Package 'tidytlg' reference manual

Title:	Create TLGs using the 'tidyverse'
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.
Authors:	Nicholas Masel [aut], Steven Haesendonckx [aut], Pelagia Alexandra Papadopoulou [aut], Sheng-Wei Wang [aut], Eli Miller [aut] , Nathan Kosiba [aut] , Aidan Ceney [aut] , Janssen R&D [cph, fnd], David Hugh-Jones [cph] (Author of included huxtable library), Konrad Pagacz [aut, cre]
Maintainer:	Konrad Pagacz <[email protected]>
License:	Apache License 2.0
Version:	0.1.5
Built:	2024-11-29 04:55:17 UTC
Source:	https://github.com/pharmaverse/tidytlg

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())
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:

The huxtable.
The border_matrix with dimentions 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:

no_borders()
spanning_borders()
col_borders()
single_border()
row_border()

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:

the first - the printed huxtable object,
the second - a border matrix.

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")

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)
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)
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)
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)
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)
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")
# 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 in 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
)
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")
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
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: 1st Occurrence of Any AE Flag
AOCCSFL: 1st Occurrence of SOC Flag
AOCCPFL: 1st Occurrence of Preferred Term Flag
AOCC02FL: 1st Occurrence 02 Flag for Serious
AOCC03FL: 1st Occurrence 03 Flag for Serious SOC
AOCC04FL: 1st Occurrence 04 Flag for Serious PT
CQ01NAM: Customized Query 01 Name
AOCC01FL: 1st 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
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: Completers 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 Lim
BR2A1HI: Base Ratio to Analysis Range 1 Upper Lim
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
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: Completers of Week 8 Population Flag
COMP16FL: Completers of Week 16 Population Flag
COMP24FL: Completers 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 Trt Visit (Vis 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
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)
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)
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)
col_borders(col, rows)

Arguments

`col`	`numeric` the column of the table
`rows`	`numeric` the range of rows to include

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
column_metadata

Format

A data frame with one row per column for each table type and 6 variables:

tbltype: identifier used to group a table column layout
coldef: distinct variable values used, typically numeric and typically a treatment/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,
  ...
)
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\ (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 releveling 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")
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
)
generate_results(
  table_metadata,
  column_metadata_file = NULL,
  column_metadata = NULL,
  env = parent.frame(),
  tbltype = NULL,
  add_count = FALSE
)

Arguments

`table_metadata`	dataframe 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 dataframe containing the column metadata that is passed to tlgsetup (see `tlgsetup()` for details). 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`.
`env`	environment to find dataframe 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()
)
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()
)

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 eg "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 (eg 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` arugment. 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") / 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.

Value

A list of formatted huxtables with desired properties for output to an RTF/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/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 [email protected]

Pelagia Alexandra Papadopoulou [email protected]

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"
)

# 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")
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"
)

# 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")

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 mediations.

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,
  ...
)
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)
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)
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.

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)
replace_na_with_blank(x)

Arguments

`x`	variable to check for NA and replace with "".

Value

x with NA's 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")))
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"
)
rmdpstitle(
  df,
  tblid,
  idvar = "tblid",
  identifier = "identifier",
  text = "text"
)

Arguments

`df`	dataframe with three variables; table name, row identifier (TITLE or FOOTNOTEn), and title/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]]
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)
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 https://stackoverflow.com/questions/12688717/round-up-from-5

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")

### 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)
row_border(row)

Arguments

row

numeric the row of the table

Adds a border under a cell

Description

Adds a border under a cell

Usage

single_border(row, col)
single_border(row, col)

Arguments

`row`	`numeric` the row of the cell
`col`	`numeric` the column of the cell

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

Arguments

`row`	`numeric` the row of the table
`cols`	`numeric` the columns of the row to consider

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)
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)
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, ...)
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:

n
n/N
n (x.x)
n (x.x%)
n/N (x.x)
n/N (x.x%)

The following other configurations are supported:

denoms_by - Controls what groupings of variables should define the denominator. Variables should be passed as a quoted vector
distinct - A boolean value. Should the numerator reflect distinct USUBJIDs or event counts. Defaults to TRUE which captures distinct subjects.
distinct_by - A character value used to select the variable that should be used to "distinct" the freq tables. Defaults to USUBJID.
zero_denom - The string to display when there are no records found in an entire denominator group. Defaults to "-"
zero_n - The string to display when there are no records found for a numerator. Defaults to "0".

Statlists for univar statlists

N
SUM
MEAN
GeoMEAN
SD
SE
CV
GSD
GSE
MEANSD
MEANSE
MEDIAN
MIN
MAX
RANGE
Q1
Q3
IQRANGE
MEDRANGE
MEDIQRANGE
MEAN_CI
GeoMEAN_CI

where GeoMEAN: Geometric Mean, CV: Coefficient of Variation, GSD: Geometric Std. Dev., GSE: Geometric Std. 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_")
  )
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
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 (eg. "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 (eg. "ETHNIC, AGEGR1")
tablebyvar: repeat the entire table summary by this variable/s, comma separated for multiple (eg. "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)
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 pre-processing 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
)
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)
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 statitstics 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,
  ...
)
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")
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")

Package 'tidytlg'

Help Index

Adds bottom borders to a huxtable

Description

Usage

Arguments

Details

Value

border_matrix details

Functions transforming the border matrix

Examples

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

Description

Usage

Arguments

Value

Examples

Add indentation variable to the results dataframe

Description

Usage

Arguments

Details

Value

Examples

Add the newrows variable to the results dataframe.

Description

Usage

Arguments

Value

Examples

Bind a set of tidytlg tables together with formatting variables

Description

Usage

Arguments

Value

Examples

ADAE data created from subsetting the CDISC ADAE dataset

Description

Usage

Format

Source

ADLB data created from subsetting the CDISC ADLB dataset

Description

Usage

Format

Source

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

Description

Usage

Format

Source

ADVS data created from subsetting the CDISC ADVS dataset

Description

Usage

Format

Source

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

Description

Usage

Arguments

Value

Examples

Adds borders under cells in a column

Description

Usage

Arguments

See Also

Metadata describing table column layouts

Description

Usage

Format

Frequency counts and percentages

Description

Usage

Arguments

Value

Sorting a 'freq' table

Examples

Generate Results using Table and Column Metadata

Description

`border_matrix` details

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

Statlists for `freq()` and `nested_freq()`