R ggplot2 Quickstart: Fast Recipes for Reliable Charts

Problem

Analysts struggle to produce consistent R charts when deadlines are tight. Switching between geoms, scales, and themes without a template leads to noisy visuals and repeated fixes.

Agitate

Misplaced aesthetics, forgotten legends, and mismatched color scales create charts that mislead stakeholders. Time is wasted tweaking defaults instead of focusing on signal.

Solution

Use a compact set of ggplot2 recipes that keep aesthetics explicit, separate mappings from fixed settings, and apply a clean theme. The patterns below cover the most requested plot types and how to facet for comparisons.

Scatter: relationships and trend lines

library(ggplot2)
 
ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +
  geom_point(size = 3, alpha = 0.8) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.8) +
  labs(color = "Cylinders", x = "Weight", y = "MPG") +
  theme_minimal()

Line: time series or ordered categories

ggplot(economics, aes(x = date, y = unemploy)) +
  geom_line(color = "#1f77b4", linewidth = 1) +
  labs(x = "Date", y = "Unemployment") +
  scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
  theme_minimal()

Bar: counts or totals

ggplot(diamonds, aes(x = cut, fill = cut)) +
  geom_bar(width = 0.7, color = "white") +
  labs(x = "Cut", y = "Count") +
  theme_minimal() +
  theme(legend.position = "none")

Box and violin: distribution comparisons

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin(alpha = 0.4, color = NA) +
  geom_boxplot(width = 0.18, outlier.shape = NA, alpha = 0.8) +
  labs(x = NULL, y = "Sepal Length") +
  theme_minimal() +
  theme(legend.position = "none")

Facets for quick comparisons

ggplot(diamonds, aes(carat, price, color = cut)) +
  geom_point(alpha = 0.4, size = 1.5) +
  facet_wrap(~ color) +
  labs(x = "Carat", y = "Price") +
  theme_light()

Geoms and best use cases

Geom	Data type	Best for	Tip
`geom_point()`	numeric vs numeric	relationships, clusters	Map color/shape to categories; add `geom_smooth()` for trend.
`geom_line()`	ordered x, numeric y	time series, sequences	Keep one line per group with `group=`; avoid sorting mistakes.
`geom_bar()` (count) / `geom_col()` (pre-agg)	categorical	counts or totals	Use `position = "fill"` for percentages.
`geom_boxplot()`	categorical vs numeric	comparing medians/IQR	Combine with `coord_flip()` for long labels.
`geom_violin()`	categorical vs numeric	distribution shape	Pair with boxplots to show quartiles.
`geom_histogram()`	single numeric	distribution	Set `bins` or `binwidth` explicitly.

Aesthetics vs. fixed settings

Map data inside aes(): aes(color = species).
Set fixed values outside aes(): geom_point(color = "steelblue").
Use scale_* functions to format values and control palettes.

Theme and labeling checklist

Start with theme_minimal() or theme_light() for clean defaults.
Add labs(title, subtitle, caption) for context; keep axes short.
Hide redundant legends with theme(legend.position = "none") when colors duplicate facets or labels.
For publication, adjust font sizes via theme(text = element_text(size = 12)).

Starter template

base_plot <- ggplot(data, aes(x = x_var, y = y_var)) +
  geom_point(color = "#4e79a7", alpha = 0.7) +
  labs(
    title = "Headline metric",
    x = "X axis",
    y = "Y axis",
    caption = "Source: internal data"
  ) +
  theme_minimal()

Related guides

Clean your data before plotting: R dplyr Data Wrangling Pipeline
Heatmap-specific options: pheatmap in R
Create base tables quickly: How to Create a Dataframe in R