from data to Viz is a new web site related to data analysis and R. Its aim is to make it easier to choose among different types of data visualisations. It looks beautiful, is easy to navigate, includes “trees” displaying a classification of visualizations and multiple individual examples with the corresponding R code. Highly recommended!
To access the website and/or to buy the printed poster visit from Data to Viz.
by Sean Lopp If you spend time with an excellent programmer, one thing that immediately jumps out is how quickly she can write code. It often appears to be magic, the number of keystrokes simply can’t equal the number of characters on the screen. The secret: it doesn’t! Most programmers use a series of tricks to
Polynomial equation as annotation to a ggplot.
I have collected some code snippets I used recently for working with polynomials. The idea of writing this page came from a question I was asked a few days ago by Titta Kotilainen. I should emphasize that the page linked below concerns only polynomials, and excludes all other linear models that can be fit with lm(). Even models that are polynomials but formulated not as a regular polynomial equation (e.g. x ~ I(x^2) + x instead of the expected x + I(x^2) will quietly fail! In contrast, use of poly() as in y ~ poly(x, 2) is fine.
I used Rmarkdown in RStudio to generate a page with knitted examples, if interested the source file is also available.
