About

The goal of this app is to provide an interactive interface to help learn and teach basic quantitative variables univariate and bivariate analysis and visualization.

You can display basic statistics and visualizations, you can then play around with parameters, zoom, pan, drag data points, and everything should be updated dynamically with transitions.

Data sources

Data can be manually typed, randomly generated from several distributions, or you can load a simple included dataset. Note that when a dataset is loaded, you won't be able to drag the points (as you shouldn't be able to do that in a real analysis).

Univariate datasets

TitleDescriptionNoteSource
Life expectancy by country Life expectancy at birth, by country, in 2014 World bank
Mother age at child birth Sample of 2000 mother ages at child birth, in France, in 2015. Only ages from 18 to 45 (included) are taken into account. An example of integer-only quantitative variable, following a slightly skewed normal distribution. Insee
Departments population Population of each french department in 2013 Wikipedia
Departments population density Population density of each french department (in people per kmĀ²) in 2013 An example of the influence of outliers on statistics and visualization. Wikipedia
!Kung people heights Heights (in cm) from partial census data for the Dobe area !Kung San, compiled from interviews conducted by Nancy Howell in the late 1960s. A bimodal dataset due to men and women heights having different distributions. Statistical rethinking book dataset

Bivariate datasets

TitleDescriptionNoteSource
Life expectancy vs GDP per capita European countries, 2007 Gapminder R package
Life expectancy vs GDP per capita World countries, 2007 Example of non linear relationship Gapminder R package
928 children height and their mid-parents heights Francis Galton, 1886 Historical dataset, used to show the concept of regression towards the mean. Galton R package

Links and credits