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Plot the relationship between two continuous variables

Source: R/plot_correlation.R
plot_correlation.Rd

Create a publication-ready scatterplot that is aligned with correlation(). The minimal call is plot_correlation(data, x, y). Incomplete pairs are excluded and the analysed number of complete pairs is shown in the caption.

Usage

plot_correlation(
  data,
  x = NULL,
  y = NULL,
  method = c("auto", "pearson", "spearman"),
  trend = c("auto", "linear", "smooth", "none"),
  show_ci = TRUE,
  show_correlation = FALSE,
  conf.level = 0.95,
  digits = 2,
  point_color = "#4472C4",
  line_color = "#ED7D31",
  base_size = 14,
  title = NULL,
  caption = NULL,
  xlab = NULL,
  ylab = NULL,
  triangle = NULL,
  show_diagonal = NULL,
  show_values = TRUE,
  low_color = "#355C7D",
  mid_color = "#FFFFFF",
  high_color = "#C06C5B"
)

Arguments

data

A data frame, or a matrix result returned by correlation().

x, y

Continuous variables, supplied as bare names or character strings.

method

Correlation method: "auto", "pearson", or "spearman".

trend

Fitted trend: "auto", "linear", "smooth", or "none".

show_ci

Logical; display the confidence band around a fitted trend.

show_correlation

Logical; report the correlation result in the caption.

conf.level

Confidence level passed to correlation().

digits

Number of decimal places used in the correlation annotation.

point_color, line_color

Colours used for observations and the trend.

base_size

Base font size.

title, caption

Optional plot title and caption.

xlab, ylab

Optional axis labels.

triangle

Matrix cells to show when data is a correlation-matrix result: "lower", "upper", or "full". The matrix object's setting is inherited when NULL.

show_diagonal

Logical; show self-correlations in a matrix heatmap. The matrix object's setting is inherited when NULL.

show_values

Logical; print coefficients inside heatmap cells.

low_color, mid_color, high_color

Colours used by the matrix heatmap.

Value

A ggplot object.

Details

With trend = "auto", a linear trend is used for Pearson correlation and a smooth trend for Spearman correlation. Set trend = "none" to display the observations alone. When show_correlation = TRUE, the caption reports the same method, coefficient, confidence interval when available, and p-value as correlation(). The returned object is a standard ggplot, so ordinary ggplot2 layers can be added.

Examples

plot_correlation(mtcars, x = mpg, y = wt)


plot_correlation(
  mtcars,
  x = mpg,
  y = wt,
  show_correlation = TRUE
)


matrix_result <- correlation(mtcars, vars = c(mpg, disp, hp, wt))
plot_correlation(matrix_result)