Title: Create TLGs using the 'tidyverse'
Version: 0.11.0
Language: en-US
Description: Generate tables, listings, and graphs (TLG) using 'tidyverse'. Tables can be created functionally, using a standard TLG process, or by specifying table and column metadata to create generic analysis summaries. The 'envsetup' package can also be leveraged to create environments for table creation.
License: Apache License 2.0
URL: https://pharmaverse.github.io/tidytlg/main/, https://github.com/pharmaverse/tidytlg
BugReports: https://github.com/pharmaverse/tidytlg/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
Suggests: testthat (≥ 2.1.0), knitr (≥ 1.23), rmarkdown (≥ 2.10), renv (≥ 0.13.2), shiny (≥ 1.3.2), kableExtra (≥ 1.3.4), haven (≥ 2.4.1), usethis (≥ 1.6.3), withr (≥ 2.3.0)
Imports: cli (≥ 3.6.0), dplyr (≥ 1.1.0), tibble (≥ 2.1.3), magrittr (≥ 1.5), rlang (≥ 0.4.10), tidyr (≥ 1.0.0), stats (≥ 3.6.0), stringr (≥ 1.4.0), forcats (≥ 0.5.1), purrr (≥ 0.3.4), huxtable (≥ 5.1.0), assertthat (≥ 0.2.1), glue (≥ 1.4.2), crayon (≥ 1.4.1), methods, readxl (≥ 1.3.1), cellranger (≥ 1.1.0), png (≥ 0.1-7), ggplot2 (≥ 3.3.2), rstudioapi (≥ 0.13)
Depends: R (≥ 4.1.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-12-19 07:23:49 UTC; kpagacz
Author: Nicholas Masel [aut], Steven Haesendonckx [aut], Pelagia Alexandra Papadopoulou [aut], Sheng-Wei Wang [aut], Eli Miller ORCID iD [aut], Nathan Kosiba ORCID iD [aut], Aidan Ceney ORCID iD [aut], Janssen R&D [cph, fnd], David Hugh-Jones [cph] (Author of included 'huxtable' library), Konrad Pagacz [aut, cre]
Maintainer: Konrad Pagacz <kpagacz@its.jnj.com>
Repository: CRAN
Date/Publication: 2025-12-19 08:00:02 UTC

tidytlg: A package for producing tables, listings, and graphs (TLGs) using tidyverse packages.

Description

The tidytlg package provide a set of function allowing you to produce TLGs using table metadata.

tidytlg Options

Default Stat Labels

stat label
N N
SUM Sum
MEAN Mean
GeoMEAN Geometric Mean
SD Std. Deviation
SE Std. Error
GSD Geometric Std. Deviation
GSE Geometric Std. Error
CV CV
MEANSD Mean (SD)
MEANSE Mean (SE)
MEDIAN Median
MIN Minimum
MAX Maximum
RANGE Range
Q1 First quartile
Q3 Third quartile
IQRANGE IQ range
MEDRANGE Median (Range)
MEDIQRANGE Median (Q1; Q3)
MEAN_CI Mean (95% C.I.)
GeoMEAN_CI Geometric Mean (95% C.I.)

Default Precision Extra

stat extra
N 0
SUM 0
MEAN 1
GeoMEAN 1
SD 2
SE 2
GSD 2
GSE 2
CV 1
MEDIAN 1
MIN 0
MAX 0
Q1 1
Q3 1
LCL_MEAN 2
UCL_MEAN 2
LCL_GeoMEAN 2
UCL_GeoMEAN 2

Updating Options

Author(s)

Maintainer: Konrad Pagacz kpagacz@its.jnj.com

Authors:

Other contributors:

See Also

Useful links:

Adds bottom borders to a huxtable

Description

Adds bottom borders to a huxtable

Usage

add_bottom_borders(ht, border_matrix = no_borders(ht), transform_fns = list())

Arguments

ht

huxtable A huxtable object

border_matrix

(optional) matrix A matrix indicating where to add the bottom borders. If NULL, then no borders are added.

transform_fns

(optional) list of function A list of functions applied to the border_matrix. The functions have to accept two arguments:

  1. The huxtable.

  2. The border_matrix with dimensions matching huxtable.

The functions in the list are applied sequentially to border_matrix.

Details

Adds bottom borders to a huxtable based on a matrix indicating where the borders should be put.

This function is responsible for adding bottom borders to a huxtable object. It supports borders spanning multiple columns and borders that are under neighbouring, single cells (or merged cells), but separate (see examples).

This feature has limitations. Mainly, it does not support both versions of the borders (continuous and separate) on the same line. In such a case, the borders in the resulting RTF look misaligned.

Value

A huxtable with added borders.

border_matrix details

You mark where the bottom borders should go in the table by passing a matrix. The matrix has to have the same number of columns as the passed huxtable and the number of rows lower by one than the passed huxtable. Each cell in border_matrix corresponds to a cell in huxtable (starting from the first row).

Internally, the function adds the first row of 0s to border_matrix before the execution. At that point, border_matrix's dimensions match ht's dimensions.

Table:

foo bar
baz bim

A border matrix:

1 1
0 0

The above border matrix puts a bottom border across the entire first row and no borders in the second row.

A border matrix:

1 2
0 0

The above border matrix puts one border under the first cell in the first row; and another border (separate from the first one) under the second cell in the first row. The second row stays without any borders.

Functions transforming the border matrix

The below functions can be passed to gentlg()'s border_fns argument to modify how gentlg renders the borders under the cells.

Border functions:

border_fns will accept your own, custom functions as long as they adhere to the format. All the functions passed to border_fns need to accept two arguments:

They also must return a matrix interpreted the same way as border_matrix passed to add_bottom_borders or gentlg().

Examples

border_matrix <- matrix(c(1, 1, 2, 0, 1, 1, 0, 0, 0), nrow = 3, ncol = 3)
ht <- huxtable::as_huxtable(
  data.frame(a = c(1, 2, 3), b = c("a", "b", "c"), c = c(TRUE, FALSE, TRUE))
)
# By default adds no borders
add_bottom_borders(ht, border_matrix)
# Adds spanning borders under cells with text in the second row
add_bottom_borders(ht, transform_fns = list(spanning_borders(2)))
# Adds spanning borders under cells with text in the second row and a border
# under a cell in row 3 and column 3
add_bottom_borders(ht, transform_fns = list(spanning_borders(2), single_border(3, 3)))

final <- data.frame(
  label = c(
    "Overall", "Safety Analysis Set",
    "Any Adverse event{\\super a}", "- Serious Adverse Event"
  ),
  Drug_A = c("", "40", "10 (25%)", "0"),
  Drug_B = c("", "40", "10 (25%)", "0"),
  anbr = c(1, 2, 3, 4),
  roworder = c(1, 1, 1, 1),
  boldme = c(1, 0, 0, 0),
  newrows = c(0, 0, 1, 0),
  indentme = c(0, 0, 0, 1),
  newpage = c(0, 0, 0, 0)
)
# Add spanning bottom borders under the cells in the first row
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  ),
  border_fns = list(no_borders, spanning_borders(1))
)

# Tables with no bottom borders
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  ),
  border_fns = list(no_borders)
)

# Tables with a border under cell in the 3nd row and 3rd column,
# and borders under cells in the first row
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  ),
  border_fns = list(no_borders, spanning_borders(1), single_border(3, 3))
)

# We discourage, but you can pass the border matrix directly
mat <- matrix(rep(0, 8 * 3), ncol = 3, nrow = 8)
mat[3, 3] <- 1
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  ),
  bottom_borders = mat, # The same as a single border under 3nd row and 3rd column
  border_fns = list()
)

# clean up.
file.remove("tsfaex.rtf")

Add the formatting variables of indentme, newrows, newpage, and roworder to the results dataframe

Description

Add the formatting variables of indentme, newrows, newpage, and roworder to the results dataframe

Usage

add_format(df, tableby = NULL, groupby = NULL, .keep = FALSE)

Arguments

df

(required) dataframe of results and must contain the anbr variable.

tableby

(optional) character vector containing table by variables.

groupby

(optional) character vector containing group by variables.

.keep

(optional) should tableby and groupby variables be kept in the final dataframe. (default = FALSE).

Value

dataframe with the formatting variables indentme, newrows, newpage, and roworder added.

Examples

df <- tibble::tibble(
  row_type =
    c(
      "TABLE_BY_HEADER", "HEADER", "BY_HEADER1", "N", "VALUE",
      "COUNTS", "UNIVAR", "NESTED", "NESTED"
    ),
  nested_level = c(NA, NA, NA, NA, NA, NA, NA, 1, 2),
  group_level = c(0, 0, 0, 0, 0, 0, 0, 0, 0),
  label = c(NA, NA, NA, NA, NA, "N", NA, NA, NA),
  by = c(NA, NA, NA, NA, NA, NA, NA, NA, NA),
  tableby = c(NA, NA, NA, NA, NA, NA, NA, NA, NA),
  anbr = c(1:9)
)
add_format(df)

Add indentation variable to the results dataframe

Description

Add the indentme variable to your results data. This drives the number of indents for the row label text (e.g. 0, 1, 2, etc.).

Usage

add_indent(df)

Arguments

df

dataframe of results that contains row_type and label and the optional nested_level and group_level variables.

Details

The group_level variable, which is added to the results dataframe by freq() and univar() calls, is needed to define indentation when by variables are used for summary.

The nested_level variable, which is added to the results dataframe by nested_freq(), is needed to define indentation for each level of nesting.

Both of these are added to the default indentation which is driven by row_type.

row_type default indentation
TABLE_BY_HEADER 0
BY_HEADER[1-9] 0
HEADER 0
N 1
VALUE 2
NESTED 0

Value

dataframe with the indentme variable added.

Examples

df <- tibble::tibble(
  row_type = c(
    "TABLE_BY_HEADER", "HEADER",
    "BY_HEADER1", "N", "VALUE", "COUNTS", "UNIVAR", "NESTED", "NESTED"
  ),
  nested_level = c(NA, NA, NA, NA, NA, NA, NA, 1, 2),
  group_level = c(0, 0, 0, 0, 0, 0, 0, 0, 0),
  label = c(NA, NA, NA, NA, NA, "N", NA, NA, NA),
  by = c(NA, NA, NA, NA, NA, NA, NA, NA, NA),
  tableby = c(NA, NA, NA, NA, NA, NA, NA, NA, NA)
)
add_indent(df)

Add the newrows variable to the results dataframe.

Description

The newrows variable is used by gentlg() to define when to add a blank row to the output. Data will be grouped by anbr and the variables passed into the tableby and groupby parameters. newrows will be set to 1 for the first record in each group, except for the first row in the data. The first row will always be set to 0.

Usage

add_newrows(df, tableby = NULL, groupby = NULL)

Arguments

df

dataframe of results. must contain the anbr variable that is added by add_format().

tableby

character vector containing table by variables used to generate the results.

groupby

character vector containing group by variables used to generate the results.

Value

dataframe with the variable newrows and roworder added. newrows is used by gentlg to insert line breaks.

Examples

# Example showing how newrows is set to one for each new anbr except
# the first
tbl <-
  structure(
    list(
      rowvar = c("RANDFL", "AGE", "AGE", "AGE", "AGE", "AGE"),
      anbr = c(1L, 2L, 2L, 2L, 2L, 2L),
      label = c(
        "Analysis set: Subjects Randomized", "Age (Years)", "N",
        "Mean (SD)", "Range", "IQ Range"
      ),
      row_type = c("COUNT", "UNIVAR", "UNIVAR", "UNIVAR", "UNIVAR", "UNIVAR")
    ),
    row.names = c(NA, -6L),
    class = c("tbl_df", "tbl", "data.frame")
  )

add_newrows(tbl)

# Example of use when you have results summarized by one or more variables
tbl2 <- tibble::tribble(
  ~anbr, ~SEX, ~label, ~row_type,
  "01", "F", "Sex : F", "TABLE_BY_HEADER",
  "01", "F", "<65", "VALUE",
  "01", "F", "65-80", "VALUE",
  "01", "F", ">80", "VALUE",
  "01", "M", "Sex : M", "TABLE_BY_HEADER",
  "01", "M", "<65", "VALUE",
  "01", "M", "65-80", "VALUE",
  "01", "M", ">80", "VALUE"
)

add_newrows(tbl2, tableby = "SEX")

tbl3 <- tibble::tribble(
  ~anbr, ~SEX, ~ETHNIC, ~label, ~row_type,
  "01", "F", NA, "Sex : F", "TABLE_BY_HEADER",
  "01", "F", "HISPANIC OR LATINO", "HISPANIC OR LATINO", "BY_HEADER1",
  "01", "F", "HISPANIC OR LATINO", "<65", "VALUE",
  "01", "F", "HISPANIC OR LATINO", ">80", "VALUE",
  "01", "F", "HISPANIC OR LATINO", "65-80", "VALUE",
  "01", "F", "NOT HISPANIC OR LATINO", "NOT HISPANIC OR LATINO", "BY_HEADER1",
  "01", "F", "NOT HISPANIC OR LATINO", "<65", "VALUE",
  "01", "F", "NOT HISPANIC OR LATINO", "65-80", "VALUE",
  "01", "F", "NOT HISPANIC OR LATINO", ">80", "VALUE",
  "01", "M", NA, "Sex : M", "TABLE_BY_HEADER",
  "01", "M", "HISPANIC OR LATINO", "HISPANIC OR LATINO", "BY_HEADER1",
  "01", "M", "HISPANIC OR LATINO", "<65", "VALUE",
  "01", "M", "HISPANIC OR LATINO", "65-80", "VALUE",
  "01", "M", "HISPANIC OR LATINO", ">80", "VALUE",
  "01", "M", "NOT HISPANIC OR LATINO", "NOT HISPANIC OR LATINO", "BY_HEADER1",
  "01", "M", "NOT HISPANIC OR LATINO", "<65", "VALUE",
  "01", "M", "NOT HISPANIC OR LATINO", "65-80", "VALUE",
  "01", "M", "NOT HISPANIC OR LATINO", ">80", "VALUE"
)

add_newrows(tbl3, tableby = "SEX", groupby = "ETHNIC")

Bind a set of tidytlg tables together with formatting variables

Description

bind_table combines analysis results with formatting variables (indentme, newrows, newpage) based on by variables (tablebyvar, rowbyvar), such that appropriate formatting (indentation, line break, page break) can be applied while creating the output. It can also attach the column metadata attribute, which will be automatically used in gentlg for creating output.

Usage

bind_table(
  ...,
  colvar = NULL,
  tablebyvar = NULL,
  rowbyvar = NULL,
  prefix = NULL,
  add_count = FALSE,
  add_format = TRUE,
  column_metadata_file = NULL,
  column_metadata = NULL,
  tbltype = NULL
)

Arguments

...

(required) a set of tidytlg tables to bind together

colvar

(required) treatment variable within df to use to summarize. Required if add_count is TRUE.

tablebyvar

(optional) repeat entire table by variable within df.

rowbyvar

(optional) any rowbyvar values used to create the table.

prefix

(optional) text to prefix the values of tablebyvar with.

add_count

(optional) Should a count be included in the tablebyvar? (default = TRUE)

add_format

(optional) Should format be added to the output table? This is done using the add_format function. (default = TRUE)

column_metadata_file

(optional) An excel file for column_metadata. Does not change the behavior of the function binds the column metadata for gentlg. If a column_metadata dataframe is passed in too, this is ignored.

column_metadata

(optional) A dataframe containing the column metadata. This will be used in place of column_metadata_file.

tbltype

(optional) A value used to subset the column_metadata_file.

Value

The tidytlg tables bound together reflecting the tablebyvars used.

Examples

library(magrittr)

# bind tables together
t1 <- cdisc_adsl %>%
  freq(
    colvar = "TRT01PN",
    rowvar = "ITTFL",
    statlist = statlist("n"),
    subset = ITTFL == "Y",
    rowtext = "Analysis set: ITT"
  )

t2 <- cdisc_adsl %>%
  univar(
    colvar = "TRT01PN",
    rowvar = "AGE",
    decimal = 0,
    row_header = "Age, years"
  )

bind_table(t1, t2)

# bind tables together w/by groups
t1 <- cdisc_adsl %>%
  freq(
    colvar = "TRT01PN",
    rowvar = "ITTFL",
    rowbyvar = "SEX",
    statlist = statlist("n"),
    subset = ITTFL == "Y",
    rowtext = "Analysis set: ITT"
  )

t2 <- cdisc_adsl %>%
  univar(
    colvar = "TRT01PN",
    rowvar = "AGE",
    rowbyvar = "SEX",
    decimal = 0,
    row_header = "Age, years"
  )

bind_table(t1, t2, rowbyvar = "SEX")

# bind tables together w/table by groups
t1 <- cdisc_adsl %>%
  freq(
    colvar = "TRT01PN",
    rowvar = "ITTFL",
    tablebyvar = "SEX",
    statlist = statlist("n"),
    subset = ITTFL == "Y",
    rowtext = "Analysis set: ITT"
  )

t2 <- cdisc_adsl %>%
  univar(
    colvar = "TRT01PN",
    rowvar = "AGE",
    tablebyvar = "SEX",
    decimal = 0,
    row_header = "Age, years"
  )

bind_table(t1, t2, tablebyvar = "SEX")

# w/prefix
bind_table(t1, t2, tablebyvar = "SEX", prefix = "Gender: ")

# w/counts
bind_table(t1, t2, tablebyvar = "SEX", add_count = TRUE, colvar = "TRT01PN")

ADAE data created from subsetting the CDISC ADAE dataset

Description

ADAE data created from subsetting the CDISC ADAE dataset

Usage

cdisc_adae

Format

A data frame with 84 rows and 55 variables:

STUDYID

Study Identifier

SITEID

Study Site Identifier

USUBJID

Unique Subject Identifier

SUBJID

Subject Identifier for the Study

TRTA

Actual Treatment

TRTAN

Actual Treatment (N)

AGE

Age

AGEGR1

Pooled Age Group 1

AGEGR1N

Pooled Age Group 1 (N)

RACE

Race

RACEN

Race (N)

SEX

Sex

SAFFL

Safety Population Flag

TRTSDT

Date of First Exposure to Treatment

TRTEDT

Date of Last Exposure to Treatment

ASTDT

Analysis Start Date

ASTDTF

Analysis Start Date Imputation Flag

ASTDY

Analysis Start Relative Day

AENDT

Analysis End Date

AENDY

Analysis End Relative Day

ADURN

AE Duration (N)

ADURU

AE Duration Units

AETERM

Reported Term for the Adverse Event

AELLT

Lowest Level Term

AELLTCD

Lowest Level Term Code

AEDECOD

Dictionary-Derived Term

AEPTCD

Preferred Term Code

AEHLT

High Level Term

AEHLTCD

High Level Term Code

AEHLGT

High Level Group Term

AEHLGTCD

High Level Group Term Code

AEBODSYS

Body System or Organ Class

AESOC

Primary System Organ Class

AESOCCD

Primary System Organ Class Code

AESEV

Severity/Intensity

AESER

Serious Event

AESCAN

Involves Cancer

AESCONG

Congenital Anomaly or Birth Defect

AESDISAB

Persist or Signif Disability/Incapacity

AESDTH

Results in Death

AESHOSP

Requires or Prolongs Hospitalization

AESLIFE

Is Life Threatening

AESOD

Occurred with Overdose

AEREL

Causality

AEACN

Action Taken with Study Treatment

AEOUT

Outcome of Adverse Event

AESEQ

Sequence Number

TRTEMFL

Treatment Emergent Analysis Flag

AOCCFL

First Occurrence of Any AE Flag

AOCCSFL

First Occurrence of SOC Flag

AOCCPFL

First Occurrence of Preferred Term Flag

AOCC02FL

First Occurrence 02 Flag for Serious

AOCC03FL

First Occurrence 03 Flag for Serious SOC

AOCC04FL

First Occurrence 04 Flag for Serious PT

CQ01NAM

Customized Query 01 Name

AOCC01FL

First Occurrence 01 Flag for CQ01

Source

CDISC SDTM/ADAM Pilot Project.

ADLB data created from subsetting the CDISC ADLB dataset

Description

ADLB data created from subsetting the CDISC ADLB dataset

Usage

cdisc_adlb

Format

A data frame with 2154 rows and 46 variables:

STUDYID

Study Identifier

SUBJID

Subject Identifier for the Study

USUBJID

Unique Subject Identifier

TRTA

Actual Treatment

TRTAN

Actual Treatment (N)

TRTSDT

Date of First Exposure to Treatment

TRTEDT

Date of Last Exposure to Treatment

AGE

Age

AGEGR1

Pooled Age Group 1

AGEGR1N

Pooled Age Group 1 (N)

RACE

Race

RACEN

Race (N)

SEX

Sex

COMP24FL

Finishers of Week 24 Population Flag

DSRAEFL

Discontinued due to AE?

SAFFL

Safety Population Flag

AVISIT

Analysis Visit

AVISITN

Analysis Visit (N)

ADY

Analysis Relative Day

ADT

Analysis Date

VISIT

Visit Name

VISITNUM

Visit Number

PARAM

Parameter

PARAMCD

Parameter Code

PARAMN

Parameter (N)

PARCAT1

Parameter Category 1

AVAL

Analysis Value

BASE

Baseline Value

CHG

Change from Baseline

A1LO

Analysis Range 1 Lower Limit

A1HI

Analysis Range 1 Upper Limit

R2A1LO

Ratio to Analysis Range 1 Lower Limit

R2A1HI

Ratio to Analysis Range 1 Upper Limit

BR2A1LO

Base Ratio to Analysis Range 1 Lower Limit

BR2A1HI

Base Ratio to Analysis Range 1 Upper Limit

ANL01FL

Analysis 01 - Special Interest Flag

ALBTRVAL

Amount Threshold Range

ANRIND

Analysis Reference Range Indicator

BNRIND

Baseline Reference Range Indicator

ABLFL

Baseline Record Flag

AENTMTFL

Last value in treatment visit

LBSEQ

Sequence Number

LBNRIND

Reference Range Indicator

LBSTRESN

Numeric Result/Finding in Standard Units

Source

CDISC SDTM/ADAM Pilot Project.

ADSL data created from subsetting the CDISC ADSL with 15 subjects (5 subjects in each arm)

Description

ADSL data created from subsetting the CDISC ADSL with 15 subjects (5 subjects in each arm)

Usage

cdisc_adsl

Format

A data frame with 15 rows and 49 variables:

STUDYID

Study Identifier

USUBJID

Unique Subject Identifier

SUBJID

Subject Identifier for the Study

SITEID

Study Site Identifier

SITEGR1

Pooled Site Group 1

ARM

Description of Planned Arm

TRT01P

Planned Treatment for Period 01

TRT01PN

Planned Treatment for Period 01 (N)

TRT01A

Actual Treatment for Period 01

TRT01AN

Actual Treatment for Period 01 (N)

TRTSDT

Date of First Exposure to Treatment

TRTEDT

Date of Last Exposure to Treatment

TRTDUR

Duration of Treatment (days)

AVGDD

Avg Daily Dose (as planned)

CUMDOSE

Cumulative Dose (as planned)

AGE

Age

AGEGR1

Pooled Age Group 1

AGEGR1N

Pooled Age Group 1 (N)

AGEU

Age Units

RACE

Race

RACEN

Race (N)

SEX

Sex

ETHNIC

Ethnicity

SAFFL

Safety Population Flag

ITTFL

Intent-To-Treat Population Flag

EFFFL

Efficacy Population Flag

COMP8FL

Finishers of Week 8 Population Flag

COMP16FL

Finishers of Week 16 Population Flag

COMP24FL

Finishers of Week 24 Population Flag

DISCONFL

Did the Subject Discontinue the Study?

DSRAEFL

Discontinued due to AE?

DTHFL

Subject Died?

BMIBL

Baseline BMI (kg/m^2)

BMIBLGR1

Pooled Baseline BMI Group 1

HEIGHTBL

Baseline Height (cm)

WEIGHTBL

Baseline Weight (kg)

EDUCLVL

Years of Education

DISONSDT

Date of Onset of Disease

DURDIS

Duration of Disease (Months)

DURDSGR1

Pooled Disease Duration Group 1

VISIT1DT

Date of Visit 1

RFSTDTC

Subject Reference Start Date/Time

RFENDTC

Subject Reference End Date/Time

VISNUMEN

End of Treatment Visit (Visit 12 or Early Term.)

RFENDT

Date of Discontinuation/Completion

DCDECOD

Standardized Disposition Term

EOSSTT

End of Study Status

DCREASCD

Reason for Discontinuation

MMSETOT

MMSE Total

Source

CDISC SDTM/ADAM Pilot Project.

ADVS data created from subsetting the CDISC ADVS dataset

Description

ADVS data created from subsetting the CDISC ADVS dataset

Usage

cdisc_advs

Format

A data frame with 1938 rows and 35 variables:

STUDYID

Study Identifier

SITEID

Study Site Identifier

USUBJID

Unique Subject Identifier

AGE

Age

AGEGR1

Pooled Age Group 1

AGEGR1N

Pooled Age Group 1 (N)

RACE

Race

RACEN

Race (N)

SEX

Sex

SAFFL

Safety Population Flag

TRTSDT

Date of First Exposure to Treatment

TRTEDT

Date of Last Exposure to Treatment

TRTP

Planned Treatment

TRTPN

Planned Treatment (N)

TRTA

Actual Treatment

TRTAN

Actual Treatment (N)

PARAMCD

Parameter Code

PARAM

Parameter

PARAMN

Parameter (N)

ADT

Analysis Date

ADY

Analysis Relative Day

ATPTN

Analysis Timepoint (N)

ATPT

Analysis Timepoint

AVISIT

Analysis Visit

AVISITN

Analysis Visit (N)

AVAL

Analysis Value

BASE

Baseline Value

BASETYPE

Baseline Value

CHG

Change from Baseline

PCHG

Percent Change from Baseline

VISITNUM

Visit Number

VISIT

Visit Name

VSSEQ

Sequence Number

ANL01FL

Analysis 01 - Special Interest Flag

ABLFL

Baseline Record Flag

Source

CDISC SDTM/ADAM Pilot Project.

Convert character variable to a factor based off it's numeric variable counterpart.

Description

Convert character variable to a factor based off it's numeric variable counterpart.

Usage

char2factor(df, c_var, n_var)

Arguments

df

data frame.

c_var

character variable within the data frame.

n_var

numeric variable counter part within the data frame to control the levels.

Value

A factor.

Examples

df <- tibble::tribble(
  ~TRT01P, ~TRT01PN,
  "Placebo", 1,
  "Low Dose", 2,
  "High Dose", 3
)

# alphabetical order
dplyr::arrange(df, TRT01P)

# change to factor with char2factor
df$TRT01P <- char2factor(df, "TRT01P", "TRT01PN")

# factor order
dplyr::arrange(df, TRT01P)

Adds borders under cells in a column

Description

Adds borders under cells in a column

Usage

col_borders(col, rows)

Arguments

col

numeric the column of the table

rows

numeric the range of rows to include

See Also

Other border_functions: no_borders(), row_border(), single_border(), spanning_borders()

Metadata describing table column layouts

Description

This is used by tlgsetup to prepare you input data to support the desired column layout.

Usage

column_metadata

Format

A data frame with one row per column for each table type and six variables:

tbltype

identifier used to group a table column layout

coldef

distinct variable values used, typically numeric and typically a treatment or main effect variable, think TRT01PN

decode

decode of coldef that will display as a column header in the table

span1

spanning header to display across multiple columns

span2

spanning header to display across multiple columns, second level

span3

spanning header to display across multiple columns, third level

Frequency counts and percentages

Description

Frequency counts and percentages for a variable by treatment and/or group.

Usage

freq(
  df,
  denom_df = df,
  colvar = NULL,
  tablebyvar = NULL,
  rowvar = NULL,
  rowbyvar = NULL,
  statlist = getOption("tidytlg.freq.statlist.default"),
  decimal = 1,
  nested = FALSE,
  cutoff = NULL,
  cutoff_stat = "pct",
  subset = TRUE,
  descending_by = NULL,
  display_missing = FALSE,
  rowtext = NULL,
  row_header = NULL,
  .keep = TRUE,
  .ord = FALSE,
  pad = TRUE,
  ...
)

Arguments

df

(required) dataframe containing records to summarize by treatment.

denom_df

(optional) dataframe used for population based denominators (default = df).

colvar

(required) treatment variable within df to use to summarize

tablebyvar

(optional) repeat entire table by variable within df

rowvar

(required) character vector of variables to summarize within the dataframe.

rowbyvar

(optional) repeat rowvar by variable within df

statlist

(optional) statlist object of stats to keep of length 1 or 2 specifying list of statistics and format desired (e.g statlist(c("N", "n (x.x\%)"))) (default = statlist(c("n (x.x)"))).

decimal

(optional) decimal precision root level default (default = 1).

nested

(optional) INTERNAL USE ONLY. The default should not be changed. Switch on when this function is called by nested_freq() so we will not include the by variables as part of the group denominators (default = FALSE).

cutoff

(optional) percentage cutoff threshold. This can be passed as a numeric cutoff, in that case any rows with greater than or equal to that cutoff will be preserved, others will be dropped. To specify a single column to define the cutoff logic, pass a character value of the form ⁠<colName> >= <value>⁠ and only that column will be used.

cutoff_stat

(optional) The value to cutoff by, n or pct. (default = 'pct'). Can be done with multiple columns by adding & or | ex. col1 >= val1 & col2 >= val2.

subset

(optional) An R expression that will be passed to a dplyr::filter() function to subset the data.frame. This is performed on the numerator before any other derivations. Denominators must be preprocessed and passed through using denom_df.

descending_by

(optional) The column or columns to sort descending counts. Can also provide a named list to do ascending order ex. c("VarName1" = "asc", "VarName2" = "desc") would sort by VarName1 in ascending order and VarName2 in descending order. In case of a tie in count or descending_by not provided, the columns will be sorted alphabetically.

display_missing

(optional) Should the "missing" values be displayed? If missing values are displayed, denominators will include missing values. (default = FALSE).

rowtext

(optional) A character vector used to rename the label column. If named, names will give the new level and values will be the replaced value. If unnamed, and the table has only one row, the rowtext will rename the label of the row. If the rowtext is unnamed, the table has no rows, and there is a subset, the table will be populated with zeros and the label will be the only row.

row_header

(optional) A character vector to be added to the table.

.keep

(optional) Should the rowbyvar and tablebyvar be output in the table. If FALSE, rowbyvar will still be output in the label column. (Default = TRUE).

.ord

Should the ordering columns be output with the table? This is useful if a table needs to be merged or reordered in any way after build.

pad

(optional) A boolean that controls if levels with zero records should be included in the final table. (default = TRUE).

...

(optional) Named arguments to be included as columns on the table.

Value

A dataframe of results

Sorting a 'freq' table

By default, a frequency table is sorted based on the factor level of the rowvar variable. If the rowvar variable isn't a factor, it will be sorted alphabetically. This behavior can be modified in two ways, the first is the char2factor() function that offers a interface for discretization a variable based on a numeric variable, like VISITN. The second is based on the descending_by argument which will sort based on counts on a variable.

Examples

adsl <- data.frame(
  USUBJID = c("DEMO-101", "DEMO-102", "DEMO-103"),
  RACE = c("WHITE", "BLACK", "ASIAN"),
  SEX = c("F", "M", "F"),
  colnbr = factor(c("Placebo", "Low", "High"))
)

# Unique subject count of a single variable
freq(adsl,
  colvar = "colnbr",
  rowvar = "RACE",
  statlist = statlist("n")
)

# Unique subject count and percent of a single variable
freq(adsl,
  colvar = "colnbr",
  rowvar = "RACE",
  statlist = statlist(c("N", "n (x.x%)"))
)

# Unique subject count of a variable by another variable
freq(adsl,
  colvar = "colnbr",
  rowvar = "RACE",
  rowbyvar = "SEX",
  statlist = statlist("n")
)

# Unique subject count of a variable by another variable using colvar and
# group to define the denominator
freq(adsl,
  colvar = "colnbr",
  rowvar = "RACE",
  rowbyvar = "SEX",
  statlist = statlist("n (x.x%)", denoms_by = c("colnbr", "SEX"))
)

# Cut records where count meets threshold for any column
freq(cdisc_adsl,
  rowvar = "ETHNIC",
  colvar = "TRT01P",
  statlist = statlist("n (x.x%)"),
  cutoff = "5",
  cutoff_stat = "n"
)

# Cut records where count meets threshold for a specific column
freq(cdisc_adsl,
  rowvar = "ETHNIC",
  colvar = "TRT01P",
  statlist = statlist("n (x.x%)"),
  cutoff = "Placebo >= 3",
  cutoff_stat = "n"
)

# Below illustrates how to make the same calls to freq() as above, using
# table and column metadata.

# Unique subject count of a single variable
table_metadata <- tibble::tribble(
  ~anbr, ~func, ~df, ~rowvar, ~statlist, ~colvar,
  1, "freq", "cdisc_adsl", "ETHNIC", statlist("n"), "TRT01PN"
)

generate_results(table_metadata,
  column_metadata = column_metadata,
  tbltype = "type1"
)

# Unique subject count and percent of a single variable
table_metadata <- tibble::tribble(
  ~anbr, ~func, ~df, ~rowvar, ~statlist, ~colvar,
  "1", "freq", "cdisc_adsl", "ETHNIC", statlist(c("N", "n (x.x%)")), "TRT01PN"
)

generate_results(table_metadata,
  column_metadata = column_metadata,
  tbltype = "type1"
)

# Cut records where count meets threshold for any column
table_metadata <- tibble::tibble(
  anbr = "1", func = "freq", df = "cdisc_adsl", rowvar = "ETHNIC",
  statlist = statlist("n (x.x%)"), colvar = "TRT01PN", cutoff = 5,
  cutoff_stat = "n"
)

generate_results(table_metadata,
  column_metadata = column_metadata,
  tbltype = "type1"
)

# Cut records where count meets threshold for a specific column
table_metadata <- tibble::tibble(
  anbr = 1, func = "freq", df = "cdisc_adsl", rowvar = "ETHNIC",
  statlist = statlist("n (x.x%)"), colvar = "TRT01PN",
  cutoff = "col1 >= 3", cutoff_stat = "n"
)

generate_results(table_metadata,
  column_metadata = column_metadata,
  tbltype = "type1"
)

Generate Results using Table and Column Metadata

Description

Generate Results using Table and Column Metadata

Usage

generate_results(
  table_metadata,
  column_metadata_file = NULL,
  column_metadata = NULL,
  env = parent.frame(),
  tbltype = NULL,
  add_count = FALSE
)

Arguments

table_metadata

a data frame containing table metadata (see ?table_metadata for details)

column_metadata_file

An excel file with the data for column_metadata. The file is read in with readxl::read_excel(). Should not be used with column_metadata argument. Results in a data frame containing the column metadata that is passed to tlgsetup (see tlgsetup() for details). If a column_metadata data frame is passed in too, this is ignored.

column_metadata

A data frame containing the column metadata. This will be used in place of column_metadata_file.

env

environment to find data frame specified in the table metadata (defaults to parent environment).

tbltype

If used, this will be used to subset the column_metadata based on the tbltype column.

add_count

Passed to bind_table() should counts be added for tablebyvars?

Value

dataframe of results

Output a tidytlg table

Description

Generate and output a huxtable with desired properties During this function call, the huxtable can be written to an RTF or displayed in HTML. gentlg is vectorized, see parameter descriptions to learn for which arguments.

Usage

gentlg(
  huxme = NULL,
  tlf = "Table",
  format = "rtf",
  colspan = NULL,
  idvars = NULL,
  plotnames = NULL,
  plotwidth = NULL,
  plotheight = NULL,
  wcol = 0.45,
  orientation = "portrait",
  opath = ".",
  title_file = NULL,
  file = NULL,
  title = NULL,
  footers = NULL,
  print.hux = TRUE,
  watermark = NULL,
  colheader = NULL,
  pagenum = FALSE,
  bottom_borders = "old_format",
  border_fns = list(),
  alignments = list()
)

Arguments

huxme

(optional) For tables and listings, A list of input dataframes containing all columns of interest. For graphs, either NULL or a list of ggplot objects. Vectorized.

tlf

(optional) String, representing the output choice. Choices are "Table" "Listing" "Figure". Abbreviations are allowed e.g. "T" for Table. Strings can be either upper- or lowercase. Vectorized. (Default = "Table")

format

(optional) String, representing the output format. Choices are "rtf" and "html". Strings can be either upper- or lowercase.(Default = "rtf")

colspan

(optional) A list of character vectors representing the spanning headers to be used for the table or listing. The first vector represents the top spanning header, etc. Each vector should have a length equal to the number of columns in the output data frame. A spanning header is identified through the use of the same column name in adjacent elements. Vectorized.

idvars

(optional) Character vector defining the columns of a listing where repeated values should be removed recursively. If NULL then all column names are used in the algorithm. If NA, then the listing remains as is.

plotnames

(optional) Character vector containing the names of the PNG files, with their extension to be incorporated for figure outputs. The PNG files need to be located in the path defined by the parameter opath.

plotwidth

(optional) Numerical value that indicates the plot width in cm for figure outputs. (Default = 6)

plotheight

(optional) Numerical value that indicates the plot height in cm for figure outputs. (Default = 5)

wcol

(optional) Can be a single numerical value that represents the width of the first column or a vector, specifying the lengths of all columns in the final table or listing.
When a single numerical value is used, this will be taken as the column width for the first column. The other columns will be equally spaced across the remainder of the available space. Alternatively, a vector can be used to represent the widths of all columns in the final output. The order of the arguments needs to correspond to the order of the columns in the huxme dataset, that are not part of the formatting algorithms (e.g. anbr, roworder, newpage, newrow, indentme, boldme, by_value, by_order). The sum of the widths in the vector needs to be less or equal to one. When format="HTML" wcol can take only one value, the width of the first column. (Default = 0.45).

orientation

(optional) String: "portrait" or "landscape". (Default = "portrait")

opath

(optional) File path pointing to the output files (including PNG files for graphs). (Default = ".").

title_file

An Excel file that will be read in with readxl::read_excel() to be used as the title and footers argument. The use of title or footers will override the values passed by this argument. The file should be either an xls or xlsx file with the columns ⁠TABLE ID⁠, IDENTIFIER, and TEXT. The file will be read in, subset to where the tblid matches the tlf argument, and identifiers with 'title' or 'footnote' will be used to populate the table.

file

(required) String. Output identifier. File name will be adjusted to be lowercase and have - and ⁠_⁠ removed, this will not affect table title.

title

(required) String. Title of the output. Vectorized.

footers

(optional) Character vector, containing strings of footnotes to be included. Vectorized.

print.hux

(optional) Logical, indicating whether the output should be printed to RTF ('format' = "rtf") or displayed as HTML ('format' = "HTML"). (Default = TRUE). Note that RTF is written using quick_rtf_jnj() function and that the HTML is displayed via the huxtable::print_html function.

watermark

(optional) String containing the desired watermark for RTF outputs. Vectorized.

colheader

(optional) Character vector that contains the column labels for a table or listing. Default uses the column labels of huxme. Vectorized.

pagenum

(optional) Logical. When true page numbers are added on the right side of the footer section in the format page x/y. Vectorized. (Default = FALSE).

bottom_borders

(optional) Matrix or "old_format". A matrix indicating where to add the bottom borders. Vectorized. See add_bottom_borders() for more information. If "old_format", then borders are added to the colspan and colheader rows. (Default = "old_format").

border_fns

(optional) List. A list of functions that transform the matrix passed to bottom_borders. Vectorized. See add_bottom_borders() for more information.

alignments

(optional) List of named lists. Vectorized. (Default = list()) Used to specify individual column or cell alignments. Each named list contains row, col, and value, which are passed to huxtable::set_align() to set the alignments.

Value

A list of formatted huxtables with desired properties for output to an RTF or HTML.

Huxme Details

For tables and listings, formatting of the output can be dictated through the formatting columns (newrows, indentme, boldme, newpage), present in the input dataframe. The final huxtable will display all columns of the input dataframe, except any recognized formatting or sorting columns. For tables, the algorithm uses the column label as first column. The remaining columns are treated as summary columns. For graphs, you can pass a ggplot object directly into huxme and gentlg will save a PNG with with ggplot2::ggsave() and output an RTF.

Author(s)

Steven Haesendonckx shaesen2@its.jnj.com

Pelagia Alexandra Papadopoulou ppapadop@its.jnj.com

References

https://github.com/hughjonesd/huxtable

Examples


final <- data.frame(
  label = c(
    "Overall", "Safety Analysis Set",
    "Any Adverse event{\\super a}", "- Serious Adverse Event"
  ),
  Drug_A = c("", "40", "10 (25%)", "0"),
  Drug_B = c("", "40", "10 (25%)", "0"),
  anbr = c(1, 2, 3, 4),
  roworder = c(1, 1, 1, 1),
  boldme = c(1, 0, 0, 0),
  newrows = c(0, 0, 1, 0),
  indentme = c(0, 0, 0, 1),
  newpage = c(0, 0, 0, 0)
)

# Produce output in rtf format
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  )
)

# Pass in column headers instead of using variable name
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  )
)

# Add spanning bottom borders under the cells in the second row
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  ),
  border_fns = list(spanning_borders(2))
)

# Use a watermark
gentlg(
  huxme = final,
  wcol = c(0.70, 0.15, 0.15),
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  ),
  watermark = "Confidential"
)

# Set alignments
gentlg(
  huxme = final,
  file = "TSFAEX",
  alignments = list(
    # Align the second column to the left
    list(row = 1:7, col = 2, value = "left"),

    # Align cell "Drug: B" to the right
    list(row = 2, col = 3, value = "right")
  )
)

# Produce output in HTML format
hux <- gentlg(
  huxme = final,
  file = "TSFAEX",
  colheader = c("", "Drug A", "Drug B"),
  title = "This is Amazing Demonstration 1",
  footers = c(
    "Note: For demonstrative purposes only",
    "{\\super a} Subjects are counted once for any given event."
  ),
  watermark = "Confidential",
  format = "HTML",
  print.hux = FALSE
)

# Export to HTML page
huxtable::quick_html(hux, file = "TSFAEX.html", open = FALSE)

# clean up.
file.remove("TSFAEX.html", "tsfaex.rtf")

Inserts empty rows into a data frame

Description

Inserts empty rows into a data frame

Usage

insert_empty_rows(huxme, newrows = huxme$newrows)

Arguments

huxme

data.frame The input data frame.

Details

gentlg allows for formatting the input table based on formatting columns (see gentlg()). One of the formatting mechanisms is empty row insertion. This function inserts the empty rows based on the newrows column in the data frame. The new rows are inserted before the rows with value 1 in the newrows column.

Value

A data frame with added new empty rows.

Generate nested count/percent for two or three levels

Description

This will call freq() multiple times and combine the levels together. This is useful for adverse event and concomitant medications.

Usage

nested_freq(
  df,
  denom_df = df,
  colvar = NULL,
  tablebyvar = NULL,
  rowvar = NULL,
  rowbyvar = NULL,
  statlist = getOption("tidytlg.nested_freq.statlist.default"),
  decimal = 1,
  cutoff = NULL,
  cutoff_stat = "pct",
  subset = TRUE,
  descending_by = NULL,
  display_missing = FALSE,
  rowtext = NULL,
  row_header = NULL,
  .keep = TRUE,
  .ord = FALSE,
  ...
)

Arguments

df

(required) dataframe containing the two levels to summarize

denom_df

(optional) dataframe containing records to use as the denominator (default = df)

colvar

(required) treatment variable within df to use to summarize

tablebyvar

(optional) repeat entire table by variable within df.

rowvar

(required) nested levels separated by a star, for example AEBODSYS*AEDECOD, this can handle up to three levels.

rowbyvar

(optional) repeat rowvar by variable within df

statlist

(optional) count/percent type to return (default = "n (x.x)")

decimal

(optional) decimal precision root level (default = 1)

cutoff

(optional) numeric value used to cut the data to a percentage threshold, if any column meets the threshold the entire record is kept.

cutoff_stat

(optional) The value to cutoff by, n or pct. (default = 'pct')

subset

(optional) An R expression that will be passed to a dplyr::filter() function to subset the data.frame

descending_by

(optional) The column or columns to sort descending values by. Can also provide a named list to do ascending order. ex. c("VarName1" = "asc", "VarName2" = "desc") would sort by VarName1 in ascending order and VarName2 in descending order. If not provided, the columns will be sorted alphabetically.

display_missing

(optional) Should the "missing" values be displayed? (default = FALSE)

rowtext

(optional) A character vector used to rename the label column. If named, names will give the new level and values will be the replaced value. If unnamed, and the table has only one row, the rowtext will rename the label of the row.

row_header

(optional) A character vector to be added to the table.

.keep

(optional) Should the rowbyvar and tablebyvar be output in the table. If FALSE, rowbyvar will still be output in the label column. (default = TRUE).

.ord

Should the ordering columns be output with the table? This is useful if a table needs to be merged or reordered in any way after build.

...

(optional) Named arguments to be included as columns on the table.

Value

A dataframe of nested results by colvar and optional tablebyvar. There are a few additional variable sets added to support multiple requirements.

The level variables (level1_, level2_, level3_) will carry down the counts for each level to every record. This allows for easy sorting of nested groups.

The header variables (header1, header2, header3) will flag the header for each level to ensure each level header is sorted to the top of the level.

The n variables ("n_") provide a numeric variable containing frequency for each colvar. This can be used to sort and filter records.

The pct variables ("pct_") provide a numeric variable containing percentages for each colvar. This can be used to sort and filter records.

Examples

adae <- data.frame(
  SITEID = c("100", "100", "100", "200", "200", "200"),
  USUBJID = c(
    "Demo1-101", "Demo1-102", "Demo1-103",
    "Demo1-104", "Demo1-105", "Demo1-106"
  ),
  AEBODSYS = c(
    "Cardiac disorders", "Cardiac disorders",
    "Respiratory, thoracic and mediastinal disorders",
    "Infections and infestations",
    "Skin and subcutaneous tissue disorders",
    "Infections and infestations"
  ),
  AEDECOD = c(
    "Arrhythmia supraventricular", "Cardiac failure",
    "Chronic obstructive pulmonary disease", "Pneumonia",
    "Pustular psoriasis", "Upper respiratory tract infection"
  ),
  colnbr = structure(
    c(1L, 2L, 3L, 1L, 2L, 3L),
    .Label = c("Active", "Placebo", "Comparator"),
    class = "factor"
  )
)

# Frequency and percent for two levels of nesting
nested_freq(adae,
  colvar = "colnbr",
  rowvar = "AEBODSYS*AEDECOD",
  statlist = statlist("n (x.x%)")
)

# Frequency and percent for three levels of nesting (for illustrative
# purpose)
nested_freq(adae,
  colvar = "colnbr",
  rowvar = "SITEID*AEBODSYS*AEDECOD",
  statlist = statlist("n (x.x%)")
)

# Cut records where pct meets threshold for a any column
nested_freq(cdisc_adae,
  colvar = "TRTA",
  rowvar = "AEBODSYS*AEDECOD",
  statlist = statlist("n (x.x%)", distinct = TRUE),
  cutoff = 2,
  cutoff_stat = "n"
)

# Cut records where pct meets threshold for a specific column
nested_freq(cdisc_adae,
  rowvar = "AEBODSYS*AEDECOD",
  colvar = "TRTAN",
  statlist = statlist("n (x.x%)", distinct = TRUE),
  cutoff = "54 >= 2",
  cutoff_stat = "n"
)

# Frequency and percent for two levels of nesting and sort by descending
# active
nested_freq(adae,
  colvar = "colnbr",
  rowvar = "AEBODSYS*AEDECOD",
  statlist = statlist("n (x.x%)"),
  descending = "Active"
)

# Below illustrates how make the same calls to nested_freq() as above, using
# table and # column metadata along with generate_results().

column_metadata <- tibble::tribble(
  ~tbltype, ~coldef, ~decode,
  "type1", "1", "Placebo",
  "type1", "2", "Low",
  "type1", "3", "High"
)

# Frequency and percent for two levels of nesting
table_metadata <- tibble::tribble(
  ~anbr, ~func, ~df, ~rowvar, ~tbltype, ~colvar, ~statlist,
  "1", "nested_freq", "cdisc_adae", "AEBODSYS*AEDECOD", "type1", "TRTP",
  statlist("n (x.x%)")
)
# generate_results(table_metadata,
# column_metadata_file = tidytlg_metadata(path)


# Frequency and percent for three levels of nesting (for illustrative purpose)
table_metadata <- tibble::tribble(
  ~anbr, ~func, ~df, ~rowvar, ~tbltype, ~colvar,
  ~statlist,
  "1", "nested_freq", "cdisc_adae", "SITEID*AEBODSYS*AEDECOD", "type1",
  "TRTP", statlist("n (x.x%)")
)
# Commented out because it takes too long
# generate_results(table_metadata, column_metadata)

# Cut records where pct meets threshold for a any column
column_metadata <- tibble::tribble(
  ~tbltype, ~coldef, ~decode,
  "type2", "1", "Placebo",
  "type2", "2", "Active"
)
table_metadata <- tibble::tibble(
  anbr = "1", func = "nested_freq", df = "cdisc_adae",
  rowvar = "AEBODSYS*AEDECOD",
  tbltype = "type2", colvar = "TRTP", statlist = statlist("n (x.x%)"),
  dotdotdot = "cutoff = 5"
)
# generate_results(table_metadata,
# column_metadata_file = tidytlg_metadata(path)

# Cut records where pct meets threshold for a specific column
table_metadata <- tibble::tibble(
  anbr = "1", func = "nested_freq", df = "cdisc_adae",
  rowvar = "AEBODSYS*AEDECOD",
  tbltype = "type2", colvar = "TRTP", statlist = statlist("n (x.x%)"),
  dotdotdot = "cutoff = 'col1 >= 5'"
)
# generate_results(table_metadata,
# column_metadata_file = tidytlg_metadata(path)

# Frequency and percent for two levels of nesting and sort by descending col1
table_metadata <- tibble::tibble(
  anbr = "1", func = "nested_freq", df = "cdisc_adae",
  rowvar = "AEBODSYS*AEDECOD",
  tbltype = "type2", colvar = "TRTP", statlist = statlist("n (x.x%)"),
  dotdotdot = "descending = 'col1'"
)
# generate_results(table_metadata,
# column_metadata_file = tidytlg_metadata(path)

Removes all borders from the table

Description

Removes all borders from the table

Usage

no_borders(ht, matrix = NULL)

Arguments

ht

huxtable object.

matrix

matrix of bottom borders. Ignored. Included for the sake of compatibility with the interface of all border mutating functions.

See Also

Other border_functions: col_borders(), row_border(), single_border(), spanning_borders()

Adds bottom borders according to the old formatting

Description

This function is vectorized over its arguments.

Usage

old_format(ht, colspan, colheader, tlf)

Arguments

ht

the hux object passed to gentlg()

colspan

colspan argument to gentlg()

colheader

colheader argument to gentlg()

tlf

character type of the output

Value

a bottom border matrix for use with add_bottom_borders() or NULL if ht is NULL

Removes no-op padding from an RTF markup

Description

Removes no-op padding from an RTF markup

Usage

remove_zero_padding(rtf)

Arguments

rtf

character(1) RTF markup.

Details

Removes no-op padding, which is padding that has a thickness of zero from an RTF markup.

Value

character(1) RTF markup with removed zero padding

Replace NA with ""

Description

Used to swap in "" for by variables so the headers sort correctly to the top

Usage

replace_na_with_blank(x)

Arguments

x

variable to check for NA and replace with "".

Value

x with NAs replaced with "". Factors will add "" as the first level.

Examples

replace_na_with_blank(c("a", "b", NA))

replace_na_with_blank(factor(c("a", "b", NA), levels = c("a", "b")))

Get Titles and Footnotes for all TLGs or one specific TLG

Description

Get Titles and Footnotes for all TLGs or one specific TLG

Usage

rmdpstitle(
  df,
  tblid,
  idvar = "tblid",
  identifier = "identifier",
  text = "text"
)

Arguments

df

dataframe with three variables; table name, row identifier (TITLE or FOOTNOTEn), and title or footnote text to display.

tblid

character vector containing the table id, optional, used to subset df to a specific table (defaults to tblid).

idvar

character vector containing the variable in df that contains your table id.

identifier

character vector containing the variable name in df that contains your record identifier (defaults to "identifier").

text

character vector containing the variable name in df that contains your title and footnote text (defaults to "text").

Value

list of length two, the first element contains the titles as a tibble and the second contains the footnotes as a list.

Examples

tblid <- "TSIDEM01"

titles <- tibble::tribble(
  ~tblid, ~identifier, ~text,
  "TSIDEM01", "TITLE", "Demographics Example",
  "TSIDEM01", "FOOTNOTE1", "Example footnote."
)

title_foot <- rmdpstitle(titles, tblid)

title_foot[[1]]
title_foot[[2]]

SAS rounding in R

Description

roundSAS is an alternative rounding function, ensuring that decimals equal or bigger than 5 are rounded upwards to the nearest number and returned as character vector.

Usage

roundSAS(x, digits = 0, as_char = FALSE, na_char = NULL)

Arguments

x

Numeric vector.

digits

An integer specifying the number of decimal places to be displayed after rounding. Default is 0.

as_char

logical value indicating conversion of rounded numerical vector to character vector; default is FALSE.

na_char

A character string indicating missing value; if not specified, "NA" is created.

Details

At the midpoint of a decimal place (e.g. 0.5, 1.5), the round function in R rounds to the nearest even number (i.e. 0.5 is rounded to 0; 1.5 is rounded to 2), whereas SAS rounds to the nearest number (i.e. 0.5 is rounded to 1; 1.5 is rounded to 2). The roundSAS function is an alternative rounding function for R that ensures rounding to the nearest number, as done in SAS. roundSAS comes from this Stack Overflow post.

Value

character vector of rounded values

Examples

### input data vector with midpoint decimals
x <- c(-2.5, -1.5, -0.5, 0.5, 1.5, 2.5)

### rounds to integer
roundSAS(x, digits = 0)

### input data vector with a missing value
y <- c(8.65, 8.75, NA, 9.85, 9.95)

### rounds to tenths and label the missing value with "NE"
roundSAS(y, digits = 1, as_char = TRUE, na_char = "NE")

Adds a continuous bottom border under a row

Description

Adds a continuous bottom border under a row

Usage

row_border(row)

Arguments

row

numeric the row of the table

See Also

Other border_functions: col_borders(), no_borders(), single_border(), spanning_borders()

Adds a border under a cell

Description

Adds a border under a cell

Usage

single_border(row, col)

Arguments

row

numeric the row of the cell

col

numeric the column of the cell

See Also

Other border_functions: col_borders(), no_borders(), row_border(), spanning_borders()

Adds borders under cells in a row, excluding the first column.

Description

Adds borders under cells that are not empty in a given row, omitting the first column of the row. The borders do not touch each other - they are separate.

Usage

spanning_borders(row, cols = c(-1))

Arguments

row

numeric the row of the table

cols

numeric the columns of the row to consider

See Also

Other border_functions: col_borders(), no_borders(), row_border(), single_border()

Spanning headers for outputs

Description

This will create the list object to be passed to gentlg() You can create as many spanning headers as you like, just add variables prefixed with span to the column metadata.

Usage

spanning_headers(column_metadata)

Arguments

column_metadata

dataframe containing the column metadata that is passed to tlgsetup() (see tlgsetup() for details)

Value

List of character vectors containing column headers for an output.

Examples

column_metadata <-
  tibble::tribble(
    ~tbltype, ~coldef, ~decode,                ~span1,
    "type1",  "0",     "Placebo",              "",
    "type1",  "54",    "Low Dose",             "Xanomeline",
    "type1",  "81",    "High Dose",            "Xanomeline",
    "type1",  "54+81", "Total Xanomeline",     ""
  )

spanning_headers(column_metadata)

Create a statlist interface for a table

Description

The statlist is the interface for the presentation of data in a tidytlg table.

Usage

statlist(stats, ...)

Arguments

stats

(required) A character vector of statistics to display in the table.

...

(optional) Additional configuration for stats. See sections below for allowable arguments.

Value

A statlist object that can be passed in the statlist argument of freq, nested_freq, or univar.

Statlists for freq() and nested_freq()

freq() statlists can be composed of n (count), N (denominator), and x.x (percentage, formatted with or without a percent sign). Denominators will include missing values if the 'display_missing' argument is TRUE, otherwise they will be excluded. They can be arranged in the following ways:

The following other configurations are supported:

Statlists for univar statlists

where GeoMEAN: Geometric Mean, CV: Coefficient of Variation, GSD: Geometric standard deviation, GSE: Geometric standard error, MEAN_CI: Mean (95% C.I.), GeoMEAN_CI: Geometric Mean (95% C.I.). In calculating geometric statistics, if there are zero values in the inputs, zero values will be excluded before calculating geometric statistics.

Examples


freq(
  mtcars,
  colvar = "gear",
  rowvar = "cyl",
  rowbyvar = "am",
  statlist = statlist("n/N (x.x)",
    distinct = FALSE,
    denoms_by = c("gear", "am"),
    zero_denom = "_0_"
  )
)

Metadata describing the data, functions and arguments needed to produce your results.

Description

Metadata describing the data, functions and arguments needed to produce your results.

Usage

table_metadata

Format

A data frame with one row per function call and 16 variables:

func

name of the function you wish to call

df

data frame to pass to the function call

subset

filter df records, this is passed directly to filter, ex. "AESER == 'Y'"

rowvar

variable being summarized that will pass to the function call

rowtext

row label text to display in the table

row_header

header text to display above row summary

statlist

list of statistics in the analysis, see individual functions for what is available per function (e.g. "N, n (x.x)")

colvar

variable used to determine the columns of the table

decimal

decimal precision

rowbyvar

repeat rowvar summary by this variable/s, comma separated for multiple (e.g. "ETHNIC, AGEGR1")

tablebyvar

repeat the entire table summary by this variable/s, comma separated for multiple (e.g. "ETHNIC, AGEGR1")

denom_df

used to set denominators if df does not contain all required records

Helper functions for returning files used in gentlg

Description

Helper functions for returning files used in gentlg

Usage

tidytlg_titles(path)

tidytlg_metadata(path)

Arguments

path

Working directory of the project

Value

A character vector to the requested file.

Setup data to support the specified column type

Description

tlgsetup is useful for preprocessing total columns and columns composed of other columns. tlgsetup is called internally by generate_results() and can be run manually for custom tables.

Usage

tlgsetup(
  df,
  var,
  column_metadata_file = NULL,
  column_metadata = NULL,
  tbltype = NULL
)

Arguments

df

dataframe of records for analysis.

var

character vector that identifies the numeric column/treatment variable.

column_metadata_file

A file containing the column metadata. Read in with readxl::read_excel(). If a column_metadata dataframe is passed in too, this is ignored.

column_metadata

A dataframe containing the column metadata. This will be used in place of column_metadata_file.

tbltype

A value used to subset the column_metadata, both this and the file requirements are needed to bind the data to the table.

Value

dataframe with observations added to support the column type as well as the factor variable colnbr which is used as our new column summary variable. Regardless of if a coldef exists in data, the column will exist in the table.

Examples

df <-
  tibble::tribble(
    ~TRT01AN, ~USUBJID,
    0, "A",
    54, "B",
    81, "C"
  )

tlgsetup(df, "TRT01AN", column_metadata = column_metadata)

# Using a dataframe of column metadata
column_metadata <-
  tibble::tribble(
    ~tbltype, ~coldef, ~decode,                ~span1,
    "type1",  "0",     "Placebo",              "",
    "type1",  "54",    "Low Dose",             "Xanomeline",
    "type1",  "81",    "High Dose",            "Xanomeline",
    "type1",  "54+81", "Total Xanomeline",     ""
  )

tlgsetup(df, "TRT01AN", column_metadata = column_metadata)

Descriptive statistics

Description

Univariate statistics for a variables by treatment and/or group.

Usage

univar(
  df,
  colvar = NULL,
  tablebyvar = NULL,
  rowvar = NULL,
  rowbyvar = NULL,
  statlist = getOption("tidytlg.univar.statlist.default"),
  decimal = 1,
  precisionby = NULL,
  precisionon = NULL,
  wide = FALSE,
  alpha = 0.05,
  rowtext = NULL,
  row_header = NULL,
  .keep = TRUE,
  .ord = FALSE,
  ...
)

Arguments

df

(required) dataframe containing records to summarize by treatment.

colvar

(required) character vector of the treatment variable within the dataframe.

tablebyvar

(optional) repeat entire table by variable within df.

rowvar

(required) character vector of variable to summarize within the dataframe.

rowbyvar

(optional) repeat rowvar by variable within df.

statlist

(optional) statlist object of stats to keep (default = statlist(c("N", "MEANSD", "MEDIAN", "RANGE", "IQRANGE"))).

decimal

(optional) decimal precision root level, when using presisionby this will be used as the base decimal cap (default = 1).

precisionby

(optional) vector of by variable(s) to use when calculating parameter based precision.

precisionon

(optional) variable to use when calculating parameter based precision. If precisionby is specified but not precisionon this will default to rowvar.

wide

(optional) logical indicating to convert labels to column and columns to labels (default = FALSE).

alpha

(optional) alpha level for 2-sided confidence interval (default = 0.05).

rowtext

(optional) A text string to replace the label value on the table. Useful for tables with a single row.

row_header

(optional) A row to add as a header for the table.

.keep

(optional) Should the rowbyvar and tablebyvar be output in the table. If FALSE, rowbyvar will still be output in the label column. (default = TRUE).

.ord

Should the ordering columns be output with the table? This is useful if a table needs to be merged or reordered in any way after build.

...

(optional) Named arguments to be included as columns on the table.

Value

dataframe of results

Examples

adsl <-
  structure(
    list(
      USUBJID = c(
        "DEMO-101", "DEMO-102", "DEMO-103", "DEMO-104",
        "DEMO-105", "DEMO-106"
      ),
      AGE = c(59, 51, 57, 65, 21, 80),
      SEX = c("F", "M", "F", "M", "F", "M"),
      WEIGHTBL = c(83.6, 75, 84, 90, 65, 70),
      colnbr = structure(
        c(1L, 3L, 2L, 2L, 3L, 1L),
        .Label = c("Placebo", "Low", "High"),
        class = "factor"
      )
    ),
    row.names = c(NA, 6L),
    class = "data.frame"
  )

# N, Mean(SD), Median, Range, IQ Range for a rowvar by colvar
univar(adsl,
  colvar = "colnbr",
  rowvar = "AGE"
)

# N and Mean for a rowvar by colvar
univar(adsl,
  colvar   = "colnbr",
  rowvar   = "AGE",
  statlist = statlist(c("N", "MEAN"))
)

# N and Mean for a rowvar by colvar and a by variable
univar(adsl,
  colvar   = "colnbr",
  rowvar   = "AGE",
  rowbyvar = "SEX",
  statlist = statlist(c("N", "MEAN"))
)

# Below illustrates how make the same calls to univar() as above, using table
# and column metadata # along with generate_results().

column_metadata <- tibble::tribble(
  ~tbltype, ~coldef, ~decode,
  "type1", "0", "Placebo",
  "type1", "54", "Low",
  "type1", "81", "High"
)

# N, Mean(SD), Median, Range, IQ Range for a rowvar by colvar
table_metadata <- tibble::tribble(
  ~anbr, ~func, ~df, ~rowvar, ~tbltype, ~colvar,
  "1", "univar", "cdisc_adae", "AGE", "type1", "TRTA"
)

generate_results(table_metadata,
  column_metadata = column_metadata,
  tbltype = "type1"
)


# N and Mean for a rowvar by colvar
table_metadata <- tibble::tribble(
  ~anbr, ~func, ~df, ~rowvar, ~tbltype, ~colvar, ~statlist,
  "1", "univar", "cdisc_adae", "AGE", "type1", "TRTA",
  statlist(c("N", "MEAN"))
)

generate_results(table_metadata,
  column_metadata = column_metadata,
  tbltype = "type1"
)


# N and Mean for a rowvar by colvar and a by variable
table_metadata <- tibble::tribble(
  ~anbr, ~func, ~df, ~rowvar, ~tbltype, ~colvar, ~statlist, ~by,
  "1", "univar", "cdisc_adae", "AGE", "type1", "TRTA",
  statlist(c("N", "MEAN")), "SEX"
)

generate_results(table_metadata,
  column_metadata = column_metadata,
  tbltype = "type1"
)