You can cite this package/vignette as:
To cite package 'ggstatsplot' in publications use: Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167A BibTeX entry for LaTeX users is @Article{, doi = {10.21105/joss.03167}, url = {https://doi.org/10.21105/joss.03167}, year = {2021}, publisher = {{The Open Journal}}, volume = {6}, number = {61}, pages = {3167}, author = {Indrajeet Patil}, title = {{Visualizations with statistical details: The {'ggstatsplot'} approach}}, journal = {{Journal of Open Source Software}}, }Lifecycle:
The functionggcorrmat() provides a quick way to producepublication-ready correlation matrix (akacorrelalogram) plot. The function can also be used for quickdata exploration. In addition to the plot, it can alsobe used to get a correlation coefficient matrix or the associatedp-value matrix. This function is a convenient wrapper aroundggcorrplot::ggcorrplot() function with some additionalfunctionality.
We will see examples of how to use this function in this vignettewith thegapminder anddiamonds dataset.
To begin with, here are some instances where you would want to useggcorrmat-
- to easily visualize a correlation matrix usingggplot2
- to quickly explore correlation between (all) numeric variables inthe dataset
Correlation matrix plot withggcorrmat()
For the first example, we will use thegapminder dataset(available in eponymouspackage on CRAN)provides values for life expectancy, Gross Domestic Product (GDP) percapita, and population, every five years, from 1952 to 2007, for each of142 countries and was collected by the Gapminder Foundation. Let’s havea look at the data-
library(gapminder)library(dplyr)dplyr::glimpse(gapminder)#> Rows: 1,704#> Columns: 6#> $country<fct> "Afghanistan","Afghanistan","Afghanistan","Afghanistan",…#> $continent<fct> Asia,Asia,Asia,Asia,Asia,Asia,Asia,Asia,Asia,Asia,…#> $year<int> 1952,1957,1962,1967,1972,1977,1982,1987,1992,1997,…#> $lifeExp<dbl> 28.801,30.332,31.997,34.020,36.088,38.438,39.854,40.8…#> $pop<int> 8425333,9240934,10267083,11537966,13079460,14880372,12…#> $gdpPercap<dbl> 779.4453,820.8530,853.1007,836.1971,739.9811,786.1134,…Let’s say we are interested in studying correlation betweenpopulation of a country, average life expectancy, and GDP per capitaacross countries only for the year 2007.
The simplest way to get a correlation matrix is to stick to thedefaults-
## select data only from the year 2007gapminder_2007<-dplyr::filter(gapminder::gapminder,year==2007)## producing the correlation matrixggcorrmat( data=gapminder_2007,## data from which variable is to be taken cor.vars=lifeExp:gdpPercap## specifying correlation matrix variables)
This plot can be further modified with additional arguments-
ggcorrmat( data=gapminder_2007,## data from which variable is to be taken cor.vars=lifeExp:gdpPercap,## specifying correlation matrix variables cor.vars.names=c("Life Expectancy","population","GDP (per capita)"), type="np",## which correlation coefficient is to be computed lab.col="red",## label color ggtheme=ggplot2::theme_light(),## selected ggplot2 theme## turn off default ggestatsplot theme overlay matrix.type="lower",## correlation matrix structure colors=NULL,## turning off manual specification of colors palette="category10_d3",## choosing a color palette package="ggsci",## package to which color palette belongs title="Gapminder correlation matrix",## custom title subtitle="Source: Gapminder Foundation"## custom subtitle)
As seen from this correlation matrix, although there is norelationship between population and life expectancy worldwide, at leastin 2007, there is a strong positive relationship between GDP, awell-established indicator of a country’s economic performance.
Given that there were only three variables, this doesn’t look thatimpressive. So let’s work with another example fromggplot2 package: thediamondsdataset.This dataset contains the prices and other attributes of almost 54,000diamonds.
Let’s have a look at the data-
library(ggplot2)dplyr::glimpse(ggplot2::diamonds)#> Rows: 53,940#> Columns: 10#> $carat<dbl> 0.23,0.21,0.23,0.29,0.31,0.24,0.24,0.26,0.22,0.23,0.…#> $cut<ord> Ideal,Premium,Good,Premium,Good,Very Good,Very Good,Ver…#> $color<ord> E,E,E,I,J,J,I,H,E,H,J,J,F,J,E,E,I,J,J,J,I,…#> $clarity<ord> SI2,SI1,VS1,VS2,SI2,VVS2,VVS1,SI1,VS2,VS1,SI1,VS1,…#> $depth<dbl> 61.5,59.8,56.9,62.4,63.3,62.8,62.3,61.9,65.1,59.4,64…#> $table<dbl> 55,61,65,58,58,57,57,55,61,61,55,56,61,54,62,58…#> $price<int> 326,326,327,334,335,336,336,337,337,338,339,340,34…#> $x<dbl> 3.95,3.89,4.05,4.20,4.34,3.94,3.95,4.07,3.87,4.00,4.…#> $y<dbl> 3.98,3.84,4.07,4.23,4.35,3.96,3.98,4.11,3.78,4.05,4.…#> $z<dbl> 2.43,2.31,2.31,2.63,2.75,2.48,2.47,2.53,2.49,2.39,2.…Let’s see the correlation matrix between different attributes of thediamond and the price.
## let's use just 5% of the data to speed it upggcorrmat( data=dplyr::sample_frac(ggplot2::diamonds, size=0.05), cor.vars=c(carat,depth:z),## note how the variables are getting selected cor.vars.names=c("carat","total depth","table","price","length (in mm)","width (in mm)","depth (in mm)"), ggcorrplot.args=list(outline.color="black", hc.order=TRUE))
We can make a number of changes to this basic correlation matrix. Forexample, since we were interested in relationship between price andother attributes, let’s make theprice column to the thefirst column.
## let's use just 5% of the data to speed it upggcorrmat( data=dplyr::sample_frac(ggplot2::diamonds, size=0.05), cor.vars=c(price,carat,depth:table,x:z),## note how the variables are getting selected cor.vars.names=c("price","carat","total depth","table","length (in mm)","width (in mm)","depth (in mm)"), type="np", title="Relationship between diamond attributes and price", subtitle="Dataset: Diamonds from ggplot2 package", colors=c("#0072B2","#D55E00","#CC79A7"), pch="square cross",## additional aesthetic arguments passed to `ggcorrmat()` ggcorrplot.args=list( lab_col="yellow", lab_size=6, tl.srt=90, pch.col="white", pch.cex=14))+## modification outside `{ggstatsplot}` using `{ggplot2}` functionsggplot2::theme( axis.text.x=ggplot2::element_text( margin=ggplot2::margin(t=0.15, r=0.15, b=0.15, l=0.15, unit="cm")))
As seen here, and unsurprisingly, the strongest predictor of thediamond price is its carat value, which a unit of mass equal to 200 mg.In other words, the heavier the diamond, the more expensive it is goingto be.
Grouped analysis withgrouped_ggcorrmat
What if we want to do the same analysis separately for each qualityof the diamondcut (Fair, Good, Very Good, Premium,Ideal)?
ggstatsplot provides a special helper function forsuch instances:grouped_ggcorrmat(). This is merely awrapper function aroundcombine_plots(). It appliesggcorrmat() across alllevels of aspecifiedgrouping variable and then combines list ofindividual plots into a single plot.
grouped_ggcorrmat(## arguments relevant for `ggcorrmat()` data=ggplot2::diamonds, cor.vars=c(price,carat,depth), grouping.var=cut,## arguments relevant for `combine_plots()` plotgrid.args=list(nrow=3), annotation.args=list( tag_levels="a", title="Relationship between diamond attributes and price across cut", caption="Dataset: Diamonds from ggplot2 package"))
Note that this function also makes it easy to run the samecorrelation matrix across different levels of a factor/groupingvariable.
Data frame
If you want a data frame of (grouped) correlation matrix, usecorrelation::correlation() instead. It can also do groupedanalysis when used with output fromdplyr::group_by().
Grouped analysis withggcorrmat() +{purrr}
Althoughgrouped_ function is good for quickly exploringthe data, it reduces the flexibility with which this function can beused. This is the because the common parameters used are applied toplots corresponding to all levels of the grouping variable and there isno way to customize the arguments for different levels of the groupingvariable. We will see how this can be done using thepurrr package.
See the associated vignette here:https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/purrr_examples.html
Summary of graphics and tests
Details about underlying functions used to create graphics andstatistical tests carried out can be found in the functiondocumentation:https://indrajeetpatil.github.io/ggstatsplot/reference/gghistostats.html
Suggestions
If you find any bugs or have any suggestions/remarks, please file anissue onGitHub:https://github.com/IndrajeetPatil/ggstatsplot/issues