Histogram Plots • ospsuite.plots

1. Introduction

This vignette documents and illustrates workflows for producing histograms using the function plotHistogram from the ospsuite.plots package.

1.1 Setup

This vignette uses the ospsuite.plots and tidyr libraries. We will use the default settings of ospsuite.plots (see vignette(“ospsuite.plots”, package = “ospsuite.plots”)) but will adjust the legend position.

library(ospsuite.plots)
#> Loading required package: ggplot2
library(tidyr)

# Set Defaults
oldDefaults <- ospsuite.plots::setDefaults()

# Place default legend position above the plot for clearer histogram plots
theme_update(legend.position = "top")
theme_update(legend.direction = "horizontal")
theme_update(legend.title = element_blank())

1.2 Example Data

This vignette uses the following datasets:

Data Set 1:

histData <- exampleDataCovariates %>%
  dplyr::filter(SetID == "DataSet1") %>%
  dplyr::select(c("ID", "Sex", "Age", "AgeBin", "Ratio"))

# Metadata
metaData <- attr(exampleDataCovariates, "metaData")
metaData <- metaData[intersect(names(histData), names(metaData))]

knitr::kable(head(histData), digits = 2, caption = "First rows of example data.")

First rows of example data.
ID	Sex	Age	AgeBin	Ratio
1	Male	48	Adults	0.72
2	Male	36	Adults	1.31
3	Male	52	Adults	0.96
4	Male	47	Adults	0.81
5	Male	0	Peds	2.69
6	Male	48	Adults	2.16

knitr::kable(metaData2DataFrame(metaData), digits = 2, caption = "List of meta data")

List of meta data
	Age	Ratio
dimension	Age	Ratio
unit	yrs

Data Set 2:

histDataDistr <- exampleDataCovariates %>%
  dplyr::filter(SetID == "DataSet2") %>%
  dplyr::select(c("ID", "AgeBin", "Sex", "Obs"))

# Metadata for Distribution Data
metaDataDistr <- attr(exampleDataCovariates, "metaData")
metaDataDistr <- metaDataDistr[intersect(names(histDataDistr), names(metaDataDistr))]

knitr::kable(head(histDataDistr), digits = 2, caption = "First rows of distribution data.")

First rows of distribution data.
ID	AgeBin	Sex	Obs
1	adult	Female	28.81
2	adult	Male	77.48
3	adult	Female	35.86
4	adult	Male	62.71
5	adult	Female	30.48
6	adult	Male	74.24

knitr::kable(metaData2DataFrame(metaDataDistr), digits = 2, caption = "List of meta data for distribution data")

List of meta data for distribution data
	Obs
dimension	Clearance
unit	dL/h/kg

2. Examples

2.1 Illustration of Basic Histograms

2.1.1 Basic Example

Histogram of the “Ratio” column mapped to x, stratified by the “Sex” column mapped to fill.

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex),
  metaData = metaData
)

Histogram showing the distribution of ratio values stratified by sex. The overlapping histograms use different fill colors for male and female groups, with semi-transparent bars allowing visualization of overlapping distributions.

2.1.2 Basic Example: Change of Defaults

The variable geomHistAttributes is set by default to getDefaultGeomAttributes("Hist"), which is a list with entries bins = 10 and position = ggplot2::position_nudge().

In the example below, the variable geomHistAttributes is set to a list with entry position = "dodge". This changes the position, but note that the default value of geomHistAttributes contains the entry bins = 10, which is now overwritten, and the default {ggplot} number of 30 is used.

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, groupby = Sex),
  metaData = metaData,
  geomHistAttributes = list(position = "dodge")
)

Histogram showing ratio distribution with dodge positioning. The histograms for male and female groups are positioned side-by-side rather than overlapping, making it easier to compare the distributions. Note the increased number of bins (30) due to ggplot defaults.

2.1.3 Basic Example: Change of Position but Keep Number of Bins

To preserve the default settings, we modified the variable with utils::modifyList(getDefaultGeomAttributes("Hist"), list(position = "stack")). This changes the position but preserves the number of bins.

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, groupby = Sex),
  metaData = metaData,
  geomHistAttributes = utils::modifyList(
    getDefaultGeomAttributes("Hist"),
    list(position = "stack")
  )
)

Histogram showing ratio distribution with stacked positioning. The histograms for male and female groups are stacked on top of each other, preserving the default bin count (10) while showing the contribution of each sex to the total distribution in each bin.

2.1.4 Basic Example: Overlay of Histograms

By setting the position to identity and setting alpha to a value below 1, an overlay of histograms is produced.

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex),
  metaData = metaData,
  geomHistAttributes = utils::modifyList(
    getDefaultGeomAttributes("Hist"),
    list(position = "identity", binwidth = 1, alpha = 0.5)
  )
)

Histogram showing ratio distribution with overlapping semi-transparent bars. Male and female distributions are overlaid with 50% transparency (alpha=0.5) and fixed bin width, allowing direct comparison of distribution shapes while showing overlap regions.

2.1.5 Omit Data Points Flagged as Missing Dependent Variable (MDV)

If some of the data should be omitted, we can do this by mapping a boolean to the aesthetic mdv. Below, we exclude data above the value of 4:

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex, mdv = Ratio > 4),
  metaData = metaData
)

Histogram showing ratio distribution with missing dependent variable (MDV) exclusion. Data points with ratio values greater than 4 are excluded from the analysis, demonstrating how to filter out extreme values or outliers during visualization.

2.1.6 Stratified by a Combination of Columns

To stratify by a combination of columns, use the function interaction for the mapping to groupby:

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, groupby = interaction(Sex, AgeBin, sep = "-")),
  geomHistAttributes = utils::modifyList(
    getDefaultGeomAttributes("Hist"),
    list(position = "dodge")
  ),
  metaData = metaData
)

Histogram showing ratio distribution stratified by the interaction of sex and age bin. Each combination of sex and age group is shown as a separate histogram with dodge positioning, creating multiple side-by-side distributions for detailed demographic analysis.

2.1.7 Customization of Binning

Use the input variable geomHistAttributes to change the binning. The entries of this list are passed to ggplot2::geom_histogram, which provides many possibilities to customize the binning. Below, we define the bin boundaries by adding the entry breaks to geomHistAttributes.

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex),
  geomHistAttributes = list(position = position_nudge(), breaks = seq(0, 5, 0.5)),
  metaData = metaData
)

Histogram showing ratio distribution with custom bin boundaries. Bins are defined at 0.5-unit intervals from 0 to 5, providing fine-grained resolution for the distribution analysis. Histograms are stratified by sex with overlapping semi-transparent bars.

You could also map a binning function to the aesthetic x. Below, ggplot2::cut_number is used to create 3 bins with equal numbers of observations. The data is now displayed as categorical data.

plotHistogram(
  data = histData,
  mapping = aes(x = cut_number(Ratio, n = 3, labels = c("low", "mean", "high")), fill = Sex),
  geomHistAttributes = list(position = position_nudge()),
  metaData = metaData
) + labs(x = "Ratio")

Histogram showing ratio distribution using quantile-based binning. Data is divided into three equal-frequency bins labeled as 'low', 'mean', and 'high', transforming continuous data into categorical representation. Bars are stratified by sex.

2.2 Frequency

If the variable plotAsFrequency is set to TRUE and:

position is stack: frequency is calculated as count per bin / total (A)
position is NOT stack: frequency is calculated as count per bin / per group (B)

# A
plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, groupby = Sex),
  metaData = metaData,
  plotAsFrequency = TRUE,
  geomHistAttributes = list(bins = 10, position = "stack")
) + labs(tag = "A", caption = "Frequency is calculated as count per bin / total")

# B
plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, groupby = Sex),
  metaData = metaData,
  plotAsFrequency = TRUE
) + labs(tag = "B", caption = "Frequency is calculated as count per bin / per group")

Two histograms (A and B) showing frequency calculations with different positioning. Plot A shows stacked histograms where frequency is calculated as count per bin divided by total sample size. Plot B shows side-by-side histograms where frequency is calculated as count per bin divided by group size.

Both plots could also be calculated by directly setting y in the mapping:

position is stack: frequency is calculated as count per bin / total (C)
position is NOT stack: frequency is calculated as count per bin / per group (D)

# C
plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex, y = after_stat(count / sum(count))),
  metaData = metaData,
  plotAsFrequency = FALSE,
  geomHistAttributes = list(bins = 10, position = "stack")
) + labs(tag = "C", caption = "Frequency is calculated as count per bin / total")

# D
plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex, y = after_stat(density)),
  metaData = metaData,
  plotAsFrequency = FALSE
) + labs(tag = "D", caption = "Frequency is calculated as count per bin / per group")

Two histograms (C and D) demonstrating manual frequency calculations using ggplot2 after_stat functions. Plot C uses stacked positioning with manual count/sum calculation, while Plot D uses density calculation for group-wise normalization.

3. Distribution Fit

The optional input variable distribution provides the possibility of fitting the data distribution. All distributions from the package {stats} are available (see ?stats::distributions). Internally, ggh4x::stat_theodensity is used for the fit. Check the help for more details.

For the most common distributions, the keys “normal” (instead of norm) and “lognormal” (instead of lnorm) are also accepted.

The vertical line indicates the mean. The function to calculate the mean is determined by the input variable meanFunction. Available options are:

none (no line is plotted)
mean (arithmetic mean)
geomean (geometric mean)
median
auto (default, selects the mean function according to the selected distribution)

Below are examples for:

3.1 Fit of a Normal Distribution with Mean as Vertical Line

# Plot normal distribution
plotHistogram(
  data = histDataDistr,
  mapping = aes(x = Obs, fill = Sex),
  metaData = metaDataDistr,
  distribution = "normal"
)

Histogram showing distribution fit with normal distribution overlay. The histogram displays observed values by sex with fitted normal distribution curves overlaid and a vertical line indicating the mean. This demonstrates automatic distribution fitting capabilities.

3.2 Fit of a Chi-Squared Distribution without Vertical Line

plotHistogram(
  data = histDataDistr,
  mapping = aes(x = Obs, groupby = Sex),
  metaData = metaDataDistr,
  distribution = "chisq",
  meanFunction = "none"
)

Histogram showing chi-squared distribution fit without mean line. The histogram displays observed values grouped by sex with fitted chi-squared distribution curves overlaid, demonstrating how to disable the mean line indicator using meanFunction = 'none'.

3.3 Fit of Stacked Data

With the option stack, it is also possible to get the distribution of the sum only.

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex),
  metaData = metaData,
  geomHistAttributes = utils::modifyList(
    getDefaultGeomAttributes("Hist"),
    list(position = "stack")
  ),
  distribution = "normal"
)

Histogram showing stacked distribution fitting. The stacked histogram displays the sum distribution of both sex groups combined, with a single normal distribution curve fitted to the total data rather than individual group distributions.

3.4 Fit with Frequency TRUE

To fit a frequency, select a distribution (here “normal”) and set the variable plotAsFrequency to TRUE.

plotHistogram(
  data = histDataDistr,
  mapping = aes(x = Obs, fill = Sex),
  metaData = metaDataDistr,
  distribution = "normal",
  plotAsFrequency = TRUE
)

Frequency histogram with normal distribution fit. The histogram displays frequency values (normalized counts) on the y-axis with fitted normal distribution curves. The vertical line shows the mean, and the fit is applied to the frequency-scaled data.

3.5 Fit with Frequency TRUE and Stacked Data

plotHistogram(
  data = histData,
  mapping = aes(x = Ratio, fill = Sex),
  metaData = metaData,
  geomHistAttributes = utils::modifyList(
    getDefaultGeomAttributes("Hist"),
    list(position = "stack")
  ),
  distribution = "normal",
  plotAsFrequency = TRUE
)

Frequency histogram with stacked positioning and normal distribution fit. The stacked frequency histogram combines both sex groups with frequency scaling, and a single normal distribution is fitted to the combined frequency data.

3.6 X-Axis on Log Scale for Distribution Fit

As the fit is based on binning, and binning is dependent on scale, a log scale has to be set before the distribution fit. Please use the variable xscale = 'log' and do not add a {ggplot} like scale_x_log10.

plotHistogram(
  data = histDataDistr,
  mapping = aes(x = Obs, fill = Sex),
  metaData = metaDataDistr,
  xscale = "log",
  distribution = "norm",
  meanFunction = "none"
) + labs(tag = "A")

Histogram with logarithmic x-axis scale and normal distribution fit. The log-scaled histogram shows observed values by sex with distribution fitting applied after log transformation. No mean line is displayed due to meanFunction = 'none' setting.

4. Histogram for Categorical Data

The function plotHistogram can also be used to plot categorical data with a bar plot. Internally, the function switches from geom_histogram to geom_bar. With default inputs, the function switches automatically to a bar plot if the data is a factor or non-numeric. (See plot A). It can also be done manually by setting the variable asBarPlot to TRUE (see plot B).

# A Input is factor
plotHistogram(
  data = histData,
  mapping = aes(x = AgeBin, fill = Sex),
  metaData = metaData
) + labs(tag = "A")

# B Set asBarPlot = TRUE to convert input to factor
plotHistogram(
  data = histData,
  mapping = aes(x = round(histData$Age / 10) * 10, fill = Sex),
  asBarPlot = TRUE,
  metaData = metaData
) + labs(x = "Age rounded to 10 years", tag = "B")

Two bar plots (A and B) showing categorical data visualization. Plot A shows age bins as factor data with automatic bar plot detection. Plot B demonstrates manual bar plot creation by setting asBarPlot = TRUE and rounding age values to 10-year intervals. Both plots are stratified by sex.

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