A key practical idea

In fingerPro, each mixture must be analysed independently. Optimum tracer selection depends on the combined information from both the sources and the mixture. Therefore, tracer selection must be performed separately for each mixture.

For this reason, it is strongly recommended to organize the analysis using one folder per mixture. Each folder should contain the input database, together with all figures and output files generated during the analysis.

Using different sets of optimum tracers for different mixtures is not a limitation of the method. Instead, it reflects the adaptation of the model to the specific characteristics of each dataset. Therefore, comparisons between results obtained for different mixtures remain valid even when different tracer sets have been selected.

Installation

Install from CRAN:

install.packages("fingerPro")

Or from a local file:

install.packages("FingerPro_2.1.tar.gz", repos = NULL, type = "source")

Load package

library(fingerPro)

Organizing your project folder

When working with your own .csv file, set the working directory to the folder containing the input database:

setwd("C:/your/project/folder")

Reading and validating your data

To read and validate your own input database, place the .csv file in your project folder and use read_dataset():

data <- read_database("my_input_database.csv")

Preparing your own database

Before starting, it is important to prepare your input database following the structure of the example datasets provided in the package.

A valid database should include:

• an ID column with unique values
• a samples column identifying the different sources and the mixture
• the corresponding tracer variables

In all cases, the mixture must be placed at the end of the dataset. If multiple mixture samples are available, they must share the same name in the samples column but have different ID values.

To retain conservative tracers for subsequent analyses, it is recommended to perform a basic data cleaning beforehand:

• replace BDL (below detection limit) values with a small positive number
• exclude tracers whose mixture value and at least one source value are BDL or zero
• optionally, remove tracers with predominantly BDL values

Supported input formats

Example datasets

The package includes four example datasets:

• example_geochemical_3s_raw.csv

  Raw dataset for 3 sources and 1 mixture with 17 scalar tracers (geochemical elements).

• example_isotopic_3s_raw.csv

  Raw dataset for 3 sources and 1 mixture with 5 isotopic tracers (ratios and their corresponding contents).

• example_geochemical_3s_mean.csv

  Averaged dataset (mean, standard deviation, and number of samples) for 3 sources and 1 mixture with 17 scalar tracers (geochemical elements). In this case, the mixture has a standard deviation equal to 0; if replicates of the mixture are available, the corresponding standard deviation can be included.

• example_isotopic_3s_mean.csv

  Averaged dataset (mean, standard deviation, and number of samples) for 3 sources and 1 mixture with 5 isotopic tracers (ratios and their corresponding contents). In this case, the mixture has a standard deviation equal to 0; if replicates of the mixture are available, the corresponding standard deviation can be included.

Next steps

Once your dataset has been validated, you are ready to continue with exploratory analysis, tracer selection, and source apportionment.

For a complete worked example, see the vignette: Workflow step-by-step