Package 'doudpackage'

Title: Create Elegant Table 1 in HTML/'LaTeX' for Bio-Statistics
Description: Creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate and bivariate analysis, and parse the result into an HTML or 'LaTeX' table ready to paste into a paper.
Authors: Edouard Baudouin [aut, cre] (ORCID: <https://orcid.org/0000-0002-8324-6908>)
Maintainer: Edouard Baudouin <[email protected]>
License: GPL (>= 3)
Version: 2.2.0
Built: 2026-09-26 10:19:00 UTC
Source: https://github.com/tiago972/doudpackage

Help Index

Method to access S4 Var elements

Description

Method to access parseClass initialize,parseClass-method() elements by name

Usage

## S4 method for signature 'parseClass'
x[i]

Arguments

x

: Object
i

: Element name

Value

object

Method to access S4 Var elements

Description

Method to access Var elements by name

Usage

## S4 method for signature 'Var'
x[i]

Arguments

x

: object
i

: value

Value

object of Var

Method to access S4 Var elements

Description

Method to access VarGroup initialize,VarGroup-method() elements by name

Usage

## S4 method for signature 'VarGroup'
x[i]

Arguments

x

: object
i

: value

Value

object element

Method to modify S4 Var elements

Description

Method to modify parseClass initialize,parseClass-method() elements by name

Usage

## S4 replacement method for signature 'parseClass'
x[i] <- value

Arguments

x

: Object
i

: Element name
value

: Value to be added

Value

parseClass Object

Method to access S4 Var elements

Description

Method to modify Var elements by name

Usage

## S4 replacement method for signature 'Var'
x[i] <- value

Arguments

x

: object
i

: Element name
value

: Value to be added

Value

object

Method to access S4 Var elements

Description

Method to modify VarGroup initialize,VarGroup-method() elements by name

Usage

## S4 replacement method for signature 'VarGroup'
x[i] <- value

Arguments

x

Object
i

Element name
value

Value to be added

Value

object

anaBiv generic function

Description

Generic function of anaBiv which gives bivariate analysis according to group

Usage

anaBiv(var, group, parallel, ...)

Arguments

var

listVar object or data.frame
group

Name of the factor variable to make subgroups with
parallel

Logical. Make analysis using parallel from parallel::mclapply().
...

Further arguments: normality and digits.p, as in descTab().

Value

A list of VarGroup object or data.frame

anaBiv data.frame function

Description

Generic function of anaBiv which gives bivariate analysis according to group

Usage

## S4 method for signature 'data.frame,character'
anaBiv(var, group, parallel, normality = "normal", digits.p = 3L, ...)

Arguments

var

listVar object or data.frame
group

Name of the factor variable to make subgroups with
parallel

Logical. Make analysis using parallel from parallel::mclapply().
normality

One of "normal", "non normal" or "assess", as in descTab().
digits.p

Integer. Significant digits for p value.
...

Further arguments: normality and digits.p, as in descTab().

Value

A list of VarGroup object or data.frame

Examples

# A small simulated clinical trial
set.seed(42)
n <- 200
patients <- data.frame(
  arm      = factor(sample(c("Placebo", "Treatment"), n, replace = TRUE)),
  age      = round(rnorm(n, mean = 65, sd = 10)),
  crp      = round(rlnorm(n, meanlog = 2, sdlog = 1), 1),
  sex      = factor(sample(c("Female", "Male"), n, replace = TRUE)),
  diabetes = factor(sample(c("No", "Yes"), n, replace = TRUE, prob = c(0.7, 0.3))),
  nyha     = factor(sample(c("I", "II", "III", "IV"), n, replace = TRUE),
                    ordered = TRUE)
)
patients$crp[sample(n, 15)] <- NA

# p values only, without the descriptive table
res <- anaBiv(patients, group = "arm", parallel = FALSE, normality = "assess")
data.frame(variable = sapply(res, function(x) x["name"]),
           pvalue = sapply(res, function(x) x["pvalue"]))

anaBiv data.frame function

Description

Generic function of anaBiv which gives bivariate analysis according to group

Usage

## S4 method for signature 'listVar,character'
anaBiv(var, group, parallel, ...)

Arguments

var

listVar object or data.frame
group

Name of the factor variable to make subgroups with
parallel

Logical. Make analysis using parallel from parallel::mclapply().
...

Further arguments: normality and digits.p, as in descTab().

Value

A list of VarGroup object or data.frame

Create a table of descriptive analysis of a dataset

Description

Displays together the univariate analysis (mean/median; SD/IQR; proportions) and the bivariate analysis (t test/Wilcoxon, ANOVA/Kruskal-Wallis, Chi2 or Fisher) of every variable of a dataset. The univariate analysis can be sub-grouped by a variable of interest of n levels; the test applied to each variable follows from its type and from normality.

Usage

descTab(
  data,
  group = "",
  quanti = TRUE,
  quali = TRUE,
  na.print = FALSE,
  pvalue = TRUE,
  digits.p = 3L,
  digits.qt = 1L,
  digits.ql = 1L,
  normality = "normal",
  parallel = FALSE,
  mc.cores = 0
)

Arguments

data

A dataset. Needs to be a data.frame or a tibble.
group

Optional. The name of the factor variable to make sub-groups comparisons with; it must have at least two non empty levels. Omitted, "" or NULL, the table has a single Total column. Rows with a missing value for it are dropped, and its unused levels are dropped, both with a warning.
quanti, quali, na.print, pvalue

Logical. If false, won't display quantitative/qualitative/Missing values/pvalues variable results. quanti and quali cannot both be FALSE.
digits.p

Integer. Significant digits for p value
digits.qt

Integer. Significant digits for mean/median, SD/IQR
digits.ql

Integer. Significant digits for proportions
normality

One of "normal", "non normal" or "assess". "normal" applies the parametric tests (mean (SD), t test/ANOVA) to every quantitative variable, "non normal" the non parametric ones (median (IQR), Wilcoxon/Kruskal-Wallis), and "assess" decides variable by variable with a Shapiro-Wilk test at the 5% level.
parallel

Logical. Make analysis using parallel from parallel::mclapply().
mc.cores

If parallel is TRUE, how many cores to use. The default, 0, uses all the cores but one.

Details

Only numeric, integer and factor (including ordered factor) variables are described. A variable of any other type is ignored with a warning, as is a variable whose test cannot be computed: its p value is then NA and the rest of the table is still produced.

Value

An S4 object of class parseClass. Its ⁠["table"]⁠ element is a data.frame (whether or not there is a group, and also when data is a tibble) with a var column, one column per level of group, a Total column and, if there is a group and pvalue = TRUE, a pvalue column.

See Also

parseClassFun() to turn the result into an HTML/LaTeX table.

Examples

# A small simulated clinical trial
set.seed(42)
n <- 200
patients <- data.frame(
  arm      = factor(sample(c("Placebo", "Treatment"), n, replace = TRUE)),
  age      = round(rnorm(n, mean = 65, sd = 10)),
  crp      = round(rlnorm(n, meanlog = 2, sdlog = 1), 1),
  sex      = factor(sample(c("Female", "Male"), n, replace = TRUE)),
  diabetes = factor(sample(c("No", "Yes"), n, replace = TRUE, prob = c(0.7, 0.3))),
  nyha     = factor(sample(c("I", "II", "III", "IV"), n, replace = TRUE),
                    ordered = TRUE)
)
patients$crp[sample(n, 15)] <- NA

# Compare the two arms. With normality = "assess", age (normal) is described
# by mean (SD) with a t test, crp (skewed) by median (IQR) with a Wilcoxon test
tab <- descTab(patients, group = "arm", normality = "assess", na.print = TRUE)
tab["table"]

# No group: a single Total column, no test
descTab(patients)["table"]

# Quantitative variables only, two decimals
descTab(patients, group = "arm", quali = FALSE, digits.qt = 2)["table"]

# Render it (see parseClassFun() for the layout options)
parseClassFun(tab)

This function is depreciated, please use anaBiv(). anaBiv()

Description

This function is depreciated, please use anaBiv(). anaBiv()

Usage

ft_ana_biv(...)

Arguments

...

None

Value

No return value, depreciated

This function is depreciated, please use anaBiv(). descTab()

Description

This function is depreciated, please use anaBiv(). descTab()

Usage

ft_desc_tab(...)

Arguments

...

None

Value

No return value, depreciated

This function is depreciated, please use parseClassFun()

Description

This function is depreciated, please use parseClassFun()

Usage

ft_parse(...)

Arguments

...

None

Value

No return value, depreciated

S4 class initialization function

Description

Initialization function for parseClass object initialize,parseClass-method()

Usage

## S4 method for signature 'parseClass'
initialize(
  .Object,
  table,
  group,
  pvalue,
  na.print,
  quanti,
  quali,
  var_list,
  data,
  digits.qt,
  digits.ql
)

Arguments

.Object

The object to create
table

The result of descTab
group

The variable from which to make subgroups
pvalue, na.print, quanti, quali

Values from descTab descTab()
var_list

An object of listVar listVar-class()
data

The dataset provided in descTab
digits.qt, digits.ql

As provided in descTab

Value

parseClass object

S4 class initialization function

Description

Initialization function for Var initialize,Var-method()

Usage

## S4 method for signature 'Var'
initialize(.Object, name, type, normal)

Arguments

.Object

Object to be initialized
name

A character taking name of the variable
type

A character taking name of the variable type
normal

Logical, if variable, is numeric; is it normal

Value

Var Object

S4 class initialization function

Description

Initialization function for VarGroup initialize,VarGroup-method()

Usage

## S4 method for signature 'VarGroup'
initialize(
  .Object,
  x,
  group_var,
  pvalue,
  parsed_name,
  value,
  missing.value,
  missing.value.name
)

Arguments

.Object

Object to be initialized
x

A Var object
group_var

The subgroup for which proportions, mean/sd were calculated and missing values
pvalue

The calculated pvalue
parsed_name

The name of the variable parsed with the n (%), mean (SD)
value

The values calculated parsed
missing.value

Missing values numbers and proportions n (%)
missing.value.name

Missing values concatenate with the level of the variable if it factor

Value

VarGroup object

S4 class

Description

A class of list of Var object

Slots

List

a list of Var

S4 class initialization function

Description

Initialization function for parseClass object initialize,parseClass-method()

Usage

parseClass(
  table,
  group,
  pvalue,
  na.print,
  quanti,
  quali,
  var_list,
  data,
  digits.qt,
  digits.ql
)

Arguments

table

The result of descTab
group

The variable from which to make subgroups
pvalue, na.print, quanti, quali

Values from descTab descTab()
var_list

An object of listVar listVar-class()
data

The dataset provided in descTab
digits.qt, digits.ql

As provided in descTab

Value

parseClass object

S4 class

Description

A S4 class containing all the information needed for parsClassFun the missing values and the group for which it was calculated

Slots

table

The result of descTab

group

The variable from which to make subgroups

pvalue,na.print,quanti,quali

Values from descTab descTab()

var_list

An object of listVar listVar-class()

data

The dataset provided in descTab

digits.qt,digits.ql

As provided in descTab

Make the LaTeX/HTML table. Generic function

Description

Make the LaTeX/HTML table. Generic function

Usage

parseClassFun(
  table,
  col.order = NULL,
  levels_to_keep = NULL,
  group_rows_labels = NULL,
  font = "arial"
)

Arguments

table

The output of descTab() or anaBiv(), an S4 object.
col.order

Optional. A vector containing the column order. If set, must contain at least all levels of group, without duplicates. Three columns created are "var", "Total", and "pvalue" which can be present in the vector
levels_to_keep

Optional, named list. If the variable is binary, which level to keep. Default is the last level of levels(variable). Must be as: list("variable name" = "level to keep").
group_rows_labels

Optional, named list. Create row labels in order to regroup them. Must be as list("label" = c("var1", "var2"), "label2" = c("var3", "var4")). Variable names are matched exactly, and a variable with no row in the table is an error. Rows are reordered to follow the order of the labels and of the variables within each label.
font

The HTML font of the table. Default "arial".

Value

An HTML/LaTex file which can be used directly in Rmarkdown and copy paste

See Also

descTab() which produces the object this function renders.

Examples

# A small simulated clinical trial
set.seed(42)
n <- 200
patients <- data.frame(
  arm      = factor(sample(c("Placebo", "Treatment"), n, replace = TRUE)),
  age      = round(rnorm(n, mean = 65, sd = 10)),
  crp      = round(rlnorm(n, meanlog = 2, sdlog = 1), 1),
  sex      = factor(sample(c("Female", "Male"), n, replace = TRUE)),
  diabetes = factor(sample(c("No", "Yes"), n, replace = TRUE, prob = c(0.7, 0.3))),
  nyha     = factor(sample(c("I", "II", "III", "IV"), n, replace = TRUE),
                    ordered = TRUE)
)
patients$crp[sample(n, 15)] <- NA

tab <- descTab(patients, group = "arm", normality = "assess", na.print = TRUE)

# Default layout
parseClassFun(tab)

# One line for the binary variables, rows grouped under labels, the Total
# column after the arms, and a custom font
parseClassFun(
  tab,
  levels_to_keep = list(sex = "Female", diabetes = "Yes"),
  group_rows_labels = list("Demographics" = c("age", "sex"),
                           "Clinical" = c("crp", "diabetes", "nyha")),
  col.order = c("var", "Placebo", "Treatment", "Total", "pvalue"),
  font = "Times New Roman"
)

Make the LaTeX/HTML table

Description

This functions takes the S4 output of descTab to create an HTML parsed table

Usage

## S4 method for signature 'parseClass'
parseClassFun(
  table,
  col.order = NULL,
  levels_to_keep = NULL,
  group_rows_labels = NULL,
  font = "arial"
)

Arguments

table

The output of descTab() or anaBiv(), an S4 object.
col.order

Optional. A vector containing the column order. If set, must contain at least all levels of group, without duplicates. Three columns created are "var", "Total", and "pvalue" which can be present in the vector
levels_to_keep

Optional, named list. If the variable is binary, which level to keep. Default is the last level of levels(variable). Must be as: list("variable name" = "level to keep").
group_rows_labels

Optional, named list. Create row labels in order to regroup them. Must be as list("label" = c("var1", "var2"), "label2" = c("var3", "var4")). Variable names are matched exactly, and a variable with no row in the table is an error. Rows are reordered to follow the order of the labels and of the variables within each label.
font

The HTML font of the table. Default "arial".

Value

An HTML/LaTex file which can be used directly in Rmarkdown and copy paste

See Also

descTab() which produces the object this function renders.

Examples

# A small simulated clinical trial
set.seed(42)
n <- 200
patients <- data.frame(
  arm      = factor(sample(c("Placebo", "Treatment"), n, replace = TRUE)),
  age      = round(rnorm(n, mean = 65, sd = 10)),
  crp      = round(rlnorm(n, meanlog = 2, sdlog = 1), 1),
  sex      = factor(sample(c("Female", "Male"), n, replace = TRUE)),
  diabetes = factor(sample(c("No", "Yes"), n, replace = TRUE, prob = c(0.7, 0.3))),
  nyha     = factor(sample(c("I", "II", "III", "IV"), n, replace = TRUE),
                    ordered = TRUE)
)
patients$crp[sample(n, 15)] <- NA

tab <- descTab(patients, group = "arm", normality = "assess", na.print = TRUE)

# Default layout
parseClassFun(tab)

# One line for the binary variables, rows grouped under labels, the Total
# column after the arms, and a custom font
parseClassFun(
  tab,
  levels_to_keep = list(sex = "Female", diabetes = "Yes"),
  group_rows_labels = list("Demographics" = c("age", "sex"),
                           "Clinical" = c("crp", "diabetes", "nyha")),
  col.order = c("var", "Placebo", "Treatment", "Total", "pvalue"),
  font = "Times New Roman"
)

S4 class initialization function

Description

Initialization function for Var initialize,Var-method()

Usage

Var(name, type = "", normal = TRUE)

Arguments

name

A character taking name of the variable
type

A character taking name of the variable type
normal

Logical, if variable, is numeric; is it normal

Value

Var Object

S4 class

Description

A S4 class containing name, type and normality assessment of variable

Slots

name

A character taking name of the variable

type

A character taking name of the variable type

normal

Logical, if variable, is numeric; is it normal

S4 class

Description

A S4 class containing Var initialize,Var-method() It also contains the pvalue, the parsed value the missing values and the group for which it was calculated

Slots

group_var

The subgroup for which proportions, mean/sd were calculated and missing values

pvalue

The calculated pvalue

parsed_name

The name of the variable parsed with the n (%), mean (SD)

value

The values calculated parsed

missing.value

Missing values numbers and proportions n (%)

missing.value.name

Missing values concatenate with the level of the variable if it factor