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Overview of duke

Source: vignettes/duke.Rmd
duke.Rmd

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)

Scatter Plot - Continuous Color 

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()

Scatter Plot - Discrete Color 

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()

Bar Plot 

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()

Histogram 

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`.

Box Plot 

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()

Density Plot 

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()

Jitter Plot - Discrete Color 

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()

## 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()

Line Plot 

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

Mosaic Plot 

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()

Label Plot 

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()

Quantile Plot 

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()

Area Plot 

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`.

Dot Plot 

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`.

Frequency Polynomial Plot 

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`.

Column Plot 

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()

Violin Plot 

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()

Count Plot 

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()

Step Plot 

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()

Coordinate Polar