,
Tesla Adeck [cph],
Amina Adeck [cph]| Title: | Cleans and Normalizes FLUOstar DBF and DAT Files from 'Liposome' Flux Assays |
|---|---|
| Description: | Cleans and Normalizes FLUOstar DBF and DAT Files obtained from liposome flux assays. Users should verify extended usage of the package on files from other assay types. |
| Authors: | Tingwei Adeck [aut, cre, cph] ,
Tesla Adeck [cph],
Amina Adeck [cph] |
| Maintainer: | Tingwei Adeck <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 2.0.0 |
| Built: | 2025-03-06 05:50:18 UTC |
| Source: | https://github.com/alphaprime7/normfluodbf |
The %there% operator
dfile %there% dirpathdfile %there% dirpath
dfile |
file |
dirpath |
directory |
logical
## Not run: fpath <- system.file("extdata", package = "normfluodbf", mustWork = TRUE) "dat_1.dat" %there% fpath ## End(Not run)## Not run: fpath <- system.file("extdata", package = "normfluodbf", mustWork = TRUE) "dat_1.dat" %there% fpath ## End(Not run)
Add Package Namespace
add_package_namespace(dir, package)add_package_namespace(dir, package)
dir |
dir |
package |
package |
modified file
## Not run: add_package_namespace(dir, package)## Not run: add_package_namespace(dir, package)
Analyze
analyze_ready(plate) ## Default S3 method: analyze_ready(plate) ## S3 method for class ''96well_plate'' analyze_ready(plate) ## S3 method for class ''384well_plate'' analyze_ready(plate) ## S3 method for class ''1536well_plate_t1'' analyze_ready(plate) ## S3 method for class ''1536well_plate_t2'' analyze_ready(plate)analyze_ready(plate) ## Default S3 method: analyze_ready(plate) ## S3 method for class ''96well_plate'' analyze_ready(plate) ## S3 method for class ''384well_plate'' analyze_ready(plate) ## S3 method for class ''1536well_plate_t1'' analyze_ready(plate) ## S3 method for class ''1536well_plate_t2'' analyze_ready(plate)
plate |
plate |
plate
plate
plate
plate
plate
plate
plate
## Not run: analyze_ready(plate)## Not run: analyze_ready(plate)
Capitalize
average_fluorescence_by_row_cycle(plate)average_fluorescence_by_row_cycle(plate)
plate |
plate |
capital letter
## Not run: average_fluorescence_by_row_cycle(plate)## Not run: average_fluorescence_by_row_cycle(plate)
Capitalize
capitalize(x)capitalize(x)
x |
well |
capital letter
## Not run: capitalize('a1')## Not run: capitalize('a1')
Check Broken Packages
check_broken_packages()check_broken_packages()
broken packages
Check Dirt
check_dirt(plate)check_dirt(plate)
plate |
plate |
plate
Other plate_utils:
get_wells_used(),
plate_data_summary(),
platename,
remove_leading_zero(),
saveloadutils,
set_plate_version(),
type()
Check package or function Usage
check_package_usage(directory, package_name)check_package_usage(directory, package_name)
directory |
directory |
package_name |
package or string |
use location
## Not run: check_package_usage('R','capitalize')## Not run: check_package_usage('R','capitalize')
Child Type
child_plate_type(plate, child_type = NULL) ## Default S3 method: child_plate_type(plate, child_type = NULL) ## S3 method for class 'normfluodbf_plate' child_plate_type(plate, child_type = NULL)child_plate_type(plate, child_type = NULL) ## Default S3 method: child_plate_type(plate, child_type = NULL) ## S3 method for class 'normfluodbf_plate' child_plate_type(plate, child_type = NULL)
plate |
plate |
child_type |
child type |
plate
plate
## Not run: child_plate_type()## Not run: child_plate_type()
The function takes the dirty data frame obtained from reading the FLUOstar DAT file and applies a function called comma_cleaner() to the dirty data frame, which automatically inserts NAs in place of the special characters, and rows with NAs only are removed.
clean_commas(df)clean_commas(df)
df |
A dirty data frame obtained from the FLUOstar DAT file. |
A clean data frame with clean NA values retained.
Tingwei Adeck
## Not run: clean_commas(df)## Not run: clean_commas(df)
The function takes the dirty data frame obtained from reading the FLUOstar DAT file, applies an original algorithm that inserts NAs in place of the special characters, and then applies a function called comma_cleaner() to the dirty data frame for the removal of commas, and finally, rows with NAs only are removed.
clean_odddat_optimus(df) clean_even_dat(df)clean_odddat_optimus(df) clean_even_dat(df)
df |
df |
A clean data frame with clean NA values retained.
df
Tingwei Adeck
## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) partial_cleaned_dat <- clean_odddat_optimus(dat_df) ## End(Not run) ## Not run: clean_even_dat(df)## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) partial_cleaned_dat <- clean_odddat_optimus(dat_df) ## End(Not run) ## Not run: clean_even_dat(df)
This modular function, in the context of this package, is responsible for removing commas from attribute(s) values. Removal of commas facilitates the conversion of attributes into the numeric class.
comma_cleaner(comma_df)comma_cleaner(comma_df)
comma_df |
A dirty data frame obtained from the FLUOstar DAT file. |
A clean data frame with numeric no-comma values for attribute(s).
Tingwei Adeck
## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- comma_cleaner(dat_df) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- comma_cleaner(dat_df) ## End(Not run)
Comment Out
comment_out_lines(input_file, output_file)comment_out_lines(input_file, output_file)
input_file |
file |
output_file |
file |
file
## Not run: comment_out_lines('tests/testthat/test_pipeline.R', 'tests/testthat/test_pipeline.R')## Not run: comment_out_lines('tests/testthat/test_pipeline.R', 'tests/testthat/test_pipeline.R')
FLUOstar .dat files used for creation of the update and unusable for immediate data analysis.
dat_1dat_1
An object of class data.frame with 320 rows and 12 columns.
FLUOstar .dat files used for creation of the update and unusable for immediate data analysis.
dat_2dat_2
An object of class data.frame with 320 rows and 12 columns.
FLUOstar .dat files used for creation of the update and unusable for immediate data analysis. This file is unique because it validates a major bug fix to ensure that users get the right output.
dat_3dat_3
An object of class data.frame with 320 rows and 12 columns.
FLUOstar .dat files used for creation of the update and unusable for immediate data analysis. This file is unique because it validates a major bug fix to ensure that users get the right output.
dat_4dat_4
An object of class data.frame with 320 rows and 1 columns.
FLUOstar .dat files used for creation of the update and unusable for immediate data analysis. This file is unique because it validates a major bug fix to ensure that users get the right output.
dat_5dat_5
An object of class data.frame with 105 rows and 1 columns.
FLUOstar .dat files used for creation of the update and unusable for immediate data analysis. This file is unique because it validates a major bug fix to ensure that users get the right output.
dat_6dat_6
An object of class data.frame with 105 rows and 2 columns.
FLUOstar .dat files used for creation of the update and unusable for immediate data analysis. This file is unique because it validates a major bug fix to ensure that users get the right output.
dat_7dat_7
An object of class data.frame with 105 rows and 3 columns.
This function is used to name attribute(s). Attribute(s) names, in this case, are equivalent to the well labels found on the microplate reader. An attribute for a sample loaded into row A - column 1 will be named A1. In short, the function takes a clean data frame and returns attribute names that match the FLUOstar plate layout often presented as an Excel file.
dat_col_names_horizontal(dat = NULL, df, rows_used = NULL, cols_used = NULL)dat_col_names_horizontal(dat = NULL, df, rows_used = NULL, cols_used = NULL)
dat |
A string ("dat_1.dat") if the file is found within the present working directory (pwd) OR a path pointing directly to a ".dat" file. |
df |
A data frame that requires attribute labels. |
rows_used |
A character vector indicating the rows or tuples used on the microplate (usually a 96-well microplate). Initialized as NULL. |
cols_used |
A numeric vector indicating the plate columns or attributes used. Initialized as NULL. |
Returns a character or numeric vector of attribute(s) names for the normalized data frame.
This function was designed to avoid the use of stringr. This function is designed to name attributes when the read direction is specified as horizontal.
Tingwei Adeck
## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_horizontal(dat=fpath,resampled_scaled, n) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_horizontal(dat=fpath,resampled_scaled, n) ## End(Not run)
This function is used to name attribute(s). Attribute(s) names, in this case, are equivalent to the well labels found on the microplate reader. An attribute for a sample loaded into row A - column 1 will be named A1. In short, the function takes a clean data frame and returns attribute names that match the FLUOstar plate layout often presented as an Excel file.
dat_col_names_optimus( dat = NULL, df, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL )dat_col_names_optimus( dat = NULL, df, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL )
dat |
A string ("dat_1.dat") if the file is found within the present working directory (pwd) OR a path pointing directly to a ".dat" file. |
df |
A data frame that requires attribute labels. |
rows_used |
A character vector indicating the rows or tuples used on the microplate (usually a 96-well microplate). Initialized as NULL. |
cols_used |
A numeric vector indicating the plate columns or attributes used. Initialized as NULL. |
user_specific_labels |
A character vector where the user manually enters the used microplate wells based on the FLUOstar plate layout. |
read_direction |
A string input with two choices, “vertical” or “horizontal.” The user indicates “vertical” if the user intends to have a final data frame with samples arranged as sample type triplets (A1, B1, C1, A1, B1, C1) OR “horizontal” if the user intends to have a final data frame with samples |
Returns a character or numeric vector of attribute(s) names for the normalized data frame.
Users are advised to input rows used but won’t be penalized for not doing so. If the user provides the rows used, then attribute names are generated for the user. The user must check to ensure that the names match the microplate layout. The user can leave the columns used as NULL if the user loaded samples from column 1 and did so in sequence. If the user fails to load in sequence from the first position, then the user must provide a numeric vector of columns used. For instance, where the user skips columns, the user will be prompted to interact with the program in order to ensure the final data frame has the correct attribute names. The user can bypass the rows used and columns used parameters if the user supplies a manually created character vector of the wells used in an experiment. The read direction parameter is used to determine the presentation of the samples in the final data frame.
Tingwei Adeck
normfluodat(), dat_col_names_rigid()
## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_optimus(dat = fpath, resampled_scaled, n) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_optimus(dat = fpath, resampled_scaled, n) ## End(Not run)
This function is used to name attribute(s). Attribute(s) names, in this case, are equivalent to the well labels found on the microplate reader. An attribute for a sample loaded into row A - column 1 will be named A1. In short, the function takes a clean data frame and returns attribute names that match the FLUOstar plate layout often presented as an Excel file.
dat_col_names_prime( dat = NULL, df, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL )dat_col_names_prime( dat = NULL, df, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL )
dat |
A string ("dat_1.dat") if the file is found within the present working directory (pwd) OR a path pointing directly to a ".dat" file. |
df |
A data frame that requires attribute labels. |
rows_used |
A character vector indicating the rows or tuples used on the microplate (usually a 96-well microplate). Initialized as NULL. |
cols_used |
A numeric vector indicating the plate columns or attributes used. Initialized as NULL. |
user_specific_labels |
A character vector where the user manually enters the used microplate wells based on the FLUOstar plate layout. |
Returns a character vector of attribute(s) names for the normalized data frame.
Tingwei Adeck
## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_prime(dat = fpath, resampled_scaled, n) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_prime(dat = fpath, resampled_scaled, n) ## End(Not run)
This function is used to name attribute(s). Attribute(s) names, in this case, are equivalent to the well labels found on the microplate reader. An attribute for a sample loaded into row A - column 1 will be named A1. In short, the function takes a clean data frame and returns attribute names that match the FLUOstar plate layout often presented as an Excel file.
dat_col_names_rigid( dat = NULL, df, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL )dat_col_names_rigid( dat = NULL, df, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL )
dat |
A string ("dat_1.dat") if the file is found within the present working directory (pwd) OR a path pointing directly to a ".dat" file. |
df |
A data frame that requires attribute labels. |
rows_used |
A character vector indicating the rows or tuples used on the microplate (usually a 96-well microplate). Initialized as NULL. |
cols_used |
A numeric vector indicating the plate columns or attributes used. Initialized as NULL. |
user_specific_labels |
A character vector where the user manually enters the used microplate wells based on the FLUOstar plate layout. |
read_direction |
A string input with two choices, “vertical” or “horizontal.” The user indicates “vertical” if the user intends to have a final data frame with samples arranged as sample type triplets (A1, B1, C1, A1, B1, C1) OR “horizontal” if the user intends to have a final data frame with samples arranged as clusters per sample type (A1, A2, A3, B1, B2, B3). |
Returns a character vector of attribute(s) names for the normalized data frame.
Users are advised to input rows used but won’t be penalized for not doing so. If the user provides the rows used, then attribute names are generated for the user. The user must check to ensure that the names match the microplate layout. The user can leave the columns used as NULL if the user loaded samples from column 1 and did so in sequence. If the user fails to load in sequence from the first position, then the user must provide a numeric vector of columns used. For instance, where the user skips columns, the user will be prompted to interact with the program in order to ensure the final data frame has the correct attribute names. The user can bypass the rows used and columns used parameters if the user supplies a manually created character vector of the wells used in an experiment. The read direction parameter is used to determine the presentation of the samples in the final data frame.
This naming function only returns a character vector hence the rigid suffix.
Tingwei Adeck
## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_rigid(dat = fpath, resampled_scaled, n) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) n = c('A','B','C') sample_col_names <- dat_col_names_rigid(dat = fpath, resampled_scaled, n) ## End(Not run)
Define Plate Parameters
define_params(plate) ## Default S3 method: define_params(plate) ## S3 method for class ''96well_plate'' define_params(plate) ## S3 method for class ''384well_plate'' define_params(plate) ## S3 method for class ''1536well_plate_t1'' define_params(plate) ## S3 method for class ''1536well_plate_t2'' define_params(plate) set_default_params(plate)define_params(plate) ## Default S3 method: define_params(plate) ## S3 method for class ''96well_plate'' define_params(plate) ## S3 method for class ''384well_plate'' define_params(plate) ## S3 method for class ''1536well_plate_t1'' define_params(plate) ## S3 method for class ''1536well_plate_t2'' define_params(plate) set_default_params(plate)
plate |
plate |
default params
default params
default params
default params
default params
plate
## Not run: define_params(plate)## Not run: define_params(plate)
Define Plate Status
define_status(plate) ## Default S3 method: define_status(plate) ## S3 method for class ''96well_plate'' define_status(plate) ## S3 method for class ''384well_plate'' define_status(plate) ## S3 method for class ''1536well_plate_t1'' define_status(plate) ## S3 method for class ''1536well_plate_t2'' define_status(plate) set_default_status(plate) update_status_list(plate) get_status_value(plate, index)define_status(plate) ## Default S3 method: define_status(plate) ## S3 method for class ''96well_plate'' define_status(plate) ## S3 method for class ''384well_plate'' define_status(plate) ## S3 method for class ''1536well_plate_t1'' define_status(plate) ## S3 method for class ''1536well_plate_t2'' define_status(plate) set_default_status(plate) update_status_list(plate) get_status_value(plate, index)
plate |
plate |
index |
index |
plate
status
status
status
status
status
status
plate
plate
plate
## Not run: define_steps(plate)## Not run: define_steps(plate)
Define Plate Steps
define_steps(plate) ## Default S3 method: define_steps(plate) ## S3 method for class 'normfluodbf_plate' define_steps(plate) ## S3 method for class ''96well_plate'' define_steps(plate) ## S3 method for class ''384well_plate'' define_steps(plate) ## S3 method for class ''1536well_plate_t1'' define_steps(plate) ## S3 method for class ''1536well_plate_t2'' define_steps(plate) set_default_steps(plate, ...) update_steps_list(plate, new_key, new_value, index)define_steps(plate) ## Default S3 method: define_steps(plate) ## S3 method for class 'normfluodbf_plate' define_steps(plate) ## S3 method for class ''96well_plate'' define_steps(plate) ## S3 method for class ''384well_plate'' define_steps(plate) ## S3 method for class ''1536well_plate_t1'' define_steps(plate) ## S3 method for class ''1536well_plate_t2'' define_steps(plate) set_default_steps(plate, ...) update_steps_list(plate, new_key, new_value, index)
plate |
plate |
... |
custom steps |
new_key |
new_key |
new_value |
new_value |
index |
index |
steps
steps
steps
steps
steps
steps
plate
plate
## Not run: define_steps(plate) ## Not run: plate <- plate %>% update_steps_list('REMOVE_OUTLIER', 'remove_outlier', 3)## Not run: define_steps(plate) ## Not run: plate <- plate %>% update_steps_list('REMOVE_OUTLIER', 'remove_outlier', 3)
Detect Outliers
detect_outliers_time_cn(plate, data) detect_outliers_cn(plate, data)detect_outliers_time_cn(plate, data) detect_outliers_cn(plate, data)
plate |
plate |
data |
data |
data frame
data frame
data frame
## Not run: detect_outliers_time_cn(plate, data)## Not run: detect_outliers_time_cn(plate, data)
A function that facilitates a users' workflow by helping to check for DBFs in a directory.
A function that facilitates a users' workflow by helping to check for DATs in a directory.
list_dbfs(pathstring) list_dats(pathstring) is_file(pathstring) is_dir(pathstring = NULL) find_known_liposome_dat_file(fpath, fname) find_known_liposome_dbf_file(fpath, fname)list_dbfs(pathstring) list_dats(pathstring) is_file(pathstring) is_dir(pathstring = NULL) find_known_liposome_dat_file(fpath, fname) find_known_liposome_dbf_file(fpath, fname)
pathstring |
path string |
fpath |
fpath |
fname |
fname |
directory utils
dbfs
dbfs
dbfs
dbfs
dbfs
dbfs
Other dirutils:
normfluodbfcomms,
sampledata
## Not run: fpath <- system.file("extdata", package = "normfluodbf", mustWork = TRUE) list_dbfs(fpath) list_dats(fpath) is_file(fpath) find_known_liposome_dat_file(fpath, 'dat_1.dat') find_known_liposome_dbf_file(fpath, 'liposomes_218') ## End(Not run)## Not run: fpath <- system.file("extdata", package = "normfluodbf", mustWork = TRUE) list_dbfs(fpath) list_dats(fpath) is_file(fpath) find_known_liposome_dat_file(fpath, 'dat_1.dat') find_known_liposome_dbf_file(fpath, 'liposomes_218') ## End(Not run)
Random Port Normfluodbf Style
find_random_port()find_random_port()
Recursively find a random port that does not fall in the unsafe group Added some more unsafe ports for like PostgresDb (5432), MySQL (3306), StreamlitApp (8501), ngrok (4040), pinggy (4300), Flask (5000), Django (8000) and ReactApp (3000). Learnt a thing or two from Dean with recursion (Really Cool!!!).
random port
Format Plate Data
format_plate_data(plate) ## Default S3 method: format_plate_data(plate) ## S3 method for class ''96well_plate'' format_plate_data(plate) ## S3 method for class ''384well_plate'' format_plate_data(plate) ## S3 method for class ''1536well_plate_t1'' format_plate_data(plate) ## S3 method for class ''1536well_plate_t2'' format_plate_data(plate)format_plate_data(plate) ## Default S3 method: format_plate_data(plate) ## S3 method for class ''96well_plate'' format_plate_data(plate) ## S3 method for class ''384well_plate'' format_plate_data(plate) ## S3 method for class ''1536well_plate_t1'' format_plate_data(plate) ## S3 method for class ''1536well_plate_t2'' format_plate_data(plate)
plate |
plate |
plate
plate
plate
plate
plate
plate
plate
## Not run: format_plate_data(plate)## Not run: format_plate_data(plate)
Wells Used
get_wells_used(pl_data)get_wells_used(pl_data)
pl_data |
data |
wells used
Other plate_utils:
check_dirt(),
plate_data_summary(),
platename,
remove_leading_zero(),
saveloadutils,
set_plate_version(),
type()
## Not run: get_wells_used(data)## Not run: get_wells_used(data)
Get File Name(s)
get_dbf_file_name(dbf_file) get_dat_file_name(dat_file) get_dat_common_name(dat_file) get_common_dat_names(dat_files)get_dbf_file_name(dbf_file) get_dat_file_name(dat_file) get_dat_common_name(dat_file) get_common_dat_names(dat_files)
dbf_file |
DBF file |
dat_file |
DAT file |
dat_files |
DAT files |
file
name
name
name
name
## Not run: get_dbf_file_name(dbf_file = "liposomes_218.dbf") get_dat_file_name(dat_file = "dat_1.dat") get_common_dat_names(dat_files = list.files(fpath, pattern = "\\.dat$")) ## End(Not run)## Not run: get_dbf_file_name(dbf_file = "liposomes_218.dbf") get_dat_file_name(dat_file = "dat_1.dat") get_common_dat_names(dat_files = list.files(fpath, pattern = "\\.dat$")) ## End(Not run)
Is Normalized
is_normalized(data, type = c("min-max", "z-score", "hundred"))is_normalized(data, type = c("min-max", "z-score", "hundred"))
data |
type |
type |
type |
boolean
boolean
## Not run: is_normalized(data,type)## Not run: is_normalized(data,type)
normfluodbf package
also provides an interactive tool that can be used to analyze liposome flux assay data
more easily. The tool will be launched in a web browser.Run the shiny App
checking dependencies in R code ... WARNING
In addition to the functions provided in this package, the normfluodbf package
also provides an interactive tool that can be used to analyze liposome flux assay data
more easily. The tool will be launched in a web browser.
launch()launch()
The simplest function utilization scenario entails an input of the path to a DBF file obtained from the FLUOstar microplate (usually a 96-well microplate) reader; In a single step, this function will create a data frame, clean the data frame, normalize the data frame, append a "Cycle_Number" attribute, perform an adjustment to the “time” attribute and return a data frame that is ready for analysis. Since the initial publication of this package, several changes have been made to improve the user experience and to give the user more options to fine-tune the output from the package to meet the users’ aesthetic needs. Users who decide to move past the simplest utility scenario have been given more options to customize the output based on the users’ needs. Notably, several normalization sub-parameters have been provided in the package which yields different outputs based on what the user is used to seeing. Just as the FLUOstar instrument is built to handle an array of assays, this function is designed to be multi-dimensional (meaning it can handle data with the same DBF extension from other assay types), on the condition that the data from assay types other than liposome flux assays follow the same data format this package was designed to handle. Of course, users of this package are advised to pre-analyze DBF files from other assay types to ensure they are compliant with this package (compliance in this scenario is simple meaning DBF files from other assays should be like DBF files from liposome flux assays).
norm_tidy_dbf( file = NULL, norm_scale = NULL, transformed = NULL, fun = NA, ... ) normfluordbf(file = NULL, norm_scale = NULL, transformed = NULL, fun = NA, ...) normfluodbf(file = NULL, norm_scale, transformed = NULL, fun = NA, ...)norm_tidy_dbf( file = NULL, norm_scale = NULL, transformed = NULL, fun = NA, ... ) normfluordbf(file = NULL, norm_scale = NULL, transformed = NULL, fun = NA, ...) normfluodbf(file = NULL, norm_scale, transformed = NULL, fun = NA, ...)
file |
A string ("liposomes_xxx.dbf") if the file is found within the present working directory (pwd) OR a path pointing directly to a ".dbf" file. |
norm_scale |
This parameter takes sub-parameters: 'raw’ , hundred’ , 'one’ , 'z-score' , or 'decimal’ , which denotes the normalization type or scale; The parameter is initialized as NULL. |
transformed |
This parameter takes input 'log', which denotes a logarithmic box-cox transformation; Initialized as NULL. |
fun |
A parameter defined as NA is used for Boolean expressions or manipulation. |
... |
An abstract placeholder or container parameter that can be used to capture extra variables if needed. |
A normalized data frame with an appended "Cycle_Number" attribute.
A normalized data frame with an appended "Cycle_Number" attribute.
A normalized data frame with an appended "Cycle_Number" attribute.
A normalized data frame with an appended "Cycle_Number" attribute.
The default normalization sub-parameter outputs values in the 0-1 range. Unless a “norm_scale” level is specified by the user, the default output is in the 0-1 range. The “norm_scale” sub-parameter “decimal” is a machine-learning tool and should be avoided; it also provides no advantage for basic research analysis as its output operates on a sliding scale just like the raw data. Logarithmic transformation provides a minuscule advantage in data analysis and could/should be avoided. Backward compatibility is maintained in all updates, so there should be no issues with using the package the way the user was used to. The favorite "norm_scale" level is "z-score" since it divides the axis into negative and positive, thus facilitating interpretation.
The norm_scale must be provided if the user chooses to use this option.
Tingwei Adeck
## Not run: fpath <- system.file("extdata", "liposomes_214.dbf", package = "normfluodbf", mustWork = TRUE) normalized_dbf <- norm_tidy_dbf(file=fpath, norm_scale = 'raw') normalized_dbf <- normfluordbf(file=fpath, norm_scale = 'raw') ## End(Not run) ## Not run: wells = normfluodbf(lipsum_214, norm_scale = 'hundred')## Not run: fpath <- system.file("extdata", "liposomes_214.dbf", package = "normfluodbf", mustWork = TRUE) normalized_dbf <- norm_tidy_dbf(file=fpath, norm_scale = 'raw') normalized_dbf <- normfluordbf(file=fpath, norm_scale = 'raw') ## End(Not run) ## Not run: wells = normfluodbf(lipsum_214, norm_scale = 'hundred')
FLUOstar .dbf file in wide format and unable to use for data analysis.
liposomes_214liposomes_214
An object of class data.frame with 11 rows and 52 columns.
FLUOstar .dbf file in wide format and unable to use for data analysis.
liposomes_215liposomes_215
An object of class data.frame with 11 rows and 52 columns.
FLUOstar .dbf file in wide format and unable to use for data analysis.
liposomes_216liposomes_216
An object of class data.frame with 8 rows and 52 columns.
FLUOstar .dbf file in wide format and unable to use for data analysis.
liposomes_218liposomes_218
An object of class data.frame with 11 rows and 52 columns.
FLUOstar .dbf file in wide format and unable to use for data analysis.
liposomes_221liposomes_221
An object of class data.frame with 38 rows and 52 columns.
FLUOstar .dbf file in wide format and unable to use for data analysis.
liposomes_227liposomes_227
An object of class data.frame with 29 rows and 52 columns.
Load Plate Data
load_plate_data(plate) load_plate_data(plate) <- valueload_plate_data(plate) load_plate_data(plate) <- value
plate |
plate |
value |
data |
plate
plate
plate
## Not run: load_plate_data(plate,value = data)## Not run: load_plate_data(plate,value = data)
Load Plate Meta
load_plate_meta(plate) load_plate_meta(plate) <- valueload_plate_meta(plate) load_plate_meta(plate) <- value
plate |
plate |
value |
metadata |
plate
plate
plate
## Not run: load_plate_meta(plate, meta)## Not run: load_plate_meta(plate, meta)
Modify Plate Data
modify_plate_data(plate) ## Default S3 method: modify_plate_data(plate) ## S3 method for class ''96well_plate'' modify_plate_data(plate) ## S3 method for class ''384well_plate'' modify_plate_data(plate) ## S3 method for class ''1536well_plate_t1'' modify_plate_data(plate) ## S3 method for class ''1536well_plate_t2'' modify_plate_data(plate)modify_plate_data(plate) ## Default S3 method: modify_plate_data(plate) ## S3 method for class ''96well_plate'' modify_plate_data(plate) ## S3 method for class ''384well_plate'' modify_plate_data(plate) ## S3 method for class ''1536well_plate_t1'' modify_plate_data(plate) ## S3 method for class ''1536well_plate_t2'' modify_plate_data(plate)
plate |
plate |
plate
plate
plate
plate
plate
plate
plate
## Not run: modify_plate_meta(plate)## Not run: modify_plate_meta(plate)
Move File
move_file(source_path, destination_path)move_file(source_path, destination_path)
source_path |
src |
destination_path |
dest |
kinetic file
## Not run: source_file <- "path/to/source/file.txt" destination_file <- "path/to/destination/file.txt" move_file(source_file, destination_file) move_file("~/Documents/Wip/R/PkgDev/pdf","~/Wip/R/PkgDev/R/pdf") ## End(Not run)## Not run: source_file <- "path/to/source/file.txt" destination_file <- "path/to/destination/file.txt" move_file(source_file, destination_file) move_file("~/Documents/Wip/R/PkgDev/pdf","~/Wip/R/PkgDev/R/pdf") ## End(Not run)
Multiplot
multiplot(..., plotlist = NULL, file, cols = 1, layout = NULL)multiplot(..., plotlist = NULL, file, cols = 1, layout = NULL)
... |
extra |
plotlist |
list |
file |
file |
cols |
cols |
layout |
layout |
grid plot
Normalize
normalize(plate) ## Default S3 method: normalize(plate) ## S3 method for class ''96well_plate'' normalize(plate) ## S3 method for class ''384well_plate'' normalize(plate) ## S3 method for class ''1536well_plate_t1'' normalize(plate) ## S3 method for class ''1536well_plate_t2'' normalize(plate) normalize_dataframe(df)normalize(plate) ## Default S3 method: normalize(plate) ## S3 method for class ''96well_plate'' normalize(plate) ## S3 method for class ''384well_plate'' normalize(plate) ## S3 method for class ''1536well_plate_t1'' normalize(plate) ## S3 method for class ''1536well_plate_t2'' normalize(plate) normalize_dataframe(df)
plate |
plate |
df |
data frame |
plate
plate
plate
plate
plate
plate
plate
plate
## Not run: normalize(plate) normalize(plate) ## End(Not run)## Not run: normalize(plate) normalize(plate) ## End(Not run)
Normalize by Well
normalize_by_well(plate) ## Default S3 method: normalize_by_well(plate) ## S3 method for class ''96well_plate'' normalize_by_well(plate) ## S3 method for class ''384well_plate'' normalize_by_well(plate) ## S3 method for class ''1536well_plate_t1'' normalize_by_well(plate) ## S3 method for class ''1536well_plate_t2'' normalize_by_well(plate)normalize_by_well(plate) ## Default S3 method: normalize_by_well(plate) ## S3 method for class ''96well_plate'' normalize_by_well(plate) ## S3 method for class ''384well_plate'' normalize_by_well(plate) ## S3 method for class ''1536well_plate_t1'' normalize_by_well(plate) ## S3 method for class ''1536well_plate_t2'' normalize_by_well(plate)
plate |
plate |
plate
plate
plate
plate
plate
plate
plate
## Not run: normalize_by_well(plate)## Not run: normalize_by_well(plate)
Normalizing Agents
min_max_norm(x) min_max_norm_df(df) min_max_norm_percent(x) min_max_norm_percent_df(df) norm_z(x) norm_z_df(df) decimal_scaling(x) decimal_scaling_df(df) log_transformation(x) roundfluor(x)min_max_norm(x) min_max_norm_df(df) min_max_norm_percent(x) min_max_norm_percent_df(df) norm_z(x) norm_z_df(df) decimal_scaling(x) decimal_scaling_df(df) log_transformation(x) roundfluor(x)
x |
value(s) |
df |
data frame |
A normalized value when applied to a single value or a normalized attribute with values between the normalizing range.
normalized value (0-1)
normalized value (0-1)
normalized value (0-100)
normalized value (0-100)
normalized value (Z = N (0,1))
normalized value (Z = N (0,1))
normalized value
normalized value
log value
rounded value
Other normfluodbf_utils:
fluorthresholdcheck
## Not run: test_df <- as.data.frame(c(seq(40))) colnames(test_df) <- "test" test_df_norm <- lapply(test_df[1:ncol(test_df)], min_max_norm) ## End(Not run)## Not run: test_df <- as.data.frame(c(seq(40))) colnames(test_df) <- "test" test_df_norm <- lapply(test_df[1:ncol(test_df)], min_max_norm) ## End(Not run)
Plot Plate - Favorite is Fluostar style
## S3 method for class ''96well_plate'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... ) ## S3 method for class ''384well_plate'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... ) ## S3 method for class ''1536well_plate_t1'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... ) ## S3 method for class ''1536well_plate_t2'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... )## S3 method for class ''96well_plate'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... ) ## S3 method for class ''384well_plate'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... ) ## S3 method for class ''1536well_plate_t1'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... ) ## S3 method for class ''1536well_plate_t2'' plot( x, whichplot = 1, fluorstarplot = 1 %in% whichplot, superimpose = 2 %in% whichplot, plate_layout = 3 %in% whichplot, plot_side_by_side = 4 %in% whichplot, legend_labels = NULL, plot_name = NULL, ... )
x |
plot requirement |
whichplot |
int |
fluorstarplot |
whichplot = 1 |
superimpose |
whichplot = 2 |
plate_layout |
whichplot = 3 |
plot_side_by_side |
whichplot = 4 |
legend_labels |
labels whichplot = 2,4 |
plot_name |
plot name |
... |
additional parameters |
plot object
print plot (return plate)
print plot (return plate)
print plot (return plate)
print plot (return plate)
## Not run: plot(plate, whichplot = 1)## Not run: plot(plate, whichplot = 1)
The simplest case scenario entails inputting the name or directory of a DAT file as a string, the number of rows denoted by the tnp (test, negative, positive) parameter, and the number of cycles (selected by the user when running the FLUOstar instrument). The program takes these three baseline parameters, performs cleaning and normalization of the DAT file, and then appends an attribute called “Cycle_Number” to the normalized data frame.
The simplest case scenario entails inputting the name or directory of a DAT file as a string, the number of rows denoted by the tnp (test, negative, positive) parameter, and the number of cycles (selected by the user when running the FLUOstar instrument). The program takes these three baseline parameters, performs cleaning and normalization of the DAT file, and then appends an attribute called “Cycle_Number” to the normalized data frame.
The simplest case scenario entails inputting the name or directory of a DAT file as a string, the number of rows denoted by the tnp (test, negative, positive) parameter, and the number of cycles (selected by the user when running the FLUOstar instrument). The program takes these three baseline parameters, performs cleaning and normalization of the DAT file, and then appends an attribute called “Cycle_Number” to the normalized data frame.
The simplest case scenario entails inputting the name or directory of a DAT file as a string, the number of rows denoted by the tnp (test, negative, positive) parameter, and the number of cycles (selected by the user when running the FLUOstar instrument). The program takes these three baseline parameters, performs cleaning and normalization of the DAT file, and then appends an attribute called “Cycle_Number” to the normalized data frame.
normfluordat( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, na_omit = NULL ) normfluodat( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, norm_scale = NULL, interval = NULL, first_end = NULL, pause_duration = NULL, end_time = NULL, normfluodbf.verbose = TRUE ) normfluodatlite( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, norm_scale = NULL ) normfluodatfull( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, norm_scale = NULL, na_omit = NULL )normfluordat( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, na_omit = NULL ) normfluodat( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, norm_scale = NULL, interval = NULL, first_end = NULL, pause_duration = NULL, end_time = NULL, normfluodbf.verbose = TRUE ) normfluodatlite( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, norm_scale = NULL ) normfluodatfull( dat, tnp, cycles, rows_used = NULL, cols_used = NULL, user_specific_labels = NULL, read_direction = NULL, norm_scale = NULL, na_omit = NULL )
dat |
A string ("dat_1.dat") if the file is found within the present working directory (pwd) OR a path pointing directly to a ".dat" file. |
tnp |
A numeric value indicating the number of rows used. TNP is used as an acronym for Test, Negative, Positive. |
cycles |
A numeric value indicating the number of cycles selected by the user when running the FLUOstar instrument. |
rows_used |
A character vector of the rows used; ru = c('A','B','C'). |
cols_used |
A numeric vector of the columns used; cu = c(1,2,3). |
user_specific_labels |
A character vector manually prepared by the user to denote the wells used on the microplate reader; usl = c('A1','B1','C1'). |
read_direction |
A string input with two choices, “vertical” or “horizontal.” The user indicates “vertical” if the user intends to have a final data frame with samples arranged as sample type triplets (A1, B1, C1, A1, B1, C1) OR “horizontal” if the user intends to have a final data frame with samples arranged as clusters per sample type (A1, A2, A3, B1, B2, B3). |
na_omit |
Takes a string "yes" OR "no". |
norm_scale |
This parameter takes sub-parameters: 'raw’ , hundred’ , 'one’ , 'z-score' , or 'decimal’ , which denotes the normalization type or scale; Initialized as NULL. |
interval |
The time interval chosen for the assay often in seconds. |
first_end |
The end time of the initial run, often the pause for the introduction of a new substance. This can be the cycle number chosen for the initial stop. |
pause_duration |
The time between the first end (pause) and resumption of the assay. |
end_time |
The final end time of the assay. |
normfluodbf.verbose |
verbose option |
A normalized data frame with an appended "Cycle_Number" attribute. The “Cycle_Number” attribute is the X-variable.
A normalized data frame with an appended "Cycle_Number" attribute. The “Cycle_Number” attribute is the X-variable.
A normalized data frame with an appended "Cycle_Number" attribute. The “Cycle_Number” attribute is the X-variable.
A normalized data frame with an appended "Cycle_Number" attribute. The “Cycle_Number” attribute is the X-variable.
This function is a single-step function leveraging several subordinate functions. It is assumed that the user has the 3 baseline parameters to get this function working. Users must double-check attribute names to ensure they end up with accurate results.
This function is a single-step function leveraging several subordinate functions. It is assumed that the user has the 3 baseline parameters to get this function working. Users must double-check attribute names to ensure they end up with accurate results.
This function is a single-step function leveraging several subordinate functions. It is assumed that the user has the 3 baseline parameters to get this function working. Users must double-check attribute names to ensure they end up with accurate results.
Tingwei Adeck
## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluordat(dat=fpath, tnp = 3, cycles = 40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluodat(dat=fpath, tnp = 3, cycles = 40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluodatlite(dat=fpath, tnp = 3, cycles = 40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluodatfull(dat=fpath, tnp = 3, cycles = 40) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluordat(dat=fpath, tnp = 3, cycles = 40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluodat(dat=fpath, tnp = 3, cycles = 40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluodatlite(dat=fpath, tnp = 3, cycles = 40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) normalized_fluo_dat <- normfluodatfull(dat=fpath, tnp = 3, cycles = 40) ## End(Not run)
Parent Type
parent_plate_type(plate, type = NULL) ## S3 method for class 'normfluodbf_plate' parent_plate_type(plate, type = NULL) ## Default S3 method: parent_plate_type(plate, type = NULL) ## S3 method for class ''96well_plate'' parent_plate_type(plate, type = NULL) ## S3 method for class ''384well_plate'' parent_plate_type(plate, type = NULL) ## S3 method for class ''1536well_plate_t1'' parent_plate_type(plate, type = NULL) ## S3 method for class ''1536well_plate_t2'' parent_plate_type(plate, type = NULL)parent_plate_type(plate, type = NULL) ## S3 method for class 'normfluodbf_plate' parent_plate_type(plate, type = NULL) ## Default S3 method: parent_plate_type(plate, type = NULL) ## S3 method for class ''96well_plate'' parent_plate_type(plate, type = NULL) ## S3 method for class ''384well_plate'' parent_plate_type(plate, type = NULL) ## S3 method for class ''1536well_plate_t1'' parent_plate_type(plate, type = NULL) ## S3 method for class ''1536well_plate_t2'' parent_plate_type(plate, type = NULL)
plate |
plate |
type |
parent type |
plate
plate
plate
plate
plate
## Not run: parent_plate_type()## Not run: parent_plate_type()
Plate
empty_plate() new_plate() init_plate(plate = NULL, type = NULL, child_type = NULL) setup_plate(plate, ...) reset_plate(plate) use_setup_plate() use_initialized_plate()empty_plate() new_plate() init_plate(plate = NULL, type = NULL, child_type = NULL) setup_plate(plate, ...) reset_plate(plate) use_setup_plate() use_initialized_plate()
plate |
plate |
type |
parent type |
child_type |
child type |
... |
dots |
plate
plate
plate
plate
plate
plate
plate
plate
## Not run: empty_plate() new_plate() init_plate() setup_plate(plate) reset_plate() ## End(Not run)## Not run: empty_plate() new_plate() init_plate() setup_plate(plate) reset_plate() ## End(Not run)
Plate Data Summary
plate_data_summary(plate)plate_data_summary(plate)
plate |
plate |
sprintf string
Other plate_utils:
check_dirt(),
get_wells_used(),
platename,
remove_leading_zero(),
saveloadutils,
set_plate_version(),
type()
Plate Types Tibble
Plate Types List
Plate Types Vector
Plate Types Global
plate_types_tbl() plate_types plate_types_vector()plate_types_tbl() plate_types plate_types_vector()
An object of class list of length 5.
The list equivalent of the tibble from plate_types_tbl.
The vector equivalent of the tibble from plate_types_tbl.
A tibble
A list
A Vector
Plate Data
plate_data(file, tnp = NULL, cycles = NULL, rows_used = NULL, ...)plate_data(file, tnp = NULL, cycles = NULL, rows_used = NULL, ...)
file |
file |
tnp |
tnp |
cycles |
cycles |
rows_used |
rows_used |
... |
dots |
plate data
plate data
## Not run: plate_data(file, tnp, cycles, rows_used = c(A,B,C), norm_scale = 'raw')## Not run: plate_data(file, tnp, cycles, rows_used = c(A,B,C), norm_scale = 'raw')
Plate Meta
plate_meta(plate, num_wells) ## Default S3 method: plate_meta(plate, num_wells = 96L) ## S3 method for class ''96well_plate'' plate_meta(plate, num_wells = 96L) ## S3 method for class ''384well_plate'' plate_meta(plate, num_wells = 384L) ## S3 method for class ''1536well_plate_t1'' plate_meta(plate, num_wells = 1536L) ## S3 method for class ''1536well_plate_t2'' plate_meta(plate, num_wells)plate_meta(plate, num_wells) ## Default S3 method: plate_meta(plate, num_wells = 96L) ## S3 method for class ''96well_plate'' plate_meta(plate, num_wells = 96L) ## S3 method for class ''384well_plate'' plate_meta(plate, num_wells = 384L) ## S3 method for class ''1536well_plate_t1'' plate_meta(plate, num_wells = 1536L) ## S3 method for class ''1536well_plate_t2'' plate_meta(plate, num_wells)
plate |
plate |
num_wells |
number of wells |
plate
plate
plate
plate
plate
plate
plate
## Not run: plate_meta(plate, num_wells = 96L)## Not run: plate_meta(plate, num_wells = 96L)
Plate Name
name(plate) name(plate) <- valuename(plate) name(plate) <- value
plate |
plate |
value |
value |
plate
plate
Other plate_utils:
check_dirt(),
get_wells_used(),
plate_data_summary(),
remove_leading_zero(),
saveloadutils,
set_plate_version(),
type()
## S3 method for class ''96well_plate'' print(x, ...) ## S3 method for class ''384well_plate'' print(x, ...) ## S3 method for class ''1536well_plate_t1'' print(x, ...) ## S3 method for class ''1536well_plate_t2'' print(x, ...)## S3 method for class ''96well_plate'' print(x, ...) ## S3 method for class ''384well_plate'' print(x, ...) ## S3 method for class ''1536well_plate_t1'' print(x, ...) ## S3 method for class ''1536well_plate_t2'' print(x, ...)
x |
print requirement |
... |
placeholder |
plate
plate
plate
plate
## Not run: plate## Not run: plate
Quiet
quiet( expr, suppress_messages = FALSE, suppress_warnings = FALSE, suppress_output = FALSE, all = FALSE )quiet( expr, suppress_messages = FALSE, suppress_warnings = FALSE, suppress_output = FALSE, all = FALSE )
expr |
expression |
suppress_messages |
logical |
suppress_warnings |
logical |
suppress_output |
logical |
all |
logical |
suppress comms
## Not run: quiet(expr)## Not run: quiet(expr)
Format Well Names
remove_leading_zero(names_vector)remove_leading_zero(names_vector)
names_vector |
column names |
vector
Other plate_utils:
check_dirt(),
get_wells_used(),
plate_data_summary(),
platename,
saveloadutils,
set_plate_version(),
type()
## Not run: remove_leading_zero(names)## Not run: remove_leading_zero(names)
Outliers
remove_outliers(plate) ## Default S3 method: remove_outliers(plate) ## S3 method for class ''96well_plate'' remove_outliers(plate) ## S3 method for class ''384well_plate'' remove_outliers(plate) ## S3 method for class ''1536well_plate_t1'' remove_outliers(plate) ## S3 method for class ''1536well_plate_t2'' remove_outliers(plate)remove_outliers(plate) ## Default S3 method: remove_outliers(plate) ## S3 method for class ''96well_plate'' remove_outliers(plate) ## S3 method for class ''384well_plate'' remove_outliers(plate) ## S3 method for class ''1536well_plate_t1'' remove_outliers(plate) ## S3 method for class ''1536well_plate_t2'' remove_outliers(plate)
plate |
plate |
plate
plate
plate
plate
plate
plate
plate
Works on a data frame not in well format.
Works on a data frame not in well format.
Works on a data frame not in well format.
Works on a data frame not in well format.
Works on a data frame not in well format.
## Not run: remove_outliers(plate)## Not run: remove_outliers(plate)
Replace Word
replace_word_in_file(file_path, old_word, new_word)replace_word_in_file(file_path, old_word, new_word)
file_path |
path |
old_word |
old func name |
new_word |
new func name |
file
Solves the inconvenient problem of renaming a function correctly and having to manually correct it.
## Not run: replace_word_in_file('R/plate_plot.R','plot_fluostar_style', 'plot_in_well')## Not run: replace_word_in_file('R/plate_plot.R','plot_fluostar_style', 'plot_in_well')
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute. NA values are retained for more control.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample. NA values are retained.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
A function that takes tuples or rows consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute. NA values are retained.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
Creates a data frame where each sample loaded into the microplate wells has a separate attribute.
resample_dat_scale(df, tnp, cycles) resample_dat_scale_naretainer(df, tnp, cycles) resample_dat_scale_alt(df, tnp, cycles, na_omit = NULL) resample_dat_scale_alt_na(df, tnp, cycles) resample_dat_scale_alt_bf_na(df, tnp, cycles) resample_dat_scale_alt_bfv(df, tnp, cycles) resample_dat_scale_optimus(df, tnp, cycles) resample_dat_scale_optimus_na(df, tnp, cycles) resample_dat_scale_optimus_backend(df, tnp, cycles, na_omit = NULL) resample_vect_scale(df, tnp, cycles, method = c("normal", "brute", "vector"))resample_dat_scale(df, tnp, cycles) resample_dat_scale_naretainer(df, tnp, cycles) resample_dat_scale_alt(df, tnp, cycles, na_omit = NULL) resample_dat_scale_alt_na(df, tnp, cycles) resample_dat_scale_alt_bf_na(df, tnp, cycles) resample_dat_scale_alt_bfv(df, tnp, cycles) resample_dat_scale_optimus(df, tnp, cycles) resample_dat_scale_optimus_na(df, tnp, cycles) resample_dat_scale_optimus_backend(df, tnp, cycles, na_omit = NULL) resample_vect_scale(df, tnp, cycles, method = c("normal", "brute", "vector"))
df |
A clean data frame with attributes or tuples containing a mixture of samples. |
tnp |
A numeric value indicating the number of rows used. TNP is used as an acronym for Test, Negative, Positive. |
cycles |
A numeric value indicating the number of cycles selected by the user when running the FLUOstar instrument. |
na_omit |
Takes a string "yes" OR "no". |
method |
A string 'normal', 'brute' or 'vector' to specify the method of resampling. |
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
This function builds on or scales-up @seealso resample_dat(), hence the suffix scale.
This function is less optimized than @seealso resample_dat_scale_optimus().
This function builds on or scales-up @seealso resample_dat(), hence the suffix scale.
This function is less optimized than @seealso resample_dat_scale_optimus().
This function builds on or scales-up @seealso resample_dat(), hence the suffix scale.
This function is more optimized than @seealso resample_dat_scale(), hence the suffix scale_optimus.
This function builds on or scales-up @seealso resample_dat(), hence the suffix scale.
This function is more optimized than @seealso resample_dat_scale(), hence the suffix scale_optimus.
This function builds on or scales-up @seealso resample_dat(), hence the suffix scale.
This function is more optimized than @seealso resample_dat_scale(), hence the suffix scale_optimus.
This is the pseudo-vectorized approach and should be a more efficient function. This function will produce a vertical layout as defined in this package. This function inspired by the lapply approach pretty much applies the
Tingwei Adeck
resample_dat_alt(), resample_dat_scale_alt()
resample_dat_alt(), resample_dat_scale_alt()
resample_dat_vect(). As a matter of fact, I took this approach to
create compatibility with lapply and rapply but that failed.
## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_naretainer(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt_na(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt_bf_na(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt_bfv(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_optimus(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_optimus_na(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_optimus_backend(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_3.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'brute') alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'normal') alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'vector') alt_test_scale_norm <- lapply(alt_test_scale, min_max_norm) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_naretainer(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt_na(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt_bf_na(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_alt_bfv(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_optimus(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_optimus_na(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_1.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) resampled_scaled <- resample_dat_scale_optimus_backend(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_3.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'brute') alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'normal') alt_test_scale <- resample_vect_scale(nocomma_dat,3,40, method = 'vector') alt_test_scale_norm <- lapply(alt_test_scale, min_max_norm) ## End(Not run)
Designed as a prototype function to take a single attribute or column consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
Designed as a prototype function to take a single attribute or column consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
: Designed as a prototype function to take a single tuple or row consisting of several samples and perform a putative resampling to yield another data frame with a separate attribute for each sample.
resample_dat_vect(df, tnp, cycles, output = NULL) resample_dat(df, tnp, cycles) resample_dat_alt(df, tnp, cycles)resample_dat_vect(df, tnp, cycles, output = NULL) resample_dat(df, tnp, cycles) resample_dat_alt(df, tnp, cycles)
df |
A clean data frame with attributes or tuples containing a mixture of samples. |
tnp |
A numeric value indicating the number of rows used. TNP is used as an acronym for Test, Negative, Positive. |
cycles |
A numeric value indicating the number of cycles selected by the user when running the FLUOstar instrument. |
output |
A choice between "df' and 'vector' outputs. Leave NULL for a data frame. |
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
A new data frame where separated samples are assigned a separate attribute or column.
This is the vectorized approach and should be a more efficient function when compared to say
Tingwei Adeck
resample_dat() or @seealso resample_dat_alt().
This function will produce a vertical layout as defined in this package.
resample_dat_scale(), resample_dat_scale_optimus()
## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) samples_delineated <- resample_dat_vect(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_5.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) samples_delineated <- resample_dat(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_5.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) samples_delineated <- resample_dat_alt(nocomma_dat, tnp=3, cycles=40) ## End(Not run)## Not run: fpath <- system.file("extdata", "dat_4.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) samples_delineated <- resample_dat_vect(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_5.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) samples_delineated <- resample_dat(nocomma_dat, tnp=3, cycles=40) ## End(Not run) ## Not run: fpath <- system.file("extdata", "dat_5.dat", package = "normfluodbf", mustWork = TRUE) dat_df <- read.table(file=fpath) nocomma_dat <- clean_odddat_optimus(dat_df) samples_delineated <- resample_dat_alt(nocomma_dat, tnp=3, cycles=40) ## End(Not run)
normfluodbf package
also provides an interactive tool that can be used to analyze liposome flux assay data
more easily. The tool will be launched in a web browser.Run the shiny App
In addition to the functions provided in this package, the normfluodbf package
also provides an interactive tool that can be used to analyze liposome flux assay data
more easily. The tool will be launched in a web browser.
run_demo()run_demo()
Run Demo Background
run_demo_bg( host = getOption("shiny.host", "127.0.0.1"), appDir = system.file("shiny/demo", package = "normfluodbf") )run_demo_bg( host = getOption("shiny.host", "127.0.0.1"), appDir = system.file("shiny/demo", package = "normfluodbf") )
host |
localhost |
appDir |
dir |
Run Demo in Background
run_demo_in_background( appDir = system.file("shiny/demo", package = "normfluodbf"), job_name, host, port )run_demo_in_background( appDir = system.file("shiny/demo", package = "normfluodbf"), job_name, host, port )
appDir |
dir |
job_name |
job name |
host |
host ip (localhost) |
port |
port |
NULL (run script)
Run Demo Script
run_demo_script( appDir = system.file("shiny/demo", package = "normfluodbf"), port, host )run_demo_script( appDir = system.file("shiny/demo", package = "normfluodbf"), port, host )
appDir |
App Dir |
port |
Port |
host |
Host |
A quick script inspired by gptstudio to aid in running the cool normfluodbf demo.
Script
Get Development Data
sample_data_dir() sample_data_file(gotofile = NULL)sample_data_dir() sample_data_file(gotofile = NULL)
gotofile |
file |
directory data
Other dirutils:
dirutils,
normfluodbfcomms
## Not run: fpath <- system.file("extdata", package = "normfluodbf", mustWork = TRUE) sample_data_dir() sample_data_file(gotofile = NULL) ## End(Not run)## Not run: fpath <- system.file("extdata", package = "normfluodbf", mustWork = TRUE) sample_data_dir() sample_data_file(gotofile = NULL) ## End(Not run)
Save and Load Plate
save_plate(plate, suffix = NULL, interactive = F) save_rds_plate(plate, save_name, use_tempfile = F) load_rds_plate(plate, interactive = F) var_str(var)save_plate(plate, suffix = NULL, interactive = F) save_rds_plate(plate, save_name, use_tempfile = F) load_rds_plate(plate, interactive = F) var_str(var)
plate |
plate |
suffix |
suffix |
interactive |
boolean |
save_name |
name |
use_tempfile |
boolean |
var |
variable |
plate
plate
plate
plate
Other plate_utils:
check_dirt(),
get_wells_used(),
plate_data_summary(),
platename,
remove_leading_zero(),
set_plate_version(),
type()
Set The Plate Types
set_assiette_type(plate, type = NULL, child_type = NULL)set_assiette_type(plate, type = NULL, child_type = NULL)
plate |
A parent plate |
type |
parent plate type |
child_type |
child plate type |
Non-recursive approach.
plate
## Not run: set_assiette_type(parent_plate_type,96well_plate)## Not run: set_assiette_type(parent_plate_type,96well_plate)
Set The Child Plate Type
set_plate_type(parent_plate, type)set_plate_type(parent_plate, type)
parent_plate |
plate |
type |
child plate type |
This is a recursive method.
plate
## Not run: x = set_plate_type(parent_plate,"96well_plate") print(class(x)) ## End(Not run)## Not run: x = set_plate_type(parent_plate,"96well_plate") print(class(x)) ## End(Not run)
Plate Version
set_plate_version(plate, pkg)set_plate_version(plate, pkg)
plate |
plate |
pkg |
package |
plate
Other plate_utils:
check_dirt(),
get_wells_used(),
plate_data_summary(),
platename,
remove_leading_zero(),
saveloadutils,
type()
## Not run: set_plate_version(plate,pkg)## Not run: set_plate_version(plate,pkg)
Set Plate Steps
steps(plate) steps(plate) <- valuesteps(plate) steps(plate) <- value
plate |
plate |
value |
value |
plate
plate
plate
Other steps:
stepsutils
## Not run: steps(plate)## Not run: steps(plate)
Random Port gptstudio style
shiny_random_port()shiny_random_port()
port
Status
status(plate) status(plate) <- value dirty(plate) dirty(plate) <- value is_plate_dirty(plate)status(plate) status(plate) <- value dirty(plate) dirty(plate) <- value is_plate_dirty(plate)
plate |
plate |
value |
value |
plate
plate
plate
plate
plate
logical
## Not run: status(plate, status)## Not run: status(plate, status)
Steps Pipeline
.next_step(plate, n = 1) next_step(plate, n = 1) run_steps(plate, reset = FALSE, ...).next_step(plate, n = 1) next_step(plate, n = 1) run_steps(plate, reset = FALSE, ...)
plate |
plate |
n |
n |
reset |
reset |
... |
dots |
plate
plate
plate
plate
Recursive function to implement steps in the plate until all steps in the pipeline are complete
Recursive function to implement steps in the plate until all steps in the pipeline are complete
## Not run: next_step(plate, n=1)## Not run: next_step(plate, n=1)
Steps Utils
step(plate, step) step_name(plate, step) .step_name(plate, step) get_step_key_by_index(steps, index) step_begin(...) step_end(...) has_step(plate, step) check_step(plate, step) steps_complete(plate)step(plate, step) step_name(plate, step) .step_name(plate, step) get_step_key_by_index(steps, index) step_begin(...) step_end(...) has_step(plate, step) check_step(plate, step) steps_complete(plate)
plate |
plate |
step |
step |
steps |
steps |
index |
index |
... |
dots |
plate
step number
step name
step name
step name
boolean
boolean
boolean
Step start time utilizing the cache
Step start time utilizing the cache
Other steps:
setsteps
## Not run: step(plate, step)## Not run: step(plate, step)
Test Boilerplate
Open Testfile
test_boilerplate(file_name = NULL) open_testfile(testfile)test_boilerplate(file_name = NULL) open_testfile(testfile)
file_name |
file name |
testfile |
test file |
test boilerplate
open test file
Solves the inconvenient process of navigating to the tests folder every time
## Not run: test_boilerplate(file_name = "test_remove_shit.R") ## Not run: open_testfile('test_pipeline.R')## Not run: test_boilerplate(file_name = "test_remove_shit.R") ## Not run: open_testfile('test_pipeline.R')
Plate Type
type(plate, all = FALSE)type(plate, all = FALSE)
plate |
plate |
all |
Boolean |
class attribute
Other plate_utils:
check_dirt(),
get_wells_used(),
plate_data_summary(),
platename,
remove_leading_zero(),
saveloadutils,
set_plate_version()
## Not run: type(plate)## Not run: type(plate)
Upload Plate Data
upload_data(plate, file, ...) ## Default S3 method: upload_data(plate, file, ...) ## S3 method for class ''96well_plate'' upload_data(plate, file, ...) ## S3 method for class ''384well_plate'' upload_data(plate, file, ...) ## S3 method for class ''1536well_plate_t1'' upload_data(plate, file, ...) ## S3 method for class ''1536well_plate_t2'' upload_data(plate, file, ...)upload_data(plate, file, ...) ## Default S3 method: upload_data(plate, file, ...) ## S3 method for class ''96well_plate'' upload_data(plate, file, ...) ## S3 method for class ''384well_plate'' upload_data(plate, file, ...) ## S3 method for class ''1536well_plate_t1'' upload_data(plate, file, ...) ## S3 method for class ''1536well_plate_t2'' upload_data(plate, file, ...)
plate |
plate |
file |
file |
... |
dots |
plate
plate
plate
plate
plate
plate
plate
## Not run: upload_data(plate, file, ...)## Not run: upload_data(plate, file, ...)
View App in Viewer Pane
viewerpane_background_normfluodbf(host, port)viewerpane_background_normfluodbf(host, port)
host |
host |
port |
port |
Validate URL
wait_for_bg_shinyapp(url)wait_for_bg_shinyapp(url)
url |
url |
Plot Coordinates
x_var_one(plate) x_var_one(plate) <- value x_var_two(plate) x_var_two(plate) <- value y_var(plate) y_var(plate) <- value x_var_one_label(plate) x_var_one_label(plate) <- value x_var_two_label(plate) x_var_two_label(plate) <- valuex_var_one(plate) x_var_one(plate) <- value x_var_two(plate) x_var_two(plate) <- value y_var(plate) y_var(plate) <- value x_var_one_label(plate) x_var_one_label(plate) <- value x_var_two_label(plate) x_var_two_label(plate) <- value
plate |
plate |
value |
value |
plate
plate
plate
plate
plate
plate
plate
plate
plate
plate
## Not run: x_var_one(plate,value)## Not run: x_var_one(plate,value)