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Plot Frequency Distribution and Density Plots

Source: R/freq_distribution.R
freq_distribution.Rd

Plot Frequency Distribution and Density Plots

Usage

freq_distribution(
  data,
  trait,
  genotype = NULL,
  hist = TRUE,
  hist.col = "gray45",
  hist.border = TRUE,
  hist.border.col = "black",
  hist.alpha = 0.8,
  bw.adjust = 0.5,
  density = TRUE,
  density.col = "black",
  density.fill = "gray45",
  density.alpha = 0.1,
  normal.curve = TRUE,
  normal.curve.col = "black",
  normal.curve.linetype = "solid",
  highlight.mean = TRUE,
  highlight.mean.col = "black",
  show.counts = TRUE,
  count.text.col = "black",
  count.text.size = 3,
  highlight.genotype.vline = FALSE,
  highlight.genotype.pointrange = FALSE,
  highlights = NULL,
  highlight.col = highlight.mean.col,
  standardize.xrange = TRUE
)

Arguments

data

The data as a data frame object. The data frame should possess columns specifying the trait (and genotypes if highlight.genotype.* = TRUE).

trait

Name of column specifying the trait as a character string. The trait column should be of type "numeric" for quantitative traits and of type "factor" for qualitative traits.

genotype

Name of column specifying the group as a character string. Required only when highlight.genotype.* = TRUE.

hist

logical. If TRUE, the histogram is plotted. Default is TRUE.

hist.col

The histograme colour.

hist.border

logical. If TRUE, histogram border is also plotted. Default is TRUE.

hist.border.col

The histogram border colour.

hist.alpha

Alpha transparency for the histogram.

bw.adjust

Multiplicative bin width adjustment. Default is 0.5 which means use half of the default bandwidth.

density

logical. If TRUE, the kernel density is plotted. Default is TRUE.

density.col

The kernel density colour.

density.fill

The kernel density fill colour.

density.alpha

Alpha transparency for the kernel density

normal.curve

logical. If TRUE, a normal curve is plotted. Default is TRUE.

normal.curve.col

The colour of the normal curve.

normal.curve.linetype

Linetype for the normal curve. See aes_linetype_size_shape.

highlight.mean

logical. If TRUE, the mean value is highlighted as a vertical line. Default is TRUE.

highlight.mean.col

The colour of the vertical line representing mean.

show.counts

If TRUE, group wise counts are plotted as a text annotation. Default is TRUE.

count.text.col

The colour of the count text annotation.

count.text.size

The size of the count text annotation.

highlight.genotype.vline

logical. If TRUE, the mean values of genotypes specified in highlights are plotted as vertical lines.

highlight.genotype.pointrange

logical. If TRUE, the mean ± stand error values of genotypes specified in highlights are plotted as a separate pointrange plot.

highlights

The genotypes to be highlighted as a character vector.

highlight.col

The colour(s) to be used to highlight genotypes specified in highlights in the plot as a character vector. Must be valid colour values in R (named colours, hexadecimal representation, index of colours [1:8] in default R palette() etc.).

standardize.xrange

logical. If TRUE, the original plot and the pointrange plot x axis ranges are standardized. Default is TRUE.

Value

Either the frequency distribution plot as a histogram or kernel density ggplot2 object, or a list containing a ggplot2 pointrange plot and the histogram/kernel density plot.

Examples

library(agridat)
library(ggplot2)
library(patchwork)

soydata <- australia.soybean
soydata$gen <- as.character(soydata$gen)
checks <- c("G01", "G05")

check_cols <- c("#B2182B", "#2166AC")


# Frequency distribution as histogram
freq_hist1 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = TRUE,
                    hist.col = "lemonchiffon",
                    density = FALSE,
                    normal.curve = FALSE, highlight.mean = FALSE,
                    show.counts = FALSE)
freq_hist1


# Frequency distribution as histogram with normal curve
freq_hist2 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = TRUE,
                    hist.col = "lemonchiffon",
                    density = FALSE,
                    normal.curve = TRUE, normal.curve.col = "blue",
                    highlight.mean = FALSE,
                    show.counts = FALSE)
freq_hist2


# Frequency distribution as histogram with mean highlighted
freq_hist3 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = TRUE,
                    hist.col = "lemonchiffon",
                    density = FALSE, normal.curve = FALSE,
                    highlight.mean = TRUE, highlight.mean.col = "red",
                    show.counts = FALSE)
freq_hist3


# Frequency distribution as histogram with count value
freq_hist4 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = TRUE,
                    hist.col = "lemonchiffon",
                    density = FALSE, normal.curve = FALSE,
                    highlight.mean = FALSE,
                    show.counts = TRUE, count.text.col = "red")
freq_hist4


# Frequency distribution as histogram with check values
# highlighted as vertical lines
freq_hist5 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = TRUE,
                    hist.col = "lemonchiffon",
                    density = FALSE, normal.curve = FALSE,
                    highlight.mean = FALSE, show.counts = FALSE,
                    genotype = "gen",
                    highlight.genotype.vline = TRUE, highlights = checks,
                    highlight.col = check_cols)
freq_hist5


# Frequency distribution as histogram with check values
# highlighted as a separate pointrange plot
freq_hist6 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = TRUE,
                    hist.col = "lemonchiffon",
                    density = FALSE, normal.curve = FALSE,
                    highlight.mean = FALSE, show.counts = FALSE,
                    genotype = "gen",
                    highlight.genotype.vline = TRUE,
                    highlight.genotype.pointrange = TRUE,
                    highlights = checks,
                    highlight.col = check_cols)
freq_hist6[[1]] <-
  freq_hist6[[1]] +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank())
wrap_plots(freq_hist6[[1]], plot_spacer(), freq_hist6[[2]],
           ncol = 1, heights = c(1, -0.5, 4))


# Frequency distribution as kernel density plot
freq_dens1 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = FALSE,
                    density = TRUE,
                    density.fill = "lemonchiffon",
                    normal.curve = FALSE, highlight.mean = FALSE,
                    show.counts = FALSE)
freq_dens1


# Frequency distribution as kernel density plot with mean highlighted
freq_dens2 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = FALSE,
                    density = TRUE,
                    density.fill = "lemonchiffon",
                    normal.curve = FALSE,
                    highlight.mean = TRUE, highlight.mean.col = "red",
                    show.counts = FALSE)
freq_dens2


# Frequency distribution as kernel density plot with count value
freq_dens3 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = FALSE,
                    density = TRUE,
                    density.fill = "lemonchiffon",
                    normal.curve = FALSE,
                    highlight.mean = FALSE,
                    show.counts = TRUE, count.text.col = "red")
freq_dens3


# Frequency distribution as kernel density plot with check values
# highlighted as vertical lines
freq_dens4 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = FALSE,
                    density = TRUE,
                    density.fill = "lemonchiffon",
                    normal.curve = FALSE,
                    highlight.mean = FALSE, show.counts = FALSE,
                    genotype = "gen",
                    highlight.genotype.vline = TRUE, highlights = checks,
                    highlight.col = check_cols)
freq_dens4


# Frequency distribution as kernel density plot with check values
# highlighted as a separate pointrange plot
freq_dens5 <-
  freq_distribution(data = soydata, trait = "lodging",
                    hist = FALSE,
                    density = TRUE,
                    density.fill = "lemonchiffon",
                    normal.curve = FALSE,
                    highlight.mean = FALSE, show.counts = FALSE,
                    genotype = "gen",
                    highlight.genotype.vline = TRUE,
                    highlight.genotype.pointrange = TRUE,
                    highlights = checks,
                    highlight.col = check_cols)
freq_dens5[[1]] <-
  freq_dens5[[1]] +
  theme(axis.ticks.x = element_blank(),
        axis.text.x = element_blank())
wrap_plots(freq_dens5[[1]], plot_spacer(), freq_dens5[[2]],
           ncol = 1, heights = c(1, -0.5, 4))