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Structure

marinakiweler edited this page Jun 7, 2019 · 3 revisions

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.

knitr::kable(tmt_LC_pump[1:2])
term term_verbose
hplc_instrument High Performance Liquid Chromatography (HPLC) Instrument
hplc_vendor HPLC Vendor
hplc_model HPLC Model
hplc_column Chromatography Column
hplc_mobilephases Chromatography Mobile Phases
hplc_samplevolume Injected Sample Volume
hplc_gradient Chromatography Gradient

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.

knitr::kable(tmt_LC_pump[3])
origin
author;miape;jpr_guidelines_ms
author;miape;jpr_guidelines_ms
author;miape;jpr_guidelines_ms
author;miape;jpr_guidelines_ms
author;miape;jpr_guidelines_ms
author;miape
author;miape;jpr_guidelines_ms

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.

knitr::kable(tmt_LC_pump[4:5])
handle_type handle
list_path Instruments.Thermo EASY-nLC.InstrumentFriendName
literal Thermo Scientific
parameter NA
parameter NA
parameter NA
list_path Instruments.Thermo EASY-nLC.Method.Sample pickup.Volume [µl]
function document_easy_nlc_gradient

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

knitr::kable(tmt_LC_pump[c(2,6)])
term_verbose example
High Performance Liquid Chromatography (HPLC) Instrument Thermo EASY-nLC
HPLC Vendor Thermo Scientific
HPLC Model NA
Chromatography Column NA
Chromatography Mobile Phases NA
Injected Sample Volume 4.00
Chromatography Gradient NA
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