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MAIDR (Multimodal Access and Interactive Data Representation) is an R package that makes data visualizations accessible to users with visual impairments. It converts ggplot2 and Base R plots into interactive, accessible formats with:
MAIDR helps data scientists and researchers create inclusive visualizations that everyone can explore, regardless of visual ability.
Install the development version from GitHub:
MAIDR works with two main functions:
show() - Display an interactive plot
in RStudio Viewer or browsersave_html() - Save a plot as a
standalone HTML filelibrary(maidr)
library(ggplot2)
# Create sample data
sales_data <- data.frame(
Product = c("A", "B", "C", "D"),
Sales = c(150, 230, 180, 290)
)
# Create a bar chart
p <- ggplot(sales_data, aes(x = Product, y = Sales)) +
geom_bar(stat = "identity", fill = "steelblue") +
labs(
title = "Product Sales by Category",
x = "Product",
y = "Sales Amount"
) +
theme_minimal()
# Display interactively
show(p)
# Or save as HTML file
save_html(p, "sales_chart.html")MAIDR also works with Base R plotting functions:
library(maidr)
# Create a simple barplot
categories <- c("A", "B", "C", "D")
values <- c(150, 230, 180, 290)
barplot(
values,
names.arg = categories,
col = "steelblue",
main = "Product Sales by Category",
xlab = "Product",
ylab = "Sales Amount"
)
# Note: For Base R plots, call show() with NO arguments
# after creating the plot
show()When you open a MAIDR plot, you can explore it using:
MAIDR plots include:
Plots can be heard through:
MAIDR supports a comprehensive range of visualizations:
facet_wrap() and
facet_grid() in ggplot2par(mfrow/mfcol) for Base RSee the Supported Plot Types vignette for detailed examples of each.
?maidr::show for function detailslibrary(maidr)
library(ggplot2)
# Normal distribution
hist_data <- data.frame(values = rnorm(1000, mean = 100, sd = 15))
p <- ggplot(hist_data, aes(x = values)) +
geom_histogram(bins = 30, fill = "skyblue", color = "black") +
labs(
title = "Distribution of Test Scores",
x = "Score",
y = "Frequency"
) +
theme_minimal()
show(p)library(maidr)
library(ggplot2)
# Create sample data
scatter_data <- data.frame(
height = rnorm(50, 170, 10),
weight = rnorm(50, 70, 8),
gender = sample(c("Male", "Female"), 50, replace = TRUE)
)
p <- ggplot(scatter_data, aes(x = height, y = weight, color = gender)) +
geom_point(size = 3, alpha = 0.7) +
labs(
title = "Height vs Weight",
x = "Height (cm)",
y = "Weight (kg)"
) +
theme_minimal()
show(p)library(maidr)
library(ggplot2)
# Time series data
months <- month.abb[1:12]
temperature <- c(5, 7, 12, 18, 22, 26, 28, 27, 23, 17, 11, 6)
temp_data <- data.frame(
Month = factor(months, levels = months),
Temperature = temperature
)
p <- ggplot(temp_data, aes(x = Month, y = Temperature, group = 1)) +
geom_line(color = "red", linewidth = 1.5) +
geom_point(color = "darkred", size = 3) +
labs(
title = "Average Monthly Temperature",
x = "Month",
y = "Temperature (°C)"
) +
theme_minimal()
show(p)?maidr::show for function documentationhelp(package = "maidr")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.