ascent.R
In kmjaniak/ascent: A Package for Manipulating Ascent Files
<<<<<<< HEAD
library(dplyr)
library(roxygen2)
=======
require(dplyr)
require(roxygen2)
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
#' Clean Ascent CSV
#' Reads Ascent csv, generates a data frame that is easy to manipulate.
#' @param batch_names List of batch names
#' @param inst Defaults to TRUE. If FALSE will not extract the instrument from the batch name
#' @param stream Defaults to TRUE. If FALSE will not extract the stream from the batch name
#' @param posix Defaults to TRUE. If FALSE will not convert AcqTime to type POSIXct
#' @param direct Defaults to FALSE. If TRUE will read batch_names input as file path, without adding folder/extension to
#' input string
#' @keywords ascent
#' @export
#' @examples
<<<<<<< HEAD
#' clean_ascent_csv(batch_names, inst = TRUE, stream = TRUE, posix = TRUE, shiny = FALSE)
clean_ascent_csv <- function(batch_names, inst = TRUE, stream = TRUE, posix = TRUE, shiny = FALSE) {
=======
<<<<<<< HEAD
#' clean_ascent_csv(batch_names)
clean_ascent_csv <- function(batch_names, inst = TRUE, stream = TRUE,
posix = TRUE, direct = FALSE) {
=======
#' clean_ascent_csv(batch_names, posix = TRUE, shiny = FALSE)
clean_ascent_csv <- function(batch_names, inst = TRUE, stream = TRUE,
posix = TRUE, shiny = FALSE) {
>>>>>>> a72ab4e7ebd5ab3f1836f514c805cc4f53558281
#create a list of file paths from the batch names(csvs are stored in "data" folder)
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
if(shiny) {
batch_paths <- batch_names
} else {
batch_paths <- lapply(batch_names,
function(x) paste("data/", x, "_alere_results.csv", sep = ""))
}
<<<<<<< HEAD
=======
<<<<<<< HEAD
=======
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
>>>>>>> a72ab4e7ebd5ab3f1836f514c805cc4f53558281
#Read in all the .csv files to "data" (creates a list of data frames for eachInstrument)
data <- lapply(batch_paths, read.csv, stringsAsFactors = FALSE)
#Combine the individual data frames into one data frame
data <- do.call("rbind", data)
data <- data %>%
select(Batch,
Index,
Sample = Sample.Name,
Type,
Nominal = Nominal.Conc.,
Analyte = Compound,
Response,
Conc = Calc..Conc.,
ISQuant = Chrom.1.Name,
Area = Chrom.1.Area,
AcqTime = Acquisition.Time)
data$Type <- factor(data$Type, levels = c("Standard", "Blank", "QC", "Unknown"))
data$Batch <- as.factor(data$Batch)
data$Analyte <- as.factor(data$Analyte)
data$ISQuant <- as.factor(data$ISQuant)
data$AcqTime <- gsub("T", " ", data$AcqTime)
data$AcqTime <- trimws(gsub("\\.000Z", "", data$AcqTime))
if(posix) {data$AcqTime <- as.POSIXct(data$AcqTime)}
if(stream){
data <- data %>%
mutate(Stream = ifelse(grepl("_A_", Batch), "Stream 1",
ifelse(grepl("_B_", Batch), "Stream 2", "Stream Unknown")))
data$Stream <- as.factor(data$Stream)
}
if(inst) {
instruments <- c("McCartney", "Hendrix", "Cash", "Janis",
"Willie", "Freddie", "Harrison", "Lennon",
"Bono", "Ringo", "Corgan",
"Jagger", "Morrison", "Axl",
"Jones,", "Casablancas", "Jett", "Dylan",
"Page", "Plant", "Berry", "Prince")
lapply(instruments, function(x) {
matched_rows <- grep(x, data$Batch, ignore.case = TRUE)
data[matched_rows, "Instrument"] <<- x
})
data$Instrument <- as.factor(data$Instrument)
}
data
}
<<<<<<< HEAD
=======
<<<<<<< HEAD
# #' Clean Ascent CSV (from full batch paths as input)
# #'
# #' Reads Ascent csv, generates a data frame that is easy to manipulate.
# #' @param batch_names List of batch names
# #' @keywords ascent
# #' @export
# #' @examples
# #' clean_ascent_csv_direct(batch_names)
#
#
# clean_ascent_csv_direct <- function(batch_names, inst = TRUE, stream = TRUE,
# posix = TRUE, shiny = TRUE) {
#
# #create a list of file paths from the batch names(csvs are stored in "data" folder)
#
# if(shiny) {
# batch_paths <- batch_names
# } else {
# batch_paths <- lapply(batch_names,
# function(x) paste("data/", x, "_alere_results.csv", sep = ""))
# }
# #Read in all the .csv files to "data" (creates a list of data frames for eachInstrument)
#
# data <- lapply(batch_paths, read.csv, stringsAsFactors = FALSE)
#
# #Combine the individual data frames into one data frame
#
# data <- do.call("rbind", data)
#
# data <- data %>%
# select(Batch,
# Index,
# Sample = Sample.Name,
# Type,
# Nominal = Nominal.Conc.,
# Analyte = Compound,
# Response,
# Conc = Calc..Conc.,
# ISQuant = Chrom.1.Name,
# Area = Chrom.1.Area,
# AcqTime = Acquisition.Time)
#
# data$Type <- factor(data$Type, levels = c("Standard", "Blank", "QC", "Unknown"))
# data$Batch <- as.factor(data$Batch)
# data$Analyte <- as.factor(data$Analyte)
# data$ISQuant <- as.factor(data$ISQuant)
#
# data$AcqTime <- gsub("T", " ", data$AcqTime)
# data$AcqTime <- trimws(gsub("\\.000Z", "", data$AcqTime))
# if(posix) {data$AcqTime <- as.POSIXct(data$AcqTime)}
#
# if(stream){
# data <- data %>%
# mutate(Stream = ifelse(grepl("_A_", Batch), "Stream 1",
# ifelse(grepl("_B_", Batch), "Stream 2", "Stream Unknown")))
#
# data$Stream <- as.factor(data$Stream)
# }
# if(inst) {
# instruments <- c("McCartney", "Hendrix", "Cash", "Janis",
# "Willie", "Freddie", "Harrison", "Lennon",
# "Bono", "Ringo", "Corgan",
# "Jagger", "Morrison", "Axl",
# "Jones,", "Casablancas", "Jett", "Dylan",
# "Page", "Plant", "Berry", "Prince")
#
# lapply(instruments, function(x) {
#
# matched_rows <- grep(x, data$Batch, ignore.case = TRUE)
# data[matched_rows, "Instrument"] <<- x
#
# })
#
# data$Instrument <- as.factor(data$Instrument)
# }
# data
#
# }
=======
>>>>>>> a72ab4e7ebd5ab3f1836f514c805cc4f53558281
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
#' Read Ascent CSVs
#'
#' This function reads one or more Ascent csv files into a single data frame.
#' @param batch_names List of batch names.
#' @keywords ascent
#' @export
#' @examples
#' read_ascent_csv()
#'
library(dplyr)
read_ascent_csv <- function(batch_names) {
#create a list of file paths from the batch names(csvs are stored in "data" folder)
batch_paths <- lapply(batch_names,
function(x) paste("data/", x, "_alere_results.csv", sep = ""))
#Read in all the .csv files to "data" (creates a list of data frames for eachInstrument)
data <- lapply(batch_paths, read.csv, stringsAsFactors = FALSE)
#Combine the individual data frames into one data frame
do.call("rbind", data)
}
#' Clean Ascent Columns
#'
#' This function takes a data frame generated from Ascent csv files and
#' selects only the relevant columns, and renames the columns so that they
#' are easier to access. Removes extraneous characters from the "Time" column.
#' Coerces various columns to factors.
#' @param data Data frame of Ascent data generated from read_ascent_csv()
#' with all columns present and column names unmodified.
#' @param posix Optionally converts AcqTime to POSIXct. Default is FALSE.
#' @keywords ascent
#' @export
#' @examples
#' clean_ascent_cols()
clean_ascent_cols <- function(data, posix = FALSE) {
data <- data %>%
select(Batch,
Index,
Sample = Sample.Name,
Type,
Nominal = Nominal.Conc.,
Analyte = Compound,
Response,
Conc = Calc..Conc.,
ISQuant = Chrom.1.Name,
Area = Chrom.1.Area,
AcqTime = Acquisition.Time)
data$Type <- factor(data$Type, levels = c("Standard", "Blank", "QC", "Unknown"))
data$Batch <- as.factor(data$Batch)
data$Analyte <- as.factor(data$Analyte)
data$ISQuant <- as.factor(data$ISQuant)
data$AcqTime <- gsub("T", " ", data$AcqTime)
data$AcqTime <- trimws(gsub("\\.000Z", "", data$AcqTime))
if(posix) {data$AcqTime <- as.POSIXct(data$AcqTime)}
data
}
remove_standards <- function(data) {
data %>%
filter(!Type == "Standard")
}
#' Add Stream Column
#'
#' This function adds a "Stream" column to an existing Ascent data frame.
#' This column is a factor with two levels, "Stream 1" and "Stream 2".
#' "Stream 1" is assigned if the batch name contains "_A_", and "Stream 2"
#' is assigned if the batch name contains "_B_".
#' @param data Data frame of Ascent data, generated from read_ascent_csv()
#' @keywords ascent
#' @export
#' @examples
#' add_stream_col(data)
add_stream_col <- function(data) {
data <- data %>%
mutate(Stream = ifelse(grepl("_A_", Batch), "Stream 1",
ifelse(grepl("_B_", Batch), "Stream 2", "Stream Unknown")))
data$Stream <- as.factor(data$Stream)
data
}
#' Add Instrument Column
#'
#' Adds an "Instrument" column to a data frame resulting from read_ascent_csv().
#' @param data Data frame of Ascent data, generated from read_ascent_csv()
#' @keywords ascent
#' @details Creates a new column called "Instrument" which contains
#' a match found between "Batch" and a pre-defined list of instruments.
#' @export
#' @examples
#' add_inst_col(data)
add_inst_col <- function(data) {
instruments <- c("McCartney", "Hendrix", "Cash", "Janis",
"Willie", "Freddie", "Harrison", "Lennon",
"Bono", "Ringo", "Corgan",
"Jagger", "Morrison", "Axl",
"Jones,", "Casablancas", "Jett", "Dylan",
"Page", "Plant", "Berry", "Prince")
lapply(instruments, function(x) {
matched_rows <- grep(x, data$Batch)
data[matched_rows, "Instrument"] <<- x
})
# data <- data %>%
# mutate(Instrument = word(as.character(Batch), -1, sep = "_"))
data$Instrument <- as.factor(data$Instrument)
data
}
#' Plot Internal Standard
#'
#' Generates a plot of IS peak area vs acquisition time.
#' @param data Data frame of Ascent data, generated from read_ascent_csv(), cleaned with
#' clean_ascent_cols(posix = TRUE)
#' @keywords ascent
#' @export
#' @examples
#' plot_IS(data)
plot_IS <- function(data) {
data %>%
filter(ISQuant == "IS") %>%
ggplot(aes(x = AcqTime, y = Area)) +
geom_point(aes(col = Type)) +
theme(axis.text.x = element_blank()) +
labs(x = "Acquisition Time", y = "IS Peak Area") +
facet_wrap(~Analyte)
}
#' Identify Outliers
#'
#' Updates the "Include" column of an Ascent data frame with FALSE if the point is an outlier
#' Points are marked as outliers if they are less than Q1 - 1.5 * IQR or greater than Q3 + 1.5*IQR
#' @param data Data frame processed by clean_ascent_csv()
#' @keywords ascent
#' @export
#' @examples
#' evaluate_outliers(data)
evaluate_outliers <- function(data) {
data <- data %>%
group_by(Analyte, Sample) %>%
mutate(Q1 = quantile(Conc, 0.25),
Q3 = quantile(Conc, 0.75),
IQR = IQR(Conc),
min_out = Q1 - 1.5*IQR,
max_out = Q3 + 1.5*IQR) %>%
ungroup()
data$Outlier <- ifelse(data$Conc < data$min_out | data$Conc > data$max_out, TRUE, FALSE)
data <- data %>%
select(-c(Q1, Q3, IQR, min_out, max_out))
return(data)
}
#' Create Summary Table of QC Verification Data
#'
#' Creates a summary table including Mean, SD, %CV, %Dev from Target, and n for each QC level for each analyte.
#' @param data Data frame processed by clean_ascent_csv() and evaluate_outliers()
#' @param exclude_outliers defaults to TRUE. Will not include data in summary if data$Outlier is TRUE (as determined by the IQR)
#' @keywords ascent
#' @export
#' @examples
#' summarize_QC(data)
summarize_QC <- function(data, exclude_outliers = TRUE) {
if(exclude_outliers) {
use_data <- data %>%
filter(!Outlier)
} else
use_data <- data
summaryTable <- use_data %>%
group_by(Analyte, Sample, Target) %>%
summarize("Mean" = mean_round(Conc),
"SD" = sd_round(Conc),
"%CV" = cv_round(Conc),
"%Dev" = acc(Conc, Target),
"n" = n()) %>%
ungroup()
return(summaryTable)
}
#' Create Summary Table of Calibrator Verification Data
#'
#' Creates a summary table including Mean, SD, %CV, %Dev from Target, and n for each calibrator level for each analyte.
#' @param data Data frame processed by clean_ascent_csv() and evaluate_outliers()
#' @param exclude_outliers defaults to TRUE. Will not include data in summary if data$Outlier is TRUE (as determined by IQR)
#' @keywords ascent
#' @export
#' @examples
#' summarize_cals(data)
summarize_cals <- function(data, exclude_outliers = TRUE) {
if(exclude_outliers) {
use_data <- data %>%
filter(!Outlier)
} else
use_data <- data
summaryTable <- use_data %>%
group_by(Analyte, Sample, Target) %>%
summarize("Mean" = mean_round(Conc),
"SD" = sd_round(Conc),
"%CV" = cv_round(Conc),
"%Dev" = acc(Conc, Target),
"n" = n()) %>%
ungroup()
return(summaryTable)
}
#' Testing
#'
#' Creates a summary table including Mean, SD, %CV, %Dev from Target, and n for each calibrator level for each analyte.
#' @param data Data frame processed by clean_ascent_csv() and evaluate_outliers()
#' @param exclude_outliers defaults to TRUE. Will not include data in summary if data$Outlier is TRUE (as determined by IQR)
#' @keywords ascent
#' @export
#' @examples
#' testdocument(data)
testdocument <- function(data, exclude_outliers = TRUE) {
if(exclude_outliers) {
use_data <- data %>%
filter(!Outlier)
} else
use_data <- data
summaryTable <- use_data %>%
group_by(Analyte, Sample, Target) %>%
summarize("Mean" = mean_round(Conc),
"SD" = sd_round(Conc),
"%CV" = cv_round(Conc),
"%Dev" = acc(Conc, Target),
"n" = n()) %>%
ungroup()
return(summaryTable)
}
kmjaniak/ascent documentation built on May 9, 2019, 9:58 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
<<<<<<< HEAD
library(dplyr)
library(roxygen2)
=======
require(dplyr)
require(roxygen2)
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
#' Clean Ascent CSV
#' Reads Ascent csv, generates a data frame that is easy to manipulate.
#' @param batch_names List of batch names
#' @param inst Defaults to TRUE. If FALSE will not extract the instrument from the batch name
#' @param stream Defaults to TRUE. If FALSE will not extract the stream from the batch name
#' @param posix Defaults to TRUE. If FALSE will not convert AcqTime to type POSIXct
#' @param direct Defaults to FALSE. If TRUE will read batch_names input as file path, without adding folder/extension to
#' input string
#' @keywords ascent
#' @export
#' @examples
<<<<<<< HEAD
#' clean_ascent_csv(batch_names, inst = TRUE, stream = TRUE, posix = TRUE, shiny = FALSE)
clean_ascent_csv <- function(batch_names, inst = TRUE, stream = TRUE, posix = TRUE, shiny = FALSE) {
=======
<<<<<<< HEAD
#' clean_ascent_csv(batch_names)
clean_ascent_csv <- function(batch_names, inst = TRUE, stream = TRUE,
posix = TRUE, direct = FALSE) {
=======
#' clean_ascent_csv(batch_names, posix = TRUE, shiny = FALSE)
clean_ascent_csv <- function(batch_names, inst = TRUE, stream = TRUE,
posix = TRUE, shiny = FALSE) {
>>>>>>> a72ab4e7ebd5ab3f1836f514c805cc4f53558281
#create a list of file paths from the batch names(csvs are stored in "data" folder)
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
if(shiny) {
batch_paths <- batch_names
} else {
batch_paths <- lapply(batch_names,
function(x) paste("data/", x, "_alere_results.csv", sep = ""))
}
<<<<<<< HEAD
=======
<<<<<<< HEAD
=======
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
>>>>>>> a72ab4e7ebd5ab3f1836f514c805cc4f53558281
#Read in all the .csv files to "data" (creates a list of data frames for eachInstrument)
data <- lapply(batch_paths, read.csv, stringsAsFactors = FALSE)
#Combine the individual data frames into one data frame
data <- do.call("rbind", data)
data <- data %>%
select(Batch,
Index,
Sample = Sample.Name,
Type,
Nominal = Nominal.Conc.,
Analyte = Compound,
Response,
Conc = Calc..Conc.,
ISQuant = Chrom.1.Name,
Area = Chrom.1.Area,
AcqTime = Acquisition.Time)
data$Type <- factor(data$Type, levels = c("Standard", "Blank", "QC", "Unknown"))
data$Batch <- as.factor(data$Batch)
data$Analyte <- as.factor(data$Analyte)
data$ISQuant <- as.factor(data$ISQuant)
data$AcqTime <- gsub("T", " ", data$AcqTime)
data$AcqTime <- trimws(gsub("\\.000Z", "", data$AcqTime))
if(posix) {data$AcqTime <- as.POSIXct(data$AcqTime)}
if(stream){
data <- data %>%
mutate(Stream = ifelse(grepl("_A_", Batch), "Stream 1",
ifelse(grepl("_B_", Batch), "Stream 2", "Stream Unknown")))
data$Stream <- as.factor(data$Stream)
}
if(inst) {
instruments <- c("McCartney", "Hendrix", "Cash", "Janis",
"Willie", "Freddie", "Harrison", "Lennon",
"Bono", "Ringo", "Corgan",
"Jagger", "Morrison", "Axl",
"Jones,", "Casablancas", "Jett", "Dylan",
"Page", "Plant", "Berry", "Prince")
lapply(instruments, function(x) {
matched_rows <- grep(x, data$Batch, ignore.case = TRUE)
data[matched_rows, "Instrument"] <<- x
})
data$Instrument <- as.factor(data$Instrument)
}
data
}
<<<<<<< HEAD
=======
<<<<<<< HEAD
# #' Clean Ascent CSV (from full batch paths as input)
# #'
# #' Reads Ascent csv, generates a data frame that is easy to manipulate.
# #' @param batch_names List of batch names
# #' @keywords ascent
# #' @export
# #' @examples
# #' clean_ascent_csv_direct(batch_names)
#
#
# clean_ascent_csv_direct <- function(batch_names, inst = TRUE, stream = TRUE,
# posix = TRUE, shiny = TRUE) {
#
# #create a list of file paths from the batch names(csvs are stored in "data" folder)
#
# if(shiny) {
# batch_paths <- batch_names
# } else {
# batch_paths <- lapply(batch_names,
# function(x) paste("data/", x, "_alere_results.csv", sep = ""))
# }
# #Read in all the .csv files to "data" (creates a list of data frames for eachInstrument)
#
# data <- lapply(batch_paths, read.csv, stringsAsFactors = FALSE)
#
# #Combine the individual data frames into one data frame
#
# data <- do.call("rbind", data)
#
# data <- data %>%
# select(Batch,
# Index,
# Sample = Sample.Name,
# Type,
# Nominal = Nominal.Conc.,
# Analyte = Compound,
# Response,
# Conc = Calc..Conc.,
# ISQuant = Chrom.1.Name,
# Area = Chrom.1.Area,
# AcqTime = Acquisition.Time)
#
# data$Type <- factor(data$Type, levels = c("Standard", "Blank", "QC", "Unknown"))
# data$Batch <- as.factor(data$Batch)
# data$Analyte <- as.factor(data$Analyte)
# data$ISQuant <- as.factor(data$ISQuant)
#
# data$AcqTime <- gsub("T", " ", data$AcqTime)
# data$AcqTime <- trimws(gsub("\\.000Z", "", data$AcqTime))
# if(posix) {data$AcqTime <- as.POSIXct(data$AcqTime)}
#
# if(stream){
# data <- data %>%
# mutate(Stream = ifelse(grepl("_A_", Batch), "Stream 1",
# ifelse(grepl("_B_", Batch), "Stream 2", "Stream Unknown")))
#
# data$Stream <- as.factor(data$Stream)
# }
# if(inst) {
# instruments <- c("McCartney", "Hendrix", "Cash", "Janis",
# "Willie", "Freddie", "Harrison", "Lennon",
# "Bono", "Ringo", "Corgan",
# "Jagger", "Morrison", "Axl",
# "Jones,", "Casablancas", "Jett", "Dylan",
# "Page", "Plant", "Berry", "Prince")
#
# lapply(instruments, function(x) {
#
# matched_rows <- grep(x, data$Batch, ignore.case = TRUE)
# data[matched_rows, "Instrument"] <<- x
#
# })
#
# data$Instrument <- as.factor(data$Instrument)
# }
# data
#
# }
=======
>>>>>>> a72ab4e7ebd5ab3f1836f514c805cc4f53558281
>>>>>>> f20c3047282626ad11a9e9042801621a1f59f8b3
#' Read Ascent CSVs
#'
#' This function reads one or more Ascent csv files into a single data frame.
#' @param batch_names List of batch names.
#' @keywords ascent
#' @export
#' @examples
#' read_ascent_csv()
#'
library(dplyr)
read_ascent_csv <- function(batch_names) {
#create a list of file paths from the batch names(csvs are stored in "data" folder)
batch_paths <- lapply(batch_names,
function(x) paste("data/", x, "_alere_results.csv", sep = ""))
#Read in all the .csv files to "data" (creates a list of data frames for eachInstrument)
data <- lapply(batch_paths, read.csv, stringsAsFactors = FALSE)
#Combine the individual data frames into one data frame
do.call("rbind", data)
}
#' Clean Ascent Columns
#'
#' This function takes a data frame generated from Ascent csv files and
#' selects only the relevant columns, and renames the columns so that they
#' are easier to access. Removes extraneous characters from the "Time" column.
#' Coerces various columns to factors.
#' @param data Data frame of Ascent data generated from read_ascent_csv()
#' with all columns present and column names unmodified.
#' @param posix Optionally converts AcqTime to POSIXct. Default is FALSE.
#' @keywords ascent
#' @export
#' @examples
#' clean_ascent_cols()
clean_ascent_cols <- function(data, posix = FALSE) {
data <- data %>%
select(Batch,
Index,
Sample = Sample.Name,
Type,
Nominal = Nominal.Conc.,
Analyte = Compound,
Response,
Conc = Calc..Conc.,
ISQuant = Chrom.1.Name,
Area = Chrom.1.Area,
AcqTime = Acquisition.Time)
data$Type <- factor(data$Type, levels = c("Standard", "Blank", "QC", "Unknown"))
data$Batch <- as.factor(data$Batch)
data$Analyte <- as.factor(data$Analyte)
data$ISQuant <- as.factor(data$ISQuant)
data$AcqTime <- gsub("T", " ", data$AcqTime)
data$AcqTime <- trimws(gsub("\\.000Z", "", data$AcqTime))
if(posix) {data$AcqTime <- as.POSIXct(data$AcqTime)}
data
}
remove_standards <- function(data) {
data %>%
filter(!Type == "Standard")
}
#' Add Stream Column
#'
#' This function adds a "Stream" column to an existing Ascent data frame.
#' This column is a factor with two levels, "Stream 1" and "Stream 2".
#' "Stream 1" is assigned if the batch name contains "_A_", and "Stream 2"
#' is assigned if the batch name contains "_B_".
#' @param data Data frame of Ascent data, generated from read_ascent_csv()
#' @keywords ascent
#' @export
#' @examples
#' add_stream_col(data)
add_stream_col <- function(data) {
data <- data %>%
mutate(Stream = ifelse(grepl("_A_", Batch), "Stream 1",
ifelse(grepl("_B_", Batch), "Stream 2", "Stream Unknown")))
data$Stream <- as.factor(data$Stream)
data
}
#' Add Instrument Column
#'
#' Adds an "Instrument" column to a data frame resulting from read_ascent_csv().
#' @param data Data frame of Ascent data, generated from read_ascent_csv()
#' @keywords ascent
#' @details Creates a new column called "Instrument" which contains
#' a match found between "Batch" and a pre-defined list of instruments.
#' @export
#' @examples
#' add_inst_col(data)
add_inst_col <- function(data) {
instruments <- c("McCartney", "Hendrix", "Cash", "Janis",
"Willie", "Freddie", "Harrison", "Lennon",
"Bono", "Ringo", "Corgan",
"Jagger", "Morrison", "Axl",
"Jones,", "Casablancas", "Jett", "Dylan",
"Page", "Plant", "Berry", "Prince")
lapply(instruments, function(x) {
matched_rows <- grep(x, data$Batch)
data[matched_rows, "Instrument"] <<- x
})
# data <- data %>%
# mutate(Instrument = word(as.character(Batch), -1, sep = "_"))
data$Instrument <- as.factor(data$Instrument)
data
}
#' Plot Internal Standard
#'
#' Generates a plot of IS peak area vs acquisition time.
#' @param data Data frame of Ascent data, generated from read_ascent_csv(), cleaned with
#' clean_ascent_cols(posix = TRUE)
#' @keywords ascent
#' @export
#' @examples
#' plot_IS(data)
plot_IS <- function(data) {
data %>%
filter(ISQuant == "IS") %>%
ggplot(aes(x = AcqTime, y = Area)) +
geom_point(aes(col = Type)) +
theme(axis.text.x = element_blank()) +
labs(x = "Acquisition Time", y = "IS Peak Area") +
facet_wrap(~Analyte)
}
#' Identify Outliers
#'
#' Updates the "Include" column of an Ascent data frame with FALSE if the point is an outlier
#' Points are marked as outliers if they are less than Q1 - 1.5 * IQR or greater than Q3 + 1.5*IQR
#' @param data Data frame processed by clean_ascent_csv()
#' @keywords ascent
#' @export
#' @examples
#' evaluate_outliers(data)
evaluate_outliers <- function(data) {
data <- data %>%
group_by(Analyte, Sample) %>%
mutate(Q1 = quantile(Conc, 0.25),
Q3 = quantile(Conc, 0.75),
IQR = IQR(Conc),
min_out = Q1 - 1.5*IQR,
max_out = Q3 + 1.5*IQR) %>%
ungroup()
data$Outlier <- ifelse(data$Conc < data$min_out | data$Conc > data$max_out, TRUE, FALSE)
data <- data %>%
select(-c(Q1, Q3, IQR, min_out, max_out))
return(data)
}
#' Create Summary Table of QC Verification Data
#'
#' Creates a summary table including Mean, SD, %CV, %Dev from Target, and n for each QC level for each analyte.
#' @param data Data frame processed by clean_ascent_csv() and evaluate_outliers()
#' @param exclude_outliers defaults to TRUE. Will not include data in summary if data$Outlier is TRUE (as determined by the IQR)
#' @keywords ascent
#' @export
#' @examples
#' summarize_QC(data)
summarize_QC <- function(data, exclude_outliers = TRUE) {
if(exclude_outliers) {
use_data <- data %>%
filter(!Outlier)
} else
use_data <- data
summaryTable <- use_data %>%
group_by(Analyte, Sample, Target) %>%
summarize("Mean" = mean_round(Conc),
"SD" = sd_round(Conc),
"%CV" = cv_round(Conc),
"%Dev" = acc(Conc, Target),
"n" = n()) %>%
ungroup()
return(summaryTable)
}
#' Create Summary Table of Calibrator Verification Data
#'
#' Creates a summary table including Mean, SD, %CV, %Dev from Target, and n for each calibrator level for each analyte.
#' @param data Data frame processed by clean_ascent_csv() and evaluate_outliers()
#' @param exclude_outliers defaults to TRUE. Will not include data in summary if data$Outlier is TRUE (as determined by IQR)
#' @keywords ascent
#' @export
#' @examples
#' summarize_cals(data)
summarize_cals <- function(data, exclude_outliers = TRUE) {
if(exclude_outliers) {
use_data <- data %>%
filter(!Outlier)
} else
use_data <- data
summaryTable <- use_data %>%
group_by(Analyte, Sample, Target) %>%
summarize("Mean" = mean_round(Conc),
"SD" = sd_round(Conc),
"%CV" = cv_round(Conc),
"%Dev" = acc(Conc, Target),
"n" = n()) %>%
ungroup()
return(summaryTable)
}
#' Testing
#'
#' Creates a summary table including Mean, SD, %CV, %Dev from Target, and n for each calibrator level for each analyte.
#' @param data Data frame processed by clean_ascent_csv() and evaluate_outliers()
#' @param exclude_outliers defaults to TRUE. Will not include data in summary if data$Outlier is TRUE (as determined by IQR)
#' @keywords ascent
#' @export
#' @examples
#' testdocument(data)
testdocument <- function(data, exclude_outliers = TRUE) {
if(exclude_outliers) {
use_data <- data %>%
filter(!Outlier)
} else
use_data <- data
summaryTable <- use_data %>%
group_by(Analyte, Sample, Target) %>%
summarize("Mean" = mean_round(Conc),
"SD" = sd_round(Conc),
"%CV" = cv_round(Conc),
"%Dev" = acc(Conc, Target),
"n" = n()) %>%
ungroup()
return(summaryTable)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.