waves (version 0.1.0)

PlotSpectra: Plot spectral data, highlighting outliers as identified using Mahalanobis distance

Description

Generates a ggplot object of given spectra, with wavelength on the x axis and given spectral values on the y. Mahalanobis distance is used to calculate outliers, which are both identified on the plot. Rows from the original dataframe are printed to the console for each outlier that is identified.

Usage

PlotSpectra(input.df, wavelengths, num.col.before.spectra = 1,
  window.size = 10, verbose = TRUE)

Arguments

input.df

data.frame object containing columns of spectra. Spectral columns must be labeled with an "X" and then the wavelength (example: "X740" = 740nm). Left-most column must be unique ID. May also contain columns of metadata between the unique ID and spectral columns. Cannot contain any missing values

wavelengths

List of wavelengths (numerical format) represented by each spectral column in input.df

num.col.before.spectra

Number of columns to the left of the spectral matrix (including unique ID). Default is 1.

window.size

number defining the size of window to use when calculating the covariance of the spectra (required to calculate Mahalanobis distance). Default is 10.

verbose

If TRUE, the number of rows removed through filtering will be printed to the console. Default is TRUE.

Value

If verbose, prints unique ID and metadata for rows identified as outliers. Returns plot of spectral data with non-outliers in blue and outliers in red. X-axis is wavelengths and y-axis is spectral values.

Examples

# NOT RUN {
library(magrittr)
ikeogu.2017 %>%
  dplyr::rename(unique.id = sample.id) %>%
  dplyr::select(unique.id, dplyr::everything(), -TCC) %>%
  na.omit() %>%
  PlotSpectra(input.df = .,
              wavelengths = 350:2500,
              num.col.before.spectra = 5,
              window.size = 15)
# }