| Title: | Read, Tidy, and Display Data from Microtiter Plates |
|---|---|
| Description: | Tools for interacting with data from experiments done in microtiter plates. Easily read in plate-shaped data and convert it to tidy format, combine plate-shaped data with tidy data, and view tidy data in plate shape. |
| Authors: | Sean Hughes [aut, cre]
|
| Maintainer: | Sean Hughes <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.5 |
| Built: | 2024-10-26 03:37:53 UTC |
| Source: | CRAN |
Converts data from plater format to a data frame with one well
per row and merges it into an existing data frame by well name.
add_plate(data, file, well_ids_column, sep = ",")add_plate(data, file, well_ids_column, sep = ",")
data |
The data frame to merge the file into. Must contain a column with well names. |
file |
The path of a .csv file formatted as described in
|
well_ids_column |
The name of the column in |
sep |
The character used to separate columns in the file (e.g. "," or ";") Defaults to ",". |
If data contains more wells than in file, NA will be added to the
merged column(s) for those wells. If the file contains more wells than
data, those wells will be added to the bottom of the result with NA
for the columns in data.
Returns data as a tibble with as many new columns as plates in
file. Empty wells are indicated with NA.
# Part of the data is tidy file <- system.file("extdata", "example-2-part-A.csv", package = "plater") data <- read.csv(file) # Part of the data is plate-shaped plate_shaped <- system.file("extdata", "example-2-part-B.csv", package = "plater") # Combine the two data <- add_plate( data = data, file = plate_shaped, well_ids_column = "Wells") # Now data are tidy head(data)# Part of the data is tidy file <- system.file("extdata", "example-2-part-A.csv", package = "plater") data <- read.csv(file) # Part of the data is plate-shaped plate_shaped <- system.file("extdata", "example-2-part-B.csv", package = "plater") # Combine the two data <- add_plate( data = data, file = plate_shaped, well_ids_column = "Wells") # Now data are tidy head(data)
Runs the provided file through a number of diagnostics to determine whether it is a valid plater format file and displays information about any deficiencies found.
check_plater_format(file, sep = ",")check_plater_format(file, sep = ",")
file |
The path of the file to check. |
sep |
The character used to separate columns in the file (e.g. "," or ";"). Defaults to ",". |
Displays a number of messages as it checks the file. Will stop with a descriptive error message if the file is not formatted correctly.
file_path <- system.file("extdata", "example-1.csv", package = "plater") data <- check_plater_format(file_path)file_path <- system.file("extdata", "example-1.csv", package = "plater") data <- check_plater_format(file_path)
Converts data from plater format to a data frame with one well
per row identified by well name.
read_plate(file, well_ids_column = "Wells", sep = ",")read_plate(file, well_ids_column = "Wells", sep = ",")
file |
The path of a .csv file formatted as described below. |
well_ids_column |
The name to give the column that will contain the well identifiers. Default "Wells". |
sep |
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
named with well_ids_column and contain the well names (A01, A02..).
There will be as many additional columns as layouts in file. Empty
wells are omitted.
plater formatThe .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:
| ColName | 1 | 2 | 3 | ... |
| A | A01 | A02 | A03 | ... |
| B | B01 | B02 | B03 | ... |
| ... | ... | ... | ... | ... |
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)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 wrapper around read_plate that handles multiple plates and combines
them all into a single data frame.
read_plates(files, plate_names = NULL, well_ids_column = "Wells", sep = ",")read_plates(files, plate_names = NULL, well_ids_column = "Wells", sep = ",")
files |
A character vector with the paths of one or more plater-formatted .csv files. |
plate_names |
A character vector the same length as |
well_ids_column |
The name to give the column that will contain the well identifiers. Default "Wells". |
sep |
The character used to separate columns in the file (e.g. "," or ";"). Defaults to ",". |
Returns a data frame like that returned by read_plate,
containing the data from all of the plates. The plates will be identified
with a column called "Plate" containing the names given in
plate_names.
# Combine multiple files into one tidy data frame file1 <- system.file("extdata", "example-1.csv", package = "plater") file2 <- system.file("extdata", "more-bacteria.csv", package = "plater") # Data are stored in plate-shaped form data <- read_plates( files = c(file1, file2), plate_names = c("Experiment 1", "Experiment 2"), well_ids_column = "Wells") # Data from both plates are tidy and in the same data frame head(data)# Combine multiple files into one tidy data frame file1 <- system.file("extdata", "example-1.csv", package = "plater") file2 <- system.file("extdata", "more-bacteria.csv", package = "plater") # Data are stored in plate-shaped form data <- read_plates( files = c(file1, file2), plate_names = c("Experiment 1", "Experiment 2"), well_ids_column = "Wells") # Data from both plates are tidy and in the same data frame head(data)
Displays the data in the form of a microtiter plate.
view_plate(data, well_ids_column, columns_to_display, plate_size = 96)view_plate(data, well_ids_column, columns_to_display, plate_size = 96)
data |
A data frame containing the data |
well_ids_column |
The name of the column in |
columns_to_display |
A vector of the names of one or more columns you'd like to display. |
plate_size |
The number of wells in the plate. Must be 6, 12, 24, 48, 96 384, or 1536. Default 96. |
A depiction of the data in columns_to_display as
though laid out on a microtiter plate with plate_size wells.
# Generate some tidy data data <- data.frame(Wells = paste0(LETTERS[1:3], 0, rep(1:4, each = 3)), Species = rep(c("Alien", "Human", "Cat"), 4), OxygenProduction = round(rnorm(12), 3)) head(data) # See which wells had cells from which species and the amount of oxygen # produced for each well view_plate(data, "Wells", c("Species", "OxygenProduction"), 12)# Generate some tidy data data <- data.frame(Wells = paste0(LETTERS[1:3], 0, rep(1:4, each = 3)), Species = rep(c("Alien", "Human", "Cat"), 4), OxygenProduction = round(rnorm(12), 3)) head(data) # See which wells had cells from which species and the amount of oxygen # produced for each well view_plate(data, "Wells", c("Species", "OxygenProduction"), 12)