) 2/17 (11.8%)
#> Total 23/134 (17.2%) 23/134 (17.2%) 13/132 (9.8%)This works!
To increase code-reusability and to have filtering control
centralised in the filters.yml file, I would recommend to
remove most filtering processes from the function, namely the following
chunk:
adlb_f <- eg_adlb %>%
dplyr::filter(ABLFL != "Y") %>%
dplyr::filter(!(AVISIT %in% c("SCREENING", "BASELINE"))) %>%
dplyr::mutate(AVISIT = droplevels(AVISIT)) %>%
formatters::var_relabel(AVISIT = "Visit")
adlb_f_crp <- adlb_f %>% dplyr::filter(PARAMCD == "CRP")For this, I’ll add two separate filters into the
filters.yml file; one to filter the right parameter, and
one to take care of the AVISIT and ABLFL.
LBCRP:
title: CRP Values
condition: PARAMCD == 'CRP'
target: adlb
type: slref
LBNOBAS:
title: Only Visits After Baseline
condition: ABLFL != "Y" & !(AVISIT %in% c("SCREENING", "BASELINE"))
target: adlb
type: slref
And the corresponding specs entry:
- program: lbt06
titles: Patient Disposition ({filter_titles("adsl")})
footnotes: 't_ds footnotes'
paper: L6
suffix: FAS_LBCRP_LBNOBAS
Now we can rewrite the function. But keep in mind: We are applying a filter to the ADSL data set but create the output on ADLB. To forward the filter to ADLB, we must semi-join the ADSL to ADLB.
lbt06 <- function(datasets) {
# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- datasets$adsl %>% tern::df_explicit_na()
adlb <- datasets$adlb %>% tern::df_explicit_na()
# join adsl to adlb
adlb <- adlb %>% semi_join(adsl, by = "USUBJID")
# Please note that df_explict_na has a na_level argument defaulting to "<Missing>",
# Please don't change the na_level to anything other than NA, empty string or the default "<Missing>".
adlb_f <- adlb %>%
dplyr::mutate(AVISIT = droplevels(AVISIT)) %>%
formatters::var_relabel(AVISIT = "Visit")
# Define the split function
split_fun <- rtables::drop_split_levels
lyt <- rtables::basic_table(show_colcounts = TRUE) %>%
rtables::split_cols_by("ARM") %>%
rtables::split_rows_by("AVISIT",
split_fun = split_fun, label_pos = "topleft",
split_label = formatters::obj_label(adlb_f_crp$AVISIT)
) %>%
tern::count_abnormal_by_baseline(
"ANRIND",
abnormal = c(Low = "LOW", High = "HIGH"),
.indent_mods = 4L
) %>%
tern::append_varlabels(adlb_f, "ANRIND", indent = 1L) %>%
rtables::append_topleft(" Baseline Status")
result <- rtables::build_table(
lyt = lyt,
df = adlb_f,
alt_counts_df = adsl
) %>%
rtables::trim_rows()
result
}lets do a dry-run before we integrate this function into the workflow:
# filter data | this step will be performed by the workflow later on
adsl <- eg_adsl
adlb <- eg_adlb
adlb_f <- adlb %>%
dplyr::filter(ABLFL != "Y") %>%
dplyr::filter(!(AVISIT %in% c("SCREENING", "BASELINE")))
adlb_f_crp <- adlb_f %>% dplyr::filter(PARAMCD == "CRP")
adsl_f <- adsl %>% filter(ITTFL == "Y")lbt06(list(adsl = adsl_f, adlb = adlb_f_crp))
#> Analysis Visit
#> Analysis Reference Range Indicator A: Drug X B: Placebo C: Combination
#> Baseline Status (N=134) (N=134) (N=132)
#> ———————————————————————————————————————————————————————————————————————————————————————
#> WEEK 1 DAY 8
#> Low
#> Not low 16/119 (13.4%) 22/113 (19.5%) 24/112 (21.4%)
#> Low 2/15 (13.3%) 2/21 (9.5%) 7/20 (35%)
#> Total 18/134 (13.4%) 24/134 (17.9%) 31/132 (23.5%)
#> High
#> Not high 21/114 (18.4%) 20/112 (17.9%) 17/115 (14.8%)
#> High 2/20 (10%) 4/22 (18.2%) 3/17 (17.6%)
#> Total 23/134 (17.2%) 24/134 (17.9%) 20/132 (15.2%)
#> WEEK 2 DAY 15
#> Low
#> Not low 26/119 (21.8%) 20/113 (17.7%) 12/112 (10.7%)
#> Low 2/15 (13.3%) 3/21 (14.3%) 4/20 (20%)
#> Total 28/134 (20.9%) 23/134 (17.2%) 16/132 (12.1%)
#> High
#> Not high 15/114 (13.2%) 17/112 (15.2%) 15/115 (13%)
#> High 2/20 (10%) 4/22 (18.2%) 4/17 (23.5%)
#> Total 17/134 (12.7%) 21/134 (15.7%) 19/132 (14.4%)
#> WEEK 3 DAY 22
#> Low
#> Not low 15/119 (12.6%) 21/113 (18.6%) 18/112 (16.1%)
#> Low 0/15 3/21 (14.3%) 0/20
#> Total 15/134 (11.2%) 24/134 (17.9%) 18/132 (13.6%)
#> High
#> Not high 22/114 (19.3%) 18/112 (16.1%) 17/115 (14.8%)
#> High 2/20 (10%) 5/22 (22.7%) 1/17 (5.9%)
#> Total 24/134 (17.9%) 23/134 (17.2%) 18/132 (13.6%)
#> WEEK 4 DAY 29
#> Low
#> Not low 30/119 (25.2%) 13/113 (11.5%) 16/112 (14.3%)
#> Low 3/15 (20%) 2/21 (9.5%) 5/20 (25%)
#> Total 33/134 (24.6%) 15/134 (11.2%) 21/132 (15.9%)
#> High
#> Not high 17/114 (14.9%) 11/112 (9.8%) 16/115 (13.9%)
#> High 2/20 (10%) 6/22 (27.3%) 3/17 (17.6%)
#> Total 19/134 (14.2%) 17/134 (12.7%) 19/132 (14.4%)
#> WEEK 5 DAY 36
#> Low
#> Not low 17/119 (14.3%) 19/113 (16.8%) 16/112 (14.3%)
#> Low 2/15 (13.3%) 3/21 (14.3%) 5/20 (25%)
#> Total 19/134 (14.2%) 22/134 (16.4%) 21/132 (15.9%)
#> High
#> Not high 19/114 (16.7%) 17/112 (15.2%) 11/115 (9.6%)
#> High 4/20 (20%) 6/22 (27.3%) 2/17 (11.8%)
#> Total 23/134 (17.2%) 23/134 (17.2%) 13/132 (9.8%)Looks like it works!
You have to keep in mind that the function you created must be in the
global environment when calling the create_outputs
function. This is the case for all autoslider.core
functions, as you attach the autoslider.core package (with
your library(autoslider.core) call), so all (exported)
function of the autoslider.core package are available.
If you store your custom function in a separate script, you would need to source that script at some point before calling the function, i.e.:
Now you just have to make sure the two .yml files are
correctly specified.
Set the path to the .yml files.
Then load the filters and generate the outputs.
filters::load_filters(filters, overwrite = TRUE)
outputs <- spec_file %>%
read_spec() %>%
generate_outputs(data) %>%
decorate_outputs()
#> ❯ Running program `t_ds_slide` with suffix 'ITT'.
#> Filter 'ITT' matched target ADSL.
#> 400/400 records matched the filter condition `ITTFL == 'Y'`.
#> ❯ Running program `t_dm_slide` with suffix 'ITT'.
#> Filter 'ITT' matched target ADSL.
#> 400/400 records matched the filter condition `ITTFL == 'Y'`.
#> ❯ Running program `lbt06` with suffix 'ITT_LBCRP_LBNOBAS'.
#> Filter 'ITT' matched target ADSL.
#> 400/400 records matched the filter condition `ITTFL == 'Y'`.
#> Filters 'LBCRP', 'LBNOBAS' matched target ADLB.
#> 2000/8400 records matched the filter condition `PARAMCD == 'CRP' & (ABLFL != 'Y' & !(AVISIT %in% c('SCREENING', 'BASELINE')))`.
outputs$lbt06_ITT_LBCRP_LBNOBAS
#> An object of class "dVTableTree"
#> Slot "tbl":
#> Patient Disposition (Intent to Treat Population)
#>
#> ———————————————————————————————————————————————————————————————————————————————————————
#> Analysis Visit
#> Analysis Reference Range Indicator A: Drug X B: Placebo C: Combination
#> Baseline Status (N=134) (N=134) (N=132)
#> ———————————————————————————————————————————————————————————————————————————————————————
#> WEEK 1 DAY 8
#> Low
#> Not low 16/119 (13.4%) 22/113 (19.5%) 24/112 (21.4%)
#> Low 2/15 (13.3%) 2/21 (9.5%) 7/20 (35%)
#> Total 18/134 (13.4%) 24/134 (17.9%) 31/132 (23.5%)
#> High
#> Not high 21/114 (18.4%) 20/112 (17.9%) 17/115 (14.8%)
#> High 2/20 (10%) 4/22 (18.2%) 3/17 (17.6%)
#> Total 23/134 (17.2%) 24/134 (17.9%) 20/132 (15.2%)
#> WEEK 2 DAY 15
#> Low
#> Not low 26/119 (21.8%) 20/113 (17.7%) 12/112 (10.7%)
#> Low 2/15 (13.3%) 3/21 (14.3%) 4/20 (20%)
#> Total 28/134 (20.9%) 23/134 (17.2%) 16/132 (12.1%)
#> High
#> Not high 15/114 (13.2%) 17/112 (15.2%) 15/115 (13%)
#> High 2/20 (10%) 4/22 (18.2%) 4/17 (23.5%)
#> Total 17/134 (12.7%) 21/134 (15.7%) 19/132 (14.4%)
#> WEEK 3 DAY 22
#> Low
#> Not low 15/119 (12.6%) 21/113 (18.6%) 18/112 (16.1%)
#> Low 0/15 3/21 (14.3%) 0/20
#> Total 15/134 (11.2%) 24/134 (17.9%) 18/132 (13.6%)
#> High
#> Not high 22/114 (19.3%) 18/112 (16.1%) 17/115 (14.8%)
#> High 2/20 (10%) 5/22 (22.7%) 1/17 (5.9%)
#> Total 24/134 (17.9%) 23/134 (17.2%) 18/132 (13.6%)
#> WEEK 4 DAY 29
#> Low
#> Not low 30/119 (25.2%) 13/113 (11.5%) 16/112 (14.3%)
#> Low 3/15 (20%) 2/21 (9.5%) 5/20 (25%)
#> Total 33/134 (24.6%) 15/134 (11.2%) 21/132 (15.9%)
#> High
#> Not high 17/114 (14.9%) 11/112 (9.8%) 16/115 (13.9%)
#> High 2/20 (10%) 6/22 (27.3%) 3/17 (17.6%)
#> Total 19/134 (14.2%) 17/134 (12.7%) 19/132 (14.4%)
#> WEEK 5 DAY 36
#> Low
#> Not low 17/119 (14.3%) 19/113 (16.8%) 16/112 (14.3%)
#> Low 2/15 (13.3%) 3/21 (14.3%) 5/20 (25%)
#> Total 19/134 (14.2%) 22/134 (16.4%) 21/132 (15.9%)
#> High
#> Not high 19/114 (16.7%) 17/112 (15.2%) 11/115 (9.6%)
#> High 4/20 (20%) 6/22 (27.3%) 2/17 (11.8%)
#> Total 23/134 (17.2%) 23/134 (17.2%) 13/132 (9.8%)
#> ———————————————————————————————————————————————————————————————————————————————————————
#>
#> t_ds footnotes
#> Confidential and for internal use only
#> GitHub repository: https://github.com/insightsengineering/autoslider.core.git
#> Git hash: ce58a63d30d6386fc98113f72c42ef6a0f843336
#>
#> Slot "titles":
#> Patient Disposition (Intent to Treat Population)
#>
#> Slot "footnotes":
#> [1] "t_ds footnotes"
#> [2] "Confidential and for internal use only"
#>
#> Slot "paper":
#> [1] "L6"
#>
#> Slot "width":
#> [1] 36 14 14 14Once this works, we can finally generate the slides.
filepath <- tempfile(fileext = ".pptx")
generate_slides(outputs, outfile = filepath)
#> [1] " Patient Disposition (Intent to Treat Population)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Demographics and Baseline Characteristics, Intent to Treat Population"
#> [1] " Patient Demographics and Baseline Characteristics, Intent to Treat Population (cont.)"
#> [1] " Patient Demographics and Baseline Characteristics, Intent to Treat Population (cont.)"
#> [1] " Patient Demographics and Baseline Characteristics, Intent to Treat Population (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"
#> [1] " Patient Disposition (Intent to Treat Population) (cont.)"Of course, you would not use a temporary file, and you might want to
use a custom .pptx template for your slides.