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 class 'easycor_test'
visualisation_recipe(
  x,
  show_data = "point",
  show_text = "subtitle",
  smooth = NULL,
  point = NULL,
  text = NULL,
  labs = NULL,
  ...
)

# S3 method for class '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 class '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{
rez <- cor_test(mtcars, "mpg", "wt")

layers <- visualisation_recipe(rez, labs = list(x = "Miles per Gallon (mpg)"))
layers
#> Layer 1
#> --------
#> Geom type: smooth
#> data = [32 x 11]
#> method = 'lm'
#> aes_string(
#>   x = 'mpg'
#>   y = 'wt'
#> )
#> 
#> Layer 2
#> --------
#> Geom type: point
#> data = [32 x 11]
#> aes_string(
#>   x = 'mpg'
#>   y = 'wt'
#> )
#> 
#> Layer 3
#> --------
#> Geom type: labs
#> subtitle = 'r = -0.87, 95% CI [-0.93, -0.74], t(30) = -9.56, p < .001'
#> x = 'Miles per Gallon (mpg)'
#> 
plot(layers)


plot(rez,
  show_text = "label",
  point = list(color = "#f44336"),
  text = list(fontface = "bold"),
  show_statistic = FALSE, show_ci = FALSE, stars = TRUE
)

# }
# \donttest{
rez <- correlation(mtcars)

x <- cor_sort(as.matrix(rez))
layers <- visualisation_recipe(x)
layers
#> Layer 1
#> --------
#> Geom type: tile
#> data = [121 x 5]
#> aes_string(
#>   y = 'Parameter1'
#>   x = 'Parameter2'
#>   fill = 'r'
#> )
#> color = 'white'
#> size = 0.6
#> 
#> Layer 2
#> --------
#> Geom type: text
#> data = [121 x 5]
#> aes_string(
#>   y = 'Parameter1'
#>   x = 'Parameter2'
#>   label = 'Text'
#> )
#> 
#> Layer 3
#> --------
#> Geom type: scale_fill_gradient2
#> low = '#F44336'
#> mid = 'white'
#> high = '#2196F3'
#> midpoint = 0
#> na.value = 'grey85'
#> limit = c(-1, 1)
#> space = 'Lab'
#> name = 'Correlation'
#> guide = 'legend'
#> 
#> Layer 4
#> --------
#> Geom type: scale_x_discrete
#> expand = c(0, 0)
#> 
#> Layer 5
#> --------
#> Geom type: scale_y_discrete
#> expand = c(0, 0)
#> 
#> Layer 6
#> --------
#> Geom type: labs
#> title = 'Correlation Matrix'
#> 
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{
rez <- correlation(iris)

layers <- visualisation_recipe(rez)
layers
#> Layer 1
#> --------
#> Geom type: ggraph::geom_edge_arc
#> strength = 0.1
#> aes_string(
#>   edge_colour = 'r'
#>   edge_width = 'width'
#> )
#> 
#> Layer 2
#> --------
#> Geom type: ggraph::geom_node_point
#> size = 22
#> 
#> Layer 3
#> --------
#> Geom type: ggraph::geom_node_text
#> aes_string(
#>   label = 'name'
#> )
#> colour = 'white'
#> 
#> Layer 4
#> --------
#> Geom type: ggraph::theme_graph
#> base_family = 'sans'
#> 
#> Layer 5
#> --------
#> Geom type: guides
#> edge_width = 'none'
#> 
plot(layers)

# }