p + theme(legend.position="top")

guides(color=guide_legend(nrow=2, byrow=TRUE))

labs(fill='Legend Title')

geom_point( ## draw horizontal lines instead of points shape = 95, size = 10, alpha = .2

direct labelling”, in which the plot region itself contains the labels for groups of points instead of using a legend. This usually makes the plot easier to read because it puts the labels closer to the data.

scale_x_discrete(guide = guide_axis(n.dodge = 2))

occupy the fill scale with a slightly darker version of the palette used for color.

Let’s fit regression line to our model:

If you're using ggplot, you can use scales::pseudo_log_trans() as your transformation object. This will replace your -inf with 0.

All figures were created using R Statistical Computing Software version 3.6.3 (R Core Team, 2020), relying primarily on the dplyr package (Wickham et al., 2015) for data manipulation and the ggplot2 package (Wickham 2016) for plotting. The code used to create each figure can be found at https://github.com/mkc9953/SARS-CoV-2-WW-EPI/tree/master.

Beautiful plotting in R: A ggplot2 cheatsheet

Boxplots and Beyond – Part II: Asymmetry

To understand why the DPI is important, consider these two plots:

additional dimension may be of course time, but also any other changing parameter suchas voltage or current or the change of material or geometric parameters