(Deprecated) Import wide CSV files

Usage

import_data_csv(
  data = NULL,
  path,
  variable_name,
  analysis_id_col = NA,
  import_metadata = TRUE,
  first_feature_column = NA,
  na_strings = "NA"
)

Arguments

data: MRMhubExperiment object
path: One or more file names with path, or a folder path, which case all *.csv files in this folder will be read.
variable_name: Variable type representing the values in the table. Must be one of "intensity", "norm_intensity", "conc", "area", "height", "response".
analysis_id_col: Column to be used as analysis_id. NA (default) used 'analysis_id' if present, or the first column if it contains unique values.
import_metadata: Import additional metadata columns (e.g. batch ID, sample type) and add to the MRMhubExperiment object. Only following metadata column names are supported: "qc_type", "batch_id", "is_quantifier", "is_istd", "analysis_order"
first_feature_column: Column number of the first column representing the feature values
na_strings: A character vector of strings which are to be interpreted as NA values. Blank fields are also considered to be missing values.

Value

MRMhubExperiment object

Details

This function is deprecated. Please use import_data_csv_wide() instead.

Examples

file_path <- system.file("extdata", "plain_wide_dataset.csv", package = "mrmhub")

mexp <- MRMhubExperiment()

mexp <- import_data_csv(
  data = mexp,
  path = file_path,
 variable_name = "conc",
 import_metadata = TRUE)
#> ! The function import_data_csv is deprecated. Please use import_data_csv_wide instead.
#> ✔ Metadata column(s) 'qc_type, batch_id' imported. To ignore, set `import_metadata = FALSE`
#> ✔ Imported 87 analyses with 5 features.
#> ✔ Analysis metadata associated with 87 analyses.
#> ✔ Feature metadata associated with 5 features.
#> ℹ Analysis order was based on `analysis_order` column of imported data. Use `set_analysis_order` to change the order.

print(mexp)
#> 
#> ── MRMhubExperiment:  ──────────────────────────────────────────────────────────
#> NA | 87 analyses and 5 features | signal: feature_conc
#> Last step: Annotated raw CONC values
#> Normalized ✖ Quantitated ✖ Drift/batch ✖ Filtered ✖
#> ℹ Use `mrmhub_status()` for the full processing and metadata report