In agryt/xrfr: Converting XRF Data from Kilo-counts per Second to Micromolar

xrfr

Overview

This package contains functions to use when importing X-ray fluorescence (XRF) data and converting the measured values from kilo counts per second (kcps) to micromolar (µM). It is created with the intention of being used at the microbiology department at University of Bergen. It was created with R version 4.0.3, and is a further development of Hedda Østgaard's XRF package.

Installation

The package is installed from GitHub using the remotes package of the devtools group of packages. If you do not have remotes or devtools installed, install this first, then use the function install_github():

``` {r installation, eval = FALSE, error = FALSE, warning = FALSE, message = FALSE}

install.packages("remotes") remotes::install_github("agryt/xrfr", build_vignettes = TRUE)

alternatively:

install.packages("devtools") devtools::install_github("agryt/xrfr", build_vignettes = TRUE)

## Usage

The xrfr package is meant to be used with the [instructions document](https://github.com/agryt/xrfr/blob/master/INSTRUCTIONS.pdf), which explains what your data files must look like for the package to work. The functions within the package can be used to transform data from the XRF machine from kcps to µM, using your raw data, information about the samples, and some basic information about the machine and elements. 

Se vignette for more information and examples of how to use the different functions: `vignette("xrfr")`. 

The two functions necessary to perform the calculations are `readxrf()` and `convertxrf()`.

```r

halfway.df <- readxrf(raw_data = rawdata.df, project_info = projectinfo.df)

calculated.df <- convertxrf(imported_data = halfway.df, base_info = baseinfo.df, year = "2019",
                            first_element = "C", last_element = "As")

If you wish to transform your data, you can use the "after" functions. These are widen(), widen_above(), widen_means(), and widen_means_above(). There is also a function that lets you see your mean blanks.

# transforms your data from long to wide format:
wide.df <- widen(project_data = calculated.df)

# same as widen() + excludes values not above the detection limit:
wide.above.df <- widen_above(project_data = calculated.df)

# same as widen() + calculates means based on one or two factors:
wide.means.df <- widen_means(project_data = calculated.df, first_factor = "Day",
                             second_factor = "Treatment")

# combination of widen_means() and widen_above():
wide.means.above.df <- widen_means_above(project_data = calculated.df, first_factor = "Day",
                                         second_factor = "Treatment")

# show mean blank values:
my.blanks.df <- show_blanks(imported_data = halfway.df, first_element = "C", last_element = "As")

If you are using the .ssd files created by the XRF machine rather than the .txt file, you will need to convert your data from hexadecimal to readable text first. To do this, follow the instructions on how to use the transformssd() function. This requires some preparation of your data before importing to R and using the function.

``` {r example transformssd, eval = FALSE, message = FALSE, warning = FALSE, error = FALSE}

sample1 <- readLines("sample1-hex.txt") sample1.df <- transformssd(hex_data = sample1)

```

You can find examples of what the created data frame of each function can look like, as well as an example of each data file needed, here.

Getting help

If you are struggling to use the package, please see the vignette (vignette("xrfr")) and the instructions document for help. If this does not help, feel free to send your questions to [e-mail]. Any issues with the code can be reported at https://github.com/agryt/xrfr/issues.

agryt/xrfr documentation built on May 13, 2021, 8:24 p.m.