Read a plater-formatted file and turn it into a tidy data frame.Source:
Converts data from
plater format to a data frame with one well
per row identified by well name.
The path of a .csv file formatted as described below.
The name to give the column that will contain the well identifiers. Default "Wells".
The character used to separate columns in the file (e.g. "," or ";"). Defaults to ",".
Returns a data frame with each well as a row. One column will be
well_ids_column and contain the well names (A01, A02..).
There will be as many additional columns as layouts in
wells are omitted.
The .csv file should be formatted as a microtiter plate. The top-left most cell contains the name to use for the column representing that plate. For example, for a 96-well plate, the subsequent wells in the top row should be labeled 1-12. The subsequent cells in the first column should be labeled A-H. That is:
In this example, the cells within the plate contain the well IDs ("A01", "A02"), but they may contain arbitrary characters: numbers, letters, or punctuation. Any cell may also be blank.
Note that Microsoft Excel will sometimes include cells that appear to be blank in the .csv files it produces, so the files may have spurious columns or rows outside of the plate, causing errors. To solve this problem, copy and paste just the cells within the plate to a fresh worksheet and save it.
Multiple columns of information about a plate can be included in a single file. After the first plate, leave one row blank, and then add another plate formatted as described above. (The "blank" row should appear as blank in a spreadsheet editor, but as a row of commas when viewed as plain text.) As many plates as necessary can be included in a single file (e.g. data measured, subject, treatment, replicate, etc.).
file_path <- system.file("extdata", "example-1.csv", package = "plater") # Data are stored in plate-shaped form data <- read_plate( file = file_path, well_ids_column = "Wells") # Now data are tidy head(data) #> # A tibble: 6 × 5 #> Wells Drug Concentration Bacteria Killing #> <chr> <chr> <dbl> <chr> <dbl> #> 1 A01 A 100 E. coli 98 #> 2 A02 A 20 E. coli 95 #> 3 A03 A 4 E. coli 92 #> 4 A04 A 0.8 E. coli 41 #> 5 A05 A 0.16 E. coli 17 #> 6 A06 A 0.032 E. coli 2