Three Tips for Better R Figures

More effective and readable plots

Recently I shared my favourite ways to ensure your R plots look great in Quarto documents. Now I want to share my most commonly used tips for building effective and readable plots in R.

As an example dataset I’ll be using chip_dataset.csv, downloaded from Vercel. Redistribution of the dataset is not permitted.

Let’s create a rough figure, and see how we can improve it in a few simple steps.

```{r}
#| dev: "CairoPNG" 
#| fig-cap: "A rough figure"

library(ggplot2)
library(dplyr)
library(lubridate)

chips <- read.csv("chip_dataset.csv")

chips <- chips |> 
  mutate(Date = mdy(Release.Date),
         transistors = as.numeric(Transistors..million.)) |>
  filter(Foundry != "" &
           Type == "GPU" &
           !is.na(Date) &
           !is.na(transistors))

chips |> 
  ggplot(aes(x = Date, 
           y = transistors, 
           color = Foundry)) +
    geom_point()
```

A rough figure

Pick a great base theme

With R being more popular than ever, the default ggplot2 theme is everywhere. In addition to being overused, the grey background can be a bit much. ggplot2::theme_minimal is an easy substitute and a great base for further customisation, but I really like hrbrthemes::theme_ipsum.

Use an effective colour palette

Effective use of colour can ensure that your plots are able to be interpreted at a glance. The viridis palettes help to ensure that different values are distinct, and are colourblind-friendly. Like the base ggplot2 theme, viridis palettes are everywhere, but it’s a hard choice to argue with!

Modify your theme

Even though we’ve chosen a great theme, it’s still important to consider whether it perfectly suits you needs. For example, I often like to drop the x-axis gridlines for a cleaner look. I will also place the legend below the plot, to allow the main contents to fill the page.

We’ll apply the tips described above, as well as rescaling the y-axis and adding some trendlines. Do you think this is a more effective figure?

```{r}
#| dev: "CairoPNG" 
#| label: feature-image
#| fig-cap: "The final figure"

library(hrbrthemes)
library(sysfonts)
library(viridis)
library(scales)

font_add_google("Roboto Condensed")

chips |>
  ggplot(aes(x = Date, y = transistors, color = Foundry)) +
  geom_point(alpha = 0.2) +
  geom_smooth(method = "lm", se = FALSE, alpha = 0.5) +
  scale_colour_viridis_d(option = "H", direction = -1) +
  scale_y_log10(labels = label_number()) +
  theme_ipsum_rc(axis_title_size = 12) +
  theme(legend.position = "bottom",
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank()) +
  labs(y = "Transistors (millions)",
       title = "GPU Transistor Count by Release Date",
       subtitle = "Data from https://chip-dataset.vercel.app/")
```

The final figure