knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(mimsy) library(knitr)
mimsy is a package designed to calculate dissolved gas concentrations of oxygen, nitrogen, and argon from Membrane Inlet Mass Spectrometer (MIMS) signal data. For more information on the gas solubility equations used in this package, please see the References section. No R expertise is required to use mimsy, and this guide is designed for novice R users.
If you find bugs in this software, or you would like to suggest new features, please let us know on the mimsy GitHub page.
mimsy is not yet released on CRAN, the official repository for R packages. To download mimsy from GitHub, use the devtools package:
# Install the devtools package install.packages("devtools") # Load devtools library(devtools) # Download mimsy from github using devtools install_github("michelleckelly/mimsy", dependencies = "Depends")
Afterwards, you can load mimsy like any other package:
# Load mimsy library(mimsy)
The general structure for running mimsy is:
mimsy.save()or an RData file using
You'll need to add some special columns to your data file before loading it into R. The easiest way to do this is to use a spreadsheet editor like Excel. We recommend saving a seperate copy of your raw data file for mimsy (add "_mimsy" to the file name) to prevent any accidents.
CSV file format:
Label or other sample identifier columns
Index, Time, 28, 32, 40, 99, N2/Ar, O2/Ar columns
# Load data into R data <- read.csv(file = "data.csv", header = TRUE, stringsAsFactors = FALSE) # Check out the structure str(data, vec.len = 2)
You must specify the barometric pressure (as
baromet.press) and its units in the function argument. Units must be one of
"Torr". All other inputs, such as background corrections or standard salinity, are optional. Check out
?mimsy for more information.
# Run the function results <- mimsy(data, baromet.press = 977.2, units = "hPa")
You'll see that
mimsy() returns a list containing five seperate dataframes (
results.full). Check out ?mimsy() for more specific information on those outputs and how they were calculated.
# Check out the structure of the output summary(results) # See the summarized results dataframe str(results$results, give.attr = FALSE)
# Save output to an Excel workbook mimsy.save(results, file = "results.xlsx") # Save output to an RData file save(results, file = "results.RData")
We don't reccomend saving results dataframes to CSV files (although you can), as you'll need multiple CSV's to preserve all of the outputs, and that gets kind of messy. A good alternative is to save both an Excel workbook copy and an RData copy, that way all of your output is preserved every time.
You can load RData files back into R using
load("results.RData"). Check out
?load() for more info.
# Install the devtools package install.packages("devtools") # Load devtools library(devtools) # Download mimsy from Github using devtools install_github("michelleckelly/mimsy", dependencies = "Depends") # Load mimsy library(mimsy) # Load data into R data <- read.csv(file = "data.csv", header = TRUE, stringsAsFactors = FALSE) # Run the mimsy function results <- mimsy(data, baromet.press = 977.2, units = "hPa") # Save the results mimsy.save(results, file = "results.xlsx") # To Excel file save(results, file = "results.RData") # To RData file # Done! :)
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