Structure

The term matching table contains links to the information needed to create the output table. Some general term matching tables can be created by the function create_term_matching_table() by specifying the used instruments and the origin key.

This function falls back on the included list of data frames tmt_list where each table is designed to fit one instrument. By calling the function with only one instrument name it will just extract the specific data frame. If called with more than one instrument (ordinarily 2) it will bind the tables together.

tmt_LC_pump <- create_term_match_table(
  instrument_list = c("Thermo EASY-nLC"))
  # no origin key set

The table always consists of 6 columns whose order is important. The count of rows differ depending on the choosen requirements and the type of instrument.

colnames(tmt_LC_pump)
#> [1] "term"         "term_verbose" "origin"       "handle_type" 
#> [5] "handle"       "example"

The first column of the resulting table is always the term. It is a short and unique description of the row. While the second, term_verbose contains a more detailed and readable title.

head(tmt_LC_pump[1:2])
#>                term
#> 1   hplc_instrument
#> 2       hplc_vendor
#> 3        hplc_model
#> 4       hplc_column
#> 5 hplc_mobilephases
#> 6 hplc_samplevolume
#>                                               term_verbose
#> 1 High Performance Liquid Chromatography (HPLC) Instrument
#> 2                                              HPLC Vendor
#> 3                                               HPLC Model
#> 4                                    Chromatography Column
#> 5                             Chromatography Mobile Phases
#> 6                                   Injected Sample Volume

The third collumn origin contains keys determining which requirements to satisfy. These keys were read in the function create_term_matching_table() and compared to the value of origin_key.

head(tmt_LC_pump[3])
#>                           origin
#> 1 author;miape;jpr_guidelines_ms
#> 2 author;miape;jpr_guidelines_ms
#> 3 author;miape;jpr_guidelines_ms
#> 4 author;miape;jpr_guidelines_ms
#> 5 author;miape;jpr_guidelines_ms
#> 6                   author;miape

In the fourth column handle_type specifys how to interpret the fifth column handle. "list_path" indicates that handle is a path in the flattened json. "literal" leads to just copy the value of handle. With "parameter" the row stays empty because the information is not represented in the json.

head(tmt_LC_pump[4:5])
#>   handle_type                                                       handle
#> 1   list_path             Instruments.Thermo EASY-nLC.InstrumentFriendName
#> 2     literal                                            Thermo Scientific
#> 3   parameter                                                         <NA>
#> 4   parameter                                                         <NA>
#> 5   parameter                                                         <NA>
#> 6   list_path Instruments.Thermo EASY-nLC.Method.Sample pickup.Volume [µl]

The last column shows an example of which value the row could have.

head(tmt_LC_pump[c(2,6)])
#>                                               term_verbose
#> 1 High Performance Liquid Chromatography (HPLC) Instrument
#> 2                                              HPLC Vendor
#> 3                                               HPLC Model
#> 4                                    Chromatography Column
#> 5                             Chromatography Mobile Phases
#> 6                                   Injected Sample Volume
#>             example
#> 1   Thermo EASY-nLC
#> 2 Thermo Scientific
#> 3              <NA>
#> 4              <NA>
#> 5              <NA>
#> 6              4.00