revdep/checks.noindex/see/old/see.Rcheck/see-Ex.R
In aloy/qqplotr: Quantile-Quantile Plot Extensions for 'ggplot2'
pkgname <- "see"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('see')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("add_plot_attributes")
### * add_plot_attributes
flush(stderr()); flush(stdout())
### Name: add_plot_attributes
### Title: Complete figure with its attributes
### Aliases: add_plot_attributes
### ** Examples
## Not run:
##D library(rstanarm)
##D library(bayestestR)
##D library(see)
##D library(ggplot2)
##D
##D model <- stan_glm(
##D Sepal.Length ~ Petal.Width + Species + Sepal.Width,
##D data = iris,
##D chains = 2, iter = 200
##D )
##D
##D result <- hdi(model, ci = c(0.5, 0.75, 0.9, 0.95))
##D data <- data_plot(result, data = model)
##D
##D p <- ggplot(
##D data,
##D aes(x = x, y = y, height = height, group = y, fill = fill)
##D ) +
##D ggridges::geom_ridgeline_gradient()
##D
##D p
##D p + add_plot_attributes(data)
## End(Not run)
cleanEx()
nameEx("bluebrown_colors")
### * bluebrown_colors
flush(stderr()); flush(stdout())
### Name: bluebrown_colors
### Title: Extract blue-brown colors as hex codes
### Aliases: bluebrown_colors
### ** Examples
bluebrown_colors()
bluebrown_colors("blue", "brown")
cleanEx()
nameEx("coord_radar")
### * coord_radar
flush(stderr()); flush(stdout())
### Name: coord_radar
### Title: Radar coordinate system
### Aliases: coord_radar
### ** Examples
# Create a radar/spider chart with ggplot:
if (require("datawizard") && require("ggplot2")) {
data(iris)
data <- aggregate(iris[-5], list(Species = iris$Species), mean)
data <- data_to_long(
data,
c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
)
ggplot(data, aes(x = name, y = value, color = Species, group = Species)) +
geom_polygon(fill = NA, linewidth = 2) +
coord_radar(start = -pi / 4)
}
cleanEx()
nameEx("data_plot")
### * data_plot
flush(stderr()); flush(stdout())
### Name: data_plot
### Title: Prepare objects for plotting or plot objects
### Aliases: data_plot
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("flat_colors")
### * flat_colors
flush(stderr()); flush(stdout())
### Name: flat_colors
### Title: Extract Flat UI colors as hex codes
### Aliases: flat_colors
### ** Examples
flat_colors()
flat_colors("dark red", "teal")
cleanEx()
nameEx("geom_binomdensity")
### * geom_binomdensity
flush(stderr()); flush(stdout())
### Name: geom_binomdensity
### Title: Add dot-densities for binary 'y' variables
### Aliases: geom_binomdensity
### ** Examples
## Don't show:
if (require("ggdist")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
library(see)
data <- iris[1:100, ]
ggplot() +
geom_binomdensity(data,
x = "Sepal.Length",
y = "Species",
fill = "red",
color = NA
)
# Different scales
data[1:70, "Species"] <- "setosa" # Create unbalanced proportions
ggplot() +
geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "auto")
ggplot() +
geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "density")
ggplot() +
geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "proportion")
ggplot() +
geom_binomdensity(data,
x = "Sepal.Length", y = "Species",
scale = list("setosa" = 0.4, "versicolor" = 0.6)
)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("geom_from_list")
### * geom_from_list
flush(stderr()); flush(stdout())
### Name: geom_from_list
### Title: Create ggplot2 geom(s) from a list
### Aliases: geom_from_list geoms_from_list
### ** Examples
## Don't show:
if (require("ggside") && require("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
# Example 1 (basic geoms and labels) --------------------------
l1 <- list(
geom = "point",
data = mtcars,
aes = list(x = "mpg", y = "wt", size = "hp", color = "hp"),
show.legend = c("size" = FALSE)
)
l2 <- list(
geom = "labs",
title = "A Title"
)
ggplot() +
geom_from_list(l1) +
geom_from_list(l2)
ggplot() +
geoms_from_list(list(l1 = l1, l2 = l2))
# Example 2 (Violin, boxplots, ...) --------------------------
l1 <- list(
geom = "violin",
data = iris,
aes = list(x = "Species", y = "Sepal.Width")
)
l2 <- list(
geom = "boxplot",
data = iris,
aes = list(x = "Species", y = "Sepal.Width"),
outlier.shape = NA
)
l3 <- list(
geom = "jitter",
data = iris,
width = 0.1,
aes = list(x = "Species", y = "Sepal.Width")
)
ggplot() +
geom_from_list(l1) +
geom_from_list(l2) +
geom_from_list(l3)
# Example 3 (2D density) --------------------------
ggplot() +
geom_from_list(list(
geom = "density_2d", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
))
ggplot() +
geom_from_list(list(
geom = "density_2d_filled", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
))
ggplot() +
geom_from_list(list(
geom = "density_2d_polygon", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
))
ggplot() +
geom_from_list(list(
geom = "density_2d_raster", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
)) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
# Example 4 (facet and coord flip) --------------------------
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width)) +
geom_point() +
geom_from_list(list(geom = "hline", yintercept = 2)) +
geom_from_list(list(geom = "coord_flip")) +
geom_from_list(list(geom = "facet_wrap", facets = "~ Species", scales = "free"))
# Example 5 (theme and scales) --------------------------
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species)) +
geom_point() +
geom_from_list(list(geom = "scale_color_viridis_d", option = "inferno")) +
geom_from_list(list(geom = "theme", legend.position = "top"))
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species)) +
geom_point() +
geom_from_list(list(geom = "scale_color_material_d", palette = "rainbow")) +
geom_from_list(list(geom = "theme_void"))
# Example 5 (Smooths and side densities) --------------------------
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width)) +
geom_from_list(list(geom = "point")) +
geom_from_list(list(geom = "smooth", color = "red")) +
geom_from_list(list(aes = list(x = "Sepal.Length"), geom = "ggside::geom_xsidedensity")) +
geom_from_list(list(geom = "ggside::scale_xsidey_continuous", breaks = NULL))
# Example 6 (ggraph) --------------------------
if (require("tidygraph", quietly = TRUE) &&
require("ggraph", quietly = TRUE)) {
# Prepare graph
nodes <- data.frame(name = c("Dom", "Mattan", "Daniel", "Brenton"))
edges <- data.frame(
from = c(1, 1, 1, 2, 3, 3, 4, 4, 4),
to = c(2, 3, 4, 1, 1, 2, 1, 2, 3)
)
data <- tidygraph::tbl_graph(nodes = nodes, edges = edges)
ggraph(data, layout = "kk") +
geom_from_list(list(geom = "ggraph::geom_edge_arc")) +
geom_from_list(list(geom = "ggraph::geom_node_point", size = 10)) +
geom_from_list(list(geom = "ggraph::geom_node_label", aes = list(label = "name")))
}
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("geom_point2")
### * geom_point2
flush(stderr()); flush(stdout())
### Name: geom_point2
### Title: Better looking points
### Aliases: geom_point2 geom_jitter2 geom_pointrange2 geom_count2
### geom_count_borderless geom_point_borderless geom_jitter_borderless
### geom_pointrange_borderless
### ** Examples
## Don't show:
if (requireNamespace("patchwork", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
library(see)
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, n_columns = 2)
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, fill = Species)) +
geom_point_borderless(size = 4) +
theme_modern()
theme_set(theme_abyss())
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, fill = Species)) +
geom_point_borderless(size = 4)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("geom_poolpoint")
### * geom_poolpoint
flush(stderr()); flush(stdout())
### Name: geom_poolpoint
### Title: Pool ball points
### Aliases: geom_poolpoint geom_pooljitter
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
geom_poolpoint(label = rownames(iris)) +
scale_color_flat_d() +
theme_modern()
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
geom_pooljitter(label = rownames(iris)) +
scale_color_flat_d() +
theme_modern()
cleanEx()
nameEx("geom_violindot")
### * geom_violindot
flush(stderr()); flush(stdout())
### Name: geom_violindot
### Title: Half-violin Half-dot plot
### Aliases: geom_violindot
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violindot() +
theme_modern()
cleanEx()
nameEx("geom_violinhalf")
### * geom_violinhalf
flush(stderr()); flush(stdout())
### Name: geom_violinhalf
### Title: Half-violin plot
### Aliases: geom_violinhalf
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violinhalf() +
theme_modern() +
scale_fill_material_d()
# To flip all half-violin geoms, use `flip = TRUE`:
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violinhalf(flip = TRUE) +
theme_modern() +
scale_fill_material_d()
# To flip the half-violin geoms for the first and third groups only
# by passing a numeric vector
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violinhalf(flip = c(1, 3)) +
theme_modern() +
scale_fill_material_d()
cleanEx()
nameEx("golden_ratio")
### * golden_ratio
flush(stderr()); flush(stdout())
### Name: golden_ratio
### Title: Golden Ratio
### Aliases: golden_ratio
### ** Examples
golden_ratio()
golden_ratio(10)
cleanEx()
nameEx("material_colors")
### * material_colors
flush(stderr()); flush(stdout())
### Name: material_colors
### Title: Extract material design colors as hex codes
### Aliases: material_colors
### ** Examples
material_colors()
material_colors("indigo", "lime")
cleanEx()
nameEx("metro_colors")
### * metro_colors
flush(stderr()); flush(stdout())
### Name: metro_colors
### Title: Extract Metro colors as hex codes
### Aliases: metro_colors
### ** Examples
metro_colors()
metro_colors("dark red", "teal")
cleanEx()
nameEx("okabeito_colors")
### * okabeito_colors
flush(stderr()); flush(stdout())
### Name: okabeito_colors
### Title: Extract Okabe-Ito colors as hex codes
### Aliases: okabeito_colors oi_colors
### ** Examples
okabeito_colors()
okabeito_colors(c("red", "light blue", "orange"))
okabeito_colors(original_names = TRUE)
okabeito_colors(black_first = TRUE)
cleanEx()
nameEx("plot.see_bayesfactor_models")
### * plot.see_bayesfactor_models
flush(stderr()); flush(stdout())
### Name: plot.see_bayesfactor_models
### Title: Plot method for Bayes Factors for model comparison
### Aliases: plot.see_bayesfactor_models
### ** Examples
library(bayestestR)
library(see)
lm0 <- lm(qsec ~ 1, data = mtcars)
lm1 <- lm(qsec ~ drat, data = mtcars)
lm2 <- lm(qsec ~ wt, data = mtcars)
lm3 <- lm(qsec ~ drat + wt, data = mtcars)
result <- bayesfactor_models(lm1, lm2, lm3, denominator = lm0)
plot(result, n_pies = "one", value = "probability", sort = TRUE) +
scale_fill_pizza(reverse = TRUE)
plot(result, n_pies = "many", value = "BF", log = TRUE) +
scale_fill_pizza(reverse = FALSE)
cleanEx()
nameEx("plot.see_check_collinearity")
### * plot.see_check_collinearity
flush(stderr()); flush(stdout())
### Name: plot.see_check_collinearity
### Title: Plot method for multicollinearity checks
### Aliases: plot.see_check_collinearity
### ** Examples
library(performance)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_collinearity(m)
result
plot(result)
cleanEx()
nameEx("plot.see_check_distribution")
### * plot.see_check_distribution
flush(stderr()); flush(stdout())
### Name: plot.see_check_distribution
### Title: Plot method for classifying the distribution of a model-family
### Aliases: plot.see_check_distribution
### ** Examples
cleanEx()
nameEx("plot.see_check_heteroscedasticity")
### * plot.see_check_heteroscedasticity
flush(stderr()); flush(stdout())
### Name: plot.see_check_heteroscedasticity
### Title: Plot method for (non-)constant error variance checks
### Aliases: plot.see_check_heteroscedasticity
### ** Examples
library(performance)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_heteroscedasticity(m)
result
plot(result, data = m) # data required for pkgdown
cleanEx()
nameEx("plot.see_check_homogeneity")
### * plot.see_check_homogeneity
flush(stderr()); flush(stdout())
### Name: plot.see_check_homogeneity
### Title: Plot method for homogeneity of variances checks
### Aliases: plot.see_check_homogeneity
### ** Examples
library(performance)
model <<- lm(len ~ supp + dose, data = ToothGrowth)
result <- check_homogeneity(model)
result
plot(result)
cleanEx()
nameEx("plot.see_check_normality")
### * plot.see_check_normality
flush(stderr()); flush(stdout())
### Name: plot.see_check_normality
### Title: Plot method for check model for (non-)normality of residuals
### Aliases: plot.see_check_normality
### ** Examples
library(performance)
m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_normality(m)
plot(result)
cleanEx()
nameEx("plot.see_check_outliers")
### * plot.see_check_outliers
flush(stderr()); flush(stdout())
### Name: plot.see_check_outliers
### Title: Plot method for checking outliers
### Aliases: plot.see_check_outliers
### ** Examples
library(performance)
data(mtcars)
mt1 <- mtcars[, c(1, 3, 4)]
mt2 <- rbind(
mt1,
data.frame(mpg = c(37, 40), disp = c(300, 400), hp = c(110, 120))
)
model <- lm(disp ~ mpg + hp, data = mt2)
plot(check_outliers(model))
cleanEx()
nameEx("plot.see_compare_parameters")
### * plot.see_compare_parameters
flush(stderr()); flush(stdout())
### Name: plot.see_compare_parameters
### Title: Plot method for comparison of model parameters
### Aliases: plot.see_compare_parameters
### ** Examples
if (require("insight") &&
require("parameters") &&
packageVersion("insight") >= "0.13.0") {
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- compare_parameters(lm1, lm2, lm3)
plot(result)
}
cleanEx()
nameEx("plot.see_compare_performance")
### * plot.see_compare_performance
flush(stderr()); flush(stdout())
### Name: plot.see_compare_performance
### Title: Plot method for comparing model performances
### Aliases: plot.see_compare_performance
### ** Examples
library(performance)
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- compare_performance(lm1, lm2, lm3)
result
plot(result)
cleanEx()
nameEx("plot.see_effectsize_table")
### * plot.see_effectsize_table
flush(stderr()); flush(stdout())
### Name: plot.see_effectsize_table
### Title: Plot method for effect size tables
### Aliases: plot.see_effectsize_table
### ** Examples
library(effectsize)
m <- aov(mpg ~ factor(am) * factor(cyl), data = mtcars)
result <- eta_squared(m)
plot(result)
cleanEx()
nameEx("plot.see_equivalence_test")
### * plot.see_equivalence_test
flush(stderr()); flush(stdout())
### Name: plot.see_equivalence_test_effectsize
### Title: Plot method for (conditional) equivalence testing
### Aliases: plot.see_equivalence_test_effectsize plot.see_equivalence_test
### plot.see_equivalence_test_lm
### ** Examples
library(effectsize)
m <- aov(mpg ~ factor(am) * factor(cyl), data = mtcars)
result <- eta_squared(m)
plot(result)
cleanEx()
nameEx("plot.see_estimate_contrasts")
### * plot.see_estimate_contrasts
flush(stderr()); flush(stdout())
### Name: plot.see_estimate_contrasts
### Title: Plot method for estimating contrasts
### Aliases: plot.see_estimate_contrasts
### ** Examples
## Don't show:
if (require("modelbased") && require("rstanarm") && require("emmeans")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_estimate_density")
### * plot.see_estimate_density
flush(stderr()); flush(stdout())
### Name: plot.see_estimate_density
### Title: Plot method for density estimation of posterior samples
### Aliases: plot.see_estimate_density
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_hdi")
### * plot.see_hdi
flush(stderr()); flush(stdout())
### Name: plot.see_hdi
### Title: Plot method for uncertainty or credible intervals
### Aliases: plot.see_hdi
### ** Examples
## Don't show:
if (require("rstanarm") && FALSE) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0)
result <- hdi(m)
result
plot(result)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_n_factors")
### * plot.see_n_factors
flush(stderr()); flush(stdout())
### Name: plot.see_n_factors
### Title: Plot method for numbers of clusters to extract or factors to
### retain
### Aliases: plot.see_n_factors
### ** Examples
if (require("parameters") && require("nFactors")) {
data(mtcars)
result <- n_factors(mtcars, type = "PCA")
result
plot(result, type = "line")
}
cleanEx()
nameEx("plot.see_p_direction")
### * plot.see_p_direction
flush(stderr()); flush(stdout())
### Name: plot.see_p_direction
### Title: Plot method for probability of direction
### Aliases: plot.see_p_direction
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_p_function")
### * plot.see_p_function
flush(stderr()); flush(stdout())
### Name: plot.see_p_function
### Title: Plot method for plotting p-functions (aka consonance functions)
### Aliases: plot.see_p_function
### ** Examples
library(parameters)
model <- lm(Sepal.Length ~ Species + Sepal.Width + Petal.Length, data = iris)
result <- p_function(model)
plot(result, n_columns = 2, show_labels = FALSE)
result <- p_function(model, keep = "Sepal.Width")
plot(result)
cleanEx()
nameEx("plot.see_p_significance")
### * plot.see_p_significance
flush(stderr()); flush(stdout())
### Name: plot.see_p_significance
### Title: Plot method for practical significance
### Aliases: plot.see_p_significance
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_parameters_brms_meta")
### * plot.see_parameters_brms_meta
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_brms_meta
### Title: Plot method for Model Parameters from Bayesian Meta-Analysis
### Aliases: plot.see_parameters_brms_meta
### ** Examples
## Don't show:
if (require("brms") && require("metafor")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Not run:
##D library(brms)
##D library(metafor)
##D data(dat.bcg)
##D
##D dat <- escalc(
##D measure = "RR",
##D ai = tpos,
##D bi = tneg,
##D ci = cpos,
##D di = cneg,
##D data = dat.bcg
##D )
##D dat$author <- make.unique(dat$author)
##D
##D # model
##D set.seed(123)
##D priors <- c(
##D prior(normal(0, 1), class = Intercept),
##D prior(cauchy(0, 0.5), class = sd)
##D )
##D model <- brm(yi | se(vi) ~ 1 + (1 | author), data = dat)
##D
##D # result
##D mp <- model_parameters(model)
##D plot(mp)
## End(Not run)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_parameters_distribution")
### * plot.see_parameters_distribution
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_distribution
### Title: Plot method for describing distributions of vectors
### Aliases: plot.see_parameters_distribution
### ** Examples
library(parameters)
set.seed(333)
x <- sample(1:100, 1000, replace = TRUE)
result <- describe_distribution(x)
result
plot(result)
cleanEx()
nameEx("plot.see_parameters_model")
### * plot.see_parameters_model
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_model
### Title: Plot method for model parameters
### Aliases: plot.see_parameters_model plot.see_parameters_sem
### ** Examples
library(parameters)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- model_parameters(m)
result
plot(result)
cleanEx()
nameEx("plot.see_parameters_pca")
### * plot.see_parameters_pca
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_pca
### Title: Plot method for principal component analysis
### Aliases: plot.see_parameters_pca
### ** Examples
library(parameters)
data(mtcars)
result <- principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
result
plot(result)
cleanEx()
nameEx("plot.see_parameters_simulate")
### * plot.see_parameters_simulate
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_simulate
### Title: Plot method for simulated model parameters
### Aliases: plot.see_parameters_simulate
### ** Examples
library(parameters)
m <<- lm(mpg ~ wt + cyl + gear, data = mtcars)
result <- simulate_parameters(m)
result
plot(result)
cleanEx()
nameEx("plot.see_performance_roc")
### * plot.see_performance_roc
flush(stderr()); flush(stdout())
### Name: plot.see_performance_roc
### Title: Plot method for ROC curves
### Aliases: plot.see_performance_roc
### ** Examples
library(performance)
data(iris)
set.seed(123)
iris$y <- rbinom(nrow(iris), size = 1, .3)
folds <- sample(nrow(iris), size = nrow(iris) / 8, replace = FALSE)
test_data <- iris[folds, ]
train_data <- iris[-folds, ]
model <- glm(y ~ Sepal.Length + Sepal.Width, data = train_data, family = "binomial")
result <- performance_roc(model, new_data = test_data)
result
plot(result)
cleanEx()
nameEx("plot.see_point_estimate")
### * plot.see_point_estimate
flush(stderr()); flush(stdout())
### Name: plot.see_point_estimate
### Title: Plot method for point estimates of posterior samples
### Aliases: plot.see_point_estimate
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_rope")
### * plot.see_rope
flush(stderr()); flush(stdout())
### Name: plot.see_rope
### Title: Plot method for Region of Practical Equivalence
### Aliases: plot.see_rope
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_si")
### * plot.see_si
flush(stderr()); flush(stdout())
### Name: plot.see_si
### Title: Plot method for support intervals
### Aliases: plot.see_si
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plots")
### * plots
flush(stderr()); flush(stdout())
### Name: plots
### Title: Multiple plots side by side
### Aliases: plots
### ** Examples
## Don't show:
if (requireNamespace("patchwork", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
library(see)
p1 <- ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point()
p2 <- ggplot(mtcars, aes(x = mpg)) +
geom_density()
p3 <- ggplot(mtcars, aes(x = factor(cyl))) +
geom_bar() +
scale_x_discrete("cyl")
plots(p1, p2)
plots(p1, p2, n_columns = 2, tags = "A")
plots(
p1, p2, p3,
n_columns = 1, tags = c("Fig. 1", "Fig. 2", "Fig. 3"),
title = "The surprising truth about mtcars"
)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("print.see_performance_pp_check")
### * print.see_performance_pp_check
flush(stderr()); flush(stdout())
### Name: print.see_performance_pp_check
### Title: Plot method for posterior predictive checks
### Aliases: print.see_performance_pp_check plot.see_performance_pp_check
### ** Examples
if (require("performance")) {
model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
check_posterior_predictions(model)
}
cleanEx()
nameEx("scale_color_bluebrown")
### * scale_color_bluebrown
flush(stderr()); flush(stdout())
### Name: scale_color_bluebrown
### Title: Blue-brown color palette
### Aliases: scale_color_bluebrown scale_color_bluebrown_d
### scale_color_bluebrown_c scale_colour_bluebrown
### scale_colour_bluebrown_c scale_colour_bluebrown_d
### scale_fill_bluebrown scale_fill_bluebrown_d scale_fill_bluebrown_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_bluebrown_d()
cleanEx()
nameEx("scale_color_colorhex")
### * scale_color_colorhex
flush(stderr()); flush(stdout())
### Name: scale_color_colorhex
### Title: Color palettes from color-hex
### Aliases: scale_color_colorhex scale_color_colorhex_d
### scale_color_colorhex_c scale_colour_colorhex scale_colour_colorhex_c
### scale_colour_colorhex_d scale_fill_colorhex scale_fill_colorhex_d
### scale_fill_colorhex_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, color = Species)) +
geom_boxplot() +
theme_modern() +
scale_color_colorhex_d(palette = 1014416)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_colorhex_d(palette = 1014416)
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_colorhex_c(palette = 1014416)
cleanEx()
nameEx("scale_color_flat")
### * scale_color_flat
flush(stderr()); flush(stdout())
### Name: scale_color_flat
### Title: Flat UI color palette
### Aliases: scale_color_flat scale_color_flat_d scale_color_flat_c
### scale_colour_flat scale_colour_flat_c scale_colour_flat_d
### scale_fill_flat scale_fill_flat_d scale_fill_flat_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_flat_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_flat_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_flat_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_material")
### * scale_color_material
flush(stderr()); flush(stdout())
### Name: scale_color_material
### Title: Material design color palette
### Aliases: scale_color_material scale_color_material_d
### scale_color_material_c scale_colour_material scale_colour_material_c
### scale_colour_material_d scale_fill_material scale_fill_material_d
### scale_fill_material_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_material_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_material_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_material_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_metro")
### * scale_color_metro
flush(stderr()); flush(stdout())
### Name: scale_color_metro
### Title: Metro color palette
### Aliases: scale_color_metro scale_color_metro_d scale_color_metro_c
### scale_colour_metro scale_colour_metro_c scale_colour_metro_d
### scale_fill_metro scale_fill_metro_d scale_fill_metro_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_metro_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_metro_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_metro_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_okabeito")
### * scale_color_okabeito
flush(stderr()); flush(stdout())
### Name: scale_color_okabeito
### Title: Okabe-Ito color palette
### Aliases: scale_color_okabeito scale_fill_okabeito scale_colour_okabeito
### scale_fill_oi
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_okabeito()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_oi(palette = "black_first")
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_oi(order = c(1, 5, 6, 2, 4, 3, 7))
cleanEx()
nameEx("scale_color_pizza")
### * scale_color_pizza
flush(stderr()); flush(stdout())
### Name: scale_color_pizza
### Title: Pizza color palette
### Aliases: scale_color_pizza scale_color_pizza_d scale_color_pizza_c
### scale_colour_pizza scale_colour_pizza_c scale_colour_pizza_d
### scale_fill_pizza scale_fill_pizza_d scale_fill_pizza_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_pizza_d()
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_pizza_c()
cleanEx()
nameEx("scale_color_see")
### * scale_color_see
flush(stderr()); flush(stdout())
### Name: scale_color_see
### Title: See color palette
### Aliases: scale_color_see scale_color_see_d scale_color_see_c
### scale_colour_see scale_colour_see_c scale_colour_see_d scale_fill_see
### scale_fill_see_d scale_fill_see_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_see_d()
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) +
geom_point() +
theme_abyss() +
scale_colour_see(palette = "light")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_see_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_social")
### * scale_color_social
flush(stderr()); flush(stdout())
### Name: scale_color_social
### Title: Social color palette
### Aliases: scale_color_social scale_color_social_d scale_color_social_c
### scale_colour_social scale_colour_social_c scale_colour_social_d
### scale_fill_social scale_fill_social_d scale_fill_social_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_social_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_social_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_social_c(palette = "rainbow")
cleanEx()
nameEx("see_colors")
### * see_colors
flush(stderr()); flush(stdout())
### Name: see_colors
### Title: Extract See colors as hex codes
### Aliases: see_colors
### ** Examples
see_colors()
see_colors("indigo", "lime")
cleanEx()
nameEx("social_colors")
### * social_colors
flush(stderr()); flush(stdout())
### Name: social_colors
### Title: Extract Social colors as hex codes
### Aliases: social_colors
### ** Examples
social_colors()
social_colors("dark red", "teal")
cleanEx()
nameEx("theme_abyss")
### * theme_abyss
flush(stderr()); flush(stdout())
### Name: theme_abyss
### Title: Abyss theme
### Aliases: theme_abyss
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point(color = "white") +
theme_abyss()
cleanEx()
nameEx("theme_blackboard")
### * theme_blackboard
flush(stderr()); flush(stdout())
### Name: theme_blackboard
### Title: Blackboard dark theme
### Aliases: theme_blackboard
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point(color = "white") +
theme_blackboard()
cleanEx()
nameEx("theme_lucid")
### * theme_lucid
flush(stderr()); flush(stdout())
### Name: theme_lucid
### Title: Lucid theme
### Aliases: theme_lucid
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point(color = "white") +
theme_lucid()
cleanEx()
nameEx("theme_modern")
### * theme_modern
flush(stderr()); flush(stdout())
### Name: theme_modern
### Title: The easystats' minimal theme
### Aliases: theme_modern
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point() +
theme_modern()
cleanEx()
nameEx("theme_radar")
### * theme_radar
flush(stderr()); flush(stdout())
### Name: theme_radar
### Title: Themes for radar plots
### Aliases: theme_radar theme_radar_dark
### ** Examples
if (require("ggplot2") && require("poorman")) {
data <- iris[-5] %>%
aggregate(list(Species = iris$Species), mean) %>%
datawizard::reshape_longer(c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"))
data %>%
ggplot(aes(
x = name,
y = value,
color = Species,
group = Species,
fill = Species
)) +
geom_polygon(linewidth = 1, alpha = 0.1) +
coord_radar() +
theme_radar()
}
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
aloy/qqplotr documentation built on Feb. 2, 2023, 4:56 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
pkgname <- "see"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('see')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("add_plot_attributes")
### * add_plot_attributes
flush(stderr()); flush(stdout())
### Name: add_plot_attributes
### Title: Complete figure with its attributes
### Aliases: add_plot_attributes
### ** Examples
## Not run:
##D library(rstanarm)
##D library(bayestestR)
##D library(see)
##D library(ggplot2)
##D
##D model <- stan_glm(
##D Sepal.Length ~ Petal.Width + Species + Sepal.Width,
##D data = iris,
##D chains = 2, iter = 200
##D )
##D
##D result <- hdi(model, ci = c(0.5, 0.75, 0.9, 0.95))
##D data <- data_plot(result, data = model)
##D
##D p <- ggplot(
##D data,
##D aes(x = x, y = y, height = height, group = y, fill = fill)
##D ) +
##D ggridges::geom_ridgeline_gradient()
##D
##D p
##D p + add_plot_attributes(data)
## End(Not run)
cleanEx()
nameEx("bluebrown_colors")
### * bluebrown_colors
flush(stderr()); flush(stdout())
### Name: bluebrown_colors
### Title: Extract blue-brown colors as hex codes
### Aliases: bluebrown_colors
### ** Examples
bluebrown_colors()
bluebrown_colors("blue", "brown")
cleanEx()
nameEx("coord_radar")
### * coord_radar
flush(stderr()); flush(stdout())
### Name: coord_radar
### Title: Radar coordinate system
### Aliases: coord_radar
### ** Examples
# Create a radar/spider chart with ggplot:
if (require("datawizard") && require("ggplot2")) {
data(iris)
data <- aggregate(iris[-5], list(Species = iris$Species), mean)
data <- data_to_long(
data,
c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
)
ggplot(data, aes(x = name, y = value, color = Species, group = Species)) +
geom_polygon(fill = NA, linewidth = 2) +
coord_radar(start = -pi / 4)
}
cleanEx()
nameEx("data_plot")
### * data_plot
flush(stderr()); flush(stdout())
### Name: data_plot
### Title: Prepare objects for plotting or plot objects
### Aliases: data_plot
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("flat_colors")
### * flat_colors
flush(stderr()); flush(stdout())
### Name: flat_colors
### Title: Extract Flat UI colors as hex codes
### Aliases: flat_colors
### ** Examples
flat_colors()
flat_colors("dark red", "teal")
cleanEx()
nameEx("geom_binomdensity")
### * geom_binomdensity
flush(stderr()); flush(stdout())
### Name: geom_binomdensity
### Title: Add dot-densities for binary 'y' variables
### Aliases: geom_binomdensity
### ** Examples
## Don't show:
if (require("ggdist")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
library(see)
data <- iris[1:100, ]
ggplot() +
geom_binomdensity(data,
x = "Sepal.Length",
y = "Species",
fill = "red",
color = NA
)
# Different scales
data[1:70, "Species"] <- "setosa" # Create unbalanced proportions
ggplot() +
geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "auto")
ggplot() +
geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "density")
ggplot() +
geom_binomdensity(data, x = "Sepal.Length", y = "Species", scale = "proportion")
ggplot() +
geom_binomdensity(data,
x = "Sepal.Length", y = "Species",
scale = list("setosa" = 0.4, "versicolor" = 0.6)
)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("geom_from_list")
### * geom_from_list
flush(stderr()); flush(stdout())
### Name: geom_from_list
### Title: Create ggplot2 geom(s) from a list
### Aliases: geom_from_list geoms_from_list
### ** Examples
## Don't show:
if (require("ggside") && require("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
# Example 1 (basic geoms and labels) --------------------------
l1 <- list(
geom = "point",
data = mtcars,
aes = list(x = "mpg", y = "wt", size = "hp", color = "hp"),
show.legend = c("size" = FALSE)
)
l2 <- list(
geom = "labs",
title = "A Title"
)
ggplot() +
geom_from_list(l1) +
geom_from_list(l2)
ggplot() +
geoms_from_list(list(l1 = l1, l2 = l2))
# Example 2 (Violin, boxplots, ...) --------------------------
l1 <- list(
geom = "violin",
data = iris,
aes = list(x = "Species", y = "Sepal.Width")
)
l2 <- list(
geom = "boxplot",
data = iris,
aes = list(x = "Species", y = "Sepal.Width"),
outlier.shape = NA
)
l3 <- list(
geom = "jitter",
data = iris,
width = 0.1,
aes = list(x = "Species", y = "Sepal.Width")
)
ggplot() +
geom_from_list(l1) +
geom_from_list(l2) +
geom_from_list(l3)
# Example 3 (2D density) --------------------------
ggplot() +
geom_from_list(list(
geom = "density_2d", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
))
ggplot() +
geom_from_list(list(
geom = "density_2d_filled", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
))
ggplot() +
geom_from_list(list(
geom = "density_2d_polygon", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
))
ggplot() +
geom_from_list(list(
geom = "density_2d_raster", data = iris,
aes = list(x = "Sepal.Width", y = "Petal.Length")
)) +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
# Example 4 (facet and coord flip) --------------------------
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width)) +
geom_point() +
geom_from_list(list(geom = "hline", yintercept = 2)) +
geom_from_list(list(geom = "coord_flip")) +
geom_from_list(list(geom = "facet_wrap", facets = "~ Species", scales = "free"))
# Example 5 (theme and scales) --------------------------
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species)) +
geom_point() +
geom_from_list(list(geom = "scale_color_viridis_d", option = "inferno")) +
geom_from_list(list(geom = "theme", legend.position = "top"))
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width, color = Species)) +
geom_point() +
geom_from_list(list(geom = "scale_color_material_d", palette = "rainbow")) +
geom_from_list(list(geom = "theme_void"))
# Example 5 (Smooths and side densities) --------------------------
ggplot(iris, aes(x = Sepal.Length, y = Petal.Width)) +
geom_from_list(list(geom = "point")) +
geom_from_list(list(geom = "smooth", color = "red")) +
geom_from_list(list(aes = list(x = "Sepal.Length"), geom = "ggside::geom_xsidedensity")) +
geom_from_list(list(geom = "ggside::scale_xsidey_continuous", breaks = NULL))
# Example 6 (ggraph) --------------------------
if (require("tidygraph", quietly = TRUE) &&
require("ggraph", quietly = TRUE)) {
# Prepare graph
nodes <- data.frame(name = c("Dom", "Mattan", "Daniel", "Brenton"))
edges <- data.frame(
from = c(1, 1, 1, 2, 3, 3, 4, 4, 4),
to = c(2, 3, 4, 1, 1, 2, 1, 2, 3)
)
data <- tidygraph::tbl_graph(nodes = nodes, edges = edges)
ggraph(data, layout = "kk") +
geom_from_list(list(geom = "ggraph::geom_edge_arc")) +
geom_from_list(list(geom = "ggraph::geom_node_point", size = 10)) +
geom_from_list(list(geom = "ggraph::geom_node_label", aes = list(label = "name")))
}
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("geom_point2")
### * geom_point2
flush(stderr()); flush(stdout())
### Name: geom_point2
### Title: Better looking points
### Aliases: geom_point2 geom_jitter2 geom_pointrange2 geom_count2
### geom_count_borderless geom_point_borderless geom_jitter_borderless
### geom_pointrange_borderless
### ** Examples
## Don't show:
if (requireNamespace("patchwork", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
library(see)
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, n_columns = 2)
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, fill = Species)) +
geom_point_borderless(size = 4) +
theme_modern()
theme_set(theme_abyss())
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, fill = Species)) +
geom_point_borderless(size = 4)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("geom_poolpoint")
### * geom_poolpoint
flush(stderr()); flush(stdout())
### Name: geom_poolpoint
### Title: Pool ball points
### Aliases: geom_poolpoint geom_pooljitter
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
geom_poolpoint(label = rownames(iris)) +
scale_color_flat_d() +
theme_modern()
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
geom_pooljitter(label = rownames(iris)) +
scale_color_flat_d() +
theme_modern()
cleanEx()
nameEx("geom_violindot")
### * geom_violindot
flush(stderr()); flush(stdout())
### Name: geom_violindot
### Title: Half-violin Half-dot plot
### Aliases: geom_violindot
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violindot() +
theme_modern()
cleanEx()
nameEx("geom_violinhalf")
### * geom_violinhalf
flush(stderr()); flush(stdout())
### Name: geom_violinhalf
### Title: Half-violin plot
### Aliases: geom_violinhalf
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violinhalf() +
theme_modern() +
scale_fill_material_d()
# To flip all half-violin geoms, use `flip = TRUE`:
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violinhalf(flip = TRUE) +
theme_modern() +
scale_fill_material_d()
# To flip the half-violin geoms for the first and third groups only
# by passing a numeric vector
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violinhalf(flip = c(1, 3)) +
theme_modern() +
scale_fill_material_d()
cleanEx()
nameEx("golden_ratio")
### * golden_ratio
flush(stderr()); flush(stdout())
### Name: golden_ratio
### Title: Golden Ratio
### Aliases: golden_ratio
### ** Examples
golden_ratio()
golden_ratio(10)
cleanEx()
nameEx("material_colors")
### * material_colors
flush(stderr()); flush(stdout())
### Name: material_colors
### Title: Extract material design colors as hex codes
### Aliases: material_colors
### ** Examples
material_colors()
material_colors("indigo", "lime")
cleanEx()
nameEx("metro_colors")
### * metro_colors
flush(stderr()); flush(stdout())
### Name: metro_colors
### Title: Extract Metro colors as hex codes
### Aliases: metro_colors
### ** Examples
metro_colors()
metro_colors("dark red", "teal")
cleanEx()
nameEx("okabeito_colors")
### * okabeito_colors
flush(stderr()); flush(stdout())
### Name: okabeito_colors
### Title: Extract Okabe-Ito colors as hex codes
### Aliases: okabeito_colors oi_colors
### ** Examples
okabeito_colors()
okabeito_colors(c("red", "light blue", "orange"))
okabeito_colors(original_names = TRUE)
okabeito_colors(black_first = TRUE)
cleanEx()
nameEx("plot.see_bayesfactor_models")
### * plot.see_bayesfactor_models
flush(stderr()); flush(stdout())
### Name: plot.see_bayesfactor_models
### Title: Plot method for Bayes Factors for model comparison
### Aliases: plot.see_bayesfactor_models
### ** Examples
library(bayestestR)
library(see)
lm0 <- lm(qsec ~ 1, data = mtcars)
lm1 <- lm(qsec ~ drat, data = mtcars)
lm2 <- lm(qsec ~ wt, data = mtcars)
lm3 <- lm(qsec ~ drat + wt, data = mtcars)
result <- bayesfactor_models(lm1, lm2, lm3, denominator = lm0)
plot(result, n_pies = "one", value = "probability", sort = TRUE) +
scale_fill_pizza(reverse = TRUE)
plot(result, n_pies = "many", value = "BF", log = TRUE) +
scale_fill_pizza(reverse = FALSE)
cleanEx()
nameEx("plot.see_check_collinearity")
### * plot.see_check_collinearity
flush(stderr()); flush(stdout())
### Name: plot.see_check_collinearity
### Title: Plot method for multicollinearity checks
### Aliases: plot.see_check_collinearity
### ** Examples
library(performance)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_collinearity(m)
result
plot(result)
cleanEx()
nameEx("plot.see_check_distribution")
### * plot.see_check_distribution
flush(stderr()); flush(stdout())
### Name: plot.see_check_distribution
### Title: Plot method for classifying the distribution of a model-family
### Aliases: plot.see_check_distribution
### ** Examples
cleanEx()
nameEx("plot.see_check_heteroscedasticity")
### * plot.see_check_heteroscedasticity
flush(stderr()); flush(stdout())
### Name: plot.see_check_heteroscedasticity
### Title: Plot method for (non-)constant error variance checks
### Aliases: plot.see_check_heteroscedasticity
### ** Examples
library(performance)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_heteroscedasticity(m)
result
plot(result, data = m) # data required for pkgdown
cleanEx()
nameEx("plot.see_check_homogeneity")
### * plot.see_check_homogeneity
flush(stderr()); flush(stdout())
### Name: plot.see_check_homogeneity
### Title: Plot method for homogeneity of variances checks
### Aliases: plot.see_check_homogeneity
### ** Examples
library(performance)
model <<- lm(len ~ supp + dose, data = ToothGrowth)
result <- check_homogeneity(model)
result
plot(result)
cleanEx()
nameEx("plot.see_check_normality")
### * plot.see_check_normality
flush(stderr()); flush(stdout())
### Name: plot.see_check_normality
### Title: Plot method for check model for (non-)normality of residuals
### Aliases: plot.see_check_normality
### ** Examples
library(performance)
m <<- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- check_normality(m)
plot(result)
cleanEx()
nameEx("plot.see_check_outliers")
### * plot.see_check_outliers
flush(stderr()); flush(stdout())
### Name: plot.see_check_outliers
### Title: Plot method for checking outliers
### Aliases: plot.see_check_outliers
### ** Examples
library(performance)
data(mtcars)
mt1 <- mtcars[, c(1, 3, 4)]
mt2 <- rbind(
mt1,
data.frame(mpg = c(37, 40), disp = c(300, 400), hp = c(110, 120))
)
model <- lm(disp ~ mpg + hp, data = mt2)
plot(check_outliers(model))
cleanEx()
nameEx("plot.see_compare_parameters")
### * plot.see_compare_parameters
flush(stderr()); flush(stdout())
### Name: plot.see_compare_parameters
### Title: Plot method for comparison of model parameters
### Aliases: plot.see_compare_parameters
### ** Examples
if (require("insight") &&
require("parameters") &&
packageVersion("insight") >= "0.13.0") {
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- compare_parameters(lm1, lm2, lm3)
plot(result)
}
cleanEx()
nameEx("plot.see_compare_performance")
### * plot.see_compare_performance
flush(stderr()); flush(stdout())
### Name: plot.see_compare_performance
### Title: Plot method for comparing model performances
### Aliases: plot.see_compare_performance
### ** Examples
library(performance)
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- compare_performance(lm1, lm2, lm3)
result
plot(result)
cleanEx()
nameEx("plot.see_effectsize_table")
### * plot.see_effectsize_table
flush(stderr()); flush(stdout())
### Name: plot.see_effectsize_table
### Title: Plot method for effect size tables
### Aliases: plot.see_effectsize_table
### ** Examples
library(effectsize)
m <- aov(mpg ~ factor(am) * factor(cyl), data = mtcars)
result <- eta_squared(m)
plot(result)
cleanEx()
nameEx("plot.see_equivalence_test")
### * plot.see_equivalence_test
flush(stderr()); flush(stdout())
### Name: plot.see_equivalence_test_effectsize
### Title: Plot method for (conditional) equivalence testing
### Aliases: plot.see_equivalence_test_effectsize plot.see_equivalence_test
### plot.see_equivalence_test_lm
### ** Examples
library(effectsize)
m <- aov(mpg ~ factor(am) * factor(cyl), data = mtcars)
result <- eta_squared(m)
plot(result)
cleanEx()
nameEx("plot.see_estimate_contrasts")
### * plot.see_estimate_contrasts
flush(stderr()); flush(stdout())
### Name: plot.see_estimate_contrasts
### Title: Plot method for estimating contrasts
### Aliases: plot.see_estimate_contrasts
### ** Examples
## Don't show:
if (require("modelbased") && require("rstanarm") && require("emmeans")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_estimate_density")
### * plot.see_estimate_density
flush(stderr()); flush(stdout())
### Name: plot.see_estimate_density
### Title: Plot method for density estimation of posterior samples
### Aliases: plot.see_estimate_density
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_hdi")
### * plot.see_hdi
flush(stderr()); flush(stdout())
### Name: plot.see_hdi
### Title: Plot method for uncertainty or credible intervals
### Aliases: plot.see_hdi
### ** Examples
## Don't show:
if (require("rstanarm") && FALSE) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0)
result <- hdi(m)
result
plot(result)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_n_factors")
### * plot.see_n_factors
flush(stderr()); flush(stdout())
### Name: plot.see_n_factors
### Title: Plot method for numbers of clusters to extract or factors to
### retain
### Aliases: plot.see_n_factors
### ** Examples
if (require("parameters") && require("nFactors")) {
data(mtcars)
result <- n_factors(mtcars, type = "PCA")
result
plot(result, type = "line")
}
cleanEx()
nameEx("plot.see_p_direction")
### * plot.see_p_direction
flush(stderr()); flush(stdout())
### Name: plot.see_p_direction
### Title: Plot method for probability of direction
### Aliases: plot.see_p_direction
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_p_function")
### * plot.see_p_function
flush(stderr()); flush(stdout())
### Name: plot.see_p_function
### Title: Plot method for plotting p-functions (aka consonance functions)
### Aliases: plot.see_p_function
### ** Examples
library(parameters)
model <- lm(Sepal.Length ~ Species + Sepal.Width + Petal.Length, data = iris)
result <- p_function(model)
plot(result, n_columns = 2, show_labels = FALSE)
result <- p_function(model, keep = "Sepal.Width")
plot(result)
cleanEx()
nameEx("plot.see_p_significance")
### * plot.see_p_significance
flush(stderr()); flush(stdout())
### Name: plot.see_p_significance
### Title: Plot method for practical significance
### Aliases: plot.see_p_significance
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_parameters_brms_meta")
### * plot.see_parameters_brms_meta
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_brms_meta
### Title: Plot method for Model Parameters from Bayesian Meta-Analysis
### Aliases: plot.see_parameters_brms_meta
### ** Examples
## Don't show:
if (require("brms") && require("metafor")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Not run:
##D library(brms)
##D library(metafor)
##D data(dat.bcg)
##D
##D dat <- escalc(
##D measure = "RR",
##D ai = tpos,
##D bi = tneg,
##D ci = cpos,
##D di = cneg,
##D data = dat.bcg
##D )
##D dat$author <- make.unique(dat$author)
##D
##D # model
##D set.seed(123)
##D priors <- c(
##D prior(normal(0, 1), class = Intercept),
##D prior(cauchy(0, 0.5), class = sd)
##D )
##D model <- brm(yi | se(vi) ~ 1 + (1 | author), data = dat)
##D
##D # result
##D mp <- model_parameters(model)
##D plot(mp)
## End(Not run)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_parameters_distribution")
### * plot.see_parameters_distribution
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_distribution
### Title: Plot method for describing distributions of vectors
### Aliases: plot.see_parameters_distribution
### ** Examples
library(parameters)
set.seed(333)
x <- sample(1:100, 1000, replace = TRUE)
result <- describe_distribution(x)
result
plot(result)
cleanEx()
nameEx("plot.see_parameters_model")
### * plot.see_parameters_model
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_model
### Title: Plot method for model parameters
### Aliases: plot.see_parameters_model plot.see_parameters_sem
### ** Examples
library(parameters)
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
result <- model_parameters(m)
result
plot(result)
cleanEx()
nameEx("plot.see_parameters_pca")
### * plot.see_parameters_pca
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_pca
### Title: Plot method for principal component analysis
### Aliases: plot.see_parameters_pca
### ** Examples
library(parameters)
data(mtcars)
result <- principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
result
plot(result)
cleanEx()
nameEx("plot.see_parameters_simulate")
### * plot.see_parameters_simulate
flush(stderr()); flush(stdout())
### Name: plot.see_parameters_simulate
### Title: Plot method for simulated model parameters
### Aliases: plot.see_parameters_simulate
### ** Examples
library(parameters)
m <<- lm(mpg ~ wt + cyl + gear, data = mtcars)
result <- simulate_parameters(m)
result
plot(result)
cleanEx()
nameEx("plot.see_performance_roc")
### * plot.see_performance_roc
flush(stderr()); flush(stdout())
### Name: plot.see_performance_roc
### Title: Plot method for ROC curves
### Aliases: plot.see_performance_roc
### ** Examples
library(performance)
data(iris)
set.seed(123)
iris$y <- rbinom(nrow(iris), size = 1, .3)
folds <- sample(nrow(iris), size = nrow(iris) / 8, replace = FALSE)
test_data <- iris[folds, ]
train_data <- iris[-folds, ]
model <- glm(y ~ Sepal.Length + Sepal.Width, data = train_data, family = "binomial")
result <- performance_roc(model, new_data = test_data)
result
plot(result)
cleanEx()
nameEx("plot.see_point_estimate")
### * plot.see_point_estimate
flush(stderr()); flush(stdout())
### Name: plot.see_point_estimate
### Title: Plot method for point estimates of posterior samples
### Aliases: plot.see_point_estimate
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_rope")
### * plot.see_rope
flush(stderr()); flush(stdout())
### Name: plot.see_rope
### Title: Plot method for Region of Practical Equivalence
### Aliases: plot.see_rope
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plot.see_si")
### * plot.see_si
flush(stderr()); flush(stdout())
### Name: plot.see_si
### Title: Plot method for support intervals
### Aliases: plot.see_si
### ** Examples
## Don't show:
if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("plots")
### * plots
flush(stderr()); flush(stdout())
### Name: plots
### Title: Multiple plots side by side
### Aliases: plots
### ** Examples
## Don't show:
if (requireNamespace("patchwork", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)({ # examplesIf
## End(Don't show)
library(ggplot2)
library(see)
p1 <- ggplot(mtcars, aes(x = disp, y = mpg)) +
geom_point()
p2 <- ggplot(mtcars, aes(x = mpg)) +
geom_density()
p3 <- ggplot(mtcars, aes(x = factor(cyl))) +
geom_bar() +
scale_x_discrete("cyl")
plots(p1, p2)
plots(p1, p2, n_columns = 2, tags = "A")
plots(
p1, p2, p3,
n_columns = 1, tags = c("Fig. 1", "Fig. 2", "Fig. 3"),
title = "The surprising truth about mtcars"
)
## Don't show:
}) # examplesIf
## End(Don't show)
cleanEx()
nameEx("print.see_performance_pp_check")
### * print.see_performance_pp_check
flush(stderr()); flush(stdout())
### Name: print.see_performance_pp_check
### Title: Plot method for posterior predictive checks
### Aliases: print.see_performance_pp_check plot.see_performance_pp_check
### ** Examples
if (require("performance")) {
model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
check_posterior_predictions(model)
}
cleanEx()
nameEx("scale_color_bluebrown")
### * scale_color_bluebrown
flush(stderr()); flush(stdout())
### Name: scale_color_bluebrown
### Title: Blue-brown color palette
### Aliases: scale_color_bluebrown scale_color_bluebrown_d
### scale_color_bluebrown_c scale_colour_bluebrown
### scale_colour_bluebrown_c scale_colour_bluebrown_d
### scale_fill_bluebrown scale_fill_bluebrown_d scale_fill_bluebrown_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_bluebrown_d()
cleanEx()
nameEx("scale_color_colorhex")
### * scale_color_colorhex
flush(stderr()); flush(stdout())
### Name: scale_color_colorhex
### Title: Color palettes from color-hex
### Aliases: scale_color_colorhex scale_color_colorhex_d
### scale_color_colorhex_c scale_colour_colorhex scale_colour_colorhex_c
### scale_colour_colorhex_d scale_fill_colorhex scale_fill_colorhex_d
### scale_fill_colorhex_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, color = Species)) +
geom_boxplot() +
theme_modern() +
scale_color_colorhex_d(palette = 1014416)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_colorhex_d(palette = 1014416)
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_colorhex_c(palette = 1014416)
cleanEx()
nameEx("scale_color_flat")
### * scale_color_flat
flush(stderr()); flush(stdout())
### Name: scale_color_flat
### Title: Flat UI color palette
### Aliases: scale_color_flat scale_color_flat_d scale_color_flat_c
### scale_colour_flat scale_colour_flat_c scale_colour_flat_d
### scale_fill_flat scale_fill_flat_d scale_fill_flat_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_flat_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_flat_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_flat_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_material")
### * scale_color_material
flush(stderr()); flush(stdout())
### Name: scale_color_material
### Title: Material design color palette
### Aliases: scale_color_material scale_color_material_d
### scale_color_material_c scale_colour_material scale_colour_material_c
### scale_colour_material_d scale_fill_material scale_fill_material_d
### scale_fill_material_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_material_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_material_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_material_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_metro")
### * scale_color_metro
flush(stderr()); flush(stdout())
### Name: scale_color_metro
### Title: Metro color palette
### Aliases: scale_color_metro scale_color_metro_d scale_color_metro_c
### scale_colour_metro scale_colour_metro_c scale_colour_metro_d
### scale_fill_metro scale_fill_metro_d scale_fill_metro_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_metro_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_metro_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_metro_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_okabeito")
### * scale_color_okabeito
flush(stderr()); flush(stdout())
### Name: scale_color_okabeito
### Title: Okabe-Ito color palette
### Aliases: scale_color_okabeito scale_fill_okabeito scale_colour_okabeito
### scale_fill_oi
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_okabeito()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_oi(palette = "black_first")
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_oi(order = c(1, 5, 6, 2, 4, 3, 7))
cleanEx()
nameEx("scale_color_pizza")
### * scale_color_pizza
flush(stderr()); flush(stdout())
### Name: scale_color_pizza
### Title: Pizza color palette
### Aliases: scale_color_pizza scale_color_pizza_d scale_color_pizza_c
### scale_colour_pizza scale_colour_pizza_c scale_colour_pizza_d
### scale_fill_pizza scale_fill_pizza_d scale_fill_pizza_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_pizza_d()
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_pizza_c()
cleanEx()
nameEx("scale_color_see")
### * scale_color_see
flush(stderr()); flush(stdout())
### Name: scale_color_see
### Title: See color palette
### Aliases: scale_color_see scale_color_see_d scale_color_see_c
### scale_colour_see scale_colour_see_c scale_colour_see_d scale_fill_see
### scale_fill_see_d scale_fill_see_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_see_d()
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) +
geom_point() +
theme_abyss() +
scale_colour_see(palette = "light")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_see_c(palette = "rainbow")
cleanEx()
nameEx("scale_color_social")
### * scale_color_social
flush(stderr()); flush(stdout())
### Name: scale_color_social
### Title: Social color palette
### Aliases: scale_color_social scale_color_social_d scale_color_social_c
### scale_colour_social scale_colour_social_c scale_colour_social_d
### scale_fill_social scale_fill_social_d scale_fill_social_c
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_modern() +
scale_fill_social_d()
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_violin() +
theme_modern() +
scale_fill_social_d(palette = "ice")
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
geom_point() +
theme_modern() +
scale_color_social_c(palette = "rainbow")
cleanEx()
nameEx("see_colors")
### * see_colors
flush(stderr()); flush(stdout())
### Name: see_colors
### Title: Extract See colors as hex codes
### Aliases: see_colors
### ** Examples
see_colors()
see_colors("indigo", "lime")
cleanEx()
nameEx("social_colors")
### * social_colors
flush(stderr()); flush(stdout())
### Name: social_colors
### Title: Extract Social colors as hex codes
### Aliases: social_colors
### ** Examples
social_colors()
social_colors("dark red", "teal")
cleanEx()
nameEx("theme_abyss")
### * theme_abyss
flush(stderr()); flush(stdout())
### Name: theme_abyss
### Title: Abyss theme
### Aliases: theme_abyss
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point(color = "white") +
theme_abyss()
cleanEx()
nameEx("theme_blackboard")
### * theme_blackboard
flush(stderr()); flush(stdout())
### Name: theme_blackboard
### Title: Blackboard dark theme
### Aliases: theme_blackboard
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point(color = "white") +
theme_blackboard()
cleanEx()
nameEx("theme_lucid")
### * theme_lucid
flush(stderr()); flush(stdout())
### Name: theme_lucid
### Title: Lucid theme
### Aliases: theme_lucid
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point(color = "white") +
theme_lucid()
cleanEx()
nameEx("theme_modern")
### * theme_modern
flush(stderr()); flush(stdout())
### Name: theme_modern
### Title: The easystats' minimal theme
### Aliases: theme_modern
### ** Examples
library(ggplot2)
library(see)
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
geom_point() +
theme_modern()
cleanEx()
nameEx("theme_radar")
### * theme_radar
flush(stderr()); flush(stdout())
### Name: theme_radar
### Title: Themes for radar plots
### Aliases: theme_radar theme_radar_dark
### ** Examples
if (require("ggplot2") && require("poorman")) {
data <- iris[-5] %>%
aggregate(list(Species = iris$Species), mean) %>%
datawizard::reshape_longer(c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"))
data %>%
ggplot(aes(
x = name,
y = value,
color = Species,
group = Species,
fill = Species
)) +
geom_polygon(linewidth = 1, alpha = 0.1) +
coord_radar() +
theme_radar()
}
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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