# RaMS and friends

Table of contents:

- Standard export to CSV
- Fancier export to Excel
- Exporting to SQL database
- Interfacing with Python via `reticulate`

The strength of `RaMS` is its simple data format. Table-like data structures are common in most programming languages, and they can always be converted to the nigh-universal matrix format. The goal of this vignette is to illustrate this strength by exporting MS data to several formats that can be used outside of R.

## Standard export to CSV

As with all rectangular data, `RaMS` objects can be easily exported to CSV files with base R functions. This works best with a few chromatograms at a time, as the millions of data points found in most MS files can overwhelm common file readers.

    library(RaMS)

    # Locate an MS file
    single_file <- system.file("extdata", "LB12HL_AB.mzML.gz", package = "RaMS")

    # Grab the MS data
    msdata <- grabMSdata(single_file, grab_what = "everything")

    ##
    ## Reading file LB12HL_AB.mzML.gz... 0.12 secs
    ## Reading MS1 data...0.11 secs
    ## Reading MS2 data...0.01 secs
    ## Reading BPC...0.06 secs
    ## Reading TIC...0.06 secs
    ## Reading file metadata...0.1 secs
    ## Binding files together into single data.table
    ## Total time: 0.46 secs

    # Write out MS1 data to .csv file
    write.csv(x = msdata$MS1, file = "MS1_data.csv")

    # Clean up afterward
    file.remove("MS1_data.csv")

    ## [1] TRUE

## Fancier export to Excel

Excel workbooks are a common format because of their intuitive GUI and widespread adoption. They can also encode more information than CSV files due to their multiple “sheets” within a single workbook - perfect for encoding both MS1 and MS2 information in one place. This vignette uses the `openxlsx` package, although there are several alternatives with identical functionality.

    library(openxlsx)

    # Locate an MS2 file
    MS2_file <- system.file("extdata", "S30657.mzML.gz", package = "RaMS")

    # Grab the MS1 and MS2 data
    msdata <- grabMSdata(MS2_file, grab_what=c("MS1", "MS2"))

    ##
    ## Reading file S30657.mzML.gz... 0.2 secs
    ## Reading MS1 data...0.14 secs
    ## Reading MS2 data...0.04 secs
    ## Binding files together into single data.table
    ## Total time: 0.38 secs

    # Write out MS data to Excel file
    # openxlsx writes each object in a list to a unique sheet
    # Produces one sheet for MS1 and one for MS2
    write.xlsx(msdata, file = "MS2_data.xlsx")

    # Clean up afterward
    file.remove("MS2_data.xlsx")

    ## [1] TRUE

## Exporting to SQL database

For more robust data processing and storage, or to work with larger-than-memory data sets, SQL databases are an excellent choice. This vignette will demo the `RSQLite` package’s engine, although several other database engines have similar functionality.

    library(DBI)
    # Get data from multiple files to show off
    mzml_files <- system.file(c("extdata/LB12HL_AB.mzML.gz",
                                "extdata/LB12HL_CD.mzML.gz"),
                              package = "RaMS")
    msdata <- grabMSdata(mzml_files)

    ##
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    ## Total time: 0.95 secs

    # Create the sqlite database and connect to it
    MSdb <- dbConnect(RSQLite::SQLite(), "MSdata.sqlite")

    # Export MS1 and MS2 data to sqlite tables
    dbWriteTable(MSdb, "MS1", msdata$MS1)
    dbWriteTable(MSdb, "MS2", msdata$MS2)
    dbListTables(MSdb)

    ## [1] "MS1" "MS2"

    # Perform a simple query to ensure data was exported correctly
    dbGetQuery(MSdb, 'SELECT * FROM MS1 LIMIT 3')

    ##      rt       mz        int          filename
    ## 1 4.009 139.0503 1800550.12 LB12HL_AB.mzML.gz
    ## 2 4.009 148.0967  206310.81 LB12HL_AB.mzML.gz
    ## 3 4.009 136.0618   71907.15 LB12HL_AB.mzML.gz

    # Perform EIC extraction in SQL rather than in R
    EIC_query <- 'SELECT * FROM MS1 WHERE mz BETWEEN :lower_bound AND :upper_bound'
    query_params <- list(lower_bound=118.086, upper_bound=118.087)
    EIC <- dbGetQuery(MSdb, EIC_query, params = query_params)

    # Append with additional files
    extra_file <- system.file("extdata", "LB12HL_EF.mzML.gz", package = "RaMS")
    extra_msdata <- grabMSdata(extra_file, grab_what = "everything")

    ##
    ## Reading file LB12HL_EF.mzML.gz... 0.12 secs
    ## Reading MS1 data...0.1 secs
    ## Reading MS2 data...0.01 secs
    ## Reading BPC...0.07 secs
    ## Reading TIC...0.06 secs
    ## Reading file metadata...0.11 secs
    ## Binding files together into single data.table
    ## Total time: 0.47 secs

    dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1') # Initial number of rows

    ##   COUNT(*)
    ## 1    42313

    dbAppendTable(MSdb, "MS1", extra_msdata$MS1)

    ## [1] 22124

    # Confirm three different files exist in DB
    dbGetQuery(MSdb, 'SELECT DISTINCT filename FROM MS1')

    ##            filename
    ## 1 LB12HL_AB.mzML.gz
    ## 2 LB12HL_CD.mzML.gz
    ## 3 LB12HL_EF.mzML.gz

    # Confirm new rows have been added
    dbGetQuery(MSdb, 'SELECT COUNT(*) FROM MS1')

    ##   COUNT(*)
    ## 1    64437

    # Disconnect after export
    dbDisconnect(MSdb)

    # Clean up afterward
    unlink("MSdata.sqlite")

## Interfacing with Python via `reticulate`

R and Python are commonly used together, and the `reticulate` package makes this even easier by enabling a Python interpreter within R. RStudio, in which this vignette was written, supports both R and Python code chunks as shown below.

### R code chunk: {r}

    # Locate a couple MS files
    data_dir <- system.file("extdata", package = "RaMS")
    file_paths <- list.files(data_dir, pattern = "HL.*mzML", full.names = TRUE)

    msdata <- grabMSdata(files = file_paths, grab_what = "BPC")$BPC

    ##
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    ## Total time: 0.3 secs

### Python code chunk: {python}

    # Not run to pass R CMD check on GitHub
    # Make sure python, matplotlib, and seaborn are installed

    import seaborn as sns
    import matplotlib.pyplot as plt

    sns.relplot(data=r.msdata, kind="line", x="rt", y="int", hue="filename")
    plt.show()