library(duke)
library(palmerpenguins)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(ggplot2)
library(ggmosaic)This vignette aims to comprehensively demonstrate the use and
functionality of the package duke. duke is
fully integrated with the ggplot2 and allows for the
creation of Duke official branded visualizations that are color blind
friendly.
student_names <- c("Jack", "Annie", "Paul", "Aidan", "Jake", "Josh", "Grace", "Suzy", "Beth", "Taylor", "Tanner", "Lisa", "Jimmy", "Larry", "Patricia", "Laura", "Yasmin", "Tim")
student_grades <- c("A+", "B", "A+", "C", "D", "A+", "E", "C", "B-", "B-", "D", "A-", "B+", "A-", "A-", "D", "B", "E")
students <- tibble(student = student_names, grade = student_grades)plot <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
geom_point(aes(color = body_mass_g)) +
labs(
title = "Bill Length vs. Bill Depth",
x = "Bill Length (mm)",
y = "Bill Depth (mm)"
)
plot +
scale_duke_continuous() +
theme_duke()
plot +
geom_point(aes(shape = species)) +
scale_duke_continuous() +
theme_duke()
plot +
scale_duke_continuous() +
theme_minimal()
plot1 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, color = species)) +
geom_point(size = 2) +
labs(title = "Bill Length vs. Bill Depth", x = "Bill Length (mm)", y = "Bill Depth (mm)")
ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
geom_point(aes(color = species)) +
labs(
title = "Bill Length vs. Bill Depth",
subtitle = "This is the subtitle",
caption = "All text is in Duke Royal Blue",
x = "Bill Length (mm)",
y = "Bill Depth (mm)"
) +
facet_wrap(~species) +
theme_duke() +
scale_duke_color_discrete()
plot1 +
theme_duke() +
scale_duke_color_discrete()
plot1 +
scale_duke_color_discrete() +
theme_minimal()
plot2 <- ggplot(penguins, aes(x = species, fill = species)) +
geom_bar() +
labs(title = "Distribution of Penguin Species", x = "Species", y = "Count")
m_penguins <- penguins %>%
dplyr::filter(sex == "male")
plot2.1 <- ggplot(m_penguins, aes(x = sex, fill = sex)) +
geom_bar()
plot2.1 +
scale_duke_fill_discrete() +
theme_duke()
# 8-category plot
plot2.2 <- ggplot(students, aes(x = grade, fill = grade)) +
geom_bar()
plot2.2 +
scale_duke_fill_discrete() +
theme_duke()
# 7-category plot
plot2.3 <- students %>%
slice(-13) %>%
ggplot(aes(x = grade, fill = grade)) +
geom_bar()
plot2.3 +
scale_duke_fill_discrete() +
theme_duke()
# 6-category plot
plot2.4 <- students %>%
slice(-c(9, 10, 13)) %>%
ggplot(aes(x = grade, fill = grade)) +
geom_bar()
plot2.4 +
scale_duke_fill_discrete() +
theme_duke()
# 5-category plot
plot2.4 <- students %>%
slice(-c(9, 10, 13, 7, 18)) %>%
ggplot(aes(x = grade, fill = grade)) +
geom_bar()
plot2.4 +
scale_duke_fill_discrete() +
theme_duke()
# 4-category plot
plot2.5 <- students %>%
slice(-c(9, 10, 13, 7, 18, 4, 8)) %>%
ggplot(aes(x = grade, fill = grade)) +
geom_bar()
plot2.5 +
scale_duke_fill_discrete() +
theme_duke()
plot3 <- ggplot2::ggplot(penguins, aes(body_mass_g)) +
geom_histogram(ggplot2::aes(fill = species), alpha = 0.8) +
labs(title = "Distribution of Penguin Body Mass", caption = "(Colors used) \n Duke Royal Blue, Duke Navy Blue, Copper", x = "Body Mass (g)", y = "Count")
plot3 +
scale_duke_fill_discrete() +
theme_duke()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot3 +
scale_duke_fill_discrete() +
theme_minimal()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot4 <- ggplot2::ggplot(penguins, ggplot2::aes(sex, body_mass_g)) +
ggplot2::geom_boxplot() +
ggplot2::labs(title = "Comparison of Body Mass By Sex", x = "Sex", y = "Body Mass (g)")
plot4 +
theme_duke()
plot4 +
theme_minimal()
plot5 <- ggplot2::ggplot(penguins, ggplot2::aes(bill_depth_mm)) +
ggplot2::geom_density(ggplot2::aes(fill = species)) +
ggplot2::labs(title = "Density of Penguin Bill Depth", x = "Bill Depth (mm)", y = "Densiy")
plot5 +
scale_duke_fill_discrete() +
theme_duke()