Introduction to Analitica

In Analitica: Exploratory Data Analysis and Group Comparison Tools

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
library(Analitica)
data(d_e, package = "Analitica")

Overview

The Analitica package provides essential tools for:

• Descriptive statistical summaries
• Exploratory visualizations
• Homoscedasticity tests
• Outlier detection
• Parametric and non-parametric group comparisons

It is suitable for researchers, educators, and analysts seeking quick and interpretable workflows.

1. Descriptive Analysis

Use descripYG() to explore a numeric variable, optionally grouped by a categorical variable:

data(d_e, package = "Analitica")
descripYG(d_e, vd = Sueldo_actual)
descripYG(d_e, vd = Sueldo_actual, vi = labor)

2. Homogeneity of Variance Tests

You can assess variance assumptions using manual implementations:

Levene.Test(Sueldo_actual ~ labor, data = d_e)
BartlettTest(Sueldo_actual ~ labor, data = d_e)
FKTest(Sueldo_actual ~ labor, data = d_e)

3. Outlier Detection

Detect univariate outliers with Grubbs' test:

res <- grubbs_outliers(d_e, Sueldo_actual)
head(res[res$outL == TRUE, ])

4. Multiple Comparisons (Post Hoc Tests)

Fit an ANOVA model and apply post hoc tests:

mod <- aov(Sueldo_actual ~ as.factor(labor), data = d_e)
resultado <- GHTest(mod)
summary(resultado)
plot(resultado)

Other methods include TukeyTest(), ScheffeTest(), DuncanTest(), SNKTest(), T2Test(), and T3Test().

5. Non-Parametric Tests

When assumptions are violated, try:

g1 <- d_e$Sueldo_actual[d_e$labor == 1]
g2 <- d_e$Sueldo_actual[d_e$labor == 2]
MWTest(g1, g2)
BMTest(g1, g2)
BMpTest(g1, g2)

Conclusion

Analitica integrates descriptive analysis with robust comparison methods for applied data exploration.

For detailed documentation, see ?Analitica or function-specific help pages like ?GHTest or ?descripYG.

Analitica documentation built on June 14, 2025, 9:07 a.m.