peak.table
accurate_align(peak.table, simple.data, use.int.tol)peak.table
simple.data
use.int.tol
result
#' @title baRTplot #' @description baRTplot #' @author Xiaotao Shen #' shenxt1990@outlook.com #' @param simple.data simple.data #' @return result baRTplot <- function(simple.data) my.theme <- ggplot2::theme_bw() + ggplot2::theme( axis.title.x = ggplot2::element_text(size = 18), axis.title.y = ggplot2::element_text(size = 18) ) + ggplot2::theme( axis.text.x = ggplot2::element_text(size = 15), axis.text.y = ggplot2::element_text(size = 15) ) + ggplot2::theme(legend.title = ggplot2::element_text(size = 12)) + ggplot2::theme(legend.text = ggplot2::element_text(size = 10)) rt.error <- simple.data[[2]]$rt - simple.data[[1]]$rt rt1 <- simple.data[[1]]$rt temp.data <- data.frame(rt1, rt.error, stringsAsFactors = FALSE) rt.error.sd <- sd(abs(rt.error)) rt.error.sd <- paste("RT error standard:", round(rt.error.sd, 2), "second") rt.plot <- ggplot2::ggplot(data = temp.data, ggplot2::aes(x = rt1, y = rt.error)) + ggplot2::geom_point() + my.theme + ggplot2::labs(x = "Retention time (s, Batch 1)", y = "RT deviation (second)") + ggplot2::ggtitle(paste("RT vs. RT deviation;", rt.error.sd)) + ggplot2::geom_hline(aes(yintercept = 0)) + ggplot2::annotate( geom = "text", x = -Inf, y = Inf, hjust = -0.2, vjust = 2, label = rt.error.sd ) return(rt.plot) #' @title baINTplot #' @description baINTplot #' @author Xiaotao Shen #' shenxt1990@outlook.com #' @param simple.data simple.data #' @return result
baINTplot <- function(simple.data) my.theme <- ggplot2::theme_bw() + ggplot2::theme( axis.title.x = ggplot2::element_text(size = 18), axis.title.y = ggplot2::element_text(size = 18) ) + ggplot2::theme( axis.text.x = ggplot2::element_text(size = 15), axis.text.y = ggplot2::element_text(size = 15) ) + ggplot2::theme(legend.title = ggplot2::element_text(size = 12)) + ggplot2::theme(legend.text = ggplot2::element_text(size = 10)) int1 <- log(apply(simple.data[[1]][, -c(seq_len(3))], 1, function(x) mean(x, na.rm = TRUE)) + 1, 10) int2 <- log(apply(simple.data[[2]][, -c(seq_len(3))], 1, function(x) mean(x, na.rm = TRUE)) + 1, 10) int.error <- int2 - int1 int.error.sd <- sd(abs(int.error)) int.error.sd <- paste("Log10int error standard:", round(int.error.sd, 2)) temp.data <- data.frame(int1, int.error, stringsAsFactors = FALSE) int.plot <- ggplot2::ggplot(data = temp.data, ggplot2::aes(x = int1, y = int.error)) + ggplot2::geom_point() + my.theme + ggplot2::labs(x = "Log10intensity (mean, Batch 1)", y = "Log10intensity deviation (second)") + ggplot2::ggtitle(paste("Log10int vs. Log10int deviation;", int.error.sd)) + ggplot2::geom_hline(aes(yintercept = 0)) + ggplot2::annotate( geom = "text", x = -Inf, y = Inf, hjust = -0.2, vjust = 2, label = int.error.sd ) return(int.plot)