README.md
In SATVILab/UtilsGGSV: Utility functions for ggplot2

UtilsGGSV

The goal of UtilsGGSV is to provide utility functions for plotting in R using .

You can install UtilsGGSV from GitHub with:

if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
remotes::install_github("SATVILab/UtilsGGSV")

library(UtilsGGSV)

Fix axis limits to be equal between x- and y-axes, and/or expand axis coordinates. The primary use of axis_limits is forcing the x- and y-axes to have the same limits “automatically” (i.e. by inspecting the ggplot object, thus not requiring the user to automatically calculate limits to pass to ggplot2::expand_limits).

data("cars", package = "datasets")
library(ggplot2)
theme_set(cowplot::theme_cowplot())

p0 <- ggplot(cars, aes(speed, dist)) +
  cowplot::background_grid(major = "xy") +
  geom_point() +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(title = "Axes unadjusted") +
  labs(x = "Speed", y = "Distance")
p1 <- axis_limits(
  p = p0,
  limits_equal = TRUE
) +
  labs(title = "Axes limits equal")
p2 <- axis_limits(
  p = p0,
  limits_expand = list(
    x = c(0, 50),
    y = c(-10, 200)
  )
) +
  labs(title = "Axes limits expanded")
cowplot::plot_grid(p0, p1, p2)

Add a column of text easily to a plot, regardless of underlying transformation, using add_text_column.

data_mod <- data.frame(x = rnorm(mean = 1, 10)^2)
data_mod$y <- data_mod$x * 3 + rnorm(10, sd = 0.5)
fit <- lm(y ~ x, data = data_mod)
coef_tbl <- coefficients(summary(fit))
results_vec <- c(
  paste0(
    "Intercept: ",
    signif(coef_tbl[1, "Estimate"][[1]], 2),
    " (",
    signif(coef_tbl[1, 1][[1]] - 2 * coef_tbl[1, 2][[1]], 3),
    ", ",
    signif(coef_tbl[1, 1][[1]] + 2 * coef_tbl[1, 2][[1]], 3),
    "; p = ",
    signif(coef_tbl[1, 4][[1]], 3),
    ")"
  ),
  paste0(
    "Slope: ",
    signif(coef_tbl[2, "Estimate"][[1]], 2),
    " (",
    signif(coef_tbl[2, 1][[1]] - 2 * coef_tbl[2, 2][[1]], 3),
    ", ",
    signif(coef_tbl[2, 1][[1]] + 2 * coef_tbl[2, 2][[1]], 3),
    "; p = ",
    signif(coef_tbl[2, 4][[1]], 3),
    ")"
  )
)
p <- ggplot(
  data = data_mod,
  aes(x = x, y = y)
) +
  geom_point() +
  cowplot::background_grid(major = "xy") 
add_text_column(
  p = p,
  x = data_mod$x,
  y = data_mod$y,
  text = results_vec,
  coord = c(0.05, 0.95),
  skip = 0.07
)

Note that add_text_column places text in the same position, regardless of underlying transformation.

p <- p +
  scale_y_continuous(
    trans = UtilsGGSV::get_trans("asinh")
  )
add_text_column(
  p = p,
  x = data_mod$x,
  y = data_mod$y,
  text = results_vec,
  trans = UtilsGGSV::get_trans("asinh"),
  coord = c(0.05, 0.95),
  skip = 0.07
)

The utility function get_trans returns trans objects (as implemented by the scales package) when given characters. It also adds various higher roots (such as cubic and quartic) and adds the asinh transformation.

get_trans("log10")
#> Transformer: log-10 [1e-100, Inf]

The function ggcorr plots correlation coefficients:

set.seed(3)
response_vec_a <- rnorm(5)
response_tbl <- data.frame(
  group = rep(letters[1:3], each = 5),
  response = c(
    response_vec_a,
    response_vec_a * 1.2 + rnorm(5, sd = 0.2),
    response_vec_a * 2 + rnorm(5, sd = 2)
  ),
  pid = rep(paste0("id_", 1:5), 3)
)

ggcorr(
  data = response_tbl |> dplyr::filter(group %in% c("a", "b")),
  grp = "group",
  y = "response",
  id = "pid"
)

We can display multiple correlation coefficients:

ggcorr(
  data = response_tbl |> dplyr::filter(group %in% c("a", "b")),
  grp = "group",
  y = "response",
  id = "pid",
  corr_method = c("spearman", "pearson")
)

We can compare more than two groups:

ggcorr(
  data = response_tbl,
  grp = "group",
  y = "response",
  id = "pid",
  corr_method = "kendall"
)

We can compare more than two groups and multiple correlation coefficients:

ggcorr(
  data = response_tbl,
  grp = "group",
  y = "response",
  id = "pid",
  corr_method = c("spearman", "pearson")
)

Specific functionality to make appropriate plots for the concordance correlation coefficient is available:

ggcorr(
  data = response_tbl |> dplyr::filter(group %in% c("a", "b")),
  grp = "group",
  y = "response",
  id = "pid",
  corr_method = "concordance",
  abline = TRUE,
  limits_equal = TRUE
)

Text in table can be moved around and resized:

ggcorr(
  data = response_tbl |> dplyr::filter(group %in% c("a", "b")),
  grp = "group",
  y = "response",
  id = "pid",
  corr_method = c("spearman", "pearson", "concordance"),
  abline = TRUE,
  limits_equal = TRUE,
  coord = c(0.4, 0.17),
  font_size = 3,
  skip = 0.04,
  pval_signif = 2,
  est_signif = 2,
  ci_signif = 2
)

Finally, the text placement is kept consistent when the axes are visually transformed:

ggcorr(
  data = response_tbl |> dplyr::mutate(response = abs(response + 1)^4),
  grp = "group",
  y = "response",
  id = "pid",
  corr_method = "spearman",
  abline = TRUE,
  limits_equal = TRUE,
  trans = "log10",
  skip = 0.06
)