plot_scatter {inferCSN}

plot_scatter

Description

plot_scatter

Usage

plot_scatter(
  data,
  smoothing_method = "lm",
  group_colors = RColorBrewer::brewer.pal(9, "Set1"),
  title_color = "black",
  title = NULL,
  col_title = NULL,
  row_title = NULL,
  legend_title = NULL,
  legend_position = "bottom",
  margins = "both",
  marginal_type = NULL,
  margins_size = 10,
  compute_correlation = TRUE,
  compute_correlation_method = "pearson",
  keep_aspect_ratio = FALSE,
  facet = FALSE,
  se = FALSE,
  pointdensity = TRUE
)

Arguments

data

Input data

smoothing_method

Method for smoothing curve, "lm" or "loess".

group_colors

Colors for different groups.

title_color

Color for the title.

title

Main title for the plot.

col_title

Title for the x-axis.

row_title

Title for the y-axis.

legend_title

Title for the legend.

legend_position

The position of legend.

margins

The position of marginal figure ("both", "x", "y").

marginal_type

The type of marginal figure ("density", "histogram", "boxplot", "violin", "densigram").

margins_size

The size of marginal figure, note the bigger size the smaller figure.

compute_correlation

Whether to compute and print correlation on the figure.

compute_correlation_method

Method to compute correlation ("pearson" or "spearman").

keep_aspect_ratio

Logical value, whether to set aspect ratio to 1:1.

facet

Faceting variable. If setting TRUE, all settings about margins will be inalidation.

se

Display confidence interval around smooth.

pointdensity

Plot point density when only provide 1 cluster.

Value

ggplot object

Examples

data("example_matrix")
test_data <- data.frame(
  example_matrix[1:200, c(1, 7)],
  c = c(
    rep("c1", 40),
    rep("c2", 40),
    rep("c3", 40),
    rep("c4", 40),
    rep("c5", 40)
  )
)

p1 <- plot_scatter(
  test_data,
  keep_aspect_ratio = TRUE
)
p2 <- plot_scatter(
  test_data,
  marginal_type = "boxplot",
  keep_aspect_ratio = TRUE
)
p1 + p2

p3 <- plot_scatter(
  test_data,
  facet = TRUE,
  keep_aspect_ratio = TRUE
)
p3

p4 <- plot_scatter(
  test_data[, 1:2],
  marginal_type = "histogram",
  keep_aspect_ratio = TRUE
)
p4