Overview

In the first simplevis blog post about simplevis, we discussed how simplevis provides simple families of functions for when the user is colouring or facetting by a variable or both or neither. If you haven’t read that post, please read that one before this one. In the current post, we will discus the simplified and consistent method for colouring that simplevis has adopted.

In simplevis, users adhere to the following rules for adjusting colour:

1. Always define the colours to use via the pal argument (short for palette)
2. If colouring by a variable, use a *_col() or *_col_facet() function, and define the col_var
3. For gg_sf_col*() and gg_point_col*() functions where the col_var is numeric, also define the col_method of bin or quantile, and the col_cuts to use.

library(simplevis)
library(dplyr)
library(palmerpenguins)

1. Always define the colours to use via the pal argument

The colour palette can be changed from the default viridis colours by providing a character vector of hex codes to the pal argument.

gg_point(penguins, bill_length_mm, body_mass_g, pal = "#e7298a")

Users can get access to a large amount of colour palettes through the pals package.

2. If colouring by a variable, use a *_col() or *_col_facet() function, and define the col_var

To colour by a variable, use a *_col() function and then define that variable to be coloured using the col_var argument.

gg_point_col(penguins, bill_length_mm, body_mass_g, species)

3. For gg_sf_col*() and gg_point_col*() functions where colouring by a numeric variable, also define the col_method and col_cuts

All simplevis *_col() and *_col_facet() functions support colouring by a categorical variable.

In addition, sf and point *_col() and *_col_facet() functions support colouring by a numeric variable.

You do this by specifying whether you want to do this by:

• defining whether the col_method is to be by bin or quantile
• defining a vector or col_cuts. These should be between 0 and infinity (Inf) for bin and between 0 and 1 for quantile

plot_data <- ggplot2::diamonds %>% 
  slice_sample(prop = 0.01)

plot_data
#> # A tibble: 539 x 10
#>    carat cut       color clarity depth table price     x     y     z
#>    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#>  1  1.22 Ideal     E     VS1      62.4    54 10622  6.77  6.88  4.26
#>  2  0.56 Premium   D     SI1      61      60  1605  5.28  5.25  3.21
#>  3  0.33 Very Good E     VS1      58.3    62   886  4.49  4.57  2.64
#>  4  0.52 Premium   D     SI1      61.5    55  1651  5.21  5.19  3.2 
#>  5  1.02 Very Good F     VS1      62.8    58  7539  6.4   6.44  4.03
#>  6  1.52 Good      F     VS2      64.2    59 11696  7.16  7.2   4.61
#>  7  0.37 Ideal     F     VS1      61.1    56   846  4.64  4.65  2.84
#>  8  0.83 Ideal     G     VS1      62.1    55  4989  6.02  6.05  3.75
#>  9  0.32 Ideal     F     VS1      61.6    57   716  4.39  4.42  2.71
#> 10  1.06 Premium   D     SI2      61.6    61  4903  6.59  6.53  4.04
#> # ... with 529 more rows

gg_point_col(plot_data, 
                 x_var = carat, 
                 y_var = price, 
                 col_var = z,
                 col_method = "quantile",
                 col_cuts = c(0, 0.25, 0.5, 0.75, 1))

gg_point_col(plot_data, 
                 x_var = carat, 
                 y_var = price, 
                 col_var = z,
                 col_method = "bin",
                 col_cuts = c(0, 1, 2, 3, 4, 5, Inf))

Further information

More blogs to come on simplevis. In the meantime, see the vignette and articles on the simplevis website.