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Scatter plots for all variable pairs in a data frame

Source: R/GLM_LINEAR_REGRESSION.R
plot_scatterplot.Rd

Generates a scatter plot with a smoothing line and marginal histograms for every pair of numeric columns in df. Each plot includes Pearson r, explained variance, the regression equation, and the regression angle in its caption (when the default y ~ x formula is used).

When the number of pairs exceeds four times the available CPU cores the plots are produced in parallel via future.apply, otherwise sequentially.

Usage

plot_scatterplot(
  df,
  method = lm,
  formula = y ~ x,
  base_size = 10,
  coord_equal = FALSE,
  all_orders = FALSE,
  title = "",
  combinations = NULL,
  str_aes = TRUE
)

Arguments

df

A data frame of numeric variables. When df has exactly two columns the first is treated as the predictor and the second as the outcome.

method

Smoothing method passed to geom_smooth. It accepts "lm", "glm", "gam", "loess", or a function such as MASS::rlm. Default lm.

formula

Formula passed to geom_smooth. Default y ~ x. When a non-default formula is supplied the caption shows only pairwise n.

base_size

Base font size in pt passed to theme_bw. Default 10.

coord_equal

Logical. If TRUE both axes share the same scale and limits. Default FALSE.

all_orders

Logical. If TRUE both (X, Y) and (Y, X) orderings are plotted for each pair. Default FALSE.

title

Character. Plot title applied to every panel. Default "".

combinations

A two-column data frame specifying which pairs to plot. Column 1 is the x-variable name, column 2 is the y-variable name. When NULL (default) all pairs are derived automatically from df.

str_aes

Logical. If TRUE variable names are passed through str_aes() to clean axis labels. Default TRUE.

Value

A named list of ggplot objects, one per variable pair. Names follow the pattern "x_y".

Examples

result <- plot_scatterplot(df = mtcars, title = "", coord_equal = TRUE, base_size = 10)
plot_multiplot(plotlist = result[1:12], cols = 4)


# Two-column data frame: first column = predictor, second = outcome
plot_scatterplot(df = mtcars[, 1:2], base_size = 10, coord_equal = TRUE,
                 all_orders = FALSE)
#> $mpg_cyl

#> 

# Custom variable pairs
plot_scatterplot(
  df = mtcars, base_size = 10, coord_equal = TRUE,
  combinations = data.frame(
    x = c("mpg", "mpg", "mpg"),
    y = c("cyl", "hp", "disp")
  )
)
#> $mpg_cyl

#> 
#> $mpg_hp

#> 
#> $mpg_disp

#> 

# Simulated near-perfect correlation
x <- rnorm(1000)
y <- x + rnorm(x, sd = .1)
plot_scatterplot(df = data.frame(x, y), title = "Random Simulation", coord_equal = TRUE)
#> $x_y

#>