Skip to contents

visStatistics

Visualization of the statistical hypothesis test with the highest statistical power between two groups of categorical or numerical data.

visStatistics is an R package for rapid visualization and statistical analysis of raw data. It automatically selects and applies the most appropriate hypothesis test to evaluate the relationship between a response (varsample) and a feature (varfactor) within a data.frame.

A minimal function call looks of its main function visstat() looks like:

visstat(dataframe, varsample = "response", varfactor = "feature")

The input must be a column-based data.frame, and varsample and varfactor are character strings naming columns of that data frame.

The function selects a statistical test based on the class of the response and feature variables, the number of levels in categorical variables, and conditions such as normality and homoscedasticity.

The automatically generated output figures illustrate the selected statistical test, display the main test statistics, and include assumption checks and post hoc comparisons when applicable. The primary test results are returned as a list object.

This automated workflow is particularly suited for browser-based interfaces or server-side R applications that interact with databases.

For a detailed description of the decision logic see

vignette("visStatistics")

Installation of latest stable version from CRAN

  1. Install the package
install.packages("visStatistics")
  1. Load the package
library(visStatistics)

Installation of the development version from GitHub

  1. Install devtools from CRAN if not already installed:

    install.packages("devtools")

  2. Load the devtools package:

  3. Install the visStatistics package from GitHub:

    install_github("shhschilling/visStatistics")

  4. Load the visStatistics package:

  5. View help for the main function:

    ?visstat

Examples

Welch t-test

InsectSprays data set 

insect_sprays_a_b <-
  InsectSprays[which(InsectSprays$spray == "A" | InsectSprays$spray == "B"), ]
insect_sprays_a_b$spray <- factor(insect_sprays_a_b$spray)
visstat(insect_sprays_a_b, "count", "spray")

mtcars data set 

mtcars$am <- as.factor(mtcars$am)
t_test_statistics <- visstat(mtcars, "mpg", "am")

Wilcoxon rank sum test 

grades_gender <- data.frame(
  sex = as.factor(c(rep("girl", 21), rep("boy", 23))),
  grade = c(
    19.3, 18.1, 15.2, 18.3, 7.9, 6.2, 19.4,
    20.3, 9.3, 11.3, 18.2, 17.5, 10.2, 20.1, 13.3, 17.2, 15.1, 16.2, 17.0,
    16.5, 5.1, 15.3, 17.1, 14.8, 15.4, 14.4, 7.5, 15.5, 6.0, 17.4, 7.3, 14.3,
    13.5, 8.0, 19.5, 13.4, 17.9, 17.7, 16.4, 15.6, 17.3, 19.9, 4.4, 2.1
  )
)

wilcoxon_statistics <- visstat(grades_gender, "grade", "sex")

ANOVA 

insect_sprays_tr <- InsectSprays
insect_sprays_tr$count_sqrt <- sqrt(InsectSprays$count)
visstat(insect_sprays_tr, "count_sqrt", "spray")

One-way test 

one_way_npk <- visstat(npk, "yield", "block")

Kruskal-Wallis test

The generated graphs can be saved in all available formats of the Cairo package. Here we save the graphical output of type “pdf” in the plotDirectory tempdir():

visstat(iris, "Petal.Width", "Species",
  graphicsoutput = "pdf", plotDirectory = tempdir()
)

Linear Regression 

linreg_cars <- visstat(cars, "dist", "speed")

Increasing the confidence level conf.level from the default 0.95 to 0.99 leads two wider confidence and prediction bands:

Pearson’s Chi-squared test

Count data sets are often presented as multidimensional arrays, so-called contingency tables, whereas visstat() requires a data.frame with a column structure. Arrays can be transformed to this column wise structure with the helper function counts_to_cases():

hair_eye_color_df <- counts_to_cases(as.data.frame(HairEyeColor))
visstat(hair_eye_color_df, "Hair", "Eye")

Fisher’s exact test 

hair_eye_color_male <- HairEyeColor[, , 1]
# Slice out a 2 by 2 contingency table
black_brown_hazel_green_male <- hair_eye_color_male[1:2, 3:4]
# Transform to data frame
black_brown_hazel_green_male <- counts_to_cases(as.data.frame(black_brown_hazel_green_male))
# Fisher test
fisher_stats <- visstat(black_brown_hazel_green_male, "Hair", "Eye")

Implemented tests

Data of class "numeric" or "integer" are referred to as numerical, while data of class "factor" are referred to as categorical.

Numerical response ~ categorical feature

When the response is numerical and the feature is categorical, test of central tendencies are selected:

t.test(), wilcox.test(), aov(), oneway.test(),kruskal.test()

Normality assumption check

shapiro.test() and ad.test()

Homoscedasticity assumption check

bartlett.test()

Post-hoc tests

The decision below tree summarises the underlying decision logic for tests of central tendencies.

knitr::include_graphics("man/figures/decision_tree.png")
*Decision tree used to select the appropriate statistical test for a categorical predictor and numerical response, based on the number of factor levels, normality and homoscedasticity.*

Decision tree used to select the appropriate statistical test for a categorical predictor and numerical response, based on the number of factor levels, normality and homoscedasticity.

Numerical response ~ numerical feature

When both the response and feature are numerical, a simple linear regression model is fitted:

lm()

Categorical response ~ categorical predictor

When both variables are categorical, visstat() tests the null hypothesis of independence using one of the following: