Title: Create Elegant Table 1 in HTML for Bio-Statistics
Version: 2.1.0
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, bivariate analysis and parse it into HTML. It also allows you to visualize all your data with graphic representation.
License: GPL (≥ 3)
Encoding: UTF-8
Language: en-US
RoxygenNote: 7.2.3
Imports: dplyr, kableExtra, methods, parallel, purrr, stats, stringi, tibble, tidyr
NeedsCompilation: no
Packaged: 2023-06-29 08:24:33 UTC; tiago2
Author: Edouard Baudouin ORCID iD [aut, cre]
Maintainer: Edouard Baudouin <edouardpierre.baudouin@aphp.fr>
Repository: CRAN
Date/Publication: 2023-06-29 17:20:06 UTC

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

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

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 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 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

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

Variable to make subgroups with

parallel

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

...

digits.p can be specified as 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, ...)

Arguments

var

listVar object or data.frame

group

Variable to make subgroups with

parallel

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

...

digits.p can be specified as 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 'listVar,character'
anaBiv(var, group, parallel, ...)

Arguments

var

listVar object or data.frame

group

Variable to make subgroups with

parallel

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

...

digits.p can be specified as descTab

Value

A list of VarGroup object or data.frame

Generic function to create a table of descriptive analysis of a dataset

Description

This function allows you to display all together all univariate analysis (median/mean; IQR/SD; proportions) and bivariates analysis (Wilcoxon, Chi² or Fisher). The univariate analysis can be sub-grouped by a variable of interest of n levels. Appropriate statistics test will be applied

Usage

descTab(
  data,
  group = NULL,
  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 datasaset. Needs to be a data.frame/tibble object

group

Optional. The name of the variable to make sub-groups comparisons.

quanti, quali, na.print, pvalue

Logical. If false, won't display quantitative/qualitative/Missing values/pvalues variable results

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 "assess", "normal", "manual", "non normal". See details

parallel

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

mc.cores

If parallel is TRUE, how many Cores to used.

Value

A S4 objects parseClass() containing the main table accessible by ["table"] subscript.

Examples

data(iris)
library(stringi)
iris$fact_1<-as.factor(as.character(sample(1:5, 150, replace = TRUE)))
n_na<-sample(1:150, 30)
iris[n_na, "fact_1"]<-NA
iris$fact_2<-as.factor(as.character(sample(1:2, 150, replace = TRUE)))
n_na<-sample(1:150, 10)
iris[n_na, "fact_2"]<-NA
iris$fact_3<-as.factor(as.character(stri_rand_strings(150, 1, '[A-B]')))
iris$num<-runif(150, min = 0, max = 100)
n_na<-sample(1:150, 5)
iris[n_na, "num"]<-NA
iris_test<-descTab(iris, group = "Species", na.print = TRUE)

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 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 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

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
)

Arguments

table

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

col.order

Optional. A vector containing the column order. If set, must contains at least all levels of group. 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")).

Value

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

Examples

data(iris)
library(stringi)
iris$fact_1<-as.factor(as.character(sample(1:5, 150, replace = TRUE)))
n_na<-sample(1:150, 30)
iris[n_na, "fact_1"]<-NA
iris$fact_2<-as.factor(as.character(stri_rand_strings(150, 1, '[A-B]')))
iris$num<-runif(150, min = 0, max = 100)
n_na<-sample(1:150, 5)
iris[n_na, "num"]<-NA
iris_test<-descTab(iris, group = "Species", na.print = TRUE)
testParse<-parseClassFun(iris_test, levels_to_keep = list("fact_2" =  "A"),
group_rows_labels = list("Size" = c("Petal.Length", "Petal.Width"),
"My_f" = c("num", "fact_2")))

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
)

Arguments

table

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

col.order

Optional. A vector containing the column order. If set, must contains at least all levels of group. 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")).

Value

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

Examples

data(iris)
library(stringi)
iris$fact_1<-as.factor(as.character(sample(1:5, 150, replace = TRUE)))
n_na<-sample(1:150, 30)
iris[n_na, "fact_1"]<-NA
iris$fact_2<-as.factor(as.character(stri_rand_strings(150, 1, '[A-B]')))
iris$num<-runif(150, min = 0, max = 100)
n_na<-sample(1:150, 5)
iris[n_na, "num"]<-NA
iris_test<-descTab(iris, group = "Species", na.print = TRUE)
testParse<-parseClassFun(iris_test, levels_to_keep = list("fact_2" =  "A"),
group_rows_labels = list("Size" = c("Petal.Length", "Petal.Width"),
"My_f" = c("num", "fact_2")))