Package 'RastaRocket' reference manual

Title:	Rocket-Fast Clinical Research Reporting
Description:	Description of the tables, both grouped and not grouped, with some associated data management actions, such as sorting the terms of the variables and deleting terms with zero numbers.
Authors:	USMR CHU de Bordeaux [aut, cre], Valentine Renaudeau [aut], Marion Kret [aut], Matisse Decilap [aut], Sahardid Mohamed Houssein [aut], Mohamedou Sow [aut], Thomas Ferté [aut]
Maintainer:	USMR CHU de Bordeaux <[email protected]>
License:	GPL (>= 3)
Version:	1.1.5
Built:	2026-05-22 16:33:38 UTC
Source:	https://github.com/biostatusmr/rastarocket

Add missing value information to a gtsummary table

Description

This function merge missing data row into label row of gtsummary object

Usage

add_missing_info(base_table)
add_missing_info(base_table)

Arguments

base_table

A gtsummary table object.

Value

A gtsummary table object with missing value information and modifications applied.

Add p-values and separate footnotes to a gtsummary object

Description

This function adds p-values to a gtsummary table using the specified tests and separates the p-value footnotes.

Usage

add_pvalues(res, tests)
add_pvalues(res, tests)

Arguments

res

A gtsummary table object.

tests

A list of tests to pass to gtsummary::add_p(), or TRUE to use default tests.

Value

A gtsummary table object with p-values added and footnotes separated.

Examples

library(gtsummary)
tbl <- trial %>% tbl_summary(by = trt)
tbl <- add_pvalues(tbl, tests = TRUE)

library(gtsummary)
tbl <- trial %>% tbl_summary(by = trt)
tbl <- add_pvalues(tbl, tests = TRUE)

Add "n (dm ; %dm)" to Variable Labels

Description

This function appends the text "n (dm ; %dm)" to the labels of all variables in a dataset. It uses the labelled package to modify and update variable labels in-place.

Usage

ajouter_label_ndm(data, col_to_skip = NULL)
ajouter_label_ndm(data, col_to_skip = NULL)

Arguments

data

A data frame containing the dataset whose variable labels need to be updated.

col_to_skip

A column to skip when adding "n (dm ; %dm)". Default is NULL.

Details

The function iterates over all columns in the dataset and performs the following steps:

Retrieves the current label of each variable using labelled::var_label.
Creates a new label by appending the text "n (dm ; %dm)" to the existing label.
Updates the variable's label using labelled::set_variable_labels.

This is useful when preparing a dataset for descriptive analysis, where it is helpful to display missing data statistics (n, dm, and ⁠%dm⁠) alongside variable labels in summary tables.

Value

A data frame with updated variable labels.

Examples

# Example usage:
library(labelled)

# Create a sample dataset
data <- data.frame(
  var1 = c(1, 2, NA),
  var2 = c("A", "B", NA)
)

# Assign initial labels
data <- labelled::set_variable_labels(
  data,
  var1 = "Variable 1",
  var2 = "Variable 2"
)

# Add "n (dm ; %dm)" to labels
data <- ajouter_label_ndm(data)

# Check updated labels
labelled::var_label(data)

# Example usage:
library(labelled)

# Create a sample dataset
data <- data.frame(
  var1 = c(1, 2, NA),
  var2 = c("A", "B", NA)
)

# Assign initial labels
data <- labelled::set_variable_labels(
  data,
  var1 = "Variable 1",
  var2 = "Variable 2"
)

# Add "n (dm ; %dm)" to labels
data <- ajouter_label_ndm(data)

# Check updated labels
labelled::var_label(data)

Create a Summary Table with Grouping and Custom Formatting

Description

This function generates a summary table from a data frame with specified grouping and variable types. It uses the gtsummary package to create descriptive statistics for categorical and continuous variables, with options for customizing the rounding and labels.

Usage

base_table(
  data1,
  show_missing_data,
  by_group = FALSE,
  var_group,
  quali = NULL,
  quanti = NULL,
  stat_var_quanti = c("{mean} ({sd})", "{median} ({p25} ; {p75})", "{min} ; {max}"),
  digits = list(r_quanti = 1, r_quali = 1),
  freq_relevel = FALSE
)
base_table(
  data1,
  show_missing_data,
  by_group = FALSE,
  var_group,
  quali = NULL,
  quanti = NULL,
  stat_var_quanti = c("{mean} ({sd})", "{median} ({p25} ; {p75})", "{min} ; {max}"),
  digits = list(r_quanti = 1, r_quali = 1),
  freq_relevel = FALSE
)

Arguments

data1

A data frame containing the dataset to be analyzed.

show_missing_data

Default to NULL. Should the missing data be displayed. Can be either :

FALSE: No missing data displayed
TRUE: Missing data displayed
NULL (default): will be switch to anyNA(data1) value.

by_group

A boolean (default is FALSE) to analyse by group.

var_group

A variable used for grouping (if applicable). Defaults to NULL.

quali

A vector of qualitative variables to be described. Defaults to NULL.

quanti

A vector of quantitative variables to be described. Defaults to NULL.

stat_var_quanti

A character vector specifying the statistics to display for continuous variables. Default is c("{mean} ({sd})", "{median} ({p25}; {p75})", "{min}; {max}").

digits

A list, the number of decimal places to round categorical and continuous variable. r_quanti and r_quali can be a single integer or a vector of integer. Default is list(r_quanti = 1, r_quali = 1)

freq_relevel

Boolean (default = FALSE). If TRUE, reorder factors by frequency (most to least frequent) using gtsummary.

Value

A gtsummary table summarizing the specified variables, grouped by var_group if provided, with customizable statistics and rounding options.

Examples

# Example usage with the iris dataset
base_table(iris, var_group = "Species", show_missing_data = TRUE)

# Example usage with the iris dataset
base_table(iris, var_group = "Species", show_missing_data = TRUE)

css_generator

Description

Generate css to be included in quarto.

Usage

css_generator(path_logo = NULL)
css_generator(path_logo = NULL)

Arguments

path_logo

The path to logo, will automatically be guessed on the package.

Value

A css string

Custom formatting for `gtsummary` and `gt` tables

Description

This function takes a gtsummary or gt table and applies custom formatting. It allows you to align columns, apply bold text to certain rows, and adjust column widths if specified.

Usage

custom_format(gt_table, align = "right", column_size = NULL)
custom_format(gt_table, align = "right", column_size = NULL)

Arguments

gt_table

A gt table object (also handles gtsummary tables by converting them).

align

A character string defining the alignment of specific columns. Passed to the gt::cols_align() function (e.g., "left", "right", "center"). Default is "right".

column_size

A named list or vector defining the width of columns (optional). The list should specify the width for one or more columns. If not provided, column widths will not be modified.

Value

A gt table object with the specified formatting applied. The table will have columns aligned according to the align parameter, and cells in the "label" rows will have bold text. If column_size is provided, the column widths will be adjusted accordingly.

Examples

# Example usage
tbl <- RastaRocket::desc_var(iris,
  table_title = "test",
  group = TRUE,
  var_group = "Species")
formatted_tbl <- custom_format(tbl,
  align = "center",
  column_size = list(label ~ gt::pct(50)))
formatted_tbl

# Example usage
tbl <- RastaRocket::desc_var(iris,
  table_title = "test",
  group = TRUE,
  var_group = "Species")
formatted_tbl <- custom_format(tbl,
  align = "center",
  column_size = list(label ~ gt::pct(50)))
formatted_tbl

Modify gtsummary table headers and add a spanning header

Description

This function customizes the column headers, optional spanning header, and table caption for a gtsummary table. It supports adding a feature name, total label, group title, and formats missing data presentation.

Usage

custom_headers(
  base_table_missing,
  var_characteristic = NULL,
  show_missing_data = TRUE,
  show_n_per_group = TRUE,
  var_tot = NULL,
  var_group = NULL,
  group_title = NULL,
  table_title
)
custom_headers(
  base_table_missing,
  var_characteristic = NULL,
  show_missing_data = TRUE,
  show_n_per_group = TRUE,
  var_tot = NULL,
  var_group = NULL,
  group_title = NULL,
  table_title
)

Arguments

base_table_missing

A gtsummary table object (typically output from previous steps).

var_characteristic

Optional. A string to label the features column.

show_missing_data

Logical. If TRUE, adds missing data info to column headers.

show_n_per_group

A boolean indicating whether to display group sizes (n) for each level of the grouping variable.

var_tot

Optional. A string to label the total column.

var_group

Optional. Name of a grouping variable for adding a spanning header.

group_title

Optional. Title for the spanning header. If NULL, uses the variable label or name.

table_title

Title for the entire table.

Value

A gtsummary table object with updated headers, spanning header, and caption.

Custom Round and Format

Description

Rounds a numeric value to a specified number of decimal places and formats it to always show the specified number of decimal places, including trailing zeros.

Usage

custom_round(x, digits = 1)
custom_round(x, digits = 1)

Arguments

x

A numeric vector to be rounded and formatted.

digits

An integer indicating the number of decimal places to round to. Defaults to 1.

Value

A character vector with the rounded and formatted numbers.

Examples

RastaRocket::custom_round(3.14159)      # "3.1"
RastaRocket::custom_round(3.14159, 3)   # "3.142"
RastaRocket::custom_round(c(2, 2.5), 2) # "2.00" "2.50"

RastaRocket::custom_round(3.14159)      # "3.1"
RastaRocket::custom_round(3.14159, 3)   # "3.142"
RastaRocket::custom_round(c(2, 2.5), 2) # "2.00" "2.50"

Customize a Summary Table with Grouping, Missing Data, and Custom Titles

Description

This function customizes a gtsummary summary table by adding an overall column, handling missing data, applying group-specific statistics, and updating headers and captions. It provides flexible options for grouping, displaying missing data, and customizing table titles.

Usage

customize_table(
  base_table,
  by_group = FALSE,
  var_group,
  add_total,
  show_missing_data,
  show_n_per_group,
  group_title,
  table_title,
  var_title,
  var_tot = NULL,
  var_characteristic = NULL
)
customize_table(
  base_table,
  by_group = FALSE,
  var_group,
  add_total,
  show_missing_data,
  show_n_per_group,
  group_title,
  table_title,
  var_title,
  var_tot = NULL,
  var_characteristic = NULL
)

Arguments

base_table

A gtsummary table object, typically generated using functions like gtsummary::tbl_summary.

by_group

A boolean (default is FALSE) to analyse by group.

var_group

A variable used for grouping (if applicable). Defaults to NULL.

add_total

A boolean (default is TRUE) to add total column or not when var_group is specified.

show_missing_data

Default to NULL. Should the missing data be displayed. Can be either :

FALSE: No missing data displayed
TRUE: Missing data displayed
NULL (default): will be switch to anyNA(data1) value.

show_n_per_group

Default to FALSE. Should the 'N' appears in the column header of the groups. Can be either :

FALSE: No 'N' is shown
TRUE: 'N' is shown

group_title

A character string specifying the title for the grouping variable. Default is NULL and get the label or the variable.

table_title

A character string specifying the title of the table.

var_title

A character string for the title of the variable column in the table. Defaults to "Variable".

var_tot

A string specifying the name of total column. Default is NULL and will guess from theme_gtsummary_language().

var_characteristic

A string specifying the name of characteristic column. Default is NULL and will guess from theme_gtsummary_language().

Details

The show_missing_data parameter determines whether missing data counts and percentages are displayed:
- If TRUE, missing data columns are added.
- If FALSE, only non-missing data counts are displayed.
Headers for columns and spanning headers are customized using the group_title, table_title, and var_title arguments.

Value

A customized gtsummary table object with added columns, headers, captions, and modifications based on the provided arguments.

Examples

# Example usage with a sample gtsummary table
library(gtsummary)
base_table <- trial %>%
  gtsummary::tbl_summary(
    type = list(
      gtsummary::all_continuous() ~ "continuous2"
    ),
    by = "trt",
    missing = "always",
    missing_stat = "{N_nonmiss} ({N_miss})",
    statistic = list(
      gtsummary::all_continuous2() ~ c("{mean} ({sd})",
                                       "{median} ({p25} ; {p75})",
                                       "{min} ; {max}")
    ))

customize_table(
  base_table,
  var_group = "trt",
  add_total = TRUE,
  show_missing_data = TRUE,
  show_n_per_group = FALSE,
  group_title = "Treatment Group",
  table_title = "Summary Statistics",
  var_title = "Variables",
  var_tot = "Total"
)

# Example usage with a sample gtsummary table
library(gtsummary)
base_table <- trial %>%
  gtsummary::tbl_summary(
    type = list(
      gtsummary::all_continuous() ~ "continuous2"
    ),
    by = "trt",
    missing = "always",
    missing_stat = "{N_nonmiss} ({N_miss})",
    statistic = list(
      gtsummary::all_continuous2() ~ c("{mean} ({sd})",
                                       "{median} ({p25} ; {p75})",
                                       "{min} ; {max}")
    ))

customize_table(
  base_table,
  var_group = "trt",
  add_total = TRUE,
  show_missing_data = TRUE,
  show_n_per_group = FALSE,
  group_title = "Treatment Group",
  table_title = "Summary Statistics",
  var_title = "Variables",
  var_tot = "Total"
)

Customize Table Body

Description

This function modifies a data frame by updating the stat_0 column. If any values in stat_0 are missing (NA), they are replaced by the values from the n column. After the replacement, the n column is removed from the data frame.

Usage

customize_table_body(data)
customize_table_body(data)

Arguments

data

A data frame that must contain at least two columns:

stat_0: A column whose missing (NA) values are to be replaced.
n: A column providing replacement values for stat_0 when its values are missing.

Details

The function uses dplyr::case_when to conditionally update the stat_0 column.
After the replacement process, the n column is dropped using dplyr::select(-n).
This function is particularly useful for cleaning and preparing table data.

Value

A modified data frame with:

Updated stat_0 values (replaced with n values where NA is found).
The n column removed after integration.

Examples

# Example data
data <- data.frame(
  stat_0 = c(NA, "B", "C"),
  n = c(10, 20, 30)
)

# Apply the function
modified_data <- RastaRocket::customize_table_body(data)
print(modified_data)

# Example data
data <- data.frame(
  stat_0 = c(NA, "B", "C"),
  n = c(10, 20, 30)
)

# Apply the function
modified_data <- RastaRocket::customize_table_body(data)
print(modified_data)

desc_ei_per_grade

Description

A function to describe adverse events (AE) by grade.

Usage

desc_ei_per_grade(
  df_pat_grp,
  df_pat_grade,
  id_col = "USUBJID",
  group_col = "RDGRPNAME",
  ei_num_col = "EINUM",
  ei_grdm_col = "EIGRDM",
  ei_grav_col = "EIGRAV",
  severity = TRUE,
  digits = 1,
  language = "fr"
)
desc_ei_per_grade(
  df_pat_grp,
  df_pat_grade,
  id_col = "USUBJID",
  group_col = "RDGRPNAME",
  ei_num_col = "EINUM",
  ei_grdm_col = "EIGRDM",
  ei_grav_col = "EIGRAV",
  severity = TRUE,
  digits = 1,
  language = "fr"
)

Arguments

df_pat_grp

A dataframe with two columns: USUBJID (Patient id) and RDGRPNAME (the RCT arm).

df_pat_grade

A dataframe with four columns: USUBJID (Patient id), EINUM (the AE id), EIGRDM (the AE grade) and EIGRAV (the AE severity which must be "Grave" and "Non grave").

id_col

Patient id column (default: "USUBJID").

group_col

group column, the rct arm (default: "RDGRPNAME").

ei_num_col

AE id column (default: "EINUM").

ei_grdm_col

AE grade column (default: "EIGRDM").

ei_grav_col

AE severity column (default: "EIGRAV").

severity

A boolean to show severe adverse event line or not (default: TRUE).

digits

Number of digits for percentages

language

'fr' default or 'en'

Value

A gt table summarizing the AE by grade.

Examples

df_pat_grp <- data.frame(USUBJID = paste0("ID_", 1:10),
                         RDGRPNAME = c(rep("A", 3), rep("B", 3), rep("C", 4)))

df_pat_grade <- data.frame(USUBJID = c("ID_1", "ID_1",
                                      "ID_2",
                                      "ID_8",
                                      "ID_9"),
                           EINUM = c(1, 2,
                                      1,
                                      1,
                                      1),
                           EIGRDM = c(1, 3,
                                     4,
                                     2,
                                     4),
                           EIGRAV = c("Grave", "Non grave",
                                      "Non grave",
                                      "Non grave",
                                      "Grave"))

desc_ei_per_grade(df_pat_grp = df_pat_grp,
                  df_pat_grade = df_pat_grade)


df_pat_grp <- data.frame(USUBJID = paste0("ID_", 1:10),
                         RDGRPNAME = c(rep("A", 3), rep("B", 3), rep("C", 4)))

df_pat_grade <- data.frame(USUBJID = c("ID_1", "ID_1",
                                      "ID_2",
                                      "ID_8",
                                      "ID_9"),
                           EINUM = c(1, 2,
                                      1,
                                      1,
                                      1),
                           EIGRDM = c(1, 3,
                                     4,
                                     2,
                                     4),
                           EIGRAV = c("Grave", "Non grave",
                                      "Non grave",
                                      "Non grave",
                                      "Grave"))

desc_ei_per_grade(df_pat_grp = df_pat_grp,
                  df_pat_grade = df_pat_grade)

desc_ei_per_pt

Description

A function to describe AE by soc and pt

Usage

desc_ei_per_pt(
  df_pat_grp,
  df_pat_llt,
  id_col = "USUBJID",
  group_col = "RDGRPNAME",
  ei_num_col = "EINUM",
  ei_llt_col = "EILLTN",
  ei_soc_col = "EISOCPN",
  ei_pt_col = "EIPTN",
  language = "fr",
  order_by_freq = TRUE,
  digits = 1
)
desc_ei_per_pt(
  df_pat_grp,
  df_pat_llt,
  id_col = "USUBJID",
  group_col = "RDGRPNAME",
  ei_num_col = "EINUM",
  ei_llt_col = "EILLTN",
  ei_soc_col = "EISOCPN",
  ei_pt_col = "EIPTN",
  language = "fr",
  order_by_freq = TRUE,
  digits = 1
)

Arguments

df_pat_grp

A dataframe with two columns: id_pat and grp (the rct arm)

df_pat_llt

A dataframe with two columns: id_pat (patient id), num_ae (AE id), llt (AE LLT), pt (AE PT), soc (AE)

id_col

Patient id column (default: "USUBJID").

group_col

group column, the rct arm (default: "RDGRPNAME").

ei_num_col

AE id column (default: "EINUM").

ei_llt_col

AE LLT column (default: "EILLTN").

ei_soc_col

AE SOC column (default: "EISOCPN").

ei_pt_col

AE PT column (default: "EIPTN")

language

'fr' default or 'en'

order_by_freq

Logical. Should PT and SOC be ordered by frequency? Defaults to TRUE. If FALSE, PT and SOC are ordered alphabetically.

digits

Number of digits for percentages

Value

A gt table

Examples

df_pat_grp <- data.frame(USUBJID = paste0("ID_", 1:10),
                         RDGRPNAME = c(rep("A", 3), rep("B", 3), rep("C", 4)))

df_pat_llt <- data.frame(USUBJID = c("ID_1", "ID_1",
                                    "ID_2",
                                    "ID_4",
                                    "ID_9"),
                         EINUM = c(1, 2, 1, 1, 1),
                         EILLTN = c("llt1", "llt1",
                                 "llt4", "llt3",
                                 "llt1"),
                         EIPTN = c("Arrhythmia", "Myocardial Infarction",
                                 "Arrhythmia", "Pneumonia",
                                 "Pneumonia"),
                         EISOCPN = c("Cardiac Disorders", "Cardiac Disorders",
                                 "Cardiac Disorders", "Infections",
                                 "Infections"))

desc_ei_per_pt(df_pat_grp = df_pat_grp,
               df_pat_llt = df_pat_llt)

df_pat_grp <- data.frame(USUBJID = paste0("ID_", 1:10),
                         RDGRPNAME = c(rep("A", 3), rep("B", 3), rep("C", 4)))

df_pat_llt <- data.frame(USUBJID = c("ID_1", "ID_1",
                                    "ID_2",
                                    "ID_4",
                                    "ID_9"),
                         EINUM = c(1, 2, 1, 1, 1),
                         EILLTN = c("llt1", "llt1",
                                 "llt4", "llt3",
                                 "llt1"),
                         EIPTN = c("Arrhythmia", "Myocardial Infarction",
                                 "Arrhythmia", "Pneumonia",
                                 "Pneumonia"),
                         EISOCPN = c("Cardiac Disorders", "Cardiac Disorders",
                                 "Cardiac Disorders", "Infections",
                                 "Infections"))

desc_ei_per_pt(df_pat_grp = df_pat_grp,
               df_pat_llt = df_pat_llt)

Generate Descriptive Tables for Variables

Description

This function creates descriptive tables for variables in a dataset. It can handle qualitative and quantitative variables, grouped or ungrouped, and supports multiple configurations for handling missing data (DM).

Usage

desc_var(
  data1,
  table_title = "",
  quali = NULL,
  quanti = NULL,
  add_total = TRUE,
  var_title = "Variable",
  by_group = FALSE,
  var_group = NULL,
  group_title = NULL,
  stat_var_quanti = c("{mean} ({sd})", "{median} ({p25} ; {p75})", "{min} ; {max}"),
  digits = list(r_quanti = 1, r_quali = 0),
  drop_levels = TRUE,
  freq_relevel = FALSE,
  tests = FALSE,
  show_n_per_group = FALSE,
  show_missing_data = NULL,
  var_tot = NULL,
  var_characteristic = NULL,
  include_all_na_cat = TRUE
)
desc_var(
  data1,
  table_title = "",
  quali = NULL,
  quanti = NULL,
  add_total = TRUE,
  var_title = "Variable",
  by_group = FALSE,
  var_group = NULL,
  group_title = NULL,
  stat_var_quanti = c("{mean} ({sd})", "{median} ({p25} ; {p75})", "{min} ; {max}"),
  digits = list(r_quanti = 1, r_quali = 0),
  drop_levels = TRUE,
  freq_relevel = FALSE,
  tests = FALSE,
  show_n_per_group = FALSE,
  show_missing_data = NULL,
  var_tot = NULL,
  var_characteristic = NULL,
  include_all_na_cat = TRUE
)

Arguments

data1

A data frame containing the dataset to be analyzed.

table_title

A character string specifying the title of the table.

quali

A vector of qualitative variables to be described. Defaults to NULL.

quanti

A vector of quantitative variables to be described. Defaults to NULL.

add_total

A boolean (default is TRUE) to add total column or not when var_group is specified.

var_title

A character string for the title of the variable column in the table. Defaults to "Variable".

by_group

A boolean (default is FALSE) to analyse by group.

var_group

A variable used for grouping (if applicable). Defaults to NULL.

group_title

A character string specifying the title for the grouping variable. Default is NULL and get the label or the variable.

stat_var_quanti

A character vector specifying the statistics to display for continuous variables. Default is c("{mean} ({sd})", "{median} ({p25}; {p75})", "{min}; {max}").

digits

A list, the number of decimal places to round categorical and continuous variable. r_quanti and r_quali can be a single integer or a vector of integer. Default is list(r_quanti = 1, r_quali = 1)

drop_levels

Boolean (default = TRUE). Drop unused levels.

freq_relevel

Boolean (default = FALSE). If TRUE, reorder factors by frequency (most to least frequent) using gtsummary.

tests

A value in order to add p value. Default to FALSE OPTION :

FALSE: No p-value add
TRUE: Add p-value made by default by gtsummary. See gtsummary add_p() options.
list(): To force tests. See gtsummary add_p() options.

show_n_per_group

Default to FALSE. Should the 'N' appears in the column header of the groups. Can be either :

FALSE: No 'N' is shown
TRUE: 'N' is shown

show_missing_data

Default to NULL. Should the missing data be displayed. Can be either :

FALSE: No missing data displayed
TRUE: Missing data displayed
NULL (default): will be switch to anyNA(data1) value.

var_tot

A string specifying the name of total column. Default is NULL and will guess from theme_gtsummary_language().

var_characteristic

A string specifying the name of characteristic column. Default is NULL and will guess from theme_gtsummary_language().

include_all_na_cat

Should the categorical variable with a missing levels (all values are NA) be displayed. Default to TRUE

Details

The function processes the dataset according to the specified parameters and generates descriptive tables.

It first uses the ajouter_label_ndm() function to append missing data statistics to variable labels.
Depending on the group and DM arguments, it adjusts the dataset and creates tables using helper functions like desc_group, desc_degroup, and desc_degroup_group.
Qualitative variables are reordered, and unused levels are dropped when necessary.

Value

A gtsummary table object containing the descriptive statistics.

Examples

# Example usage:
library(dplyr)

# Sample dataset
data1 <- data.frame(
  group = c("A", "B", "B", "C"),
  var1 = c(1, 2, 3, NA),
  var2 = c("X", "Y", "X", NA)
)

# Generate descriptive table
table <- desc_var(
  data1 = data1,
  table_title = "Descriptive Table",
  quanti = "var1"
)

# Order categorical features by frequency
table1 <- desc_var(
  data1 = data1,
  table_title = "Descriptive Table",
  quanti = "var1",
  freq_relevel = TRUE)

  # Round quantitative and qualitative features using  a vector of integer
  table2 <- desc_var(
    data1 = iris,
    quanti = "Sepal.Length",
    stat_var_quanti = c("{sum}", "{mean} ({sd})"),
    digits = list(r_quanti = c(1, 3, 2), r_quali = c(0, 2))
    )


# Example usage:
library(dplyr)

# Sample dataset
data1 <- data.frame(
  group = c("A", "B", "B", "C"),
  var1 = c(1, 2, 3, NA),
  var2 = c("X", "Y", "X", NA)
)

# Generate descriptive table
table <- desc_var(
  data1 = data1,
  table_title = "Descriptive Table",
  quanti = "var1"
)

# Order categorical features by frequency
table1 <- desc_var(
  data1 = data1,
  table_title = "Descriptive Table",
  quanti = "var1",
  freq_relevel = TRUE)

  # Round quantitative and qualitative features using  a vector of integer
  table2 <- desc_var(
    data1 = iris,
    quanti = "Sepal.Length",
    stat_var_quanti = c("{sum}", "{mean} ({sd})"),
    digits = list(r_quanti = c(1, 3, 2), r_quali = c(0, 2))
    )

Prepare a dataframe for creating AE plots

Description

Prepare a dataframe for creating AE plots

Usage

df_builder_ae(df_pat_grp, df_pat_llt, ref_grp = NULL)
df_builder_ae(df_pat_grp, df_pat_llt, ref_grp = NULL)

Arguments

df_pat_grp

A data frame of patient groups. Must contain columns USUBJID (patient ID) and RDGRPNAME (group assignment).

df_pat_llt

A data frame with USUBJID (subject ID), EINUM (AE ID), EILLTN (LLT identifier), EIPTN (PT identifier), EISOCPN (soc identifier) and EIGRDM (severity grade)

ref_grp

(Optional) A reference group for comparisons. Defaults to the first group in df_pat_grp.

Value

A dataframe with all the info to build AE plots

Convert a Name to an Email Address

Description

This function transforms a given name into an email address following the format [email protected].

Usage

from_name_to_adress(name = "Peter Parker")
from_name_to_adress(name = "Peter Parker")

Arguments

name

A character string representing a full name. Default is "Peter Parker".

Value

A character string containing the generated email address.

Examples

from_name_to_adress("John Doe")
from_name_to_adress()

from_name_to_adress("John Doe")
from_name_to_adress()

Text indentation for `gt` tables

Description

Text indentation for gt tables

Usage

indent_gt_table(g_table, indent = 0)
indent_gt_table(g_table, indent = 0)

Arguments

g_table

A gt table object

indent

A numerical value corresponding to the pixel value, which defines the text indentation (default = 0 corresponding to px(0)). 30 ~ px(30) should be a good compromise.

Value

A gt table object with indentation applied.

Examples

tbl_bis <- RastaRocket::desc_var(
iris,
table_title = "test",
quali = "Species")

tbl_bis |> indent_gt_table(indent = 30)
tbl_bis |> indent_gt_table(indent = 60)

tbl_bis <- RastaRocket::desc_var(
iris,
table_title = "test",
quali = "Species")

tbl_bis |> indent_gt_table(indent = 30)
tbl_bis |> indent_gt_table(indent = 60)

Text indentation for `gtsummary` tables

Description

Text indentation for gtsummary tables

Usage

indent_gtsummary_table(gts_table, indent = 4)
indent_gtsummary_table(gts_table, indent = 4)

Arguments

gts_table

A gtsummary table object

indent

A numerical value indicating how many space to indent text (default = 4). A value of 8 should be a good compromise.

Value

A gtsummary table object with indentation applied.

Examples

tbl <- iris |>  dplyr::select(Species, Sepal.Length) |> RastaRocket::desc_var(
table_title = "test",
quali = "Species")
tbl_1 <- tbl |> indent_gtsummary_table(indent = 4)
tbl_2 <- tbl |> indent_gtsummary_table(indent = 8)
tbl_3 <- tbl |> indent_gtsummary_table(indent = 16)

tbl <- iris |>  dplyr::select(Species, Sepal.Length) |> RastaRocket::desc_var(
table_title = "test",
quali = "Species")
tbl_1 <- tbl |> indent_gtsummary_table(indent = 4)
tbl_2 <- tbl |> indent_gtsummary_table(indent = 8)
tbl_3 <- tbl |> indent_gtsummary_table(indent = 16)

Intermediate Header

Description

Combines multiple descriptive tables into a single table with customized row group headers and styling. This function accepts a list of tables and corresponding group headers, applies consistent styling, and outputs a styled gt table.

Usage

intermediate_header(
  tbls,
  group_header,
  color = "#8ECAE6",
  size = 16,
  align = "center",
  weight = "bold"
)
intermediate_header(
  tbls,
  group_header,
  color = "#8ECAE6",
  size = 16,
  align = "center",
  weight = "bold"
)

Arguments

tbls

A list of descriptive tables (generated by RastaRocket::desc_var or similar functions).

group_header

A character vector specifying the headers for each group of tables. Must be the same length as tbls.

color

A character string specifying the background color for the row group headers. Default is "#8ECAE6".

size

An integer specifying the font size for the row group headers. Default is 16.

align

A character string specifying text alignment for the row group headers. Options are "left", "center", or "right". Default is "center".

weight

A character string specifying the font weight for the row group headers. Options include "normal", "bold", etc. Default is "bold".

Value

A styled gt table combining the input tables with row group headers.

Examples

# Load necessary libraries
library(RastaRocket)
library(dplyr)

# Generate sample data
data <- data.frame(
  Age = c(rnorm(45, mean = 50, sd = 10), rep(NA, 5)),
  sexe = sample(c("Femme", "Homme"), 50, replace = TRUE, prob = c(0.6, 0.4)),
  quatre_modalites = sample(c("A", "B", "C", "D"), 50, replace = TRUE)
)

# Create descriptive tables
tb1 <- data %>%
  dplyr::select(Age, sexe) %>%
  RastaRocket::desc_var(table_title = "Demographics", group = FALSE)

tb2 <- data %>%
  dplyr::select(quatre_modalites) %>%
  RastaRocket::desc_var(table_title = "Modalities", group = FALSE)

# Combine and style tables
intermediate_header(
  tbls = list(tb1, tb2),
  group_header = c("Demographics", "Modalities")
)
# Load necessary libraries
library(RastaRocket)
library(dplyr)

# Generate sample data
data <- data.frame(
  Age = c(rnorm(45, mean = 50, sd = 10), rep(NA, 5)),
  sexe = sample(c("Femme", "Homme"), 50, replace = TRUE, prob = c(0.6, 0.4)),
  quatre_modalites = sample(c("A", "B", "C", "D"), 50, replace = TRUE)
)

# Create descriptive tables
tb1 <- data %>%
  dplyr::select(Age, sexe) %>%
  RastaRocket::desc_var(table_title = "Demographics", group = FALSE)

tb2 <- data %>%
  dplyr::select(quatre_modalites) %>%
  RastaRocket::desc_var(table_title = "Modalities", group = FALSE)

# Combine and style tables
intermediate_header(
  tbls = list(tb1, tb2),
  group_header = c("Demographics", "Modalities")
)

Butterfly Stacked Bar Plot for Adverse Event Grades

Description

Creates a butterfly stacked bar plot to visualize the frequency of adverse event (AE) grades across patient groups, with system organ class (SOC) and preferred terms (PT) as labels.

Usage

plot_butterfly_stacked_barplot(
  df_pat_grp,
  df_pat_llt,
  ref_grp = NULL,
  max_text_width = 9,
  vec_fill_color = viridis::viridis(n = 4)
)
plot_butterfly_stacked_barplot(
  df_pat_grp,
  df_pat_llt,
  ref_grp = NULL,
  max_text_width = 9,
  vec_fill_color = viridis::viridis(n = 4)
)

Arguments

df_pat_grp

A data frame of patient groups. Must contain columns USUBJID (patient ID) and RDGRPNAME (group assignment).

df_pat_llt

A data frame with USUBJID (subject ID), EINUM (AE ID), EILLTN (LLT identifier), EIPTN (PT identifier), EISOCPN (soc identifier) and EIGRDM (severity grade)

ref_grp

A character string specifying the reference group (used for alignment in the plot). If NULL (default), the first level of df_pat_grp$grp is used.

max_text_width

An integer specifying the maximum width (in characters) for SOC labels before wrapping to the next line. Default is 9.

vec_fill_color

A vector of colors used for filling the AE grade bars. Default is viridis::viridis(n = 4).

Details

The function processes input data to calculate the frequency of adverse events per patient group and AE grade. It then generates a stacked bar plot where:

The x-axis represents the percentage of patients experiencing an AE.
The y-axis represents PTs (with SOCs as facets).
Bars are stacked by AE grade.
Labels for PTs are displayed in the center.
The left and right panels correspond to different patient groups.

The function utilizes the ggh4x package to adjust panel sizes and axes for a symmetrical butterfly plot.

Value

A ggplot2 object representing the butterfly stacked bar plot.

Examples


df_pat_grp <- data.frame(
 USUBJID = paste0("ID_", 1:10),
 RDGRPNAME = c(rep("A", 5), rep("B", 5))
)

df_pat_llt <- data.frame(
  USUBJID = c("ID_1", "ID_1", "ID_2", "ID_4", "ID_9"),
  EINUM = c(1, 2, 1, 1, 1),
  EILLTN = c("llt1", "llt2", "llt1", "llt3", "llt4"),
  EIPTN = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia", "Pneumonia", "Pneumonia"),
  EISOCPN = c("Cardiac Disorders", "Cardiac Disorders", "Cardiac Disorders",
  "Infections", "Infections"),
  EIGRDM = c(1, 3, 4, 2, 4)
)

plot_butterfly_stacked_barplot(df_pat_grp, df_pat_llt)

df_pat_grp <- data.frame(
 USUBJID = paste0("ID_", 1:10),
 RDGRPNAME = c(rep("A", 5), rep("B", 5))
)

df_pat_llt <- data.frame(
  USUBJID = c("ID_1", "ID_1", "ID_2", "ID_4", "ID_9"),
  EINUM = c(1, 2, 1, 1, 1),
  EILLTN = c("llt1", "llt2", "llt1", "llt3", "llt4"),
  EIPTN = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia", "Pneumonia", "Pneumonia"),
  EISOCPN = c("Cardiac Disorders", "Cardiac Disorders", "Cardiac Disorders",
  "Infections", "Infections"),
  EIGRDM = c(1, 3, 4, 2, 4)
)

plot_butterfly_stacked_barplot(df_pat_grp, df_pat_llt)

Plot a Dumbbell Chart for Adverse Events Analysis

Description

This function creates a dumbbell plot comparing the occurrence of adverse events across different patient groups. The plot includes the total number of adverse events, the proportion of patients affected, and the risk difference with confidence intervals.

Usage

plot_dumbell(
  df_pat_grp,
  df_pat_llt,
  ref_grp = NULL,
  colors_arm = c("#1b9e77", "#7570b3"),
  color_label = "Arm"
)
plot_dumbell(
  df_pat_grp,
  df_pat_llt,
  ref_grp = NULL,
  colors_arm = c("#1b9e77", "#7570b3"),
  color_label = "Arm"
)

Arguments

df_pat_grp

A data frame of patient groups. Must contain columns USUBJID (patient ID) and RDGRPNAME (group assignment).

df_pat_llt

A data frame with USUBJID (subject ID), EINUM (AE ID), EILLTN (LLT identifier), EIPTN (PT identifier), EISOCPN (soc identifier) and EIGRDM (severity grade)

ref_grp

(Optional) A reference group for comparisons. Defaults to the first group in df_pat_grp.

colors_arm

A vector of colors for the patient groups. Defaults to c("#1b9e77", "#7570b3").

color_label

A string specifying the legend label for the groups. Defaults to "Arm".

Value

A ggplot object displaying the dumbbell chart.

Examples

df_pat_grp <- data.frame(
 USUBJID = paste0("ID_", 1:10),
 RDGRPNAME = c(rep("A", 5), rep("B", 5))
)

df_pat_llt <- data.frame(
  USUBJID = c("ID_1", "ID_1", "ID_2", "ID_4", "ID_9"),
  EINUM = c(1, 2, 1, 1, 1),
  EILLTN = c("llt1", "llt2", "llt1", "llt3", "llt4"),
  EIPTN = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia", "Pneumonia", "Pneumonia"),
  EISOCPN = c("Cardiac Disorders", "Cardiac Disorders", "Cardiac Disorders",
  "Infections", "Infections"),
  EIGRDM = c(1, 3, 4, 2, 4)
)

plot_dumbell(df_pat_llt = df_pat_llt, df_pat_grp = df_pat_grp)

df_pat_grp <- data.frame(
 USUBJID = paste0("ID_", 1:10),
 RDGRPNAME = c(rep("A", 5), rep("B", 5))
)

df_pat_llt <- data.frame(
  USUBJID = c("ID_1", "ID_1", "ID_2", "ID_4", "ID_9"),
  EINUM = c(1, 2, 1, 1, 1),
  EILLTN = c("llt1", "llt2", "llt1", "llt3", "llt4"),
  EIPTN = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia", "Pneumonia", "Pneumonia"),
  EISOCPN = c("Cardiac Disorders", "Cardiac Disorders", "Cardiac Disorders",
  "Infections", "Infections"),
  EIGRDM = c(1, 3, 4, 2, 4)
)

plot_dumbell(df_pat_llt = df_pat_llt, df_pat_grp = df_pat_grp)

Plot a Patient Span Chart (Panchart)

Description

This function visualizes the timeline of adverse events (AEs), treatments, and randomization for a selected patient. The span chart helps track AE duration and treatment events relative to randomization.

Usage

plot_patient_panchart(
  df_soc_pt,
  df_pat_grp_rando,
  df_pat_pt_grade_date,
  df_pat_treatment_date,
  pat_id,
  vec_fill_color = viridis::viridis(n = 4, direction = -1, end = 0.95, option = "magma")
)
plot_patient_panchart(
  df_soc_pt,
  df_pat_grp_rando,
  df_pat_pt_grade_date,
  df_pat_treatment_date,
  pat_id,
  vec_fill_color = viridis::viridis(n = 4, direction = -1, end = 0.95, option = "magma")
)

Arguments

df_soc_pt

A data frame mapping System Organ Class (SOC) to Preferred Terms (PT).

df_pat_grp_rando

A data frame containing patient IDs, randomization groups, and randomization dates.

df_pat_pt_grade_date

A data frame with patient IDs, PT terms, AE grades, start and end dates of AEs.

df_pat_treatment_date

A data frame with patient IDs and treatment dates.

pat_id

A character string specifying the patient ID to plot.

vec_fill_color

A vector of colors for AE grades. Default is viridis::viridis(n = 4).

Value

A ggplot object representing the patient span chart.

Examples


df_pat_grp_rando <- data.frame(
  id_pat = c("ID_1", "ID_2"),
  grp = c("A", "B"),
  rando_date = c("2020-12-01", "2021-01-03")
)

df_pat_pt_grade_date <- data.frame(
  id_pat = c("ID_1", "ID_1", "ID_1", "ID_1", "ID_2"),
  pt = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia",
          "Pneumonia", "Pneumonia"),
  grade = c(4, 2, 1, 3, 4),
  start = c("2021-01-01", "2021-02-03", "2021-01-02", "2021-03-05", "2021-02-01"),
  end = c("2021-01-14", "2021-03-03", "2021-01-22", "2021-05-05", "2021-02-03")
)

df_pat_treatment_date <- data.frame(
  id_pat = c("ID_1", "ID_1", "ID_1"),
  treatment_date = c("2021-01-25", "2021-03-01", "2021-01-20")
)
df_soc_pt <- data.frame(
  pt = c("Arrhythmia", "Myocardial Infarction", "Pneumonia", "Sepsis"),
  soc = c("Cardiac Disorders", "Cardiac Disorders", "Infections", "Infections")
)

plot_patient_panchart(
  df_soc_pt = df_soc_pt,
  df_pat_grp_rando = df_pat_grp_rando,
  df_pat_pt_grade_date = df_pat_pt_grade_date,
  df_pat_treatment_date = df_pat_treatment_date,
  pat_id = "ID_1"
)
df_pat_grp_rando <- data.frame(
  id_pat = c("ID_1", "ID_2"),
  grp = c("A", "B"),
  rando_date = c("2020-12-01", "2021-01-03")
)

df_pat_pt_grade_date <- data.frame(
  id_pat = c("ID_1", "ID_1", "ID_1", "ID_1", "ID_2"),
  pt = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia",
          "Pneumonia", "Pneumonia"),
  grade = c(4, 2, 1, 3, 4),
  start = c("2021-01-01", "2021-02-03", "2021-01-02", "2021-03-05", "2021-02-01"),
  end = c("2021-01-14", "2021-03-03", "2021-01-22", "2021-05-05", "2021-02-03")
)

df_pat_treatment_date <- data.frame(
  id_pat = c("ID_1", "ID_1", "ID_1"),
  treatment_date = c("2021-01-25", "2021-03-01", "2021-01-20")
)
df_soc_pt <- data.frame(
  pt = c("Arrhythmia", "Myocardial Infarction", "Pneumonia", "Sepsis"),
  soc = c("Cardiac Disorders", "Cardiac Disorders", "Infections", "Infections")
)

plot_patient_panchart(
  df_soc_pt = df_soc_pt,
  df_pat_grp_rando = df_pat_grp_rando,
  df_pat_pt_grade_date = df_pat_pt_grade_date,
  df_pat_treatment_date = df_pat_treatment_date,
  pat_id = "ID_1"
)

Volcano Plot for Adverse Event Analysis

Description

Generates a volcano plot to visualize the association between adverse events and patient groups.

Usage

plot_volcano(
  df_pat_grp,
  df_pat_llt,
  ref_grp = NULL,
  colors_arm = c("#1b9e77", "#7570b3"),
  size = "nb_pat"
)
plot_volcano(
  df_pat_grp,
  df_pat_llt,
  ref_grp = NULL,
  colors_arm = c("#1b9e77", "#7570b3"),
  size = "nb_pat"
)

Arguments

df_pat_grp

A data frame of patient groups. Must contain columns USUBJID (patient ID) and RDGRPNAME (group assignment).

df_pat_llt

A data frame with USUBJID (subject ID), EINUM (AE ID), EILLTN (LLT identifier), EIPTN (PT identifier), EISOCPN (soc identifier) and EIGRDM (severity grade)

ref_grp

(Optional) A reference group for comparisons. Defaults to the first group in df_pat_grp.

colors_arm

A character vector of length two specifying the colors for the two patient groups in the plot. Default is c("#1b9e77", "#7570b3").

size

A character string specifying the metric used for point sizes in the plot. Options are:

"nb_pat": Number of patients (default).
"nb_ei": Number of adverse events.

Details

The function first processes input data using df_builder_ae(), then calculates relevant statistics such as risk difference (RD) and p-values. The volcano plot displays:

RD on the x-axis (risk difference between groups).
-log10(p-value) on the y-axis (significance level).
Point colors indicating which group has an increased risk.
Point sizes reflecting either the number of patients or events.
A horizontal dashed line at p = 0.05 for significance threshold.

Value

A ggplot2 object representing the volcano plot.

Examples

df_pat_grp <- data.frame(
 USUBJID = paste0("ID_", 1:10),
 RDGRPNAME = c(rep("A", 5), rep("B", 5))
)

df_pat_llt <- data.frame(
  USUBJID = c("ID_1", "ID_1", "ID_2", "ID_4", "ID_9"),
  EINUM = c(1, 2, 1, 1, 1),
  EILLTN = c("llt1", "llt2", "llt1", "llt3", "llt4"),
  EIPTN = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia", "Pneumonia", "Pneumonia"),
  EISOCPN = c("Cardiac Disorders", "Cardiac Disorders", "Cardiac Disorders",
  "Infections", "Infections"),
  EIGRDM = c(1, 3, 4, 2, 4)
)

plot_volcano(df_pat_grp, df_pat_llt)

df_pat_grp <- data.frame(
 USUBJID = paste0("ID_", 1:10),
 RDGRPNAME = c(rep("A", 5), rep("B", 5))
)

df_pat_llt <- data.frame(
  USUBJID = c("ID_1", "ID_1", "ID_2", "ID_4", "ID_9"),
  EINUM = c(1, 2, 1, 1, 1),
  EILLTN = c("llt1", "llt2", "llt1", "llt3", "llt4"),
  EIPTN = c("Arrhythmia", "Myocardial Infarction", "Arrhythmia", "Pneumonia", "Pneumonia"),
  EISOCPN = c("Cardiac Disorders", "Cardiac Disorders", "Cardiac Disorders",
  "Infections", "Infections"),
  EIGRDM = c(1, 3, 4, 2, 4)
)

plot_volcano(df_pat_grp, df_pat_llt)

Prepare a Data Frame for Summarization with Custom Missing Data Handling and Factor Ordering

Description

This function prepares a data frame for summarization by handling missing data based on the show_missing_data argument and applying the specified data manipulation (DM) option to factor variables. It provides flexibility for data cleaning and ordering before summarizing with functions like gtsummary.

Usage

prepare_table(
  data1,
  by_group = FALSE,
  var_group = NULL,
  drop_levels = TRUE,
  show_missing_data = TRUE,
  include_all_na_cat = TRUE
)
prepare_table(
  data1,
  by_group = FALSE,
  var_group = NULL,
  drop_levels = TRUE,
  show_missing_data = TRUE,
  include_all_na_cat = TRUE
)

Arguments

data1

A data frame containing the dataset to be analyzed.

by_group

A boolean (default is FALSE) to analyse by group.

var_group

A variable used for grouping (if applicable). Defaults to NULL.

drop_levels

Boolean (default = TRUE). Drop unused levels.

show_missing_data

Default to NULL. Should the missing data be displayed. Can be either :

FALSE: No missing data displayed
TRUE: Missing data displayed
NULL (default): will be switch to anyNA(data1) value.

include_all_na_cat

Should the categorical variable with a missing levels (all values are NA) be displayed. Default to TRUE

Details

The DM option defines the data manipulation to be applied to factor variables:
- "tout": Both order factor levels and drop unused levels.
- "tri": Only order factor levels.
- "remove": Drop unused factor levels without ordering.

Examples

# Example usage with the iris dataset
prepare_table(iris)

# Example usage with the iris dataset
prepare_table(iris)

Reverse Log Transformation

Description

Creates a transformation object for a reverse log scale, which can be used in ggplot2 scales.

Usage

reverselog_trans(base = exp(1))
reverselog_trans(base = exp(1))

Arguments

base

A numeric value specifying the logarithm base. Default is the natural logarithm (exp(1)).

Details

This function defines a reverse logarithmic transformation, where the transformation function is

$-\log(x, \text{base})$

and the inverse function is

$\text{base}^{-x}$

. It is useful for cases where a decreasing log scale is needed.

Value

A transformation object compatible with ggplot2 scales.

Examples

library(scales)
rev_log <- reverselog_trans(10)
rev_log$trans(100)  # -2
rev_log$inverse(-2) # 100

library(scales)
rev_log <- reverselog_trans(10)
rev_log$trans(100)  # -2
rev_log$inverse(-2) # 100

riskdifference

Description

A function from the fmsb package to compute risk difference. Calculate risk difference (a kind of attributable risk / excess risk) and its confidence intervals based on approximation, followed by null hypothesis (risk difference equals to 0) testing.

Usage

riskdifference(a, b, N1, N0, CRC = FALSE, conf.level = 0.95)
riskdifference(a, b, N1, N0, CRC = FALSE, conf.level = 0.95)

Arguments

a

The number of disease occurence among exposed cohort.

b

The number of disease occurence among non-exposed cohort.

N1

The population at risk of the exposed cohort.

N0

The population at risk of the unexposed cohort.

CRC

Logical. If TRUE, calculate confidence intervals for each risk. Default is FALSE.

conf.level

Probability for confidence intervals. Default is 0.95.

Value

A list with the results

Column selection with optional grouping variable

Description

This function extends dplyr::select() by allowing the dynamic addition of one or more grouping variables (var_group) to the selection.

Usage

select_plus(.data, ..., var_group = NULL)
select_plus(.data, ..., var_group = NULL)

Arguments

.data

A data frame.

...

Columns to select (as in dplyr::select()).

var_group

A character string or vector of column names to additionally include, typically one or more grouping variables. Can be NULL.

Details

It is especially useful when switching between an ungrouped analysis (e.g., all observations together) and a grouped analysis (e.g., stratified or including interaction terms), without rewriting code.

For instance, this allows you to write a single analysis command for both the RDD (Rapport de Démarrage des Données) and the final report, simply by changing the .qmd file, without modifying the core analysis code.

Value

A data frame with the selected columns, including var_group if specified.

Examples

library(dplyr)
df <- data.frame(x = 1:3, y = 4:6, z = 7:9)

# Simple selection
select_plus(df, x, y)

# Selection with grouping variable
select_plus(df, x, var_group = "z")
library(dplyr)
df <- data.frame(x = 1:3, y = 4:6, z = 7:9)

# Simple selection
select_plus(df, x, y)

# Selection with grouping variable
select_plus(df, x, var_group = "z")

Generate qmd, html and css files for reporting

Description

This function creates and writes a qmd file with css and html to report statistical analysis.

Usage

start_new_reporting(
  folder_path,
  output_folder,
  name = "report",
  structure = "USMR",
  path_logo = NULL,
  confidential = FALSE,
  report_type = "Data review report",
  study_id = "CHUBXYYYY/NN",
  study_name = "The Study Name",
  study_abbreviation = "TSN",
  investigator = "Investigator name",
  methodologist = "Jean Dupont",
  biostatistician = "George Frais",
  datamanager = "Peter Parker",
  methodologist_mail = NULL,
  biostatistician_mail = NULL,
  datamanager_mail = NULL,
  language = "fr"
)
start_new_reporting(
  folder_path,
  output_folder,
  name = "report",
  structure = "USMR",
  path_logo = NULL,
  confidential = FALSE,
  report_type = "Data review report",
  study_id = "CHUBXYYYY/NN",
  study_name = "The Study Name",
  study_abbreviation = "TSN",
  investigator = "Investigator name",
  methodologist = "Jean Dupont",
  biostatistician = "George Frais",
  datamanager = "Peter Parker",
  methodologist_mail = NULL,
  biostatistician_mail = NULL,
  datamanager_mail = NULL,
  language = "fr"
)

Arguments

folder_path

The folder where this should be created

output_folder

The folder where the html will be recorded.

name

The name of the files

structure

Character string indicating the organizational structure, either "USMR" or "EUCLID". Default is "USMR".

path_logo

Character string specifying the path to the logo image. If NULL, a default logo is used.

confidential

Logical value indicating whether the report should be marked as confidential. Default is FALSE.

report_type

Character string specifying the type of report. Default is "Data review report".

study_id

Character string representing the study identifier. Default is "CHUBXYYYY/NN".

study_name

Character string specifying the name of the study. Default is "The Study Name".

study_abbreviation

Character string providing the abbreviation of the study. Default is "TSN".

investigator

Character string representing the investigator's name. Default is "Investigator name".

methodologist

Character string specifying the methodologist's name. Default is "Jean Dupont".

biostatistician

Character string specifying the biostatistician's name. Default is "George Frais".

datamanager

Character string specifying the data manager's name. Default is "Peter Parker".

methodologist_mail

Character string specifying the methodologist's email. If NULL, it is generated automatically.

biostatistician_mail

Character string specifying the biostatistician's email. If NULL, it is generated automatically.

datamanager_mail

Character string specifying the data manager's email. If NULL, it is generated automatically.

language

Character string indicating the language of the report, either "fr" (French) or "en" (English). Default is "fr".

Value

None. The function writes an HTML report to the specified file path.

Generate a CSS File

Description

This function creates and writes a CSS file with predefined styling for tables and text formatting.

Usage

write_css(path)
write_css(path)

Arguments

path

Character string specifying the file path where the CSS file will be saved.

Value

None. The function writes a CSS file to the specified file path.

Generate an HTML Report File

Description

This function creates and writes an HTML report file based on specified study and structure details.

Usage

write_html_file(
  path,
  structure = "USMR",
  path_logo = NULL,
  confidential = FALSE,
  report_type = "Data review report",
  study_id = "CHUBXYYYY/NN",
  study_name = "The Study Name",
  study_abbreviation = "TSN",
  investigator = "Investigator name",
  methodologist = "Jean Dupont",
  biostatistician = "George Frais",
  datamanager = "Peter Parker",
  methodologist_mail = NULL,
  biostatistician_mail = NULL,
  datamanager_mail = NULL,
  language = "fr"
)
write_html_file(
  path,
  structure = "USMR",
  path_logo = NULL,
  confidential = FALSE,
  report_type = "Data review report",
  study_id = "CHUBXYYYY/NN",
  study_name = "The Study Name",
  study_abbreviation = "TSN",
  investigator = "Investigator name",
  methodologist = "Jean Dupont",
  biostatistician = "George Frais",
  datamanager = "Peter Parker",
  methodologist_mail = NULL,
  biostatistician_mail = NULL,
  datamanager_mail = NULL,
  language = "fr"
)

Arguments

path

Character string specifying the file path where the HTML file will be saved.

structure

Character string indicating the organizational structure, either "USMR" or "EUCLID". Default is "USMR".

path_logo

Character string specifying the path to the logo image. If NULL, a default logo is used.

confidential

Logical value indicating whether the report should be marked as confidential. Default is FALSE.

report_type

Character string specifying the type of report. Default is "Data review report".

study_id

Character string representing the study identifier. Default is "CHUBXYYYY/NN".

study_name

Character string specifying the name of the study. Default is "The Study Name".

study_abbreviation

Character string providing the abbreviation of the study. Default is "TSN".

investigator

Character string representing the investigator's name. Default is "Investigator name".

methodologist

Character string specifying the methodologist's name. Default is "Jean Dupont".

biostatistician

Character string specifying the biostatistician's name. Default is "George Frais".

datamanager

Character string specifying the data manager's name. Default is "Peter Parker".

methodologist_mail

Character string specifying the methodologist's email. If NULL, it is generated automatically.

biostatistician_mail

Character string specifying the biostatistician's email. If NULL, it is generated automatically.

datamanager_mail

Character string specifying the data manager's email. If NULL, it is generated automatically.

language

Character string indicating the language of the report, either "fr" (French) or "en" (English). Default is "fr".

Value

None. The function writes an HTML report to the specified file path.

Write a Quarto Markdown (.qmd) file

Description

This function generates a Quarto Markdown (.qmd) file with predefined metadata and a sample table.

Usage

write_qmd(path, path_html, path_css, study_abbreviation, name)
write_qmd(path, path_html, path_css, study_abbreviation, name)

Arguments

path

Character string specifying the output file path for the .qmd file.

path_html

Character string specifying the path to an HTML file to be included before the body of the document.

path_css

Character string specifying the path to a CSS file for styling the document.

study_abbreviation

Character string providing the abbreviation of the study.

name

The name of the files

Details

The function creates a Quarto Markdown file with metadata fields such as title, author, date, and format settings. The HTML file specified in path_html is included before the body, and the CSS file specified in path_css is used for styling. The generated document includes an example of a start of report.

Value

None. The function writes a .qmd file to the specified path.

write_quarto_yml

Description

Write quarto extension

Usage

write_quarto_yml(path)
write_quarto_yml(path)

Arguments

path

The path toward quarto yaml file

Value

nothing

write_rendercopy

Description

A function to write a R file rendercopy

Usage

write_rendercopy(output_folder, path, name)
write_rendercopy(output_folder, path, name)

Arguments

output_folder

The output folder

path

The path of the R script

name

The name of the files

Value

Nothing

Package 'RastaRocket'

Help Index

Add missing value information to a gtsummary table

Description

Usage

Arguments

Value

Add p-values and separate footnotes to a gtsummary object

Description

Usage

Arguments

Value

Examples

Add "n (dm ; %dm)" to Variable Labels

Description

Usage

Arguments

Details

Value

Examples

Create a Summary Table with Grouping and Custom Formatting

Description

Usage

Arguments

Value

Examples

css_generator

Description

Usage

Arguments

Value

Custom formatting for gtsummary and gt tables

Description

Usage

Arguments

Value

Examples

Modify gtsummary table headers and add a spanning header

Description

Usage

Arguments

Value

Custom Round and Format

Description

Usage

Arguments

Value

Examples

Customize a Summary Table with Grouping, Missing Data, and Custom Titles

Description

Usage

Arguments

Details

Value

Examples

Customize Table Body

Description

Usage

Arguments

Details

Value

Examples

desc_ei_per_grade

Description

Usage

Arguments

Value

Examples

desc_ei_per_pt

Description

Usage

Arguments

Value

Examples

Generate Descriptive Tables for Variables

Description

Usage

Arguments

Details

Value

Custom formatting for `gtsummary` and `gt` tables

Text indentation for `gt` tables

Text indentation for `gtsummary` tables