Get started

2019-03-08

1. Introduction

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.

2. Installation

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)

3. Running mimsy

The general structure for running mimsy is:

  1. Format your CSV file
  2. Load CSV file into R using read.csv()
  3. Run the mimsy() function
  4. Explore the results
  5. Save the results to an Excel file using mimsy.save() or an RData file using save()

3.1. Format your CSV file

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.

Figure 1. An example of a correctly formatted raw data file.

Figure 1. An example of a correctly formatted raw data file.

CSV file format:

Columns:

3.2. Load your CSV file into R using read.csv()

# Load data into R
data <- read.csv(file = "data.csv", header = TRUE, stringsAsFactors = FALSE)

# Check out the structure
str(data, vec.len = 2)
#> 'data.frame':    42 obs. of  14 variables:
#>  $ Type          : chr  "Standard" "Standard" ...
#>  $ Group         : int  1 1 1 1 1 ...
#>  $ CollectionTemp: num  24.9 24.9 24.9 26.3 26.3 ...
#>  $ RunDate       : chr  "2/1/2018" "2/1/2018" ...
#>  $ Label         : chr  NA NA ...
#>  $ Index         : int  3029 3079 3128 3212 3261 ...
#>  $ Time          : chr  "10:32:53 AM" "10:34:07 AM" ...
#>  $ X28           : num  1.20e-08 1.20e-08 1.20e-08 1.19e-08 1.18e-08 ...
#>  $ X32           : num  5.75e-09 5.75e-09 5.75e-09 5.66e-09 5.66e-09 ...
#>  $ X40           : num  2.98e-10 2.98e-10 2.98e-10 2.93e-10 2.93e-10 ...
#>  $ X99           : num  5.51e-07 5.49e-07 5.49e-07 5.41e-07 5.41e-07 ...
#>  $ N2.Ar         : num  40.3 40.3 ...
#>  $ O2.Ar         : num  19.3 19.3 ...
#>  $ Notes         : chr  "Barometric pressure =  977.2 hPa" "" ...

3.3. Run the mimsy() function

You must specify the barometric pressure (as baromet.press) and its units in the function argument. Units must be one of "atm", "hPa", "psi", "bar", or "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")
#> Calculated dissolved concentrations based on a two-point temperature standard.
#> Standard 1: 24.9 C, Standard 2: 26.3 C

3.4. Explore the results

You’ll see that mimsy() returns a list containing five seperate dataframes (results, solubility.Concentrations, calibration.Factors, calibration.DriftCorrection, and 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)
#>                             Length Class      Mode
#> results                     11     grouped_df list
#> solubility.Concentrations    3     data.frame list
#> calibration.Factors          3     data.frame list
#> calibration.DriftCorrection 12     data.frame list
#> results.full                30     grouped_df list

# See the summarized results dataframe
str(results$results, give.attr = FALSE)
#> Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame':  24 obs. of  11 variables:
#>  $ CollectionTemp: num  25.1 25.1 25.1 26.1 26.1 26.1 25.3 25.3 25.3 24.9 ...
#>  $ Label         : chr  "Sample1" "Sample2" "Sample3" "Sample4" ...
#>  $ Notes         : chr  "" "" "" "" ...
#>  $ data$Type     : chr  "Sample" "Sample" "Sample" "Sample" ...
#>  $ data$Group    : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ N2_uMol       : num  469 469 465 443 447 ...
#>  $ O2_uMol       : num  301 301 300 290 293 ...
#>  $ Ar_uMol       : num  12 12 11.9 11.6 11.6 ...
#>  $ N2_mg         : num  13.1 13.1 13 12.4 12.5 ...
#>  $ O2_mg         : num  9.63 9.62 9.61 9.29 9.37 ...
#>  $ Ar_mg         : num  0.48 0.479 0.478 0.464 0.464 ...

3.5. Save the results

# 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.

4. Putting it all together

# 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! :)