sets_read: Read sets and calculate concentrations and variability.
In bioset: Convert a Matrix of Raw Values into Nice and Tidy Data

Description Usage Arguments Value See Also Examples

Basically a wrapper around set_read(), set_calc_concentrations() and set_calc_variability().

For a gentler introduction see examples and Vignette "Introduction".

May write the processed data into two files: data_samples.csv, data_all.csv.

sets_read(sets, cal_names, cal_values, exclude_cals = list(),
  additional_vars = c("name"), additional_sep = "_", sep = ",",
  dec = ".", path = ".", file_name = "set_#NUM#.csv",
  model_func = fit_linear, plot_func = plot_linear,
  interpolate_func = interpolate_linear, write_data = TRUE,
  use_written_data = FALSE)

`sets`	The number of sets (e.g. 3`` attempts to readset_1.csv`,`set_2.csv`,`set_3.csv`), see`file_name'.
`cal_names`	A vector of strings containing the names of the samples used as calibrators.
`cal_values`	A numeric vector with the known concentrations of those samples (must be in the same order).
`exclude_cals`	A list of calibrators to exclude, e.g.: `list(set1 = c("CAL1"))`.
`additional_vars`	Vector of strings containing the names for the additional columns.
`additional_sep`	String / RegExp that separates additional vars, e.g.: `"ID_blue_cold"` with `additional_sep = "_"` will be separated into three columns containing `"ID"`, `"blue"` and `"cold"`. If the separated data would exceed the columns in `additional_vars` the last column will contain a string with separator (e.g.: `"blue_cold"`). If data is missing `NA` is inserted.
`sep`	Separator used in the csv-file, either "," or ";" (see `utils::read.csv()`).
`dec`	The character used for decimal points (see `utils::read.csv()`). "AUTO" will result in "." if `sep` is "," and "," for ";".
`path`	The path to the file (no trailing "/" or "\" !).
`file_name`	Name of the file from which to read the data. May contain "#NUM#" as a placeholder if you have multiple files.
`model_func`	A function generating a model to fit the calibrators, e.g. `fit_linear()`, `fit_lnln()`.
`plot_func`	Function used to display the fitted line.
`interpolate_func`	A function used to interpolate the concentrations of the other samples, based on the model, e.g. `interpolate_linear()`, `interpolate_lnln()`.
`write_data`	Write the calculated data into `data_all.csv` and `data_samples.csv`?
`use_written_data`	Try to read `data_all.csv` and `data_read.csv` instead of raw data. Useful if you have to re-run the script, but the raw data does not change.

A list:

$all: here you will find all the data , including calibrators, duplicates, ... (saved in data_all.csv if write_data = TRUE)
$samples: only one row per distinct sample here - no calibrators, no duplicates -> most often you will work with this data (saved in data_samples.csv if write_data = TRUE)
$set1: a list
- $plot: a plot showing you the function used to calculate the concentrations for this set. The points represent the calibrators.
- $model: the model as returned by model_func
($set2 - $setN): the same information for every set you have

Other set functions: set_calc_concentrations, set_calc_variability, set_read

# files "set_1.csv" and "set_2.csv" containing raw values and the
# corresponding lables (consisting of ID and point in time like
# "ID_TIME")
read.csv(
  file = system.file("extdata", "set_1.csv", package = "bioset"),
  header = FALSE,
  colClasses = "character"
)
read.csv(
  file = system.file("extdata", "set_2.csv", package = "bioset"),
  header = FALSE,
  colClasses = "character"
)

# the known concentration of the calibrators contained in these plates
cals <- c(10, 20, 30, 40)      # ng / ml
names(cals) <- c("CAL1", "CAL2", "CAL3", "CAL4")

# read both files into a tibble
# columns "ID" and "time" separated by "_"
# and calculate concentrations using the calibrators
result <- sets_read(
  sets = 2,                      # expect 2 plates
  path = system.file("extdata", package = "bioset"),
  additional_vars = c("ID", "time"),  # expect the labels to contain ID and
                                      # point in time
  additional_sep = "_",               # separated by "_"
  cal_names = names(cals),       # that's what they're called in the files
  cal_values = cals,             # the concentration has to be known
  write_data = FALSE             # do not store the results in csv-files
)

# inspect results (all values contained in the two original files)
result$all
# (all values except CAL1-4)
result$samples
# inspect goodness of fit
# for plate 1
result$set_1$plot
result$set_1$model
# for plate 2
result$set_2$plot
result$set_2$model