Visualisation Recipe for 'correlation' Objects

Objects from the correlation package can be easily visualized. You can simply run plot() on them, which will internally call the visualisation_recipe() method to produce a basic ggplot. You can customize this plot ad-hoc or via the arguments described below. See examples here.

Usage

# S3 method for easycor_test
visualisation_recipe(
  x,
  show_data = "point",
  show_text = "subtitle",
  smooth = NULL,
  point = NULL,
  text = NULL,
  labs = NULL,
  ...
)

# S3 method for easycormatrix
visualisation_recipe(
  x,
  show_data = "tile",
  show_text = "text",
  show_legend = TRUE,
  tile = NULL,
  point = NULL,
  text = NULL,
  scale = NULL,
  scale_fill = NULL,
  labs = NULL,
  type = show_data,
  ...
)

# S3 method for easycorrelation
visualisation_recipe(x, ...)

Arguments

x

A correlation object.

show_data

Show data. For correlation matrices, can be "tile" (default) or "point".

show_text

Show labels with matrix values.

...

Other arguments passed to other functions.

show_legend

Show legend. Can be set to FALSE to remove the legend.

tile, point, text, scale, scale_fill, smooth, labs

Additional aesthetics and parameters for the geoms (see customization example).

type

Alias for show_data, for backwards compatibility.

Examples

# \donttest{
# ==============================================
# Correlation Test
# ==============================================
if (require("see")) {
  rez <- cor_test(mtcars, "mpg", "wt")

  layers <- visualisation_recipe(rez, labs = list(x = "Miles per Gallon (mpg)"))
  layers
  plot(layers)

  plot(rez,
    show_text = "label",
    point = list(color = "#f44336"),
    text = list(fontface = "bold"),
    show_statistic = FALSE, show_ci = FALSE, stars = TRUE
  )
}
#> Loading required package: see

# }
# ==============================================
# Correlation Matrix
# ==============================================
if (require("see")) {
  rez <- correlation(mtcars)

  x <- cor_sort(as.matrix(rez))
  layers <- visualisation_recipe(x)
  layers
  plot(layers)

  #' Get more details using `summary()`
  x <- summary(rez, redundant = TRUE, digits = 3)
  plot(visualisation_recipe(x))

  # Customize
  x <- summary(rez)
  layers <- visualisation_recipe(x,
    show_data = "points",
    scale = list(range = c(10, 20)),
    scale_fill = list(
      high = "#FF5722",
      low = "#673AB7",
      name = "r"
    ),
    text = list(color = "white"),
    labs = list(title = "My Plot")
  )
  plot(layers) + theme_modern()
}

# \donttest{
# ==============================================
# Correlation Results (easycorrelation)
# ==============================================
if (require("see") && require("tidygraph") && require("ggraph")) {
  rez <- correlation(iris)

  layers <- visualisation_recipe(rez)
  layers
  plot(layers)
}
#> Loading required package: tidygraph
#> 
#> Attaching package: ‘tidygraph’
#> The following objects are masked from ‘package:poorman’:
#> 
#>     %>%, anti_join, arrange, contains, distinct, ends_with, everything,
#>     filter, full_join, group_by, group_data, group_indices, group_keys,
#>     group_size, group_vars, groups, inner_join, left_join, matches,
#>     mutate, n, n_groups, num_range, pull, rename, right_join, select,
#>     semi_join, slice, starts_with, transmute, ungroup
#> The following object is masked from ‘package:stats’:
#> 
#>     filter
#> Loading required package: ggraph

# }