Tutorial · Beginner
Definitions, reproducible R code, and figures generated directly from each example.
Choose a graphic, read what it communicates, inspect the output, and run the accompanying code in R or RStudio. Every displayed figure was produced from its example code.
Several tutorials use iris, a dataset included with R. It contains 150 flower observations from three iris species, with measurements for sepal length, sepal width, petal length, and petal width. You do not need to download a file or install a package.
Load the dataset, preview its first rows, inspect its structure, and open the official R documentation with the commands shown here.
data(iris) # Load the built-in dataset
head(iris) # Display the first six rows
str(iris) # Inspect variables and data types
summary(iris) # View descriptive statistics
?iris # Open the R help pageA barplot compares numerical values across categories. The length or height of each bar represents the value associated with that category.
Explore the detailed barplot guide →
values <- c(A = 18, B = 27, C = 22, D = 35, E = 29)
barplot(values, col = "#2563eb", border = NA,
ylab = "Value", main = "Values by category")A boxplot summarizes a numerical distribution through its median, quartiles, spread, and potential outliers, making group comparisons compact and clear.
Explore the detailed boxplot guide →
boxplot(Sepal.Length ~ Species, data = iris,
col = c("#dbeafe", "#93c5fd", "#2563eb"),
ylab = "Sepal length", main = "Distribution by species")A density plot provides a smoothed estimate of a continuous variable’s distribution. Peaks indicate ranges where observations are concentrated.
Explore the detailed density plot guide →
set.seed(123)
x <- rnorm(120)
d <- density(x)
plot(d, lwd = 3, col = "#2563eb", xlab = "x")
polygon(d, col = adjustcolor("#0891b2", 0.22), border = NA)
lines(d, lwd = 3, col = "#2563eb")A heatmap represents matrix values with color. It is useful for identifying high and low values, clusters, and correlation patterns.
Explore the detailed heatmap guide →
m <- cor(iris[, 1:4])
heatmap(m, Rowv = NA, Colv = NA, scale = "none",
col = colorRampPalette(c("#eff6ff", "#2563eb",
"#172554"))(30))A histogram divides continuous observations into intervals and uses bar heights to show how many values fall within each interval.
Explore the detailed histogram guide →
set.seed(123)
x <- rnorm(120)
hist(x, breaks = 12, col = "#2563eb", border = "white",
main = "Distribution of x", xlab = "x")A line plot connects values in sequence, most often through time, to reveal trends, changes, cycles, and turning points.
Explore the detailed line plot guide →
set.seed(123)
values <- cumsum(rnorm(24, 0.3, 1)) + 20
plot(seq_along(values), values, type = "o", pch = 16,
lwd = 2.5, col = "#2563eb", xlab = "Period",
ylab = "Value", main = "Values over time")A pairs plot is a matrix of scatterplots showing every pairwise relationship among several numerical variables.
Explore the detailed pairs plot guide →
pairs(iris[, 1:4], pch = 19,
col = adjustcolor("#2563eb", 0.45),
main = "Iris variables")A polygon plot draws a closed shape from ordered x and y coordinates. It can highlight regions or add filled areas to an existing plot.
Explore the detailed polygon plot guide →
plot(1, 1, type = "n", xlim = c(0, 2), ylim = c(0, 2),
xlab = "X", ylab = "Y")
polygon(x = c(0.35, 1.45, 1.75, 1.2, 0.45),
y = c(0.35, 0.45, 1.25, 1.75, 1.35),
col = "#2563eb", border = "#172554", lwd = 2)A quantile–quantile plot compares observed quantiles with those of a reference distribution. Points close to the reference line indicate a similar distributional shape.
Explore the detailed qqplot guide →
set.seed(123)
x <- rnorm(120)
qqnorm(x, pch = 19, col = "#2563eb")
qqline(x, col = "#dc2626", lwd = 2)A scatterplot places paired numerical observations on two axes to reveal the direction, form, strength, and unusual points in their relationship.
Explore the detailed scatterplot guide →
set.seed(123)
x <- rnorm(120)
y <- 0.7 * x + rnorm(120, sd = 0.65)
plot(x, y, pch = 19, col = adjustcolor("#2563eb", 0.6))
abline(lm(y ~ x), col = "#dc2626", lwd = 2.5)A Venn diagram uses overlapping shapes to display relationships between sets. Intersections represent elements shared by two or more sets.
Explore the detailed venn diagram guide →
plot.new()
plot.window(xlim = c(0, 10), ylim = c(0, 7), asp = 1)
symbols(c(4, 6), c(3.5, 3.5), circles = c(2.35, 2.35),
inches = FALSE, add = TRUE,
bg = c(adjustcolor("#2563eb", 0.35),
adjustcolor("#0891b2", 0.35)))
text(5, 3.5, "A ∩ B", font = 2)An area chart fills the space between a line and a baseline, emphasizing magnitude and accumulated change over an ordered axis.
Explore the detailed area chart guide →
x <- 1:24
y <- cumsum(rnorm(24, .3, .5)) + 10
plot(x, y, type = "n")
polygon(c(x, rev(x)), c(y, rep(0, length(y))), col = "#bfdbfe", border = NA)
lines(x, y, col = "#2563eb", lwd = 3)A dot plot represents values by position on a common scale, offering a compact and precise alternative to bars.
Explore the detailed dot plot guide →
values <- c(A = 18, B = 27, C = 22, D = 35, E = 29)
dotchart(values, pch = 19, col = "#2563eb", xlab = "Value")A violin plot mirrors a density estimate to compare the complete shape, spread, and concentration of distributions.
Explore the detailed violin plot guide →
library(ggplot2)
ggplot(iris, aes(Species, Sepal.Length, fill = Species)) +
geom_violin(show.legend = FALSE) + theme_minimal()A lollipop chart connects each category baseline to a marked value, producing a light and readable ranking display.
Explore the detailed lollipop chart guide →
v <- c(18, 27, 22, 35, 29)
plot(v, type = "n", ylim = c(0, 40))
segments(seq_along(v), 0, seq_along(v), v, col = "#93c5fd", lwd = 3)
points(v, pch = 19, col = "#2563eb")Pie and donut charts divide a circle into angular segments to communicate simple part-to-whole composition.
Explore the detailed pie and donut chart guide →
values <- c(A = 35, B = 25, C = 22, D = 18)
pie(values, col = hcl.colors(4, "Blues 3"), main = "Composition")A mosaic plot divides a rectangle according to contingency-table frequencies to reveal associations between categorical variables.
Explore the detailed mosaic plot guide →
tab <- xtabs(Freq ~ Class + Survived, data = as.data.frame(Titanic))
mosaicplot(tab, color = c("#bfdbfe", "#2563eb"), main = "Class and survival")A contour plot connects locations with equal surface values, translating three-dimensional structure into two dimensions.
Explore the detailed contour plot guide →
x <- seq(-3, 3, length = 60)
y <- x
z <- outer(x, y, function(a, b) exp(-(a^2 + b^2) / 2))
contour(x, y, z, col = "#2563eb", lwd = 2)A radar chart places indicators on radial axes and joins their values into a multivariate profile.
Explore the detailed radar chart guide →
values <- c(4, 7, 6, 8, 5)
angles <- seq(0, 2*pi, length.out = 6)
r <- c(values, values[1])
plot(cos(angles)*r, sin(angles)*r, type = "l", asp = 1, axes = FALSE)