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Quantile plot for visualizing data distribution.

Source: R/quantile_plot.R
quantile_plot.Rd

Quantile plot for visualizing data distribution.

Usage

quantile_plot(
  data,
  my_shape = "fill_circle",
  point_size = 1.5,
  conf_int = TRUE,
  conf_level = 0.95,
  split_panel = "Split_Panel",
  legend_pos = "right",
  legend_dir = "vertical",
  sci_fill_color = "Sci_NPG",
  sci_color_alpha = 0.75,
  ggTheme = "theme_light"
)

Arguments

data

Dataframe: Weight and Sex traits dataframe (1st-col: Weight, 2nd-col: Sex).

my_shape

Character: scatter shape. Default: "fill_circle", options: "border_square", "border_circle", "border_triangle", "plus", "times", "border_diamond", "border_triangle_down", "square_times", "plus_times", "diamond_plus", "circle_plus", "di_triangle", "square_plus", "circle_times","square_triangle", "fill_square", "fill_circle", "fill_triangle", "fill_diamond", "large_circle", "small_circle", "fill_border_circle", "fill_border_square", "fill_border_diamond", "fill_border_triangle".

point_size

Numeric: point size. Default: 1.5, min: 0.0, max: not required.

conf_int

Logical: confidence interval (CI). Default: TRUE, options: TRUE or FALSE.

conf_level

Numeric: confidence interval value. Default: 0.95, min: 0.00, max: 1.00.

split_panel

Character: split panel by groups. Default: "Split_Panel", options: "One_Panel", "Split_Panel".

legend_pos

Character: legend position. Default: "right", options: "none", "left", "right", "bottom", "top".

legend_dir

Character: legend direction. Default: "vertical", options: "horizontal", "vertical".

sci_fill_color

Character: ggsci fill or color palette. Default: "Sci_NPG", options: "Sci_AAAS", "Sci_NPG", "Sci_Simpsons", "Sci_JAMA", "Sci_GSEA", "Sci_Lancet", "Sci_Futurama", "Sci_JCO", "Sci_NEJM", "Sci_IGV", "Sci_UCSC", "Sci_D3", "Sci_Material".

sci_color_alpha

Numeric: ggsci border color alpha. Default: 0.75, min: 0.00, max: 1.00.

ggTheme

Character: ggplot2 themes. Default: "theme_light", options: "theme_default", "theme_bw", "theme_gray", "theme_light", "theme_linedraw", "theme_dark", "theme_minimal", "theme_classic", "theme_void".

Value

Plot: quantile plot for visualizing data distribution.

Author

benben-miao

Examples

# 1. Library TOmicsVis package
library(TOmicsVis)

# 2. Use example dataset
data(weight_sex)
head(weight_sex)
#>   Weight    Sex
#> 1  36.74 Female
#> 2  38.54 Female
#> 3  44.91 Female
#> 4  43.53 Female
#> 5  39.03 Female
#> 6  26.01 Female

# 3. Default parameters
quantile_plot(weight_sex)


# 4. Set split_panel = "Split_Panel"
quantile_plot(weight_sex, split_panel = "Split_Panel")


# 5. Set sci_fill_color = "Sci_Futurama"
quantile_plot(weight_sex, sci_fill_color = "Sci_Futurama")


# 6. Set conf_int = FALSE
quantile_plot(weight_sex, conf_int = FALSE)