Introduction

Isoreader supports several dual inlet IRMS data formats. This vignette shows some of the functionality for dual inlet data 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 dual inlet files is as simple as passing one or multiple file or folder paths to the iso_read_dual_inlet() function. If folders are provided, any files that have a recognized continuous flow file extensions within those folders will be processed (e.g. all .did and .caf). 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).

# 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 dual inlet examples
di_files <-
  iso_read_dual_inlet(
    iso_get_reader_example("dual_inlet_example.did"),
    iso_get_reader_example("dual_inlet_example.caf"),
    iso_get_reader_example("dual_inlet_nu_example.txt"),
    nu_masses = 49:44
  )
#> Info: preparing to read 3 data files (all will be cached)...
#> Info: reading file 'dual_inlet_example.did' from cache...
#> Info: reading file 'dual_inlet_example.caf' from cache...
#> Info: reading file 'dual_inlet_nu_example.txt' with '.txt' reader...
#> Info: finished reading 3 files in 0.83 secs

File summary

The di_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.

di_files %>% iso_get_data_summary() %>% knitr::kable()
#> Info: aggregating data summary from 3 data file(s)
file_id raw_data file_info method_info vendor_data_table file_path
dual_inlet_example.did 7 cycles, 6 ions (44,45,46,47,48,49) 16 entries standards, resistors 7 rows, 8 columns dual_inlet_example.did
dual_inlet_example.caf 8 cycles, 6 ions (44,45,46,47,48,49) 22 entries standards, resistors 8 rows, 9 columns dual_inlet_example.caf
dual_inlet_nu_example.txt 82 cycles, 6 ions (44,45,46,47,48,49) 9 entries no method info no vendor data table dual_inlet_nu_example.txt

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.

di_files %>% iso_get_problems_summary() %>% knitr::kable()
file_id error warning
di_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
di_files %>% iso_get_file_info(select = c(-file_root)) %>% knitr::kable()
#> Info: aggregating file info from 3 data file(s), selecting info columns 'c(-file_root)'
file_id file_path file_subpath file_datetime file_size Line Peak Center Pressadjust Background Identifier 1 Identifier 2 Analysis Method measurement_info MS_integration_time.s Reference Refill Weight [mg] Sample Comment Preparation Pre Script Post Script Sample Name Sample Weight Method File Name
dual_inlet_example.did dual_inlet_example.did NA 2014-10-27 11:23:54 134446 158 1 1 1 CIT Carrara 13 49077 CO2_multiply_16V.met Peak Center found at [61032] , Background: 8.87 mV,11.31 mV,12.98 mV,6.40 mV,1.90 mV,5.88 mV (old253), PressAdjust: L: 15972.5 R: 15971.6 ( Manual Adjustment ) 26 NA NA NA NA NA NA NA NA NA NA
dual_inlet_example.caf dual_inlet_example.caf NA 2017-07-03 22:57:57 651704 1 NA NA NA 2H6 CARBO IIIUvU_Clump.met 16068 CARBO IIIUvU_Clump.met NA 26 NA 113 9 NA NA NA NA NA NA NA
dual_inlet_nu_example.txt dual_inlet_nu_example.txt NA 2017-02-21 17:46:17 309491 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA ETH-01 3480 C:Stable2 4-20-8-20.rdf
# select file information
di_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 = `Sample Name`,
      # select columns without renaming
      Analysis, Method, `Peak Center`,
      # select the time stamp and rename it to `Date & Time`
      `Date & Time` = file_datetime,
      # rename weight columns from the different file types
      `Sample Weight`, `Sample Weight` = `Weight [mg]`
    ),
    # explicitly allow for file specific rename (for the new ID column)
    file_specific = TRUE
  ) %>% knitr::kable()
#> Info: aggregating file info from 3 data file(s), selecting info columns 'c(ID = `Identifier 1`, ID = `Sample Name`, Analysis, Method, `Peak Center`, `Date & Time` = file_datetime, `Sample Weight`, `Sample Weight` = `Weight [mg]`)'
file_id ID Analysis Method Peak Center Date & Time Sample Weight
dual_inlet_example.did CIT Carrara 49077 CO2_multiply_16V.met 1 2014-10-27 11:23:54 NA
dual_inlet_example.caf 2H6 16068 CARBO IIIUvU_Clump.met NA 2017-07-03 22:57:57 113
dual_inlet_nu_example.txt ETH-01 NA NA NA 2017-02-21 17:46:17 3480

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
di_files2 <- di_files %>%
  iso_select_file_info(
    ID = `Identifier 1`, ID = `Sample Name`, Analysis, Method,
    `Peak Center`, `Date & Time` = file_datetime,
    `Sample Weight`, `Sample Weight` = `Weight [mg]`,
    file_specific = TRUE
  )
#> Info: selecting/renaming the following file info:
#>  - for 1 file(s): 'file_id', 'Identifier 1'->'ID', 'Analysis', 'Method', 'Peak Center', 'file_datetime'->'Date & Time'
#>  - for 1 file(s): 'file_id', 'Identifier 1'->'ID', 'Analysis', 'Method', 'Peak Center', 'file_datetime'->'Date & Time', 'Weight [mg]'->'Sample Weight'
#>  - for 1 file(s): 'file_id', 'Sample Name'->'ID', 'file_datetime'->'Date & Time', 'Sample Weight'

# fetch all file info
di_files2 %>% iso_get_file_info() %>% knitr::kable()
#> Info: aggregating file info from 3 data file(s)
file_id ID Analysis Method Peak Center Date & Time Sample Weight
dual_inlet_example.did CIT Carrara 49077 CO2_multiply_16V.met 1 2014-10-27 11:23:54 NA
dual_inlet_example.caf 2H6 16068 CARBO IIIUvU_Clump.met NA 2017-07-03 22:57:57 113
dual_inlet_nu_example.txt ETH-01 NA NA NA 2017-02-21 17:46:17 3480

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 'CIT' in the new ID field
di_files2 %>% iso_filter_files(grepl("CIT", ID)) %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: applying file filter, keeping 1 of 3 files
#> Info: aggregating file info from 1 data file(s)
file_id ID Analysis Method Peak Center Date & Time
dual_inlet_example.did CIT Carrara 49077 CO2_multiply_16V.met 1 2014-10-27 11:23:54

# find files that were run in 2017
di_files2 %>%
  iso_filter_files(`Date & Time` > "2017-01-01" & `Date & Time` < "2018-01-01") %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: applying file filter, keeping 2 of 3 files
#> Info: aggregating file info from 2 data file(s)
file_id ID Analysis Method Peak Center Date & Time Sample Weight
dual_inlet_example.caf 2H6 16068 CARBO IIIUvU_Clump.met NA 2017-07-03 22:57:57 113
dual_inlet_nu_example.txt ETH-01 NA NA NA 2017-02-21 17:46:17 3480

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/overwrite 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.

di_files3 <- di_files2 %>%
  iso_mutate_file_info(
    # update existing column
    ID = paste("ID:", ID),
    # introduce new column
    `Run in 2017?` = `Date & Time` > "2017-01-01" & `Date & Time` < "2018-01-01",
    # 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 3 data file(s)

di_files3 %>%
  iso_get_file_info() %>%
  iso_make_units_explicit() %>%
  knitr::kable()
#> Info: aggregating file info from 3 data file(s)
file_id ID Analysis Method Peak Center Date & Time Sample Weight [mg] Run in 2017?
dual_inlet_example.did ID: CIT Carrara 49077 CO2_multiply_16V.met 1 2014-10-27 11:23:54 NA FALSE
dual_inlet_example.caf ID: 2H6 16068 CARBO IIIUvU_Clump.met NA 2017-07-03 22:57:57 113 TRUE
dual_inlet_nu_example.txt ID: ETH-01 NA NA NA 2017-02-21 17:46:17 3480 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(
      ~Analysis, ~`Run in 2017?`,  ~process,  ~info,
       NA,       TRUE,              "yes",     "2017 runs",
       NA,       FALSE,             "yes",     "other runs"
    ),
    # new information for a single specific file
    dplyr::tribble(
      ~Analysis, ~process,  ~note,
       "16068",   "no",      "did not inject properly"
    )
  )
new_info %>% knitr::kable()
Analysis Run in 2017? process info note
NA TRUE yes 2017 runs NA
NA FALSE yes other runs NA
16068 NA no NA did not inject properly

# adding it to the isofiles
di_files3 %>%
  iso_add_file_info(new_info, by1 = "Run in 2017?", by2 = "Analysis") %>%
  iso_get_file_info(select = !!names(new_info)) %>%
  knitr::kable()
#> Info: adding new file information ('process', 'info', 'note') to 3 data file(s), joining by 'Run in 2017?' then 'Analysis'...
#>  - 'Run in 2017?' join: 2/2 new info rows matched 3/3 data files - 1 of these was/were also matched by subsequent joins which took precedence
#>  - 'Analysis' join: 1/1 new info rows matched 1/3 data files
#> Info: aggregating file info from 3 data file(s), selecting info columns 'Analysis', 'Run in 2017?', 'process', 'info', 'note'
file_id Analysis Run in 2017? process info note
dual_inlet_example.did 49077 FALSE yes other runs NA
dual_inlet_example.caf 16068 TRUE no NA did not inject properly
dual_inlet_nu_example.txt NA TRUE yes 2017 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
di_files2 %>%
  iso_mutate_file_info(
    # change type of Peak Center to logical
    `Peak Center` = parse_logical(`Peak Center`),
    # retrieve first word of Method column
    Method_1st = extract_word(Method),
    # retrieve second word of Method column
    Method_2nd = extract_word(Method, 2),
    # retrieve file extension from the file_id using regular expression
    extension = extract_substring(file_id, "\\.(\\w+)$", capture_bracket = 1)
  ) %>%
  iso_get_file_info(select = c(extension, `Peak Center`, matches("Method"))) %>%
  knitr::kable()
#> Info: mutating file info for 3 data file(s)
#> Info: aggregating file info from 3 data file(s), selecting info columns 'c(extension, `Peak Center`, matches("Method"))'
file_id extension Peak Center Method Method_1st Method_2nd
dual_inlet_example.did did TRUE CO2_multiply_16V.met CO2 multiply
dual_inlet_example.caf caf NA CARBO IIIUvU_Clump.met CARBO IIIUvU
dual_inlet_nu_example.txt txt NA NA NA NA

# use parsing in iso_filter_file_info
di_files2 %>%
  iso_filter_files(parse_integer(Analysis) > 1500) %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: applying file filter, keeping 2 of 3 files
#> Info: aggregating file info from 2 data file(s)
file_id ID Analysis Method Peak Center Date & Time Sample Weight
dual_inlet_example.did CIT Carrara 49077 CO2_multiply_16V.met 1 2014-10-27 11:23:54 NA
dual_inlet_example.caf 2H6 16068 CARBO IIIUvU_Clump.met NA 2017-07-03 22:57:57 113

# use iso_parse_file_info for simplified parsing of column data types
di_files2 %>%
  iso_parse_file_info(
    integer = Analysis,
    number = `Sample Weight`,
    logical = `Peak Center`
  ) %>%
  iso_get_file_info() %>%
  knitr::kable()
#> Info: parsing 3 file info columns for 3 data file(s):
#>  - to integer: 'Analysis'
#>  - to logical: 'Peak Center'
#>  - to number: 'Sample Weight'
#> Info: aggregating file info from 3 data file(s)
file_id ID Analysis Method Peak Center Date & Time Sample Weight
dual_inlet_example.did CIT Carrara 49077 CO2_multiply_16V.met TRUE 2014-10-27 11:23:54 NA
dual_inlet_example.caf 2H6 16068 CARBO IIIUvU_Clump.met NA 2017-07-03 22:57:57 113
dual_inlet_nu_example.txt ETH-01 NA NA NA 2017-02-21 17:46:17 3480

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):

di_files %>% iso_get_resistors() %>% knitr::kable()
#> Info: aggregating resistors info from 3 data file(s)
file_id cup R.Ohm mass
dual_inlet_example.did 1 3.000000e+08 44
dual_inlet_example.did 2 3.000000e+10 45
dual_inlet_example.did 3 1.000000e+11 46
dual_inlet_example.did 4 1.000000e+12 47
dual_inlet_example.did 5 5.000000e+11 48
dual_inlet_example.did 6 1.000000e+12 49
dual_inlet_example.caf 1 2.970297e+08 44
dual_inlet_example.caf 2 1.500000e+10 45
dual_inlet_example.caf 3 5.000000e+10 46
dual_inlet_example.caf 4 5.000000e+11 47
dual_inlet_example.caf 5 9.900990e+09 48
dual_inlet_example.caf 6 5.000000e+11 49

Reference values

As well as isotopic reference values for the different gases:

# reference delta values without ratio values
di_files %>% iso_get_standards(file_id:reference) %>% knitr::kable()
#> Info: aggregating standards info from 3 data file(s)
file_id standard gas delta_name delta_value reference
dual_inlet_example.did Caltech-1960C CO2 d 13C/12C -3.56 VPDB
dual_inlet_example.did Caltech-1960C CO2 d 18O/16O 25.01 VSMOW
dual_inlet_example.caf CO2clump tank CO2 d 13C/12C -2.82 VPDB
dual_inlet_example.caf CO2clump tank CO2 d 18O/16O -4.67 VPDB
dual_inlet_example.caf CO2clump tank CO2 d 47CO2/44CO2 0.00 None
dual_inlet_example.caf CO2clump tank CO2 d 48CO2/44CO2 0.00 None
dual_inlet_example.caf CO2clump tank CO2 d 49CO2/44CO2 0.00 None
# reference values with ratios
di_files %>% iso_get_standards() %>% knitr::kable()
#> Info: aggregating standards info from 3 data file(s)
file_id standard gas delta_name delta_value reference element ratio_name ratio_value
dual_inlet_example.did Caltech-1960C CO2 d 13C/12C -3.56 VPDB C R 13C/12C 0.0111802
dual_inlet_example.did Caltech-1960C CO2 d 13C/12C -3.56 VPDB O R 18O/16O 0.0020672
dual_inlet_example.did Caltech-1960C CO2 d 13C/12C -3.56 VPDB O R 17O/16O 0.0003860
dual_inlet_example.did Caltech-1960C CO2 d 18O/16O 25.01 VSMOW H R 2H/1H 0.0001558
dual_inlet_example.did Caltech-1960C CO2 d 18O/16O 25.01 VSMOW O R 17O/16O 0.0003799
dual_inlet_example.did Caltech-1960C CO2 d 18O/16O 25.01 VSMOW O R 18O/16O 0.0020052
dual_inlet_example.caf CO2clump tank CO2 d 13C/12C -2.82 VPDB C R 13C/12C 0.0111802
dual_inlet_example.caf CO2clump tank CO2 d 13C/12C -2.82 VPDB O R 18O/16O 0.0020672
dual_inlet_example.caf CO2clump tank CO2 d 13C/12C -2.82 VPDB O R 17O/16O 0.0003860
dual_inlet_example.caf CO2clump tank CO2 d 18O/16O -4.67 VPDB C R 13C/12C 0.0111802
dual_inlet_example.caf CO2clump tank CO2 d 18O/16O -4.67 VPDB O R 18O/16O 0.0020672
dual_inlet_example.caf CO2clump tank CO2 d 18O/16O -4.67 VPDB O R 17O/16O 0.0003860
dual_inlet_example.caf CO2clump tank CO2 d 47CO2/44CO2 0.00 None NA NA NA
dual_inlet_example.caf CO2clump tank CO2 d 48CO2/44CO2 0.00 None NA NA NA
dual_inlet_example.caf CO2clump tank CO2 d 49CO2/44CO2 0.00 None NA NA NA

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)
di_files %>% iso_get_raw_data() %>% head(n=10) %>% knitr::kable()
#> Info: aggregating raw data from 3 data file(s)
file_id type cycle v44.mV v45.mV v46.mV v47.mV v48.mV v49.mV block i44.A i45.A i46.A i47.A i48.A i49.A
dual_inlet_example.did standard 0 15946.42 19001.94 21960.62 2513.280 29.78742 -181.2712 NA NA NA NA NA NA NA
dual_inlet_example.did standard 1 15941.14 18995.66 21954.19 2512.345 29.80048 -180.9752 NA NA NA NA NA NA NA
dual_inlet_example.did standard 2 15933.54 18986.64 21943.58 2511.280 29.81818 -180.7748 NA NA NA NA NA NA NA
dual_inlet_example.did standard 3 15916.58 18966.34 21919.97 2508.497 29.78074 -180.4583 NA NA NA NA NA NA NA
dual_inlet_example.did standard 4 15901.62 18948.60 21899.17 2506.104 29.76304 -180.2311 NA NA NA NA NA NA NA
dual_inlet_example.did standard 5 15896.00 18941.97 21891.49 2505.600 29.75818 -180.3095 NA NA NA NA NA NA NA
dual_inlet_example.did standard 6 15884.63 18928.51 21876.19 2503.555 29.72923 -180.2013 NA NA NA NA NA NA NA
dual_inlet_example.did standard 7 15875.89 18918.12 21863.96 2502.444 29.69513 -180.0340 NA NA NA NA NA NA NA
dual_inlet_example.did sample 1 15955.18 19122.71 22237.99 2559.245 31.09436 -181.2460 NA NA NA NA NA NA NA
dual_inlet_example.did sample 2 15945.64 19111.16 22224.65 2557.857 31.09153 -180.9976 NA NA NA NA NA NA NA
# get specific raw data and add some file information
di_files %>%
  iso_get_raw_data(
    # select just time and the two ions
    select = c(type, cycle, v44.mV, v45.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 3 data file(s), selecting data columns 'c(type, cycle, v44.mV, v45.mV)', including file info 'c(run = Analysis)'
file_id run type cycle v44.mV v45.mV
dual_inlet_example.did 49077 standard 0 15946.42 19001.94
dual_inlet_example.did 49077 standard 1 15941.14 18995.66
dual_inlet_example.did 49077 standard 2 15933.54 18986.64
dual_inlet_example.did 49077 standard 3 15916.58 18966.34
dual_inlet_example.did 49077 standard 4 15901.62 18948.60
dual_inlet_example.did 49077 standard 5 15896.00 18941.97
dual_inlet_example.did 49077 standard 6 15884.63 18928.51
dual_inlet_example.did 49077 standard 7 15875.89 18918.12
dual_inlet_example.did 49077 sample 1 15955.18 19122.71
dual_inlet_example.did 49077 sample 2 15945.64 19111.16

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 signal intensities into properly referenced isotopic measurement. 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
di_files %>% iso_get_vendor_data_table() %>% knitr::kable()
#> Info: aggregating vendor data table from 3 data file(s)
file_id cycle d 45CO2/44CO2 d 46CO2/44CO2 d 13C/12C d 18O/16O d 17O/16O AT% 13C/12C AT% 18O/16O d 47CO2/44CO2 d 48CO2/44CO2 d 49CO2/44CO2
dual_inlet_example.did 1 3.3287770 37.330647 2.1929917 37.366456 6.202321 1.108056 0.2075809 NA NA NA
dual_inlet_example.did 2 3.3209553 37.311799 2.1852948 37.347605 6.192886 1.108048 0.2075772 NA NA NA
dual_inlet_example.did 3 3.3263384 37.317514 2.1908570 37.353314 6.195743 1.108054 0.2075783 NA NA NA
dual_inlet_example.did 4 3.3200304 37.317207 2.1841057 37.353021 6.195597 1.108047 0.2075782 NA NA NA
dual_inlet_example.did 5 3.3187408 37.329768 2.1822644 37.365600 6.201893 1.108045 0.2075808 NA NA NA
dual_inlet_example.did 6 3.3177548 37.322849 2.1814601 37.358675 6.198427 1.108044 0.2075794 NA NA NA
dual_inlet_example.did 7 3.3217518 37.318368 2.1859087 37.354180 6.196176 1.108049 0.2075785 NA NA NA
dual_inlet_example.caf 1 0.7000753 7.543204 0.4669423 7.550236 6.316518 NA NA 15.65910 22.50135 -58.12863
dual_inlet_example.caf 2 0.6846506 7.518991 0.4512997 7.526031 6.304043 NA NA 15.62094 22.59733 -54.25812
dual_inlet_example.caf 3 0.6827013 7.503826 0.4497784 7.510853 6.296221 NA NA 15.56356 22.81077 -52.21288
dual_inlet_example.caf 4 0.6940694 7.513421 0.4616176 7.520432 6.301158 NA NA 15.64200 23.18259 -66.43734
dual_inlet_example.caf 5 0.6748151 7.499821 0.4414657 7.506862 6.294164 NA NA 15.56706 22.81533 -67.80591
dual_inlet_example.caf 6 0.6836593 7.492892 0.4512181 7.499905 6.290578 NA NA 15.65654 23.33585 -66.66771
dual_inlet_example.caf 7 0.6960376 7.491672 0.4645483 7.498654 6.289934 NA NA 15.56031 23.89354 -42.32076
dual_inlet_example.caf 8 0.6929899 7.499328 0.4609894 7.506326 6.293888 NA NA 15.45959 24.43994 -48.39355
# get specific parts and add some file information
di_files %>%
  iso_get_vendor_data_table(
    # select cycle and all carbon columns
    select = c(cycle, matches("C")),
    # include the Identifier 1 fron the file info and rename it to 'id'
    include_file_info = c(id = `Identifier 1`)
  ) %>% knitr::kable()
#> Info: aggregating vendor data table from 3 data file(s), including file info 'c(id = `Identifier 1`)'
file_id id cycle d 45CO2/44CO2 d 46CO2/44CO2 d 13C/12C AT% 13C/12C d 47CO2/44CO2 d 48CO2/44CO2 d 49CO2/44CO2
dual_inlet_example.did CIT Carrara 1 3.3287770 37.330647 2.1929917 1.108056 NA NA NA
dual_inlet_example.did CIT Carrara 2 3.3209553 37.311799 2.1852948 1.108048 NA NA NA
dual_inlet_example.did CIT Carrara 3 3.3263384 37.317514 2.1908570 1.108054 NA NA NA
dual_inlet_example.did CIT Carrara 4 3.3200304 37.317207 2.1841057 1.108047 NA NA NA
dual_inlet_example.did CIT Carrara 5 3.3187408 37.329768 2.1822644 1.108045 NA NA NA
dual_inlet_example.did CIT Carrara 6 3.3177548 37.322849 2.1814601 1.108044 NA NA NA
dual_inlet_example.did CIT Carrara 7 3.3217518 37.318368 2.1859087 1.108049 NA NA NA
dual_inlet_example.caf 2H6 1 0.7000753 7.543204 0.4669423 NA 15.65910 22.50135 -58.12863
dual_inlet_example.caf 2H6 2 0.6846506 7.518991 0.4512997 NA 15.62094 22.59733 -54.25812
dual_inlet_example.caf 2H6 3 0.6827013 7.503826 0.4497784 NA 15.56356 22.81077 -52.21288
dual_inlet_example.caf 2H6 4 0.6940694 7.513421 0.4616176 NA 15.64200 23.18259 -66.43734
dual_inlet_example.caf 2H6 5 0.6748151 7.499821 0.4414657 NA 15.56706 22.81533 -67.80591
dual_inlet_example.caf 2H6 6 0.6836593 7.492892 0.4512181 NA 15.65654 23.33585 -66.66771
dual_inlet_example.caf 2H6 7 0.6960376 7.491672 0.4645483 NA 15.56031 23.89354 -42.32076
dual_inlet_example.caf 2H6 8 0.6929899 7.499328 0.4609894 NA 15.45959 24.43994 -48.39355

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 <- di_files %>% iso_get_all_data()
#> Info: aggregating all data from 3 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 .di.rds will be automatically appended). These saved collections can be conveniently read back using the same iso_read_dual_inlet command used for raw data files.

# export to R data archive
di_files %>% iso_save("di_files_export.di.rds")
#> Info: exporting data from 3 iso_files into R Data Storage 'di_files_export.di.rds'

# read back the exported R data storage
iso_read_dual_inlet("di_files_export.di.rds")
#> Info: preparing to read 1 data files (all will be cached)...
#> Info: reading file 'di_files_export.di.rds' with '.di.rds' reader...
#> Info: loaded 3 data files from R Data Storage
#> Info: finished reading 1 files in 0.11 secs
#> Data from 3 dual inlet iso files: 
#> # A tibble: 3 × 6
#>   file_id     raw_data      file_info  method_info  vendor_data_tab… file_path  
#>   <chr>       <glue>        <chr>      <chr>        <chr>            <chr>      
#> 1 dual_inlet… 7 cycles, 6 … 16 entries standards, … 7 rows, 8 colum… dual_inlet…
#> 2 dual_inlet… 8 cycles, 6 … 22 entries standards, … 8 rows, 9 colum… dual_inlet…
#> 3 dual_inlet… 82 cycles, 6… 9 entries  no method i… no vendor data … dual_inlet…

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 (.di.xlsx and .di.feather, respectively).

# export to excel
di_files %>% iso_export_to_excel("di_files_export")

# data sheets available in the exported data file:
readxl::excel_sheets("di_files_export.di.xlsx")
# export to feather
di_files %>% iso_export_to_feather("di_files_export")

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