Introduction

Isoreader supports several continuous flow IRMS data formats. This vignette shows some of the functionality for continuous flow files. For additional information on operations more generally (caching, combining read files, data export, etc.), please consult the operations vignette. For details on downstream data processing and visualization, see the isoprocessor package.

# load isoreader package
library(isoreader)

Reading files

Reading continuous flow files is as simple as passing one or multiple file or folder paths to the iso_read_continuous_flow() function. If folders are provided, any files that have a recognized continuous flow file extensions within those folders will be processed (e.g. all .dxf, .cf and .iarc). Here we read several files that are bundled with the package as examples (and whose paths can be retrieved using the iso_get_reader_example() function). Note that some of the files (.cf, .dxf) are individual analysis files whereas others (.iarc) are collections of several files.

# all available examples
iso_get_reader_examples() %>% knitr::kable()
filename type software description
continuous_flow_example.cf continuous flow Isodat Continuous Flow file format (older)
continuous_flow_example.dxf continuous flow Isodat Continuous Flow file format (newer)
continuous_flow_example.iarc continuous flow ionOS Continuous Flow data archive
dual_inlet_example.caf dual inlet Isodat Dual Inlet file format (older)
dual_inlet_example.did dual inlet Isodat Dual Inlet file format (newer)
dual_inlet_nu_example.txt dual inlet Nu Dual Inlet file format
background_scan_example.scn scan Isodat Scan file format
full_scan_example.scn scan Isodat Scan file format
peak_shape_scan_example.scn scan Isodat Scan file format
time_scan_example.scn scan Isodat Scan file format
# read a few of the continuous flow examples
cf_files <-
  iso_read_continuous_flow(
    iso_get_reader_example("continuous_flow_example.cf"),
    iso_get_reader_example("continuous_flow_example.iarc"),
    iso_get_reader_example("continuous_flow_example.dxf")
  )
#> Info: preparing to read 3 data files (all will be cached)...
#> Info: reading file 'continuous_flow_example.cf' with '.cf' reader...
#> Info: reading file 'continuous_flow_example.iarc' with '.iarc' reader...
#>       unpacking isoprime archive file...
#>       found 1 processing list(s) in .iarc: 'ProcessingList_1'
#>       found 2 method(s) in .iarc: 'Method_320', 'Method_77'
#>       found 4 sample(s) in .iarc
#>       searching processing list 'ProcessingList_1' for gas configurations...
#>       found configurations for 'CO', 'SO2', 'CO2', 'H2', 'N2'
#>       processing sample '6632_WSL-2 wood' (IRMS data '133.hdf5', '135.hdf5')
#>       processing sample '6605_USGS41' (IRMS data '40.hdf5', '43.hdf5')
#>       processing sample '6617_IAEA600' (IRMS data '80.hdf5', '82.hdf5')
#>       processing sample '6630_GlutamicAcid04' (IRMS data '124.hdf5', '126.h...
#> Info: reading file 'continuous_flow_example.dxf' with '.dxf' reader...
#> Info: finished reading 3 files in 6.51 secs

File summary

The cf_files variable now contains a set of isoreader objects, one for each file. Take a look at what information was retrieved from the files using the iso_get_data_summary() function.

cf_files %>% iso_get_data_summary() %>% knitr::kable()
#> Info: aggregating data summary from 6 data file(s)
file_id raw_data file_info method_info vendor_data_table file_path
continuous_flow_example.cf 8605 time points, 2 ions (2,3) 21 entries standards, resistors 19 rows, 25 columns continuous_flow_example.cf
6632_WSL-2 wood 1952 time points, 3 ions (45,46,44) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_0b7e6b49-7756-4cca-b387-4d6617cb28e6
6605_USGS41 2387 time points, 2 ions (29,28) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_0e08e8b4-c625-4fd7-bf5b-9f4823f660cd
6617_IAEA600 1796 time points, 3 ions (45,46,44) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_1de1fdc3-0797-4e44-8357-52acbd0d4a6d
6630_GlutamicAcid04 2386 time points, 2 ions (29,28) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_261a3286-0bc6-4aca-bba2-183c02597d8b
continuous_flow_example.dxf 2435 time points, 6 ions (28,29,30,44,45,46) 21 entries standards, resistors 6 rows, 60 columns continuous_flow_example.dxf

Problems

In case there was any trouble with reading any of the files, the following functions provide an overview summary as well as details of all errors and warnings, respectively. The examples here contain no errors but if you run into any unexpected file read problems, please file a bug report in the isoreader issue tracker.

cf_files %>% iso_get_problems_summary() %>% knitr::kable()
file_id error warning
cf_files %>% iso_get_problems() %>% knitr::kable()
file_id type func details

File Information

Detailed file information can be aggregated for all isofiles using the iso_get_file_info() function which supports the full select syntax of the dplyr package to specify which columns are of interest (by default, all file information is retrieved). Additionally, file information from different file formats can be renamed to the same column name for easy of downstream processing. The following provides a few examples for how this can be used (the names of the interesting info columns may vary between different file formats):

# all file information
cf_files %>% iso_get_file_info(select = c(-file_root)) %>% knitr::kable()
#> Info: aggregating file info from 6 data file(s), selecting info columns 'c(-file_root)'
file_id file_path file_subpath file_datetime file_size Line Peak Center GC Method AS Sample AS Method Identifier 1 Identifier 2 Analysis Comment Preparation Pre Script Post Script Method H3 Factor MS_integration_time.s GlobalIdentifier Name Id AcquisitionStartDate AcquisitionEndDate CompletionState MethodId ProcessingListTypeIdentifier Dilution Type EA Method EA Sample Weight Row Check Ref. Dilution H3 Stability Type measurement_info
continuous_flow_example.cf continuous_flow_example.cf NA 2013-07-13 23:42:40 550462 19 NA Sebastian8.gcm 98 >Internal No 3 F8-5.5uL NA 6520 NA NA NA NA Sebastian\20130624_F8_CH4.met 2.79431047797221 0.2 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
6632_WSL-2 wood continuous_flow_example.iarc Task_0b7e6b49-7756-4cca-b387-4d6617cb28e6 2014-10-08 18:27:23 662269 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0b7e6b49-7756-4cca-b387-4d6617cb28e6 WSL-2 wood 6632 2014-10-08T18:27:23.4682755 2014-10-08T18:43:40.1319321 Success 320 {BC89E456-57C0-4FF1-9110-90C8E6AE1B69} Wood - 25 mg 10mg120sIRMS 29 NA NA NA NA NA
6605_USGS41 continuous_flow_example.iarc Task_0e08e8b4-c625-4fd7-bf5b-9f4823f660cd 2014-10-08 14:14:35 662269 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0e08e8b4-c625-4fd7-bf5b-9f4823f660cd USGS41 6605 2014-10-08T14:14:35.9865192 2014-10-08T14:24:49.0288173 Success 77 {BC89E456-57C0-4FF1-9110-90C8E6AE1B69} Glutamic Acid - 1mg 05mg70sIRMS_fast 1 NA NA NA NA NA
6617_IAEA600 continuous_flow_example.iarc Task_1de1fdc3-0797-4e44-8357-52acbd0d4a6d 2014-10-08 16:01:14 662269 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 1de1fdc3-0797-4e44-8357-52acbd0d4a6d IAEA600 6617 2014-10-08T16:01:14.8274991 2014-10-08T16:11:29.4289531 Success 77 {BC89E456-57C0-4FF1-9110-90C8E6AE1B69} Caffeine - 0.4mg 05mg70sIRMS_fast 0.4 NA NA NA NA NA
6630_GlutamicAcid04 continuous_flow_example.iarc Task_261a3286-0bc6-4aca-bba2-183c02597d8b 2014-10-08 18:00:50 662269 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 261a3286-0bc6-4aca-bba2-183c02597d8b GlutamicAcid04 6630 2014-10-08T18:00:50.7420188 2014-10-08T18:11:05.9675352 Success 77 {BC89E456-57C0-4FF1-9110-90C8E6AE1B69} Glutamic Acid - 1mg 05mg70sIRMS_fast 1 NA NA NA NA NA
continuous_flow_example.dxf continuous_flow_example.dxf NA 2017-02-09 19:55:40 370497 NA 1 NA NA NA acetanilide_1 0.428 2921 11 A5 NA NA N2 CO2 85%.met 0 0.2 NA NA NA NA NA NA NA NA NA N2 CO2 test.eam NA 5 0 0 Sample Set Sample Dilution to 0 [%] , Sample dilution: 000 0 , Set Reference Dilution to 69 [%] , Peak Center found at [59732] , Sample Dilution found at 0 [] , Set Sample Dilution to 0 [%] , Sample dilution: 000 0 , Detector Signal added: Ret:145.171[sec] Ampl:2321 [mV] : 12:47:38 N2, Set Reference Dilution to 57 [%] , Set Sample Dilution to 89 [%] , Sample dilution: 001 1
# select file information
cf_files %>%
  iso_get_file_info(
    select = c(
       # rename sample id columns from the different file types to a new ID column
      ID = `Identifier 1`, ID = `Name`,
      # select columns without renaming
      Analysis, `Peak Center`, `H3 Factor`,
      # select the time stamp and rename it to `Date & Time`
      `Date & Time` = file_datetime
    ),
    # explicitly allow for file specific rename (for the new ID column)
    file_specific = TRUE
  ) %>% knitr::kable()
#> Info: aggregating file info from 6 data file(s), selecting info columns 'c(ID = `Identifier 1`, ID = Name, Analysis, `Peak Center`, `H3 Factor`, `Date & Time` = file_datetime)'
file_id ID Analysis Peak Center H3 Factor Date & Time
continuous_flow_example.cf F8-5.5uL 6520 NA 2.79431047797221 2013-07-13 23:42:40
6632_WSL-2 wood WSL-2 wood NA NA NA 2014-10-08 18:27:23
6605_USGS41 USGS41 NA NA NA 2014-10-08 14:14:35
6617_IAEA600 IAEA600 NA NA NA 2014-10-08 16:01:14
6630_GlutamicAcid04 GlutamicAcid04 NA NA NA 2014-10-08 18:00:50
continuous_flow_example.dxf acetanilide_1 2921 1 0 2017-02-09 19:55:40

Select/Rename

Rather than retrieving specific file info columns using the above example of iso_get_file_info(select = ...), these information can also be modified across an entire collection of isofiles using the iso_select_file_info() and iso_rename_file_info() functions. For example, the above example could be similarly achieved with the following use of iso_select_file_info():

# select + rename specific file info columns
cf_files2 <- cf_files %>%
  iso_select_file_info(
    ID = `Identifier 1`, ID = `Name`, Analysis, `Peak Center`, `H3 Factor`,
    `Date & Time` = file_datetime,
    # recode to the same name in different files
    `Sample Weight` = `Identifier 2`, `Sample Weight` = `EA Sample Weight`,
    file_specific = TRUE
  )
#> Info: selecting/renaming the following file info:
#>  - for 4 file(s): 'file_id', 'Name'->'ID', 'file_datetime'->'Date & Time', 'EA Sample Weight'->'Sample Weight'
#>  - for 2 file(s): 'file_id', 'Identifier 1'->'ID', 'Analysis', 'Peak Center', 'H3 Factor', 'file_datetime'->'Date & Time', 'Identifier 2'->'Sample Weight'

# fetch all file info
cf_files2 %>% iso_get_file_info() %>% knitr::kable()
#> Info: aggregating file info from 6 data file(s)
file_id ID Analysis Peak Center H3 Factor Date & Time Sample Weight
continuous_flow_example.cf F8-5.5uL 6520 NA 2.79431047797221 2013-07-13 23:42:40 NA
6632_WSL-2 wood WSL-2 wood NA NA NA 2014-10-08 18:27:23 29
6605_USGS41 USGS41 NA NA NA 2014-10-08 14:14:35 1
6617_IAEA600 IAEA600 NA NA NA 2014-10-08 16:01:14 0.4
6630_GlutamicAcid04 GlutamicAcid04 NA NA NA 2014-10-08 18:00:50 1
continuous_flow_example.dxf acetanilide_1 2921 1 0 2017-02-09 19:55:40 0.428

Filter

Any collection of isofiles can also be filtered based on the available file information using the function iso_filter_files. This function can operate on any column available in the file information and supports full dplyr syntax.

# find files that have 'acetanilide' in the new ID field
cf_files2 %>% iso_filter_files(grepl("acetanilide", ID)) %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: applying file filter, keeping 1 of 6 files
#> Info: aggregating file info from 1 data file(s)
file_id ID Analysis Peak Center H3 Factor Date & Time Sample Weight
continuous_flow_example.dxf acetanilide_1 2921 1 0 2017-02-09 19:55:40 0.428

# find files that were run since 2015
cf_files2 %>%
  iso_filter_files(`Date & Time` > "2015-01-01") %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: applying file filter, keeping 1 of 6 files
#> Info: aggregating file info from 1 data file(s)
file_id ID Analysis Peak Center H3 Factor Date & Time Sample Weight
continuous_flow_example.dxf acetanilide_1 2921 1 0 2017-02-09 19:55:40 0.428

Mutate

The file information in any collection of isofiles can also be mutated using the function iso_mutate_file_info. This function can introduce new columns and operate on any existing columns available in the file information (even if it does not exist in all files) and supports full dplyr syntax. It can also be used in conjunction with iso_with_unit to generate values with implicit units.

cf_files3 <-
  cf_files2 %>%
  iso_mutate_file_info(
    # update existing column
    ID = paste("ID:", ID),
    # introduce new column
    `Run since 2015?` = `Date & Time` > "2015-01-01",
    # parse weight as a number and turn into a column with units
    `Sample Weight` = `Sample Weight` %>% parse_number() %>% iso_with_units("mg")
  )
#> Info: mutating file info for 6 data file(s)

cf_files3 %>%
  iso_get_file_info() %>%
  iso_make_units_explicit() %>%
  knitr::kable()
#> Info: aggregating file info from 6 data file(s)
file_id ID Analysis Peak Center H3 Factor Date & Time Sample Weight [mg] Run since 2015?
continuous_flow_example.cf ID: F8-5.5uL 6520 NA 2.79431047797221 2013-07-13 23:42:40 NA FALSE
6632_WSL-2 wood ID: WSL-2 wood NA NA NA 2014-10-08 18:27:23 29.000 FALSE
6605_USGS41 ID: USGS41 NA NA NA 2014-10-08 14:14:35 1.000 FALSE
6617_IAEA600 ID: IAEA600 NA NA NA 2014-10-08 16:01:14 0.400 FALSE
6630_GlutamicAcid04 ID: GlutamicAcid04 NA NA NA 2014-10-08 18:00:50 1.000 FALSE
continuous_flow_example.dxf ID: acetanilide_1 2921 1 0 2017-02-09 19:55:40 0.428 TRUE

Add

Additionally, a wide range of new file information can be added in the form of a data frame with any number of columns (usually read from a comma-separated-value/csv file or an Excel/xlsx file) using the function iso_add_file_info and specifying which existing file information should be used to merge in the new information. It is similar to dplyr’s left_join but with additional safety checks and the possibility to join the new information sequentially as illustrated below.

# this kind of information data frame is frequently read in from a csv or xlsx file
new_info <-
  dplyr::bind_rows(
    # new information based on new vs. old samples
    dplyr::tribble(
      ~file_id, ~`Run since 2015?`,  ~process,  ~info,
       NA,       TRUE,                "yes",     "new runs",
       NA,       FALSE,               "yes",     "old runs"
    ),
    # new information for a single specific file
    dplyr::tribble(
      ~file_id,        ~process,  ~note,
       "6617_IAEA600",  "no",      "did not inject properly"
    )
  )
new_info %>% knitr::kable()
file_id Run since 2015? process info note
NA TRUE yes new runs NA
NA FALSE yes old runs NA
6617_IAEA600 NA no NA did not inject properly

# adding it to the isofiles
cf_files3 %>%
  iso_add_file_info(new_info, by1 = "Run since 2015?", by2 = "file_id") %>%
  iso_get_file_info(select = !!names(new_info)) %>%
  knitr::kable()
#> Info: adding new file information ('process', 'info', 'note') to 6 data file(s), joining by 'Run since 2015?' then 'file_id'...
#>  - 'Run since 2015?' join: 2/2 new info rows matched 6/6 data files - 1 of these was/were also matched by subsequent joins which took precedence
#>  - 'file_id' join: 1/1 new info rows matched 1/6 data files
#> Info: aggregating file info from 6 data file(s), selecting info columns 'file_id', 'Run since 2015?', 'process', 'info', 'note'
file_id Run since 2015? process info note
continuous_flow_example.cf FALSE yes old runs NA
6632_WSL-2 wood FALSE yes old runs NA
6605_USGS41 FALSE yes old runs NA
6617_IAEA600 FALSE no NA did not inject properly
6630_GlutamicAcid04 FALSE yes old runs NA
continuous_flow_example.dxf TRUE yes new runs NA

Parse

Most file information is initially read as text to avoid cumbersome specifications during the read process and compatibility issues between different IRMS file formats. However, many file info columns are not easily processed as text. The isoreader package therefore provides several parsing and data extraction functions to facilitate processing the text-based data (some via functionality implemented by the readr package). See code block below for examples. For a complete overview, see the ?extract_data and ?iso_parse_file_info documentation.

# use parsing and extraction in iso_mutate_file_info
cf_files2 %>%
  iso_mutate_file_info(
    # change type of Peak Center to logical
    `Peak Center` = parse_logical(`Peak Center`),
    # retrieve first word of file_id
    file_id_1st = extract_word(file_id),
    # retrieve second word of ID column
    file_id_2nd = extract_word(file_id, 2),
    # retrieve file extension from the file_id using regular expression
    name = extract_substring(ID, "(\\w+)-?(.*)?", capture_bracket = 1)
  ) %>%
  iso_get_file_info(select = c(matches("file_id"), ID, name, `Peak Center`)) %>%
  knitr::kable()
#> Info: mutating file info for 6 data file(s)
#> Info: aggregating file info from 6 data file(s), selecting info columns 'c(matches("file_id"), ID, name, `Peak Center`)'
file_id file_id_1st file_id_2nd ID name Peak Center
continuous_flow_example.cf continuous flow F8-5.5uL F8 NA
6632_WSL-2 wood 6632 WSL WSL-2 wood WSL NA
6605_USGS41 6605 USGS41 USGS41 USGS41 NA
6617_IAEA600 6617 IAEA600 IAEA600 IAEA600 NA
6630_GlutamicAcid04 6630 GlutamicAcid04 GlutamicAcid04 GlutamicAcid04 NA
continuous_flow_example.dxf continuous flow acetanilide_1 acetanilide_1 TRUE

# use parsing in iso_filter_file_info
cf_files2 %>%
  iso_filter_files(parse_number(`H3 Factor`) > 2) %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: applying file filter, keeping 1 of 6 files
#> Info: aggregating file info from 1 data file(s)
file_id ID Analysis Peak Center H3 Factor Date & Time Sample Weight
continuous_flow_example.cf F8-5.5uL 6520 NA 2.79431047797221 2013-07-13 23:42:40 NA

# use iso_parse_file_info for simplified parsing of column data types
cf_files2 %>%
  iso_parse_file_info(
    integer = Analysis,
    number = `H3 Factor`,
    logical = `Peak Center`
  ) %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: parsing 3 file info columns for 6 data file(s):
#>  - to integer: 'Analysis'
#>  - to logical: 'Peak Center'
#>  - to number: 'H3 Factor'
#> Info: aggregating file info from 6 data file(s)
file_id ID Analysis Peak Center H3 Factor Date & Time Sample Weight
continuous_flow_example.cf F8-5.5uL 6520 NA 2.79431 2013-07-13 23:42:40 NA
6632_WSL-2 wood WSL-2 wood NA NA NA 2014-10-08 18:27:23 29
6605_USGS41 USGS41 NA NA NA 2014-10-08 14:14:35 1
6617_IAEA600 IAEA600 NA NA NA 2014-10-08 16:01:14 0.4
6630_GlutamicAcid04 GlutamicAcid04 NA NA NA 2014-10-08 18:00:50 1
continuous_flow_example.dxf acetanilide_1 2921 TRUE 0.00000 2017-02-09 19:55:40 0.428

Resistors

Additionally, some IRMS data files contain resistor information that are useful for downstream calculations (see e.g. section on signal conversion later in this vignette):

cf_files %>% iso_get_resistors() %>% knitr::kable()
#> Info: aggregating resistors info from 6 data file(s)
file_id cup R.Ohm mass
continuous_flow_example.cf 1 1e+09 2
continuous_flow_example.cf 2 1e+12 3
continuous_flow_example.dxf 1 3e+08 28
continuous_flow_example.dxf 2 3e+10 29
continuous_flow_example.dxf 3 1e+11 30
continuous_flow_example.dxf 1 3e+08 44
continuous_flow_example.dxf 2 3e+10 45
continuous_flow_example.dxf 3 1e+11 46

Reference values

As well as isotopic reference values for the different gases:

# reference delta values without ratio values
cf_files %>% iso_get_standards(file_id:reference) %>% knitr::kable()
#> Info: aggregating standards info from 6 data file(s)
file_id standard gas delta_name delta_value reference
continuous_flow_example.cf methane ref H2 d 2H/1H -151.9 VSMOW
continuous_flow_example.dxf N2_zero N2 d 15N/14N 0.0 Air-N2
continuous_flow_example.dxf CO2_zero CO2 d 13C/12C 0.0 VPDB
continuous_flow_example.dxf CO2_zero CO2 d 18O/16O 0.0 VSMOW
# reference values with ratios
cf_files %>% iso_get_standards() %>% knitr::kable()
#> Info: aggregating standards info from 6 data file(s)
file_id standard gas delta_name delta_value reference element ratio_name ratio_value
continuous_flow_example.cf methane ref H2 d 2H/1H -151.9 VSMOW H R 2H/1H 0.0001558
continuous_flow_example.cf methane ref H2 d 2H/1H -151.9 VSMOW O R 17O/16O 0.0003799
continuous_flow_example.cf methane ref H2 d 2H/1H -151.9 VSMOW O R 18O/16O 0.0020052
continuous_flow_example.dxf N2_zero N2 d 15N/14N 0.0 Air-N2 N R 15N/14N 0.0036782
continuous_flow_example.dxf CO2_zero CO2 d 13C/12C 0.0 VPDB C R 13C/12C 0.0111802
continuous_flow_example.dxf CO2_zero CO2 d 13C/12C 0.0 VPDB O R 18O/16O 0.0020672
continuous_flow_example.dxf CO2_zero CO2 d 13C/12C 0.0 VPDB O R 17O/16O 0.0003860
continuous_flow_example.dxf CO2_zero CO2 d 18O/16O 0.0 VSMOW H R 2H/1H 0.0001558
continuous_flow_example.dxf CO2_zero CO2 d 18O/16O 0.0 VSMOW O R 17O/16O 0.0003799
continuous_flow_example.dxf CO2_zero CO2 d 18O/16O 0.0 VSMOW O R 18O/16O 0.0020052

Raw Data

The raw data read from the IRMS files can be retrieved similarly using the iso_get_raw_data() function. Most data aggregation functions also allow for inclusion of file information using the include_file_info parameter, which functions identically to the select parameter of the iso_get_file_info function discussed earlier.

# get raw data with default selections (all raw data, no additional file info)
cf_files %>% iso_get_raw_data() %>% head(n=10) %>% knitr::kable()
#> Info: aggregating raw data from 6 data file(s)
file_id tp time.s v2.mV v3.mV i45.nA i46.nA i44.nA i29.nA i28.nA v28.mV v29.mV v30.mV v44.mV v45.mV v46.mV
continuous_flow_example.cf 1 0.209 194.5682 60.01971 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 2 0.418 194.5418 60.04760 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 3 0.627 194.5365 60.21315 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 4 0.836 194.5630 59.91891 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 5 1.045 194.5735 59.71668 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 6 1.254 194.5444 59.69523 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 7 1.463 194.5153 59.58589 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 8 1.672 194.5233 59.72385 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 9 1.881 194.5096 59.73885 NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 10 2.090 194.5206 59.73885 NA NA NA NA NA NA NA NA NA NA NA
# get specific raw data and add some file information
cf_files %>%
  iso_get_raw_data(
    # select just time and the m/z 2 and 3 ions
    select = c(time.s, v2.mV, v3.mV),
    # include the Analysis number fron the file info and rename it to 'run'
    include_file_info = c(run = Analysis)
  ) %>%
  # look at first few records only
  head(n=10) %>% knitr::kable()
#> Info: aggregating raw data from 6 data file(s), selecting data columns 'c(time.s, v2.mV, v3.mV)', including file info 'c(run = Analysis)'
file_id run time.s v2.mV v3.mV
continuous_flow_example.cf 6520 0.209 194.5682 60.01971
continuous_flow_example.cf 6520 0.418 194.5418 60.04760
continuous_flow_example.cf 6520 0.627 194.5365 60.21315
continuous_flow_example.cf 6520 0.836 194.5630 59.91891
continuous_flow_example.cf 6520 1.045 194.5735 59.71668
continuous_flow_example.cf 6520 1.254 194.5444 59.69523
continuous_flow_example.cf 6520 1.463 194.5153 59.58589
continuous_flow_example.cf 6520 1.672 194.5233 59.72385
continuous_flow_example.cf 6520 1.881 194.5096 59.73885
continuous_flow_example.cf 6520 2.090 194.5206 59.73885

Data Processing

The isoreader package is intended to make raw stable isotope data easily accessible. However, as with most analytical data, there is significant downstream processing required to turn these raw intensity chromatograms into peak-specific, properly referenced isotopic measurements. This and similar functionality as well as data visualization is part of the isoprocessor package which takes isotopic data through the various corrections in a transparent, efficient and reproducible manner.

That said, most vendor software also performs some of these calculations and it can be useful to be able to compare new data reduction procedures against those implemented in the vendor software. For this purpose, isoreader retrieves vendor computed data tables whenever possible, as illustrated below.

Vendor Data Table

As with most data retrieval functions, the iso_get_vendor_data_table() function also allows specific column selection (by default, all columns are selected) and easy addition of file information via the include_file_info parameter (by default, none is included).

# entire vendor data table
cf_files %>% iso_get_vendor_data_table() %>% knitr::kable()
#> Info: aggregating vendor data table from 6 data file(s)
file_id Nr. Start Rt End Ampl 2 Ampl 3 BGD 2 BGD 3 rIntensity 2 rIntensity 3 rIntensity All Intensity 2 Intensity 3 Intensity All List First Peak rR 3H2/2H2 Is Ref.? R 3H2/2H2 Ref. Name rd 3H2/2H2 d 3H2/2H2 R 2H/1H d 2H/1H AT% 2H/1H Rps 3H2/2H2 Ampl 28 Ampl 29 Ampl 30 Ampl 44 Ampl 45 Ampl 46 BGD 28 BGD 29 BGD 30 BGD 44 BGD 45 BGD 46 rIntensity 28 rIntensity 29 rIntensity 30 Intensity 28 Intensity 29 Intensity 30 Sample Dilution rR 29N2/28N2 R 29N2/28N2 rd 29N2/28N2 d 29N2/28N2 R 15N/14N d 15N/14N AT% 15N/14N Rps 29N2/28N2 rIntensity 44 rIntensity 45 rIntensity 46 Intensity 44 Intensity 45 Intensity 46 rR 45CO2/44CO2 rR 46CO2/44CO2 R 45CO2/44CO2 rd 45CO2/44CO2 d 45CO2/44CO2 R 46CO2/44CO2 rd 46CO2/44CO2 d 46CO2/44CO2 R 13C/12C d 13C/12C AT% 13C/12C R 18O/16O d 18O/16O AT% 18O/16O R 17O/16O d 17O/16O Rps 45CO2/44CO2 Rps 46CO2/44CO2
continuous_flow_example.cf 1 283.404 286.330 293.018 3977.902 1075.2188 194.4964 59.91891 18553.91 4854.296 23408.20 18.55391 0.004854296 18.55876 1 0.2616320 0 0.0002614 methane ref -10.587034 -160.8789 0.0001307 -160.8789 0.01306760 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 2 318.307 321.233 327.921 3978.824 1076.2768 194.6741 59.72170 18558.22 4858.212 23416.43 18.55822 0.004858212 18.56307 NA 0.2617823 0 0.0002615 methane ref -10.018742 -160.3969 0.0001308 -160.3969 0.01307511 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 3 606.309 611.952 634.942 4992.800 1374.8432 216.3650 65.39321 24397.80 6433.988 30831.79 24.39780 0.006433988 24.40423 NA 0.2637118 0 0.0002635 methane ref -2.721810 -154.2084 0.0001317 -154.2084 0.01317147 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 4 666.083 671.517 699.314 4906.356 1351.9323 229.7344 69.24248 24374.92 6445.498 30820.41 24.37492 0.006445498 24.38136 NA 0.2644316 1 0.0002642 methane ref 0.000000 -151.9000 0.0001321 -151.9000 0.01320741 0.0003115 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 5 740.696 747.802 768.075 5227.052 1176.7574 229.8024 69.13564 18309.28 4462.697 22771.98 18.30928 0.004462697 18.31375 NA 0.2437396 0 0.0002436 methane ref -78.076804 -218.1169 0.0001218 -218.1169 0.01217635 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 6 801.515 809.457 829.312 5044.300 1126.9858 229.8969 69.30490 20955.17 5157.040 26112.21 20.95517 0.005157040 20.96033 NA 0.2460987 0 0.0002460 methane ref -69.011701 -210.4288 0.0001230 -210.4288 0.01229606 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 7 855.437 860.662 889.504 4128.717 1130.0235 231.4799 68.98743 21029.08 5558.151 26587.23 21.02908 0.005558151 21.03464 NA 0.2643079 1 0.0002642 methane ref 0.000000 -151.9000 0.0001321 -151.9000 0.01320741 0.0003115 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 8 927.751 936.529 961.609 4534.019 1081.2981 230.6880 68.62187 20808.25 5482.657 26290.90 20.80825 0.005482657 20.81373 NA 0.2634848 0 0.0002631 methane ref -4.271874 -155.5230 0.0001315 -155.5230 0.01315100 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 9 993.377 1002.155 1023.055 4353.581 942.3458 232.3942 69.41825 19012.23 4761.478 23773.71 19.01223 0.004761478 19.01699 NA 0.2504429 0 0.0002498 methane ref -54.507987 -198.1282 0.0001249 -198.1282 0.01248759 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 10 1048.971 1055.032 1085.964 4070.049 1115.3502 233.3084 69.43352 20304.21 5382.550 25686.76 20.30421 0.005382550 20.30959 NA 0.2650953 1 0.0002642 methane ref 0.000000 -151.9000 0.0001321 -151.9000 0.01320741 0.0003115 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 11 1126.719 1135.915 1154.307 4377.211 967.1902 230.9224 68.91989 20212.07 5118.810 25330.88 20.21207 0.005118810 20.21719 NA 0.2532552 0 0.0002525 methane ref -44.315335 -189.4838 0.0001262 -189.4838 0.01262220 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 12 1191.927 1201.332 1223.068 4383.536 931.2333 232.2101 69.20323 20176.05 4992.138 25168.19 20.17605 0.004992138 20.18104 NA 0.2474289 0 0.0002467 methane ref -66.026049 -207.8967 0.0001234 -207.8967 0.01233549 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 13 1244.177 1249.402 1283.051 4160.401 1141.0546 230.8278 68.62405 20694.94 5481.328 26176.27 20.69494 0.005481328 20.70043 NA 0.2648631 1 0.0002642 methane ref 0.000000 -151.9000 0.0001321 -151.9000 0.01320741 0.0003115 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 14 1324.433 1333.629 1356.201 4316.019 980.2678 233.2191 69.48585 20458.90 5319.453 25778.35 20.45890 0.005319453 20.46422 NA 0.2600068 0 0.0002591 methane ref -19.151240 -168.1422 0.0001296 -168.1422 0.01295451 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 15 1386.297 1395.284 1416.811 3705.608 810.6083 220.9844 65.88735 16664.94 4204.549 20869.48 16.66494 0.004204549 16.66914 NA 0.2522992 0 0.0002513 methane ref -48.806364 -193.2927 0.0001256 -193.2927 0.01256289 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 16 1453.595 1459.447 1490.170 4182.669 1150.8922 216.9934 64.96403 22223.49 5898.395 28121.88 22.22349 0.005898395 22.22939 NA 0.2654126 1 0.0002642 methane ref 0.000000 -151.9000 0.0001321 -151.9000 0.01320741 0.0003115 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 17 1600.940 1608.673 1636.470 4302.989 1182.7732 229.2687 68.93950 22838.87 6044.344 28883.22 22.83887 0.006044344 22.84492 NA 0.2646516 0 0.0002634 methane ref -2.867311 -154.3318 0.0001317 -154.3318 0.01316955 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 18 1736.372 1739.716 1746.195 3973.676 1077.9145 194.8355 60.10338 18479.60 4857.156 23336.75 18.47960 0.004857156 18.48445 NA 0.2628388 0 0.0002616 methane ref -9.697427 -160.1244 0.0001308 -160.1244 0.01307935 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.cf 19 1776.709 1779.426 1786.114 3971.912 1077.5780 194.9600 60.11840 18499.15 4859.828 23358.98 18.49915 0.004859828 18.50401 NA 0.2627055 0 0.0002615 methane ref -10.199640 -160.5503 0.0001307 -160.5503 0.01307272 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.dxf 1 40.128 59.983 63.327 NA NA NA NA NA NA 110929.75 NA NA 57.97671 1 NA 0 NA N2_zero NA NA NA NA NA NA 3024.040 2194.322 731.3983 NA NA NA 20.20419 16.66439 1175.724 NA NA NA 57524.32 41739.97 11665.466 57.52432 0.4173997 0.03499640 0 0.7256056 0.0073565 0.01600287 0.01600287 0.0036783 0.01600287 0.3664779 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.dxf 2 90.915 110.770 114.114 NA NA NA NA NA NA 110486.88 NA NA 57.83398 NA NA 1 NA N2_zero NA NA NA NA NA NA 3022.789 2193.600 716.5056 NA NA NA 20.37561 16.70776 1229.999 NA NA NA 57383.21 41636.91 11466.758 57.38321 0.4163691 0.03440027 0 0.7255940 0.0073564 0.00000000 0.00000000 0.0036782 0.00000000 0.3664720 0.0073564 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.dxf 3 125.818 155.287 216.733 NA NA NA NA NA NA 98168.61 NA NA 53.13610 NA NA 0 NA N2_zero NA NA NA NA NA NA 2073.872 1507.741 306.0217 NA NA NA 20.61750 16.67424 1364.278 NA NA NA 52731.67 38301.92 7135.016 52.73167 0.3830192 0.02140505 0 0.7263552 0.0073641 1.04910065 1.04910065 0.0036821 1.04910065 0.3668551 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
continuous_flow_example.dxf 4 274.208 300.124 365.750 NA NA NA NA NA NA 240551.17 NA NA 68.82495 1 NA 0 NA CO2_zero NA NA NA NA NA NA NA NA NA 2588.446 2969.023 3634.147 NA NA NA 1.290191 1.269749 1.981168 NA NA NA NA NA NA 89 NA NA NA NA NA NA NA NA 67763.92 77523.47 95263.78 67.76392 0.7752347 0.2857913 1.144023 1.405819 0.0117810 -17.0104406 -1.701044e+01 0.0040067 -3.20667726 -3.20667726 0.0109776 -1.811690e+01 1.085845 0.0019988 -3.16962276 0.1994857 0.0004017 -1.6367818 NA NA
continuous_flow_example.dxf 5 416.537 436.183 446.215 NA NA NA NA NA NA 328751.58 NA NA 93.43697 NA NA 0 NA CO2_zero NA NA NA NA NA NA NA NA NA 4948.947 5759.321 6977.775 NA NA NA 2.972498 3.155700 4.344203 NA NA NA NA NA NA 89 NA NA NA NA NA NA NA NA 91977.27 107053.51 129720.80 91.97727 1.0705351 0.3891624 1.163913 1.410357 0.0119858 0.0797209 7.972090e-02 0.0040196 0.01142322 0.01142322 0.0111812 8.504090e-02 1.105752 0.0020052 0.01124505 0.2001210 0.0004023 0.0058024 NA NA
continuous_flow_example.dxf 6 466.906 486.761 497.002 NA NA NA NA NA NA 330289.66 NA NA 93.87689 NA NA 1 NA CO2_zero NA NA NA NA NA NA NA NA NA 4951.985 5762.556 6982.150 NA NA NA 3.138675 3.423053 4.544472 NA NA NA NA NA NA 89 NA NA NA NA NA NA NA NA 92410.41 107549.07 130330.19 92.41041 1.0754907 0.3909906 1.163820 1.410341 0.0119849 0.0000000 2.220446e-13 0.0040196 0.00000000 0.00000000 0.0111802 2.220446e-13 1.105659 0.0020052 0.00000000 0.2001187 0.0004023 0.0000000 0.0119849 0.0040196
# get specific parts and add some file information
cf_files %>%
  iso_get_vendor_data_table(
    # select peak number, ret. time, overall intensity and all H delta columns
    select = c(Nr., Rt, area = `rIntensity All`, matches("^d \\d+H")),
    # include the Analysis number fron the file info and rename it to 'run'
    include_file_info = c(run = Analysis)
  ) %>%
  knitr::kable()
#> Info: aggregating vendor data table from 6 data file(s), including file info 'c(run = Analysis)'
file_id run Nr. Rt area d 3H2/2H2 d 2H/1H
continuous_flow_example.cf 6520 1 286.330 23408.20 -160.8789 -160.8789
continuous_flow_example.cf 6520 2 321.233 23416.43 -160.3969 -160.3969
continuous_flow_example.cf 6520 3 611.952 30831.79 -154.2084 -154.2084
continuous_flow_example.cf 6520 4 671.517 30820.41 -151.9000 -151.9000
continuous_flow_example.cf 6520 5 747.802 22771.98 -218.1169 -218.1169
continuous_flow_example.cf 6520 6 809.457 26112.21 -210.4288 -210.4288
continuous_flow_example.cf 6520 7 860.662 26587.23 -151.9000 -151.9000
continuous_flow_example.cf 6520 8 936.529 26290.90 -155.5230 -155.5230
continuous_flow_example.cf 6520 9 1002.155 23773.71 -198.1282 -198.1282
continuous_flow_example.cf 6520 10 1055.032 25686.76 -151.9000 -151.9000
continuous_flow_example.cf 6520 11 1135.915 25330.88 -189.4838 -189.4838
continuous_flow_example.cf 6520 12 1201.332 25168.19 -207.8967 -207.8967
continuous_flow_example.cf 6520 13 1249.402 26176.27 -151.9000 -151.9000
continuous_flow_example.cf 6520 14 1333.629 25778.35 -168.1422 -168.1422
continuous_flow_example.cf 6520 15 1395.284 20869.48 -193.2927 -193.2927
continuous_flow_example.cf 6520 16 1459.447 28121.88 -151.9000 -151.9000
continuous_flow_example.cf 6520 17 1608.673 28883.22 -154.3318 -154.3318
continuous_flow_example.cf 6520 18 1739.716 23336.75 -160.1244 -160.1244
continuous_flow_example.cf 6520 19 1779.426 23358.98 -160.5503 -160.5503
continuous_flow_example.dxf 2921 1 59.983 110929.75 NA NA
continuous_flow_example.dxf 2921 2 110.770 110486.88 NA NA
continuous_flow_example.dxf 2921 3 155.287 98168.61 NA NA
continuous_flow_example.dxf 2921 4 300.124 240551.17 NA NA
continuous_flow_example.dxf 2921 5 436.183 328751.58 NA NA
continuous_flow_example.dxf 2921 6 486.761 330289.66 NA NA

# the data table also provides units if included in the original data file
# which can be made explicit using the function iso_make_units_explicit()
cf_files %>%
  iso_get_vendor_data_table(
    # select peak number, ret. time, overall intensity and all H delta columns
    select = c(Nr., Rt, area = `rIntensity All`, matches("^d \\d+H")),
    # include the Analysis number fron the file info and rename it to 'run'
    include_file_info = c(run = Analysis)
  ) %>%
  # make column units explicit
  iso_make_units_explicit() %>%
  knitr::kable()
#> Info: aggregating vendor data table from 6 data file(s), including file info 'c(run = Analysis)'
file_id run Nr. Rt [s] area [mVs] d 3H2/2H2 [permil] d 2H/1H [permil]
continuous_flow_example.cf 6520 1 286.330 23408.20 -160.8789 -160.8789
continuous_flow_example.cf 6520 2 321.233 23416.43 -160.3969 -160.3969
continuous_flow_example.cf 6520 3 611.952 30831.79 -154.2084 -154.2084
continuous_flow_example.cf 6520 4 671.517 30820.41 -151.9000 -151.9000
continuous_flow_example.cf 6520 5 747.802 22771.98 -218.1169 -218.1169
continuous_flow_example.cf 6520 6 809.457 26112.21 -210.4288 -210.4288
continuous_flow_example.cf 6520 7 860.662 26587.23 -151.9000 -151.9000
continuous_flow_example.cf 6520 8 936.529 26290.90 -155.5230 -155.5230
continuous_flow_example.cf 6520 9 1002.155 23773.71 -198.1282 -198.1282
continuous_flow_example.cf 6520 10 1055.032 25686.76 -151.9000 -151.9000
continuous_flow_example.cf 6520 11 1135.915 25330.88 -189.4838 -189.4838
continuous_flow_example.cf 6520 12 1201.332 25168.19 -207.8967 -207.8967
continuous_flow_example.cf 6520 13 1249.402 26176.27 -151.9000 -151.9000
continuous_flow_example.cf 6520 14 1333.629 25778.35 -168.1422 -168.1422
continuous_flow_example.cf 6520 15 1395.284 20869.48 -193.2927 -193.2927
continuous_flow_example.cf 6520 16 1459.447 28121.88 -151.9000 -151.9000
continuous_flow_example.cf 6520 17 1608.673 28883.22 -154.3318 -154.3318
continuous_flow_example.cf 6520 18 1739.716 23336.75 -160.1244 -160.1244
continuous_flow_example.cf 6520 19 1779.426 23358.98 -160.5503 -160.5503
continuous_flow_example.dxf 2921 1 59.983 110929.75 NA NA
continuous_flow_example.dxf 2921 2 110.770 110486.88 NA NA
continuous_flow_example.dxf 2921 3 155.287 98168.61 NA NA
continuous_flow_example.dxf 2921 4 300.124 240551.17 NA NA
continuous_flow_example.dxf 2921 5 436.183 328751.58 NA NA
continuous_flow_example.dxf 2921 6 486.761 330289.66 NA NA

For expert users: retrieving all data

For users familiar with the nested data frames from the tidyverse (particularly tidyr’s nest and unnest), there is an easy way to retrieve all data from the iso file objects in a single nested data frame:

all_data <- cf_files %>% iso_get_all_data()
#> Info: aggregating all data from 6 data file(s)
# not printed out because this data frame is very big

Saving collections

Saving entire collections of isofiles for retrieval at a later point is easily done using the iso_save function which stores collections or individual isoreader file objects in the efficient R data storage format .rds (if not specified, the extension .cf.rds will be automatically appended). These saved collections can be conveniently read back using the same iso_read_continuous_flow command used for raw data files.

# export to R data archive
cf_files %>% iso_save("cf_files_export.cf.rds")
#> Info: exporting data from 6 iso_files into R Data Storage 'cf_files_export.cf.rds'

# read back the exported R data archive
cf_files <- iso_read_continuous_flow("cf_files_export.cf.rds")
#> Info: preparing to read 1 data files (all will be cached)...
#> Info: reading file 'cf_files_export.cf.rds' with '.cf.rds' reader...
#> Info: loaded 6 data files from R Data Storage
#> Info: finished reading 1 files in 0.11 secs
cf_files %>% iso_get_data_summary() %>% knitr::kable()
#> Info: aggregating data summary from 6 data file(s)
file_id raw_data file_info method_info vendor_data_table file_path
continuous_flow_example.cf 8605 time points, 2 ions (2,3) 21 entries standards, resistors 19 rows, 25 columns continuous_flow_example.cf
6632_WSL-2 wood 1952 time points, 3 ions (45,46,44) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_0b7e6b49-7756-4cca-b387-4d6617cb28e6
6605_USGS41 2387 time points, 2 ions (29,28) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_0e08e8b4-c625-4fd7-bf5b-9f4823f660cd
6617_IAEA600 1796 time points, 3 ions (45,46,44) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_1de1fdc3-0797-4e44-8357-52acbd0d4a6d
6630_GlutamicAcid04 2386 time points, 2 ions (29,28) 17 entries no method info no vendor data table continuous_flow_example.iarc|Task_261a3286-0bc6-4aca-bba2-183c02597d8b
continuous_flow_example.dxf 2435 time points, 6 ions (28,29,30,44,45,46) 21 entries standards, resistors 6 rows, 60 columns continuous_flow_example.dxf

Data Export

At the moment, isoreader supports export of all data to Excel and the Feather file format (a Python/R cross-over format). Note that both export methods have similar syntax and append the appropriate file extension for each type of export file (.cf.xlsx and .cf.feather, respectively).

# export to excel
cf_files %>% iso_export_to_excel("cf_files_export")

# data sheets available in the exported data file:
readxl::excel_sheets("cf_files_export.cf.xlsx")
# export to feather
cf_files %>% iso_export_to_feather("cf_files_export")

# exported feather files
list.files(pattern = ".cf.feather")