“Damned are those who believe without seeing”
Run the following:
install.packages("devtools")
devtools::install_github("easystats/see")
library("see")
library(ggplot2)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point2() + theme_modern()
library(ggplot2)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point2() + theme_lucid()
library(rstanarm)
library(estimate)
dat <- rstanarm::stan_glm(Sepal.Width ~ poly(Petal.Length, 2),
data = iris) %>% estimate::estimate_link(keep_draws = TRUE,
length = 100, draws = 250) %>% estimate::reshape_draws()
p <- ggplot(dat, aes(x = Petal.Length, y = Draw, group = Draw_Group)) +
geom_line(color = "white", alpha = 0.05) + scale_x_continuous(expand = c(0,
0)) + scale_y_continuous(expand = c(0, 0))
p + theme_blackboard()
This is just one example of the available palettes. See this vignette for a detailed overview of palettes and color scales.
p1 <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() + theme_modern(axis.text.angle = 45) + scale_fill_material_d()
p2 <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() + theme_modern(axis.text.angle = 45) + scale_fill_material_d(palette = "ice")
p3 <- ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point2() + theme_modern() + scale_color_material_c(palette = "rainbow")
The plots()
function allows us to plot the figures side by side.
plots(p1, p2, p3, ncol = 2)
The plots()
function can also be used to add tags (i.e., labels
for subfigures).
plots(p1, p2, p3, ncol = 2, tags = paste("Fig. ", 1:3))
geom_points2()
and geom_jitter2()
allow points without borders and
contour.
normal <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point(size = 8, alpha = 0.3) + theme_modern()
new <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point2(size = 8, alpha = 0.3) + theme_modern()
plots(normal, new, ncol = 2)
Create a half-violin half-dot plot, useful for visualising the distribution and the sample size at the same time.
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violindot(fill_dots = "black") + theme_modern() + scale_fill_material_d()
library(dplyr)
library(tidyr)
data <- iris %>% group_by(Species) %>% summarise_all(mean) %>%
pivot_longer(-Species)
data %>% ggplot(aes(x = name, y = value, color = Species, group = Species)) +
geom_polygon(fill = NA, size = 2, show.legend = FALSE) +
coord_radar(start = -pi/4) + theme_minimal()
Plotting functions for the bayestestR package are demonstrated in this vignette.
Plotting functions for the parameters package are demonstrated in this vignette.
Plotting functions for the performance package are demonstrated in this vignette.
Plotting functions for the estimate package are demonstrated in this vignette.