Base Plot:
This plot is made using ggplot2 and viridis to give the plot a clean look. No real data is being used in the plot.
# Install packages if needed
# Load libraries
library(ggplot2)
Warning: package 'ggplot2' was built under R version 4.5.2
Cargando paquete requerido: viridisLite
# Example data
set.seed(123)
data <- data.frame(
category = rep(c("A", "B", "C", "D", "E"), each = 20),
value = c(rnorm(20, 50, 10),
rnorm(20, 55, 12),
rnorm(20, 45, 8),
rnorm(20, 60, 15),
rnorm(20, 50, 5))
)
# Basic vertical boxplot
ggplot(data, aes(x = category, y = value, fill = category)) +
geom_boxplot(color = "black") + # black outline
scale_fill_viridis(discrete = TRUE, option = "D") + # viridis palette for discrete categories
labs(
title = "Vertical Boxplot",
x = "Category",
y = "Value"
) +
theme_minimal()
Plot with Work Color Palette:
For work, I incorporate a custom palette for my plots.
library(ggplot2)
# Example data
set.seed(123)
data <- data.frame(
category = rep(c("A", "B", "C", "D", "E"), each = 20),
value = c(rnorm(20, 50, 10),
rnorm(20, 55, 12),
rnorm(20, 45, 8),
rnorm(20, 60, 15),
rnorm(20, 50, 5))
)
# Your custom palette
work_viridis <- colorRampPalette(c("#021C49","#1F3657","#3C5C7C",
"#7FA0C0", "#E9EEF3", "#F3F7FF"))
box_colors <- work_viridis(length(unique(data$category))) # one color per category
# Boxplot using custom palette
ggplot(data, aes(x = category, y = value, fill = category)) +
geom_boxplot(color = "#021C49") + # outline in darkest color
scale_fill_manual(values = box_colors) + # apply custom palette
labs(
title = "Vertical Boxplot with Custom Palette",
x = "Category",
y = "Value"
) +
theme_minimal() +
theme(
plot.background = element_rect(fill = "#F3F7FF", color = NA),
panel.background = element_rect(fill = "#F3F7FF", color = NA),
panel.grid.major = element_line(color = "#021C49", linewidth = 0.3),
panel.grid.minor = element_line(color = "#021C49", linewidth = 0.1),
axis.title = element_text(color = "#021C49", size = 12),
axis.text = element_text(color = "#021C49", size = 10),
plot.title = element_text(color = "#021C49", size = 14, face = "bold"),
legend.title = element_text(color = "#021C49"),
legend.text = element_text(color = "#021C49")
)
Plot with Primary & Secondary Work Palettes to Create Contrasts:
library(ggplot2)
# Example data
set.seed(123)
data <- data.frame(
category = rep(c("A", "B", "C", "D", "E"), each = 20),
value = c(rnorm(20, 50, 10),
rnorm(20, 55, 12),
rnorm(20, 45, 8),
rnorm(20, 60, 15),
rnorm(20, 50, 5))
)
# Theme palette
work_viridis <- colorRampPalette(c("#021C49","#1F3657","#3C5C7C",
"#7FA0C0", "#E9EEF3", "#F3F7FF"))
theme_colors <- work_viridis(256)
bg_light <- theme_colors[256] # light background
line_dark <- theme_colors[1] # dark text/lines
# Box fill palette
work2_viridis <- colorRampPalette(c("#55401C", "#7f602A", "#A98038","#D4B57F","#F1E6Df"))
box_colors <- work2_viridis(length(unique(data$category))) # one color per category
# Boxplot with custom palettes
ggplot(data, aes(x = category, y = value, fill = category)) +
geom_boxplot(color = line_dark) + # outline in dark theme color
scale_fill_manual(values = box_colors) + # assign separate palette to boxes
labs(
title = "Vertical Boxplot with Dual Palettes",
x = "Category",
y = "Value"
) +
theme_minimal() +
theme(
plot.background = element_rect(fill = bg_light, color = NA),
panel.background = element_rect(fill = bg_light, color = NA),
panel.grid.major = element_line(color = line_dark, linewidth = 0.3),
panel.grid.minor = element_line(color = line_dark, linewidth = 0.1),
axis.title = element_text(color = line_dark, size = 12),
axis.text = element_text(color = line_dark, size = 10),
plot.title = element_text(color = line_dark, size = 14, face = "bold"),
legend.title = element_text(color = line_dark),
legend.text = element_text(color = line_dark)
)
