Categorize a Numeric Variable into Custom Ranges

Description

category creates a new categorical variable by splitting a numeric column into specified ranges. You can provide custom labels for each range, or it will default to the range values themselves.

Usage

category(data, var, level, new_var = NULL, labels = NULL)

Arguments

Value

A data.frame or tibble with a new categorical variable.

Examples

df <- data.frame(age = c(20, 28, 40, 55, 34, 10, 24, 55))

# Categorize without custom labels
category(df, var = age, level = c("10-25", "26-35", "36-50"))

# Categorize with custom labels
category(df, var = age, level = c("10-25", "26-35", "36-50"),
         labels = c("young", "adult", "old"))

Diagnostic Accuracy Metrics with Optional 2x2 Table

Description

Calculates diagnostic accuracy measures (Sensitivity, Specificity, PPV, NPV, Accuracy, LR+, LR-, DOR) from a binary test and gold standard. Provides 95% confidence intervals using Wilson method for proportions and log method for ratios. Optionally, prints a descriptive 2x2 table.

Usage

diag_accuracy(data, test_col, gold_col, descriptive = FALSE)

Arguments

Value

A flextable object summarizing diagnostic metrics with 95% CI. If descriptive = TRUE, also prints a 2x2 table of counts.

Examples

diagnostic_data <- data.frame(
  test = c("positive","negative","positive","
  negative","positive","negative","positive","negative"),
  goldstandard = c("positive","positive","negative",
  "negative","positive","negative","positive","negative")
)
diag_accuracy(diagnostic_data, test_col = "test",
gold_col = "goldstandard",
descriptive = FALSE)

Impute Missing Values in a Data Frame

Description

This function imputes missing values in a data frame. For categorical variables (factor or character), missing values are replaced with the mode. For numeric variables, missing values can be imputed using the mean, median, or regression-based imputation. If no method is specified for numeric columns, missing values are left as NA.

Usage

impute_missing(data, method = NULL)

Arguments

Value

A data frame with missing values imputed according to the specified method.

Examples

library(dplyr)
# Impute numeric columns using regression and categorical with mode
impute_missing(starwars, method = "regression")

# Impute numeric columns using mean
impute_missing(starwars, method = "mean")

# Impute numeric columns using median
impute_missing(starwars, method = "median")

Linear Regression Table with Univariable and Multivariable Analysis

Description

Fits univariable and multivariable linear regression models for a continuous outcome, summarizing beta coefficients, 95% confidence intervals, and p-values. Factor predictors include reference levels in the table. Returns a formatted flextable and optionally provides an automatic textual interpretation of results.

Usage

linreg(data, outcome, predictors, report = TRUE)

Arguments

Value

If report = FALSE, returns a flextable summarizing univariable and multivariable beta coefficients, 95% CI, and p-values. If report = TRUE, returns a list with table (the flextable) and interpretation (textual summary of multivariable results).

Examples

# Apply linear regression on iris dataset
linreg(
  data = iris,
  outcome = "Sepal.Length",
  predictors = c("Sepal.Width", "Petal.Length", "Species"),
  report = TRUE
)

Binary Logistic Regression Table with Univariable and Multivariable Analysis

Description

Fits univariable and multivariable logistic regression models for a binary outcome, summarizing odds ratios (ORs), 95% confidence intervals, and p-values. Factor predictors include reference levels in the table. Returns a formatted flextable and optionally provides an automatic textual interpretation of results.

Arguments

Value

A flextable summarizing univariable and multivariable logistic regression results, including ORs, 95% CI, and p-values.

Examples

logreg(data=medical_data(), outcome="case" ,
   predictors= c("age" ,  "parity" ,    "induced" ), report = TRUE)

Load Infertility Dataset

Description

Load Infertility Dataset

Usage

medical_data()

Value

A data.frame containing infertility cases with labeled predictors suitable for logistic regression

Recode values in a data frame using a lookup table

Description

This function replaces values in a data frame according to a named lookup vector. All columns are converted to character, and any value matching a name in lookup will be replaced by its corresponding value.

Usage

recode_data(data, lookup)

Arguments

Value

A data frame with the same structure as data, with values recoded according to lookup.

Examples

df <- data.frame(
  gender = c("male", "F", "male", "female"),
  status = c("single", "Married", "oo", "M"),
  stringsAsFactors = FALSE
)

lookup <- c(
  "male" = "Male",
  "M" = "Married",
  "oo" = "Widow",
  "female" = "Female",
  "F" = "Female"
)

df_recode <- recode_data(df, lookup)
print(df_recode)

Summarize Correlations Between a Reference Variable and Others

Description

Computes correlations between a reference variable and one or more comparison variables. For Pearson correlations, 95% confidence intervals are also calculated. Can optionally stratify by a grouping variable. Returns a formatted flextable and optionally prints a narrative summary describing weak, moderate, and strong correlations.

Usage

sum_cor(
  data,
  ref_var,
  compare_vars,
  by = NULL,
  method = "pearson",
  digits = 3,
  report = TRUE
)

Arguments

Value

A flextable object showing correlations, 95% CI (Pearson only), p-values, and interpretation.

Examples

# Example 1: Correlations across entire dataset
sum_cor(
  data = iris,
  ref_var = "Sepal.Length",
  compare_vars = c("Petal.Length", "Petal.Width", "Sepal.Width"),
  method = "pearson",
  digits = 2,
  report = TRUE
)

# Example 2: Correlations by Species
sum_cor(
  data = iris,
  ref_var = "Sepal.Length",
  by = "Species",
  compare_vars = c("Petal.Length", "Petal.Width", "Sepal.Width"),
  method = "pearson",
  digits = 2,
  report = TRUE
)

Crosstabulation with Counts and Percentages

Description

Creates a cross-tabulation (contingency table) between two categorical variables and returns a publication-ready table as a flextable. The table displays cell counts with optional percentages (cell-wise, row-wise, or column-wise), and includes row totals (right) and column totals (bottom).

Usage

sum_crosstab(
  data,
  row_var,
  col_var,
  percent = c("none", "cell", "row", "col"),
  digits = 2
)

Arguments

Value

A flextable object containing the formatted cross-tabulation with counts, optional percentages, and marginal totals.

Examples

data(CO2)

sum_crosstab(
  data = CO2,
  row_var = "Treatment",
  col_var = "Type",
  percent = "cell",
  digits = 2
)

Normality Test Summary Table for Numeric Variables

Description

This function performs the Shapiro-Wilk normality test on all numeric variables in a dataset and returns the results in a publication-ready flextable. Extremely small p-values are displayed as "p < 0.001". The function automatically detects numeric variables and ignores non-numeric columns.

Arguments

Value

A flextable summarizing each numeric variable with Shapiro-Wilk W statistic, formatted p-value, and distribution classification ("Normal" or "Skewed").

Examples

sum_norm(iris)

Summarize Variables with Post-hoc Tests: Mutiple comparisons

Description

Produces a summary table of numeric or categorical variables grouped by a factor, optionally performing global tests (ANOVA or Kruskal-Wallis) and post-hoc comparisons (Tukey or Dunn test). Numeric variables can be summarized using mean (SD) or median (IQR). Returns a flextable suitable for reporting.

Usage

sum_posthoc(
  data,
  by,
  variables = NULL,
  digits = 2,
  format_lt = "<0.001",
  na.rm = TRUE,
  statistic = "auto",
  test_type = "auto"
)

Arguments

Value

A flextable containing the summary table, global p-values, and post-hoc results.

Examples

sum_posthoc(
  data = iris,
  by = "Species",
  variables = c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width"),
  digits = 2,
  statistic = "auto",
  test_type = "auto"
)

Summarize Continuous and Categorical Variables with Optional Grouping

Description

sum_stat provides a summary of both continuous and categorical variables in a dataset. Continuous variables can be summarized using mean (SD) or median (IQR), optionally with 95% confidence intervals. Categorical variables are summarized as counts and percentages, optionally with confidence intervals. Summaries can also be generated by a grouping variable, and a narrative interpretation is optionally printed.

Usage

sum_stat(
  data,
  by = NULL,
  statistic = "mean_sd",
  percent = "col",
  ci = FALSE,
  conf = 0.95,
  report = FALSE
)

Arguments

Value

A flextable object displaying the summarized variables, optionally including confidence intervals and group comparisons.

Examples

# Basic summary of iris dataset
sum_stat(iris, ci = FALSE, report = TRUE)

# Summary of CO2 dataset by 'Treatment' with CI
sum_stat(CO2, by = "Treatment", ci = TRUE, report = TRUE, percent = "row")

Summarize Continuous and Categorical Variables with Grouping and P-Values

Description

sum_stat_p generates a descriptive summary table for both continuous and categorical variables, stratified by a grouping variable. It automatically computes appropriate statistical tests (Chi-square, Fisher's exact, t-test, Wilcoxon, ANOVA, or Kruskal–Wallis) based on variable type, number of groups, and data distribution. Continuous variables can be summarized as mean (SD) or median (IQR), and categorical variables as counts and percentages.

Usage

sum_stat_p(data, by, statistic = "mean_sd", test_type = "auto", paired = FALSE)

Arguments

Details

The output is formatted as a flextable with footnotes indicating which summary statistics were used and which statistical tests were applied.

Value

A flextable object displaying a publication-ready summary table including:

• Counts and percentages for categorical variables

• Mean (SD) or median (IQR) for continuous variables

• P-values for group comparisons

• Footnotes describing which summary statistics and tests were used

Examples

# Summary of iris dataset by species
sum_stat_p(iris, by = "Species", statistic = "mean_sd", test_type = "auto")

# Summary of CO2 dataset by Type with paired t-test
sum_stat_p(CO2, by = "Type", statistic = "mean_sd", test_type = "t.test", paired = TRUE)

# Summary using median and IQR
sum_stat_p(iris, by = "Species", statistic = "med_iqr", test_type = "kruskal")

Summarize Variables with Optional Stratification and Statistical Tests

Description

Produces summary tables for numeric and categorical variables in a dataset, optionally stratified by a grouping variable. Numeric variables are summarized with mean (SD) or median (IQR), and categorical variables with counts and percentages. Appropriate statistical tests (t-test, Wilcoxon, ANOVA, Kruskal-Wallis, Chi-square, or Fisher's Exact) are performed depending on the variable type, number of groups, and user-specified options.

Usage

sum_stat_p_strata(
  data,
  by,
  strata = NULL,
  statistic = "mean_sd",
  test_type = "auto",
  paired = FALSE
)

Arguments

Value

A flextable object containing the summary table with optional p-values, counts, percentages, and numeric summaries. Footnotes describe the statistics used and tests performed.

Examples

# Example : Summary of CO2 dataset by Type, stratified by Treatment
sum_stat_p_strata(data = CO2, by = "Type", strata = "Treatment")