CorPairsPlot

Generate a grid of scatter correlation plots for all pairs of variables.

Usage

CorPairsPlot(
  data,
  columns = NULL,
  group_by = NULL,
  group_by_sep = "_",
  group_name = NULL,
  split_by = NULL,
  split_by_sep = "_",
  diag_type = NULL,
  diag_args = list(),
  layout = c(".\\", "\\.", "/.", "./"),
  cor_method = c("pearson", "spearman", "kendall"),
  cor_palette = "RdBu",
  cor_palcolor = NULL,
  cor_size = 3,
  cor_format = "corr: {round(corr, 2)}",
  cor_fg = "black",
  cor_bg = "white",
  cor_bg_r = 0.1,
  theme = "theme_this",
  theme_args = list(),
  palette = ifelse(is.null(group_by), "Spectral", "Paired"),
  palcolor = NULL,
  title = NULL,
  subtitle = NULL,
  facet_by = NULL,
  legend.position = "right",
  legend.direction = "vertical",
  seed = 8525,
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  ...
)

Arguments

data

A data frame.

columns

The column names of the data to be plotted. If NULL, all columns, except group_by, will be used.

group_by

Columns to group the data for plotting For those plotting functions that do not support multiple groups, They will be concatenated into one column, using group_by_sep as the separator

group_by_sep

The separator for multiple group_by columns. See group_by

group_name

The name of the group in the legend.

split_by

The column(s) to split data by and plot separately.

split_by_sep

The separator for multiple split_by columns. See split_by

diag_type

The type of the diagonal plots. Available types: "density", "violin", "histogram", "box", "none".

diag_args

A list of additional arguments to be passed to the diagonal plots.

layout

The layout of the plots. Available layouts: ".\", "\.", "/.", "./".

• '\' or '/' means the diagonal plots are on the top-left to bottom-right diagonal.

• '.' means where the scatter plots are.

cor_method

The method to calculate the correlation. Available methods: "pearson", "spearman", "kendall". The correlation will be shown in the other triangle of the scatter plots.

cor_palette

The color palette for the correlation tile plots.

cor_palcolor

Custom colors used to create a color palette for the correlation tile plots.

cor_size

The size of the correlation text.

cor_format

The format of the correlation text. Default is "corr: %.2f". It will be formatted using sprintf(cor_format, corr).

cor_fg

The color of the correlation text.

cor_bg

The background color of the correlation text.

cor_bg_r

The radius of the background of the correlation text.

theme

A character string or a theme class (i.e. ggplot2::theme_classic) specifying the theme to use. Default is "theme_this".

theme_args

A list of arguments to pass to the theme function.

palette

A character string specifying the palette to use.

palcolor

A character string specifying the color to use in the palette.

title

A character string specifying the title of the plot. A function can be used to generate the title based on the default title. This is useful when split_by is used and the title needs to be dynamic.

subtitle

A character string specifying the subtitle of the plot.

facet_by

A character string specifying the column name of the data frame to facet the plot. Otherwise, the data will be split by split_by and generate multiple plots and combine them into one using patchwork::wrap_plots

legend.position

A character string specifying the position of the legend. if waiver(), for single groups, the legend will be "none", otherwise "right".

legend.direction

A character string specifying the direction of the legend.

seed

The random seed to use. Default is 8525.

combine

Whether to combine the plots into one when facet is FALSE. Default is TRUE.

nrow

A numeric value specifying the number of rows in the facet.

ncol

A numeric value specifying the number of columns in the facet.

byrow

A logical value indicating whether to fill the plots by row.

...

Additional arguments.

Value

A patch_work::wrap_plots object or a list of them if combine is FALSE.

Examples

set.seed(8525)
data <- data.frame(x = rnorm(100))
data$y <- rnorm(100, 10, sd = 0.5)
data$z <- -data$x + data$y + rnorm(100, 20, 1)
data$g <- sample(1:4, 100, replace = TRUE)

CorPairsPlot(data, diag_type = "histogram", diag_args = list(bins = 30, palette = "Paired"),
 layout = "/.")


CorPairsPlot(data, group_by = "g", diag_type = "none", layout = "./",
 theme_args = list(axis.title = element_textbox(
     color = "black", box.color = "grey20", size = 16, halign = 0.5, fill = "grey90",
     linetype = 1, width = grid::unit(1, "npc"), padding = ggplot2::margin(5, 5, 5, 5))))


CorPairsPlot(data, group_by = "g", diag_type = "violin", layout = "\\.",
  cor_format = "{x}\n{y}\ncorr: {round(corr, 2)}")


CorPairsPlot(data, split_by = "g", diag_type = "none", layout = ".\\",
 legend.position = "bottom", legend.direction = "horizontal", group_name = "group")