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

The function visstat() visualizes the statistical hypothesis testing between two groups of data, where varsample is the dependent variable (or response) and varfactor is the independent variable (feature). The statistical hypothesis test with the highest statistical power and fulfilling the assumptions of the corresponding test is performed and visualized. A graph displaying the raw data accordingly to the chosen test as well as the test statistics is generated. Furthermore visstat() returns the corresponding test statistics as text. The automated workflow is especially suited for browser based interfaces to server-based deployments of R. Implemented tests: lm(), t.test(), wilcox.test(), aov(), oneway.test(),kruskal.test(), fisher.test(),chisqu.test().

Installation from GitHub

  1. Firstly, you need to install the devtools package. You can do this from CRAN. Invoke R and then type install.packages("devtools")
  2. Load the devtools package. library(devtools)
  3. Install the package from the github- repository install_github("shhschilling/visStatistics")
  4. Load the package library(visStatistics)
  5. Help on the function usage ?visstat


Trees data set: Linear regression


Iris data set: Kruskal-Wallis test

visstat(iris,"Petal.Width", "Species")

InsectSprays data set: ANOVA


InsectSprays data set: Welch two sample t.test

InsectSpraysAB <- InsectSprays[ which(InsectSprays$spray == 'A'| InsectSprays$spray == 'B'), ] #select only sprays 'A und 'B'

InsectSpraysAB$spray = factor(InsectSpraysAB$spray)


ToothGrowth data set: Wilcoxon rank sum test with continuity correction

visstat(ToothGrowth,"len", "supp")

HairEyeColor data set: Pearson’s Chi-squared test

HairEyeColorMale = counts_to_cases([,,1]))