update_check_log: Update the attached check log for an Olink dataset

In OlinkAnalyze: Facilitate Analysis of Proteomic Data from Olink

Update the attached check log for an Olink dataset

Description

Refreshes the check_log attached to an olink_class tibble or ArrowObject. This is useful after manually modifying or cleaning an Olink dataset, where the attached check log may no longer describe the current data.

The function can also be used to update only the column-name choices stored in the attached check log by supplying preferred_names, even when the data itself has not changed. The supplied names are validated by the same machinery used by check_npx().

If df already carries a check log, the existing log is used to preserve current preferred column-name choices before the log is regenerated. If no attached check log is found, check_log is used when supplied; otherwise check_npx() is run on df. This means update_check_log() can also be used to attach a check log to a plain tibble or ArrowObject, returning an olink_class object for tibble input or an ArrowObject with olink_check_log metadata for Arrow input.

Usage

update_check_log(df, check_log = NULL, preferred_names = NULL)

Arguments

df	A "tibble" or "ArrowObject" from read_npx.
check_log	Optional named list returned by check_npx(). If NULL, an attached check_log is used when present; otherwise check_npx() will be run internally using df.
preferred_names	A named character vector where names are internal column names and values are column names to be selected from the input data frame. Read the description for further information.

Value

The input data in its original output format with an updated check log attached. Tibbles are returned as olink_class objects and ArrowObjects are returned with refreshed olink_check_log schema metadata.

Author(s)

Klev Diamanti

Examples

## Not run: 

# get file
npx_file <- system.file(
  "extdata",
  "npx_data1.xlsx",
  package = "OlinkAnalyze"
)

# Example 1: manually modify the dataset and update the check log accordingly

# read file
npx_df <- OlinkAnalyze::read_npx(
  filename = npx_file,
  olink_platform = "Target 96"
)

# manually cleanup the data based on the warning messages from read_npx
npx_df <- npx_df |>
  # remove duplicated samples
  dplyr::filter(
    !grepl("^CONTROL", .data[["SampleID"]])
  ) |>
  # convert NPX and LOD columns to numeric
  dplyr::mutate(
    NPX = as.numeric(.data[["NPX"]]),
    LOD = as.numeric(.data[["LOD"]])
  )
# same result achieved by using clean_npx

# run update_check_log so that it describes the current status of the dataset
npx_df <- OlinkAnalyze::update_check_log(
  df = npx_df
)

# Example 2: change preferred column names to be used in the analyses

# update preferred column names without otherwise modifying the dataset
npx_df <- npx_df |>
  dplyr::mutate(
    PCNormalizedNPX = .data[["NPX"]]
  )

npx_df <- OlinkAnalyze::update_check_log(
  df = npx_df,
  preferred_names = c("quant" = "PCNormalizedNPX")
)

# Example 3: attach an existing check log to a plain tibble or ArrowObject

# attach an existing check log to a plain tibble or ArrowObject
npx_tbl <- OlinkAnalyze::rm_check_log(
  df = npx_df
)
check_log <- OlinkAnalyze::check_npx(
  df = npx_df,
  preferred_names = c("quant" = "PCNormalizedNPX")
)

# attach an existing check log to a plain tibble
npx_obj <- OlinkAnalyze::update_check_log(
  df = npx_tbl,
  check_log = check_log
)

# attach an existing check log to an ArrowObject
npx_arrow <- npx_tbl |>
  arrow::as_arrow_table() |>
  OlinkAnalyze::update_check_log(
    check_log = check_log
  )

# inspect ArrowObject has a check_log
names(npx_arrow$metadata)


## End(Not run)

OlinkAnalyze documentation built on Sept. 18, 2026, 5:06 p.m.