Examples

Making accessible data representation with maidr is easy and straightforward. If you already have data visualization code using ggplot2 or Base R, you can make your plots accessible with maidr in just a few lines of code.

Simply load the maidr package and call maidr_on(). When plots are printed in a document, maidr will automatically generate accessible versions. You can then interact with the accessible versions using keyboard shortcuts (refer to the table below).

Key	Action
Left / Right	Navigate between data points
B	Toggle braille mode
T	Toggle text mode
S	Toggle sonification (hear the data)
R	Toggle review mode

When to Use Each Plot Type

Plot Type	Best For	Example Use Case
Bar Chart	Comparing categories	Sales by product
Histogram	Showing distributions	Test score frequencies
Scatter Plot	Relationships between variables	Height vs weight
Line Plot	Trends over time/order	Stock prices
Box Plot	Distribution comparison	Salary by department
Violin Plot	Distribution shape comparison	Gene expression by group
Heatmap	Matrix relationships	Correlation matrices
Density	Smooth distributions	Probability density
Faceted	Comparing subgroups	Regional sales trends
Multi-Panel	Multiple related views	Dashboard layouts
Multi-Layered	Combining visualizations	Histogram + density overlay

Bar Plots

Simple Bar Chart

A simple bar chart compares values across categories. Each bar represents one category with its height proportional to its value.

ggplot2

bar_data <- data.frame(
  Product = c("Laptop", "Tablet", "Phone", "Monitor"),
  Sales = c(150, 230, 180, 290)
)

p <- ggplot(bar_data, aes(x = Product, y = Sales)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(
    title = "Product Sales by Category",
    x = "Product",
    y = "Sales (units)"
  ) +
  theme_minimal()

p

Base R

products <- c("Laptop", "Tablet", "Phone", "Monitor")
sales <- c(150, 230, 180, 290)

barplot(sales,
  names.arg = products,
  col = "steelblue",
  main = "Product Sales by Category",
  xlab = "Product",
  ylab = "Sales (units)"
)

Dodged / Grouped Bar Chart

A dodged bar chart places bars for each sub-group side by side, making it easy to compare values within and across categories.

ggplot2

dodged_data <- data.frame(
  Region = rep(c("North", "South", "East"), each = 2),
  Quarter = rep(c("Q1", "Q2"), 3),
  Revenue = c(120, 150, 200, 180, 160, 210)
)

p <- ggplot(dodged_data, aes(x = Region, y = Revenue, fill = Quarter)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  labs(title = "Quarterly Revenue by Region") +
  scale_fill_manual(values = c("steelblue", "coral")) +
  theme_minimal()

p

Base R

revenue_matrix <- matrix(c(120, 150, 200, 180, 160, 210), nrow = 2)
rownames(revenue_matrix) <- c("Q1", "Q2")
barplot(revenue_matrix,
  beside = TRUE,
  names.arg = c("North", "South", "East"),
  col = c("steelblue", "coral"),
  legend.text = rownames(revenue_matrix),
  main = "Quarterly Revenue by Region",
  xlab = "Region",
  ylab = "Revenue"
)

Stacked Bar Chart

A stacked bar chart layers sub-groups on top of each other within each category, showing both individual contributions and the total.

ggplot2

stacked_data <- data.frame(
  Year = rep(c("2022", "2023", "2024"), each = 3),
  Source = rep(c("Solar", "Wind", "Hydro"), 3),
  Output = c(40, 30, 50, 55, 45, 48, 70, 60, 52)
)

p <- ggplot(stacked_data, aes(x = Year, y = Output, fill = Source)) +
  geom_bar(stat = "identity", position = position_stack()) +
  labs(
    title = "Renewable Energy Output by Source",
    y = "Output (GWh)"
  ) +
  scale_fill_manual(values = c("#2ecc71", "#3498db", "#9b59b6")) +
  theme_minimal()

p

Base R

energy_matrix <- matrix(
  c(40, 30, 50, 55, 45, 48, 70, 60, 52),
  nrow = 3
)
rownames(energy_matrix) <- c("Solar", "Wind", "Hydro")
barplot(energy_matrix,
  beside = FALSE,
  names.arg = c("2022", "2023", "2024"),
  col = c("#2ecc71", "#3498db", "#9b59b6"),
  legend.text = rownames(energy_matrix),
  main = "Renewable Energy Output by Source",
  xlab = "Year",
  ylab = "Output (GWh)"
)

Histogram

A histogram shows the frequency distribution of a continuous variable by grouping values into bins. The height of each bar indicates how many observations fall within that range.

ggplot2

exam_scores <- data.frame(score = c(
  rnorm(500, mean = 72, sd = 10),
  rnorm(500, mean = 85, sd = 8)
))

p <- ggplot(exam_scores, aes(x = score)) +
  geom_histogram(bins = 25, fill = "skyblue", color = "white") +
  labs(
    title = "Distribution of Exam Scores",
    x = "Score",
    y = "Frequency"
  ) +
  theme_minimal()

p

Base R

scores <- c(rnorm(500, mean = 72, sd = 10), rnorm(500, mean = 85, sd = 8))
hist(scores,
  breaks = 25,
  col = "skyblue",
  border = "white",
  main = "Distribution of Exam Scores",
  xlab = "Score",
  ylab = "Frequency"
)

Scatter Plot

A scatter plot displays the relationship between two continuous variables. Each point represents one observation, and color can encode a third categorical variable.

ggplot2

car_data <- data.frame(
  weight = mtcars$wt,
  mpg = mtcars$mpg,
  cylinders = factor(mtcars$cyl)
)

p <- ggplot(car_data, aes(x = weight, y = mpg, color = cylinders)) +
  geom_point(size = 3, alpha = 0.8) +
  labs(
    title = "Fuel Efficiency vs Vehicle Weight",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon",
    color = "Cylinders"
  ) +
  theme_minimal()

p

Base R

colors <- c("4" = "steelblue", "6" = "coral", "8" = "forestgreen")
plot(mtcars$wt, mtcars$mpg,
  pch = 19,
  col = colors[as.character(mtcars$cyl)],
  main = "Fuel Efficiency vs Vehicle Weight",
  xlab = "Weight (1000 lbs)",
  ylab = "Miles per Gallon"
)
legend("topright",
  legend = c("4 cyl", "6 cyl", "8 cyl"),
  col = colors, pch = 19
)

Line Plots

Single Line

A line plot connects ordered data points, making it ideal for showing trends over time or sequential categories.

ggplot2

temp_data <- data.frame(
  Month = factor(month.abb, levels = month.abb),
  Temperature = c(2, 4, 10, 15, 20, 25, 28, 27, 22, 15, 8, 3)
)

p <- ggplot(temp_data, aes(x = Month, y = Temperature, group = 1)) +
  geom_line(color = "tomato", linewidth = 1.2) +
  labs(
    title = "Average Monthly Temperature",
    y = "Temperature (C)"
  ) +
  theme_minimal()

p

Base R

months <- 1:12
temps <- c(2, 4, 10, 15, 20, 25, 28, 27, 22, 15, 8, 3)
plot(months, temps,
  type = "l", col = "tomato", lwd = 2,
  main = "Average Monthly Temperature",
  xlab = "Month", ylab = "Temperature (C)",
  xaxt = "n"
)
axis(1, at = 1:12, labels = month.abb)

Multiple Lines

A multi-line plot overlays several series on the same axes, enabling direct comparison of trends across groups.

ggplot2

multi_line <- data.frame(
  Year = rep(2015:2024, 3),
  Users = c(
    10, 15, 22, 35, 50, 72, 95, 120, 150, 180,
    8, 12, 18, 25, 38, 55, 70, 88, 110, 135,
    5, 8, 14, 20, 30, 42, 58, 75, 95, 118
  ),
  Platform = rep(c("Mobile", "Desktop", "Tablet"), each = 10)
)

p <- ggplot(multi_line, aes(x = Year, y = Users, color = Platform)) +
  geom_line(linewidth = 1.2) +
  labs(
    title = "Platform Users Over Time",
    y = "Users (millions)"
  ) +
  theme_minimal()

p

Base R

years <- 2015:2024
users <- cbind(
  Mobile  = c(10, 15, 22, 35, 50, 72, 95, 120, 150, 180),
  Desktop = c(8, 12, 18, 25, 38, 55, 70, 88, 110, 135),
  Tablet  = c(5, 8, 14, 20, 30, 42, 58, 75, 95, 118)
)

matplot(years, users,
  type = "l", lwd = 2,
  col = c("steelblue", "coral", "forestgreen"),
  lty = 1,
  main = "Platform Users Over Time",
  xlab = "Year", ylab = "Users (millions)"
)
legend("topleft",
  legend = colnames(users),
  col = c("steelblue", "coral", "forestgreen"),
  lwd = 2
)

Box Plot

A box plot summarizes a distribution using its five-number summary: minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum. Outliers appear as individual points.

ggplot2

p <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot(fill = "lightblue", alpha = 0.7) +
  labs(
    title = "Sepal Length by Iris Species",
    x = "Species",
    y = "Sepal Length (cm)"
  ) +
  theme_minimal()

p

Base R

boxplot(Sepal.Length ~ Species,
  data = iris,
  col = "lightblue",
  main = "Sepal Length by Iris Species",
  xlab = "Species",
  ylab = "Sepal Length (cm)"
)

Violin Plot

A violin plot combines kernel density estimation (KDE) curves with box-summary statistics, providing a richer view of the data distribution than a box plot alone. MAIDR renders each violin as two navigable layers: a box layer (min, Q1, median, Q3, max) and a KDE layer (density curve).

Note: Violin plots are a ggplot2-only feature.

p <- ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
  geom_violin(fill = "lightblue", alpha = 0.7) +
  labs(
    title = "MPG Distribution by Cylinder Count",
    x = "Cylinders",
    y = "Miles per Gallon"
  ) +
  theme_minimal()

p

Heat Map

A heatmap uses color intensity to represent values in a two-dimensional matrix. It is useful for spotting patterns, clusters, and outliers across two categorical dimensions.

ggplot2

heatmap_data <- expand.grid(
  Day = c("Mon", "Tue", "Wed", "Thu", "Fri"),
  Hour = c("9am", "10am", "11am", "12pm", "1pm", "2pm", "3pm", "4pm")
)
heatmap_data$Visitors <- c(
  20, 35, 50, 45, 30,
  25, 40, 60, 55, 35,
  30, 55, 75, 70, 45,
  40, 65, 90, 85, 60,
  35, 50, 70, 65, 40,
  30, 45, 60, 55, 38,
  25, 40, 55, 50, 32,
  15, 30, 40, 35, 22
)

p <- ggplot(heatmap_data, aes(x = Day, y = Hour, fill = Visitors)) +
  geom_tile(color = "white") +
  scale_fill_gradient(low = "#f7fbff", high = "#08306b") +
  labs(title = "Website Visitors by Day and Hour") +
  theme_minimal()

p

Base R

visitors <- matrix(
  c(
    20, 35, 50, 45, 30,
    25, 40, 60, 55, 35,
    30, 55, 75, 70, 45,
    40, 65, 90, 85, 60,
    35, 50, 70, 65, 40,
    30, 45, 60, 55, 38,
    25, 40, 55, 50, 32,
    15, 30, 40, 35, 22
  ),
  nrow = 5
)
image(visitors,
  col = hcl.colors(20, "Blues"),
  main = "Website Visitors by Day and Hour",
  xlab = "Day",
  ylab = "Hour",
  axes = FALSE
)
axis(1,
  at = seq(0, 1, length.out = 5),
  labels = c("Mon", "Tue", "Wed", "Thu", "Fri")
)
axis(2,
  at = seq(0, 1, length.out = 8),
  labels = c("9am", "10am", "11am", "12pm", "1pm", "2pm", "3pm", "4pm")
)

KDE (Kernel Density Estimation) Plots

A density curve shows the estimated probability distribution of a continuous variable. It provides a smooth alternative to histograms for understanding the shape of data.

ggplot2

density_values <- data.frame(value = c(
  rnorm(400, mean = 25, sd = 5),
  rnorm(600, mean = 40, sd = 8)
))

p <- ggplot(density_values, aes(x = value)) +
  geom_density(fill = "lightblue", alpha = 0.5, color = "steelblue") +
  labs(
    title = "Age Distribution of Survey Respondents",
    x = "Age",
    y = "Density"
  ) +
  theme_minimal()

p

Base R

ages <- c(rnorm(400, mean = 25, sd = 5), rnorm(600, mean = 40, sd = 8))
d <- density(ages)
plot(d,
  col = "steelblue", lwd = 2,
  main = "Age Distribution of Survey Respondents",
  xlab = "Age",
  ylab = "Density"
)
polygon(d, col = rgb(0.678, 0.847, 0.902, 0.5), border = "steelblue")

Regression Plots

A scatter plot with a fitted regression line shows the relationship between two variables along with the best-fit model.

ggplot2

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(color = "steelblue", size = 3) +
  geom_smooth(method = "lm", color = "red", se = TRUE) +
  labs(
    title = "Weight vs MPG with Linear Fit",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon"
  ) +
  theme_minimal()

p

Base R

plot(mtcars$wt, mtcars$mpg,
  pch = 19, col = "steelblue",
  main = "Weight vs MPG with Linear Fit",
  xlab = "Weight (1000 lbs)", ylab = "Miles per Gallon"
)
abline(lm(mpg ~ wt, data = mtcars), col = "red", lwd = 2)

Multi-Layered Plots

Multi-layered plots combine multiple visualization types in a single chart. For example, a histogram overlaid with a density curve, or a bar chart with a line overlay.

Histogram with Density Overlay

ggplot2

p <- ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(
    aes(y = after_stat(density)),
    bins = 15, fill = "lightblue", color = "white"
  ) +
  geom_density(color = "red", linewidth = 1.2) +
  labs(title = "MPG: Histogram with Density Curve") +
  theme_minimal()

p

Base R

hist(mtcars$mpg,
  breaks = 15, freq = FALSE,
  col = "lightblue", border = "white",
  main = "MPG: Histogram with Density Curve",
  xlab = "Miles per Gallon",
  ylab = "Density"
)
lines(density(mtcars$mpg), col = "red", lwd = 2)

Bar Chart with Line Overlay

ggplot2

combo_data <- data.frame(
  month = factor(month.abb[1:6], levels = month.abb[1:6]),
  sales = c(100, 120, 90, 150, 130, 160),
  target = c(110, 110, 110, 140, 140, 140)
)

p <- ggplot(combo_data, aes(x = month)) +
  geom_bar(aes(y = sales), stat = "identity", fill = "steelblue", alpha = 0.7) +
  geom_line(aes(y = target, group = 1), color = "red", linewidth = 1.5) +
  labs(title = "Monthly Sales vs Target", y = "Value") +
  theme_minimal()

p

Multi-Panel Plots (Multiple Subplots)

Multi-panel layouts arrange several independent plots in a grid. This is useful for dashboards or comparing different views of the same dataset.

ggplot2 (patchwork)

library(patchwork)

# Line plot with currency formatting
line_df <- data.frame(
  Month = 1:8,
  Revenue = c(2500, 4200, 3100, 5500, 4300, 6700, 5600, 7800)
)
pw_line <- ggplot(line_df, aes(Month, Revenue)) +
  geom_line(color = "steelblue", linewidth = 1) +
  labs(title = "Monthly Revenue", x = "Month", y = "Revenue") +
  theme_minimal()

# Bar plot with rates
bar_df1 <- data.frame(
  Category = c("A", "B", "C", "D", "E"),
  Rate = c(0.15, 0.22, 0.18, 0.28, 0.17)
)
pw_bar1 <- ggplot(bar_df1, aes(Category, Rate)) +
  geom_bar(stat = "identity", fill = "forestgreen", alpha = 0.7) +
  labs(title = "Conversion Rates", x = "Category", y = "Rate") +
  theme_minimal()

# Bar plot with large numbers
bar_df2 <- data.frame(
  Category = c("A", "B", "C", "D", "E"),
  Count = c(125000, 98000, 145000, 112000, 88000)
)
pw_bar2 <- ggplot(bar_df2, aes(Category, Count)) +
  geom_bar(stat = "identity", fill = "royalblue", alpha = 0.7) +
  labs(title = "User Counts", x = "Category", y = "Count") +
  theme_minimal()

# Line plot with exponential growth
line_df2 <- data.frame(
  x = 1:8,
  y = 10^(seq(3, 6.5, length.out = 8))
)
pw_line2 <- ggplot(line_df2, aes(x, y)) +
  geom_line(color = "tomato", linewidth = 1) +
  labs(title = "Exponential Growth", x = "Time", y = "Value") +
  theme_minimal()

combined <- (pw_line + pw_bar1 + pw_bar2 + pw_line2) +
  plot_layout(ncol = 2)
combined

Base R (par)

par(mfrow = c(2, 2))

barplot(table(mtcars$cyl),
  col = "steelblue",
  main = "Cars by Cylinder Count",
  xlab = "Cylinders"
)

hist(mtcars$mpg,
  breaks = 12, col = "coral", border = "white",
  main = "MPG Distribution", xlab = "MPG"
)

plot(mtcars$wt, mtcars$mpg,
  pch = 19, col = "forestgreen",
  main = "MPG vs Weight",
  xlab = "Weight (1000 lbs)", ylab = "MPG"
)

boxplot(hp ~ gear,
  data = mtcars, col = "plum",
  main = "Horsepower by Gear Count",
  xlab = "Gears", ylab = "Horsepower"
)

invisible(par(mfrow = c(1, 1)))

Faceted plots split data into a grid of subplots by one or more grouping variables. Each panel shows the same type of chart for a different subset of the data. This is a ggplot2-only feature using facet_wrap() or facet_grid().

facet_data <- data.frame(
  x = rep(c("A", "B", "C", "D"), 4),
  y = c(
    30, 25, 35, 20,
    45, 30, 25, 40,
    20, 35, 30, 45,
    35, 40, 20, 30
  ),
  panel = rep(c("Group 1", "Group 2", "Group 3", "Group 4"), each = 4)
)

p <- ggplot(facet_data, aes(x = x, y = y)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  facet_wrap(~panel, ncol = 2) +
  labs(title = "Sales by Category Across Groups", x = "Category", y = "Sales") +
  theme_minimal()

p

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = 2, color = "steelblue") +
  facet_grid(vs ~ am,
    labeller = labeller(
      vs = c("0" = "V-engine", "1" = "Straight"),
      am = c("0" = "Automatic", "1" = "Manual")
    )
  ) +
  labs(
    title = "MPG vs Weight by Engine Type and Transmission",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon"
  ) +
  theme_minimal()

p

When to Use Each Plot Type

Bar Plots

Simple Bar Chart

ggplot2

Base R

Dodged / Grouped Bar Chart

ggplot2

Base R

Stacked Bar Chart

ggplot2

Base R

Histogram

ggplot2

Base R

Scatter Plot

ggplot2

Base R

Line Plots

Single Line

ggplot2

Base R

Multiple Lines

ggplot2

Base R

Box Plot

ggplot2

Base R

Violin Plot

Heat Map

ggplot2

Base R

KDE (Kernel Density Estimation) Plots

ggplot2

Base R

Regression Plots

ggplot2

Base R

Multi-Layered Plots

Histogram with Density Overlay

ggplot2

Base R

Bar Chart with Line Overlay

ggplot2

Multi-Panel Plots (Multiple Subplots)

ggplot2 (patchwork)

Base R (par)

Facet Plots

facet_wrap

facet_grid