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Plot a histogram with separate error plot

Source: R/plot_Histogram.R
plot_Histogram.Rd

Function plots a predefined histogram with an accompanying error plot as suggested by Rex Galbraith at the UK LED in Oxford 2010.

Usage

plot_Histogram(
  data,
  na.rm = TRUE,
  mtext,
  cex.global,
  se,
  rug,
  normal_curve,
  summary,
  summary.pos,
  colour,
  interactive = FALSE,
  ...
)

Arguments

data

data.frame or RLum.Results object (required): for data.frame: two columns: De (data[,1]) and De error (data[,2])

na.rm

logical (with default): excludes NA values from the data set prior to any further operations.

mtext

character (optional): further sample information (mtext).

cex.global

numeric (with default): global scaling factor.

se

logical (optional): plots standard error points over the histogram, default is FALSE.

rug

logical (optional): adds rugs to the histogram, default is TRUE.

normal_curve

logical (with default): adds a normal curve to the histogram. Mean and standard deviation are calculated from the input data. More see details section.

summary

character (optional): add statistic measures of centrality and dispersion to the plot. Can be one or more of several keywords. See details for available keywords.

summary.pos

numeric or character (with default): optional position coordinates or keyword (e.g. "topright") for the statistical summary. Alternatively, the keyword "sub" may be specified to place the summary below the plot header. However, this latter option in only possible if mtext is not used. In case of coordinate specification, y-coordinate refers to the right y-axis.

colour

numeric or character (with default): optional vector of length 4 which specifies the colours of the following plot items in exactly this order: histogram bars, rug lines, normal distribution curve and standard error points (e.g., c("grey", "black", "red", "grey")).

interactive

logical (with default): create an interactive histogram plot (requires the 'plotly' package)

...

further arguments and graphical parameters passed to plot or hist. If y-axis labels are provided, these must be specified as a vector of length 2 since the plot features two axes (e.g. ylab = c("axis label 1", "axis label 2")). Y-axes limits (ylim) must be provided as vector of length four, with the first two elements specifying the left axes limits and the latter two elements giving the right axis limits.

Details

If the normal curve is added, the y-axis in the histogram will show the probability density.

A statistic summary, i.e. a collection of statistic measures of centrality and dispersion (and further measures) can be added by specifying one or more of the following keywords:

  • "n" (number of samples),

  • "mean" (mean De value),

  • "mean.weighted" (error-weighted mean),

  • "median" (median of the De values),

  • "sdrel" (relative standard deviation in percent),

  • "sdrel.weighted" (error-weighted relative standard deviation in percent),

  • "sdabs" (absolute standard deviation),

  • "sdabs.weighted" (error-weighted absolute standard deviation),

  • "serel" (relative standard error),

  • "serel.weighted" (error-weighted relative standard error),

  • "seabs" (absolute standard error),

  • "seabs.weighted" (error-weighted absolute standard error),

  • "kurtosis" (kurtosis) and

  • "skewness" (skewness).

Note

The input data is not restricted to a special type.

Function version

0.4.5

See also

hist, plot

Author

Michael Dietze, GFZ Potsdam (Germany)
Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany) , RLum Developer Team

How to cite

Dietze, M., Kreutzer, S., 2024. plot_Histogram(): Plot a histogram with separate error plot. Function version 0.4.5. In: Kreutzer, S., Burow, C., Dietze, M., Fuchs, M.C., Schmidt, C., Fischer, M., Friedrich, J., Mercier, N., Philippe, A., Riedesel, S., Autzen, M., Mittelstrass, D., Gray, H.J., Galharret, J., Colombo, M., 2024. Luminescence: Comprehensive Luminescence Dating Data Analysis. R package version 0.9.26. https://r-lum.github.io/Luminescence/

Examples


## load data
data(ExampleData.DeValues, envir = environment())
ExampleData.DeValues <-
  Second2Gray(ExampleData.DeValues$BT998, dose.rate = c(0.0438,0.0019))

## plot histogram the easiest way
plot_Histogram(ExampleData.DeValues)


## plot histogram with some more modifications
plot_Histogram(ExampleData.DeValues,
               rug = TRUE,
               normal_curve = TRUE,
               cex.global = 0.9,
               pch = 2,
               colour = c("grey", "black", "blue", "green"),
               summary = c("n", "mean", "sdrel"),
               summary.pos = "topleft",
               main = "Histogram of De-values",
               mtext = "Example data set",
               ylab = c(expression(paste(D[e], " distribution")),
                        "Standard error"),
               xlim = c(100, 250),
               ylim = c(0, 0.1, 5, 20))