# Facets, Scales, Labels and Themes — R Programming

Source: https://www.geekswithgeeks.com/en/r-programming/v-polish

> Make publication-quality charts with facets, scales, labels and themes.

## From exploration to communication

Exploratory plots are quick; plots for reports need care. **Facets** split one plot into small multiples by a variable: `facet_wrap(~ city)` or `facet_grid(rows ~ cols)`, often clearer than crowding many colours into one panel. **Scales** control how data maps to visuals: `scale_y_continuous(labels = scales::label_comma())` or `scales::label_currency(prefix = "₹")` for readable numbers, `scale_colour_brewer()` or `scale_colour_viridis_d()` for colour-blind-friendly palettes, and log scales for skewed data. **`labs()`** sets the title, subtitle, axis labels, caption (cite your data source) and legend titles. **Themes** change the look: `theme_minimal()`, `theme_bw()`, and `theme()` adjustments for font sizes, legend position and gridlines; you can set a default with `theme_set()`. Extensions such as **patchwork** combine plots, **ggrepel** avoids overlapping labels, and **plotly** (`ggplotly()`) adds interactivity. Good charts start the y-axis at zero for bars, avoid 3D effects, and label directly where possible.

## A polished faceted chart

Readable currency labels, a colour-blind-friendly palette and a clean theme.

```r
library(ggplot2)
library(scales)

monthly <- sales |>
  dplyr::mutate(month = lubridate::floor_date(order_date, "month")) |>
  dplyr::summarise(revenue = sum(amount_inr), .by = c(city, month))

ggplot(monthly, aes(x = month, y = revenue, colour = city)) +
  geom_line(linewidth = 1) +
  geom_point() +
  facet_wrap(~ city, ncol = 2) +
  scale_y_continuous(labels = label_comma(prefix = "Rs ")) +
  scale_colour_viridis_d(guide = "none") +        # facets already identify cities
  labs(
    title = "Monthly revenue by city",
    subtitle = "Financial year 2026-27",
    x = NULL, y = "Revenue",
    caption = "Source: internal orders database"
  ) +
  theme_minimal(base_size = 12)
```

## Tidying a room before guests arrive

An exploratory plot is a room you live in: functional but messy. A report plot is the same room tidied for guests: labels on the doors, colours that match, and nothing distracting on the floor.

**Quiz:** What does facet_wrap(~ city) do?

- [ ] Changes the colour palette
- [ ] Sorts the data
- [ ] Saves the plot
- [x] Creates small multiples: one panel per city

*Answer:* Creates small multiples: one panel per city. Faceting splits the data into panels by a variable.