Package {statease}

statease: Simplified Statistical Analysis with Plain-English Interpretation

Description

statease provides a suite of functions for performing common statistical analyses and automatically interpreting the results in plain English. It is designed for students, researchers, and educators who want fast, readable statistical output without sacrificing rigour.

Main Functions

analyze

Master function — auto-detects and runs the right test

describe

Descriptive statistics with interpretation

ttest_interpret

T-tests (one-sample, independent, paired) with Cohen's d

anova_interpret

One-way ANOVA with Tukey post-hoc and eta squared

interpret_p

Standalone p-value interpreter

Typical Workflow

The simplest way to use statease is through the master analyze() function, which automatically detects what test to run based on your input:

# Descriptive statistics
analyze(x = my_vector, var_name = "My Variable")

# Independent samples t-test
analyze(x = group1, y = group2, var_name = "Scores")

# One-way ANOVA
analyze(formula = score ~ group, data = my_df)

# Interpret a p-value
interpret_p(0.03, context = "treatment vs control")

Author(s)

Uwakmfon Usen Paul

See Also

Useful links:

• https://github.com/DevWebWacky/statease

• https://devwebwacky.github.io/statease/

• Report bugs at https://github.com/DevWebWacky/statease/issues

Master Analysis Function - Auto-detects and runs the right test

Description

Master Analysis Function - Auto-detects and runs the right test

Usage

analyze(
  x = NULL,
  y = NULL,
  data = NULL,
  formula = NULL,
  mu = 0,
  paired = FALSE,
  nonparam = FALSE,
  conf.level = 0.95,
  var_name = "Variable",
  var1_name = "Variable 1",
  var2_name = "Variable 2",
  method = "pearson",
  test_type = NULL,
  effect_size = NULL,
  power = 0.8,
  n_groups = 2,
  n_predictors = 1,
  check = FALSE
)

Arguments

Value

A printed analysis report from the appropriate test

Examples

# Descriptive only
analyze(x = c(23, 45, 12, 67, 34))

# Auto t-test
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29))

# Auto ANOVA
df <- data.frame(
  score = c(23,45,12,67,34,89,56,43,78,90,11,34),
  group = rep(c("A","B","C"), each = 4)
)
analyze(formula = score ~ group, data = df)

# Power analysis
analyze(test_type = "ttest.two", effect_size = 0.5)

# Check assumptions
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
        check = TRUE)

Two-Way ANOVA with Plain-English Interpretation

Description

Uses Type-2 SS by default (safe for unbalanced designs). Automatically switches to Type-3 SS when an interaction term is detected and sets the correct contrasts. Users are warned when interpreting main effects in the presence of a significant interaction.

Usage

anova2_interpret(formula, data, type = 2, conf.level = 0.95)

Arguments

Value

An object of class statease_anova2 containing two-way ANOVA results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  score  = c(23,45,12,67,34,89,56,43,78,90,11,34),
  method = rep(c("Online","Traditional"), each = 6),
  gender = rep(c("Male","Female"), times = 6)
)
result <- anova2_interpret(score ~ method * gender, data = df)
print(result)

One-Way ANOVA with Post-Hoc Tukey and Plain-English Interpretation

Description

One-Way ANOVA with Post-Hoc Tukey and Plain-English Interpretation

Usage

anova_interpret(formula, data, conf.level = 0.95)

Arguments

Value

An object of class statease_anova containing ANOVA results, effect size, and post-hoc comparisons. Use print() to display the formatted report.

Examples

df <- data.frame(
  score = c(23,45,12,67,34,89,56,43,78,90,11,34),
  group = rep(c("A","B","C"), each = 4)
)
result <- anova_interpret(score ~ group, data = df)
print(result)

Check Statistical Assumptions Before Running a Test

Description

Check Statistical Assumptions Before Running a Test

Usage

check_assumptions(test, x = NULL, y = NULL, data = NULL, formula = NULL)

Arguments

Value

An object of class statease_assumptions containing assumption check results. Use print() to display the formatted report.

Examples

x <- c(23, 45, 12, 67, 34, 89, 56, 43, 78, 90)
y <- c(19, 38, 22, 51, 29, 74, 44, 38, 65, 80)
result <- check_assumptions("ttest", x = x, y = y)
print(result)

Chi-Square Test with Plain-English Interpretation

Description

Chi-Square Test with Plain-English Interpretation

Usage

chisq_interpret(x, y, correct = TRUE, conf.level = 0.95)

Arguments

Value

An object of class statease_chisq containing test results and interpretation. Use print() to display the formatted report.

Examples

x <- c("Yes","No","Yes","Yes","No","Yes","No","No","Yes","Yes")
y <- c("Male","Female","Male","Female","Male","Female","Male","Female","Male","Female")
result <- chisq_interpret(x, y)
print(result)

Correlation Analysis with Plain-English Interpretation

Description

Correlation Analysis with Plain-English Interpretation

Usage

cor_interpret(
  x,
  y,
  method = "pearson",
  conf.level = 0.95,
  var1_name = "Variable 1",
  var2_name = "Variable 2"
)

Arguments

Value

An object of class statease_cor containing correlation results and interpretation. Use print() to display the formatted report.

Examples

x <- c(23, 45, 12, 67, 34, 89, 56, 43, 78, 90)
y <- c(19, 42, 15, 70, 30, 85, 52, 48, 80, 88)
result <- cor_interpret(x, y)
print(result)

Descriptive Statistics with Interpretation

Description

Descriptive Statistics with Interpretation

Usage

describe(x, var_name = "Variable")

Arguments

Value

An object of class statease_describe containing descriptive statistics and interpretation. Use print() to display the formatted report.

Examples

result <- describe(c(23, 45, 12, 67, 34, 89, 56))
print(result)

Fisher's Exact Test with Plain-English Interpretation

Description

Fisher's Exact Test with Plain-English Interpretation

Usage

fisher_interpret(x, y, conf.level = 0.95, simulate.p.value = FALSE)

Arguments

Value

An object of class statease_fisher containing test results and interpretation. Use print() to display the formatted report.

Examples

x <- c("Yes","No","Yes","Yes","No","Yes","No","No","Yes","Yes")
y <- c("Male","Female","Male","Female","Male",
       "Female","Male","Female","Male","Female")
result <- fisher_interpret(x, y)
print(result)

Friedman Test with Plain-English Interpretation

Description

Friedman Test with Plain-English Interpretation

Usage

friedman_interpret(formula, data, conf.level = 0.95)

Arguments

Value

An object of class statease_friedman containing test results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  score   = c(23,45,12,67,34,89,56,43,78,90,11,34),
  time    = rep(c("T1","T2","T3"), each = 4),
  subject = rep(1:4, times = 3)
)
result <- friedman_interpret(score ~ time | subject, data = df)
print(result)

Standalone P-Value Interpreter

Description

Standalone P-Value Interpreter

Usage

interpret_p(p, alpha = 0.05, context = NULL)

Arguments

Value

An object of class statease_pvalue containing the p-value interpretation. Use print() to display the report.

Examples

result <- interpret_p(0.03)
print(result)

result2 <- interpret_p(0.12, alpha = 0.05, context = "treatment vs control")
print(result2)

Kruskal-Wallis Test with Plain-English Interpretation

Description

Kruskal-Wallis Test with Plain-English Interpretation

Usage

kruskal_interpret(formula, data, conf.level = 0.95)

Arguments

Value

An object of class statease_kruskal containing test results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  score = c(23,45,12,67,34,89,56,43,78,90,11,34),
  group = rep(c("A","B","C"), each = 4)
)
result <- kruskal_interpret(score ~ group, data = df)
print(result)

Logistic Regression with Plain-English Interpretation

Description

Logistic Regression with Plain-English Interpretation

Usage

logistic_interpret(formula, data, conf.level = 0.95)

Arguments

Value

An object of class statease_logistic containing logistic regression results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  passed      = c(1,1,0,1,0,1,1,0,1,1,0,0,1,1,0),
  study_hours = c(9,8,3,7,2,9,8,3,7,6,2,1,8,7,3),
  attendance  = c(90,85,50,80,45,95,88,55,78,70,40,35,92,83,52)
)
result <- logistic_interpret(passed ~ study_hours + attendance, data = df)
print(result)

Mann-Whitney U Test with Plain-English Interpretation

Description

Mann-Whitney U Test with Plain-English Interpretation

Usage

mannwhitney_interpret(x, y, conf.level = 0.95, var_name = "Variable")

Arguments

Value

An object of class statease_mannwhitney containing test results and interpretation. Use print() to display the formatted report.

Examples

x <- c(23, 45, 12, 67, 34, 89, 56)
y <- c(19, 38, 22, 51, 29, 74, 44)
result <- mannwhitney_interpret(x, y)
print(result)

MANOVA with Plain-English Interpretation

Description

MANOVA with Plain-English Interpretation

Usage

manova_interpret(formula, data, conf.level = 0.95)

Arguments

Value

An object of class statease_manova containing MANOVA results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  math    = c(23,45,12,67,34,89,56,43,78,90,11,34),
  english = c(34,56,23,78,45,90,67,54,89,95,22,45),
  group   = rep(c("A","B","C"), each = 4)
)
result <- manova_interpret(cbind(math, english) ~ group, data = df)
print(result)

McNemar's Test with Plain-English Interpretation

Description

McNemar's Test with Plain-English Interpretation

Usage

mcnemar_interpret(x, y, conf.level = 0.95)

Arguments

Value

An object of class statease_mcnemar containing test results and interpretation. Use print() to display the formatted report.

Examples

x <- c("Yes","No","Yes","Yes","No","Yes","No","No","Yes","Yes")
y <- c("No","No","Yes","Yes","No","Yes","Yes","No","Yes","No")
result <- mcnemar_interpret(x, y)
print(result)

Multiple Linear Regression with Plain-English Interpretation

Description

Multiple Linear Regression with Plain-English Interpretation

Usage

mlr_interpret(formula, data, conf.level = 0.95)

Arguments

Value

An object of class statease_mlr containing multiple regression results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  exam_score  = c(23,45,12,67,34,89,56,43,78,90),
  study_hours = c(2,5,1,7,3,9,6,4,8,10),
  attendance  = c(60,80,50,90,70,95,85,75,88,92)
)
result <- mlr_interpret(exam_score ~ study_hours + attendance, data = df)
print(result)

Power Analysis with Plain English Interpretation

Description

Power Analysis with Plain English Interpretation

Usage

power_interpret(
  test,
  effect_size,
  n = NULL,
  alpha = 0.05,
  power = 0.8,
  n_groups = 2,
  n_predictors = 1
)

Arguments

Value

An object of class statease_power containing power analysis results and interpretation. Use print() to display the formatted report.

Examples

# Calculate required sample size for independent t-test
result <- power_interpret("ttest.two", effect_size = 0.5)
print(result)

# Calculate achieved power for given sample size
result2 <- power_interpret("ttest.two", effect_size = 0.5, n = 30)
print(result2)

Simple Linear Regression with Plain-English Interpretation

Description

Simple Linear Regression with Plain-English Interpretation

Usage

reg_interpret(formula, data, conf.level = 0.95)

Arguments

Value

An object of class statease_reg containing regression results and interpretation. Use print() to display the formatted report.

Examples

df <- data.frame(
  exam_score = c(23,45,12,67,34,89,56,43,78,90),
  study_hours = c(2,5,1,7,3,9,6,4,8,10)
)
result <- reg_interpret(exam_score ~ study_hours, data = df)
print(result)

Launch the statease Shiny App

Description

Launches an interactive Shiny application for running statistical analyses without writing any code.

Usage

run_app(...)

Arguments

Value

Launches the Shiny app in your browser

Examples

if(interactive()){
  run_app()
}

T-Test with Plain-English Interpretation

Description

T-Test with Plain-English Interpretation

Usage

ttest_interpret(
  x,
  y = NULL,
  mu = 0,
  paired = FALSE,
  conf.level = 0.95,
  var_name = "Variable"
)

Arguments

Value

An object of class statease_ttest containing test results and interpretation. Use print() to display the formatted report.

Examples

result <- ttest_interpret(c(23,45,12,67,34), c(19,38,22,51,29))
print(result)

Wilcoxon Signed Rank Test with Plain-English Interpretation

Description

Wilcoxon Signed Rank Test with Plain-English Interpretation

Usage

wilcoxon_interpret(x, y, conf.level = 0.95, var_name = "Variable")

Arguments

Value

An object of class statease_wilcoxon containing test results and interpretation. Use print() to display the formatted report.

Examples

x <- c(23, 45, 12, 67, 34, 89, 56)
y <- c(19, 38, 22, 51, 29, 74, 44)
result <- wilcoxon_interpret(x, y)
print(result)