examples/scales_ggplot2.R

if (require("ggplot2")) {
	data("diamonds")
	diam_exp = diamonds[diamonds$price >= 15000, ]
	diam_exp$clarity[1:500] = NA

	# discrete categorical scale
	ggplot(diam_exp, aes(x = carat, y = price, color = color)) +
		geom_point(size = 2) +
		scale_color_discrete_c4a_cat("carto.safe") +
		theme_light()

	# missing values
	c4a_plot("tol.muted", 8)
	ggplot(diam_exp, aes(x = carat, y = price, fill = clarity)) +
		geom_point(size = 2, shape = 21) +
		scale_fill_discrete_c4a_cat("tol.muted") +
		theme_light()

	# discrete sequential scale
	ggplot(diam_exp, aes(x = carat, y = price, color = cut)) +
		geom_point(size = 2) +
		scale_color_discrete_c4a_seq("hcl.blues2") +
		theme_light()

	# continuous sequential scale
	ggplot(diam_exp, aes(x = carat, y = price, color = depth)) +
		geom_point(size = 2) +
		scale_color_continuous_c4a_seq("hcl.blues2", range = c(0.4, 1)) +
		theme_light()

	# continuous diverging scale
	ggplot(diam_exp, aes(x = carat, y = depth, color = price)) +
		geom_point(size = 2) +
		scale_color_continuous_c4a_div("wes.zissou1", mid = mean(diam_exp$price)) +
		theme_light()

	# binned sequential scale
	ggplot(diam_exp, aes(x = carat, y = price, color = depth)) +
		geom_point(size = 2) +
		scale_color_binned_c4a_seq("scico.batlow", range = c(0.4, 1)) +
		theme_light()
}
mtennekes/cols4all documentation built on Oct. 25, 2024, 7:04 a.m.