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Go further

David Hodge

Source: vignettes/articles/3_go_further.Rmd
3_go_further.Rmd

Overview

This article will demonstrate a random assortment of content, including some of which is more advanced.

  1. Change the stat of the layer
  2. Change the position of the layer
  3. Reorder and/or reverse categorical variables
  4. Drop unused categorical variable values
  5. Transform scales to "log" etc
  6. Correct the default orientation
  7. Avoid the ‘symmetric’ scale
  8. Change the *_position of positional axes
  9. Zoom in or out on scales
  10. Use delayed evaluation
  11. Rescale a diverging col scale
  12. Add a legend within the panel
library(dplyr)
library(tidyr)
library(forcats)
library(stringr)
library(ggplot2)
library(scales)
library(ggblanket)
library(patchwork)
library(palmerpenguins)

set_blanket()

penguins2 <- penguins |> 
  labelled::set_variable_labels(
    bill_length_mm = "Bill length (mm)",
    bill_depth_mm = "Bill depth (mm)",
    flipper_length_mm = "Flipper length (mm)",
    body_mass_g = "Body mass (g)",
  ) |> 
  mutate(sex = factor(sex, labels = c("Female", "Male")))

1. Change the stat of the layer

The default stat of each gg_* function can be changed.

penguins2 |>
  gg_pointrange(
    stat = "summary", 
    x = species,
    y = flipper_length_mm, 
  )

library(ggforce)

ggplot2::economics |>
  slice_head(n = 35) |> 
  gg_path(
    stat = "bspline", n = 100,
    x = date, 
    y = unemploy,
    y_label = "Unemployment",
    linewidth = 1,
  )

2. Change the position of the layer

The default position of each gg_* function can be changed.

penguins2 |> 
  gg_point(
    position = ggbeeswarm::position_quasirandom(),
    x = sex, 
    y = flipper_length_mm,
    col = sex,
  ) +
  theme(legend.position = "none")

3. Reorder and/or reverse categorical variables

ggblanket requires unquoted variables only for x, y, col, facet, facet2 and alpha. You can often manipulate the data prior to plotting to achieve what you want (e.g. using tidyr::drop_na, forcats::fct_rev and/or forcats::fct_reorder).

p1 <- diamonds |>
  count(color) |>
  gg_col(
    x = n,
    y = color,
    width = 0.75,
    x_labels = \(x) x / 1000,
    x_label = "Count (thousands)", 
    subtitle = "\nDefault order"
  )

p2 <- diamonds |>
  count(color) |>
  mutate(color = fct_rev(fct_reorder(color, n))) |>
  gg_col(
    x = n,
    y = color,
    width = 0.75,
    x_labels = \(x) x / 1000,
    x_label = "Count (thousands)", 
    subtitle = "\nRe-orderered"
  ) 

p1 + p2

4. Drop unused categorical variable values

ggblanket keeps unused factor levels in the plot. If users wish to drop unused levels they should likewise do it in the data prior to plotting using forcats::fct_drop.

p1 <- diamonds |> 
  count(color) |>
  filter(color %in% c("E", "G", "I")) |>
  gg_col(
    x = n,
    y = color,
    width = 0.75,
    x_labels = \(x) x / 1000,
    x_label = "Count (thousands)", 
    subtitle = "\nUnused levels kept",
  )

p2 <- diamonds |> 
  count(color) |>
  filter(color %in% c("E", "G", "I")) |>
  mutate(color = forcats::fct_drop(color)) |> 
  gg_col(
    x = n,
    y = color,
    width = 0.75,
    x_labels = \(x) x / 1000,
    x_label = "Count (thousands)", 
    subtitle = "\nUnused levels dropped",
  )

p1 + p2

5. Transform scales to "log" etc

Transform objects (e.g. transform_log() or character strings of these can be used to transform scales - including combining these.

p1 <- pressure |>
  gg_point(
    x = temperature, 
    y = pressure, 
    x_breaks_n = 4,
    y_breaks_n = 4,
    subtitle = "\nDefault",
  )

p2 <- pressure |>
  gg_point(
    x = temperature, 
    y = pressure, 
    x_breaks_n = 4,
    y_breaks_n = 4,
    y_transform = "reverse", 
    subtitle = "\nReverse",
  )

p3 <- pressure |>
  gg_point(
    x = temperature, 
    y = pressure,
    x_breaks_n = 4,
    y_breaks_n = 4,
    y_transform = "sqrt",
    subtitle = "\nSqrt", 
  )

p4 <- pressure |>
  gg_point(
    x = temperature, 
    y = pressure, 
    x_breaks_n = 4,
    y_breaks_n = 4,
    y_transform = c("sqrt", "reverse"),
    subtitle = "\nSqrt & Reverse",
  )

(p1 + p2) / (p3 + p4)

6. Correct the default orientation

The gg_* function guesses the *_orientation of the plot to determine how to make continuous axes and what side-effects to have on the provided mode. If it guesses incorrectly, use either the x_orientation or y_orientation argument.

p1 <- penguins2 |>
  gg_point(
    x = bill_depth_mm,
    y = bill_length_mm,
    subtitle = "\nDefault orientation",
  )

p2 <- penguins2 |>
  gg_point(
    x = bill_depth_mm,
    y = bill_length_mm,
    mode_orientation = "y",
    subtitle = "\nAdjusted orientation",
  ) 

p1 + p2

7. Avoid the ‘symmetric’ scale

Symmetric scales can be turned off or on using *_symmetric arguments.

p1 <- penguins2 |>
  gg_pointrange(
    x = sex,
    y = bill_length_mm,
    stat = "summary",
    position = position_dodge(),
    x_labels = \(x) str_sub(x, 1, 1),
    subtitle = "\ny_symmetric = NULL",
  ) +
  labs(y = NULL)

p2 <- penguins2 |>
  gg_pointrange(
    x = sex,
    y = bill_length_mm,
    stat = "summary",
    position = position_dodge(),
    x_labels = \(x) str_sub(x, 1, 1),
    y_symmetric = FALSE,
    subtitle = "\ny_symmetric = FALSE,",
  ) +
  labs(y = NULL)

p3 <- penguins2 |>
  gg_col(
    x = sex,
    y = bill_length_mm,
    stat = "summary",
    position = position_dodge(),
    width = 0.5,
    x_labels = \(x) str_sub(x, 1, 1),
    y_symmetric = FALSE,
    subtitle = "\ny_symmetric = FALSE,",
  ) +
  labs(y = NULL)

p1 + p2 + p3

8. Change the *_position of positional axes

Positional axes can be changed using *_position.

Note that for x_position = "top", a caption must be added or modified to make this work nicely with a *_mode_* theme.

economics |>
  gg_line(
    x = date,
    y = unemploy,
    col = date,
    y_position = "right",
    x_position = "top",
    caption = "", 
    title = "Unemployment",
    subtitle = "1967\u20132015",
  )

9. Zoom in or out on scales

There are no *_limits arguments in ggblanket.

Instead, users should use a combination of filtering the data, adding *_expand_limits and coord = coord_cartesian(xlim = ..., ylim = ...) arguments etc.

#To Zoom out, use *_expand_limits:
penguins |>
  gg_smooth(
    x =  body_mass_g, 
    y = bill_depth_mm,
    x_expand_limits = c(0),
    y_expand_limits = c(10, 25),
    se = TRUE,
  ) 

#To zoom-in when the stat equals "identity", use dplyr::filter
penguins |>
  filter(bill_depth_mm < 15) |>
  gg_point(
    x =  bill_depth_mm, 
    y = body_mass_g,
  ) 

#To zoom-in when the stat does _not_ equal "identity", use coord_cartesian 
#Then either recreate the breaks, or turn off the symmetric axis 
penguins |>
  gg_smooth(
    x =  body_mass_g, 
    y = bill_depth_mm,
    coord = coord_cartesian(ylim = c(14.8, 15)),
    y_breaks = scales::breaks_width(0.05),
    se = TRUE,
    # y_symmetric = FALSE,
  )

10. Use delayed evaluation

The mapping argument can be used for delayed evaluation with the ggplot2::after_stat function.

penguins2 |>
  gg_histogram(
    x = flipper_length_mm,
    mapping = aes(y = after_stat(density)),
    facet = species,
  )

faithfuld |>
  gg_contour(
    x = waiting,
    y = eruptions,
    z = density,
    mapping = aes(colour = after_stat(level)),
    bins = 8,
  )

11. Rescale a diverging col scale

Use col_rescale to rescale a diverging scale around a central point.

rescale_vctr <- sort(c(range(mpg$cty), 15))

mpg  |>  
  gg_point(
    x = displ, 
    y = hwy, 
    col = cty,
    col_palette = c(navy, teal, "white", orange, red),
    col_rescale = scales::rescale(rescale_vctr),
    col_breaks = scales::breaks_width(5),
    theme = dark_mode_r(),
  )

12. Add a legend within the panel 

set_blanket()

penguins2 |> 
  gg_histogram(
    x = flipper_length_mm,
    col = species,
  ) +
  theme(legend.position = "inside") +
  theme(legend.position.inside = c(1, 0.975)) +
  theme(legend.justification = c(1, 1))