Add Counts and Percentages of Missing Data by Group

Description

This function calculates and summarizes the counts and percentages of missing and non-missing values for a specified variable, grouped by another variable. It provides formatted output for integration into summary tables.

Usage

add_by_n(data, variable, by, tbl, ...)

Arguments

Details

The function performs the following steps:

1. Groups the data by the variable specified in by.

2. Computes the number of non-missing values (nb), the number of missing values (nb_NA), and the percentage of missing values (nb_percent) for the specified variable.

3. Renames and formats the output columns for clarity and readability.

4. Converts the data into a wide format suitable for integration into summary tables, with calculated statistics included in formatted strings (e.g., "value (missing_count ; missing_percent%)").

The output is designed for use with summary tools, such as gtsummary, to display detailed missing data statistics alongside descriptive statistics.

Value

A data frame in wide format, where each row represents a group (as defined by by), and columns include statistics for the target variable (variable) in a formatted string.

Examples

# Example usage:
library(dplyr)
library(tidyr)
data(mtcars)

# Add missing data statistics grouped by 'cyl'
add_by_n(
  data = mtcars,
  variable = "mpg",
  by = "cyl"
)

Add Counts by Group

Description

This function calculates and summarizes the counts and percentages of non-missing values for a specified variable, grouped by another variable. It provides formatted output for integration into summary tables.

Usage

add_by_n_noNA(data, variable, by, tbl, ...)

Arguments

Details

The function performs the following steps:

1. Groups the data by the variable specified in by.

2. Computes the number of non-missing values (nb) for the specified variable.

3. Renames and formats the output columns for clarity and readability.

4. Converts the data into a wide format suitable for integration into summary tables, with calculated statistics included in formatted strings.

The output is designed for use with summary tools, such as gtsummary, to display detailed missing data statistics alongside descriptive statistics.

Value

A data frame in wide format, where each row represents a group (as defined by by), and columns include statistics for the target variable (variable) in a formatted string.

Examples

# Example usage:
library(dplyr)
library(tidyr)
data(mtcars)

# Add  data statistics grouped by 'cyl'
add_by_n(
  data = mtcars,
  variable = "mpg",
  by = "cyl"
)

Add missing value information to a gtsummary table

Description

This function adds information about missing and non-missing data counts to a gtsummary table. It can also apply custom statistics by group and modify the table body with an external function.

Usage

add_missing_info(
  base_table,
  show_missing_data,
  var_group = NULL,
  by_group = FALSE
)

Arguments

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)

Arguments

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)

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)

Arguments

Details

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

1. Retrieves the current label of each variable using labelled::var_label.

2. Creates a new label by appending the text "n (dm ; %dm)" to the existing label.

3. 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)

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,
  by_group = FALSE,
  var_group,
  quali = NULL,
  quanti = NULL,
  digits = list(mean_sd = 1, median_q1_q3_min_max = 1, pct = 1)
)

Arguments

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")

css_generator

Description

Generate css to be included in quarto.

Usage

css_generator(path_logo = NULL)

Arguments

Value

A css string

Custom formatting for gtsummary tables

Description

This function takes a 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)

Arguments

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

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
)

Arguments

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)

Arguments

Value

A character vector with the rounded and formatted numbers.

Examples

custom_round(3.14159)      # "3.1"
custom_round(3.14159, 3)   # "3.142"
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
)

Arguments

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.

• An external function modify_table_body_func is called to further modify the table body.

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 %>%
  tbl_summary(by = "trt", missing = "no")

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)

Arguments

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 <- 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, severity = TRUE, digits = 1)

Arguments

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)

desc_ei_per_grade_df_to_gt

Description

Converts the processed AE grade dataframe into a gt table for visualization.

Usage

desc_ei_per_grade_df_to_gt(df_wide, vec_grp)

Arguments

Value

A formatted gt table.

desc_ei_per_grade_prepare_df

Description

Prepares a wide-format dataframe summarizing AE by grade and group.

Usage

desc_ei_per_grade_prepare_df(
  augmented_df_pat_grp,
  augmented_df_pat_grade_grp,
  digits = 1
)

Arguments

Value

A dataframe in wide format with AE counts and percentages by grade and group.

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,
  language = "fr",
  order_by_freq = TRUE,
  digits = 1
)

Arguments

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)

Convert AE Summary Data to GT Table

Description

This function takes a prepared wide-format dataframe summarizing adverse events and patients and converts it into a formatted gt table for easy visualization.

Usage

desc_ei_per_pt_df_to_gt(df_wide, vec_grp, language = "fr")

Arguments

Value

A gt table formatted with appropriate labels, spans, and styling.

Prepare Data for AE Description by SOC and PT

Description

This function processes patient and adverse event data to generate a wide-format summary dataframe, including total counts and percentages of events and patients per SOC (System Organ Class) and PT (Preferred Term).

Usage

desc_ei_per_pt_prepare_df(
  augmented_df_pat_grp,
  augmented_df_pat_pt_grp,
  order_by_freq = TRUE,
  unknown_ei = " Unknown",
  digits = 1
)

Arguments

Value

A wide-format dataframe summarizing adverse event occurrences and patient counts across groups.

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,
  digits = list(mean_sd = 1, median_q1_q3_min_max = 1, pct = 1),
  drop_levels = TRUE,
  freq_relevel = FALSE,
  tests = FALSE,
  show_n_per_group = FALSE,
  show_missing_data = NULL,
  var_tot = NULL,
  var_characteristic = NULL
)

Arguments

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", "A", "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"
)

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)

Arguments

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 firstname.lastname@chu-bordeaux.fr.

Usage

from_name_to_adress(name = "Peter Parker")

Arguments

Value

A character string containing the generated email address.

Examples

from_name_to_adress("John Doe")
from_name_to_adress()

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"
)

Arguments

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")
)

Modify Table Body

Description

This function modifies a table by updating the "stat_" columns with corresponding "add_n_stat_" columns if they exist. If "stat_" columns contain missing values (NA), the function replaces them with the respective "add_n_stat_" column values. Extra "add_n_stat_*" columns are removed after processing.

Usage

modify_table_body_func(data)

Arguments

Details

• The function identifies columns starting with "add_n_stat_" and attempts to use them to fill missing values in columns matching the pattern "^stat_\d+$".

• If all required "add_n_stat_*" columns exist in data, they are utilized for this replacement; otherwise, the ⁠add_n_stat_*⁠ columns are removed without modifications to the ⁠stat_*⁠ columns.

Value

A modified data frame where:

• ⁠stat_*⁠ columns are updated to replace NA values with the corresponding values from ⁠add_n_stat_*⁠.

• ⁠add_n_stat_*⁠ columns are removed after processing.

• If no ⁠add_n_stat_*⁠ columns exist, these columns are simply removed.

Examples

# Example data
data <- data.frame(
  n = c(1, 2, 3),
  stat_1 = c(NA, 5, 6),
  stat_2 = c(7, NA, 9),
  add_n_stat_1 = c(10, 11, 12),
  add_n_stat_2 = c(13, 14, 15)
)

# Apply the function
modified_data <- modify_table_body_func(data)
print(modified_data)

Reorder Levels of Qualitative Variables by Frequency

Description

This function reorders the levels of all qualitative (factor) variables in a dataset based on their frequency, in descending order. It ensures that the most frequent levels appear first when analyzing or visualizing the data.

Usage

ordonner_variables_qualitatives(data)

Arguments

Details

The function applies the following transformations:

• Identifies all columns of type factor in the dataset.

• Reorders the levels of each factor variable using the forcats::fct_infreq() function, which orders levels by decreasing frequency.

This is particularly useful for preparing datasets for visualization or analysis, where it can be helpful to have the most common levels displayed first.

Value

A data frame with reordered levels for all factor variables. Non-factor variables remain unchanged.

Examples

# Example usage:
library(dplyr)
library(forcats)

# Create a sample dataset
data <- data.frame(
  var1 = factor(c("A", "B", "A", "C", "B", "B")),
  var2 = factor(c("X", "Y", "X", "Y", "X", "Z")),
  var3 = c(1, 2, 3, 4, 5, 6)  # Non-factor variable
)

# Reorder qualitative variables by frequency
data <- ordonner_variables_qualitatives(data)

# Check the new order of levels
levels(data$var1) # Output: "B" "A" "C"
levels(data$var2) # Output: "X" "Y" "Z"

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)
)

Arguments

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)

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"
)

Arguments

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)

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")
)

Arguments

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"
)

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"
)

Arguments

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)

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,
  freq_relevel = FALSE,
  show_missing_data = TRUE
)

Arguments

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.

Value

A data frame that has been prepared based on the show_missing_data and DM arguments. The function modifies the input data frame by applying labels, ordering factor variables, and potentially dropping unused levels.

Examples

# 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))

Arguments

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

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)

Arguments

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)

Arguments

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 <- tibble(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"
)

Arguments

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)

Arguments

Value

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

write_datestamp_output_file

Description

A function to write a R file and add datestamp

Usage

write_datestamp_output_file(output_folder, path, from_file)

Arguments

Value

Nothing

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"
)

Arguments

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)

Arguments

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 table with a caption.

Value

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

write_quarto_yml

Description

Write quarto extension

Usage

write_quarto_yml(path)

Arguments

Value

nothing