p <- ggplot(data = data.frame(x = c(-5, 5)), mapping = aes(x = x)) p + stat_function(fun = dnorm, size = 2, color = "red") + xlab("z-scores") + ylab("Relative ...

#' the x axis into bins and counting the number of observations in each bin. #' Histograms (\code{geom_histogram}) display the count with bars; frequency #' polygons (\code{geom_freqpoly}), display ...

In this tutorial we create basic visualizations (histograms and box plots) using R. The purpose of these basic visualizations is to see the distribution of a particular variable. The distribution ...

For everything from styling text and customizing color palettes to creating your own geoms, these ggplot2 add-ons deserve a place in your R data visualization toolkit. Plus, a bonus list of packages ...

There’s a reason ggplot2 is one of the most popular add-on packages for R: It’s a powerful, flexible and well-thought-out platform to create data visualizations you can customize to your heart’s ...

Visualise the distribution of a single continuous variable by dividing the x axis into bins and counting the number of observations in each bin. Histograms (geom_histogram) display the count with bars ...