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()
plot5 +
scale_duke_fill_discrete() +
theme_minimal()
plot6 <- ggplot2::ggplot(penguins, ggplot2::aes(year, body_mass_g)) +
ggplot2::geom_jitter(ggplot2::aes(color = species)) +
ggplot2::labs(title = "Comparison of Body Mass By Year", x = "Year", y = "Body Mass (g)")
plot6 +
scale_duke_color_discrete() +
theme_duke()
plot6 +
scale_duke_color_discrete() +
theme_minimal()
## Jitter Plot - Discrete Color
plot6.1 <- ggplot2::ggplot(penguins, ggplot2::aes(year, body_mass_g)) +
ggplot2::geom_jitter(ggplot2::aes(color = bill_length_mm)) +
ggplot2::labs(title = "Comparison of Body Mass By Year", x = "Year", y = "Body Mass (g)")
plot6.1 +
scale_duke_continuous() +
theme_duke()
plot6.1 +
scale_duke_continuous() +
theme_minimal()
yearly_avg <- penguins %>%
filter(!is.na(bill_length_mm)) %>%
group_by(island, year) %>%
summarize(island, year, mean = mean(bill_length_mm)) %>%
distinct(island, year, .keep_all = T)
#> `summarise()` has grouped output by 'island', 'year'. You can override using
#> the `.groups` argument.
lineplot <- ggplot(data = yearly_avg, aes(x = as.factor(year), y = mean, group = island)) +
geom_line(aes(color = island), linewidth = 1) +
geom_point(aes(color = island)) +
labs(title = "Average Island Bill Length(mm) Per Year", x = "Year", y = "Average Bill Length (mm)") +
theme_duke() +
scale_duke_color_discrete()
lineplot
# with point shape and line pattern
lineplot.2 <- ggplot(data = yearly_avg, aes(x = as.factor(year), y = mean, group = island)) +
geom_line(aes(color = island, linetype = island), linewidth = 1) +
geom_point(aes(color = island, shape = island)) +
labs(title = "Average Island Bill Length(mm) Per Year", x = "Year", y = "Average Bill Length (mm)") +
theme_duke() +
scale_duke_color_discrete()
lineplot.2
plot7 <- ggplot(data = penguins) +
ggmosaic::geom_mosaic(aes(x = ggmosaic::product(sex, island), fill = sex)) +
labs(title = "Penguin Sex vs. Island", x = "Island", y = "Penguin Sex", caption = "(Color from top to bottom) \n Granite, Duke Navy Blue, Duke Royal Blue")
plot7 +
scale_duke_fill_discrete() +
theme_duke()
plot7 +
scale_duke_fill_discrete() +
theme_minimal()
plot8 <- ggplot2::ggplot(penguins, ggplot2::aes(bill_length_mm, bill_depth_mm, fill = island)) +
ggplot2::geom_label(aes(label = island)) +
ggplot2::labs(title = "Bill Length/Depth by Island", x = "Bill Length (mm)", y = "Bill Depth (mm)")
plot8 +
scale_duke_fill_discrete() +
theme_duke()
plot8 +
scale_duke_fill_discrete() +
theme_minimal()
plot9 <- ggplot2::ggplot(penguins, ggplot2::aes(bill_length_mm, bill_depth_mm, color = species)) +
ggplot2::geom_quantile() +
ggplot2::labs(title = "Bill Length/Depth Quantiles", x = "Bill Length (mm)", y = "Bill Depth (mm)")
plot9 +
scale_duke_color_discrete() +
theme_duke()
#> Smoothing formula not specified. Using: y ~ x
#> Smoothing formula not specified. Using: y ~ x
#> Smoothing formula not specified. Using: y ~ x
plot9 +
scale_duke_color_discrete() +
theme_minimal()
#> Smoothing formula not specified. Using: y ~ x
#> Smoothing formula not specified. Using: y ~ x
#> Smoothing formula not specified. Using: y ~ x
plot10 <- ggplot2::ggplot(penguins, ggplot2::aes(body_mass_g, fill = species)) +
ggplot2::geom_area(stat = "bin") +
ggplot2::labs(title = "Area of Body Mass Index", x = "BMI")
plot10 +
scale_duke_fill_discrete() +
theme_duke()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot10 +
scale_duke_fill_discrete() +
theme_minimal()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot11 <- ggplot2::ggplot(penguins, ggplot2::aes(body_mass_g)) +
ggplot2::geom_dotplot(aes(fill = species)) +
ggplot2::labs(title = "Dotplot of BMI", x = "BMI")
plot11 +
scale_duke_fill_discrete() +
theme_duke()
#> Bin width defaults to 1/30 of the range of the data. Pick better value with
#> `binwidth`.
plot11 +
scale_duke_fill_discrete() +
theme_minimal()
#> Bin width defaults to 1/30 of the range of the data. Pick better value with
#> `binwidth`.
plot12 <- ggplot2::ggplot(penguins, ggplot2::aes(body_mass_g)) +
ggplot2::geom_freqpoly(aes(color = species)) +
ggplot2::labs(title = "Frequency Polynomial Plot of BMI", x = "BMI")
plot12 +
scale_duke_color_discrete() +
theme_duke()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot12 +
scale_duke_color_discrete() +
theme_minimal()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot13 <- ggplot2::ggplot(penguins, ggplot2::aes(species, body_mass_g, color = species)) +
ggplot2::geom_col() +
ggplot2::labs(title = "BMI By Species of BMI", x = "Species", y = "BMI")
plot13 +
scale_duke_color_discrete() +
theme_duke()
plot13 +
scale_duke_color_discrete() +
theme_minimal()
plot14 <- ggplot2::ggplot(penguins, ggplot2::aes(species, body_mass_g, fill = species)) +
geom_violin(scale = "area") +
ggplot2::labs(title = "BMI By Species", x = "Species", y = "BMI")
plot14 +
scale_duke_fill_discrete() +
theme_duke()
plot14 +
scale_duke_fill_discrete() +
theme_minimal()
plot15 <- ggplot2::ggplot(penguins, ggplot2::aes(species, island, color = species)) +
geom_count() +
ggplot2::labs(title = "Species Count by Island", x = "Species", y = "Island")
plot15 +
scale_duke_color_discrete() +
theme_duke()
plot15 +
scale_duke_color_discrete() +
theme_minimal()
plot16 <- ggplot2::ggplot(penguins, ggplot2::aes(year, body_mass_g, color = species)) +
geom_step()
ggplot2::labs(title = "BMI By Year", x = "Year", y = "BMI")
#> $x
#> [1] "Year"
#>
#> $y
#> [1] "BMI"
#>
#> $title
#> [1] "BMI By Year"
#>
#> attr(,"class")
#> [1] "labels"
plot16 +
scale_duke_color_discrete() +
theme_duke()
plot16 +
scale_duke_color_discrete() +
theme_minimal()
plot1 +
facet_wrap(~species) +
theme_bw() +
scale_duke_color_discrete()