Merge pull request #27 from RECETOX/hechth/issue8

Removed SNplot duplicate

MFAssignR/DESCRIPTION

@@ -1,28 +1,28 @@
-Package: MFAssignR
-Type: Package
-Title: An R Package for Data Preparation and Molecular Formula Assignment
-Version: 1.0.2
-Author: Simeon Schum, Lynn Mazzoleni, Laura Brown
-Maintainer: Simeon Schum, <[email protected]>
-Description: This package contains functions that can estimate
-	the signal-to-noise for raw mass spectra, provide
-	preliminary identification of potential isotopic masses, 
-	recalibrate mass spectral data, and assign molecular formulas 
-	to exact mass measurements from ultrahigh resolution mass spectral
-	data using either Orbitrap MS or FT-ICR MS. The core algorithm for
-	formula assignment is the CHOFIT algorithm developed by 
-	Green and Perdue, Anal. Chem. (2015). This algorithm has 
-	been expanded on, using formula extension to increase its
-	performance and data quality.
-License: GNU GPL-2
-Encoding: UTF-8
-LazyData: true
-Depends: R (>= 3.4.1)
-Imports: dplyr (>= 0.7.6),
-         tidyr (>= 0.8.1),
-         ggplot2 (>= 3.0.0),
-	 colorRamps (>= 2.3)
-RoxygenNote: 7.1.1
-Suggests: knitr,
-    rmarkdown
-VignetteBuilder: knitr
+Package: MFAssignR
+Type: Package
+Title: An R Package for Data Preparation and Molecular Formula Assignment
+Version: 1.0.2
+Author: Simeon Schum, Lynn Mazzoleni, Laura Brown
+Maintainer: Simeon Schum, <[email protected]>
+Description: This package contains functions that can estimate
+	the signal-to-noise for raw mass spectra, provide
+	preliminary identification of potential isotopic masses, 
+	recalibrate mass spectral data, and assign molecular formulas 
+	to exact mass measurements from ultrahigh resolution mass spectral
+	data using either Orbitrap MS or FT-ICR MS. The core algorithm for
+	formula assignment is the CHOFIT algorithm developed by 
+	Green and Perdue, Anal. Chem. (2015). This algorithm has 
+	been expanded on, using formula extension to increase its
+	performance and data quality.
+License: GNU GPL-2
+Encoding: UTF-8
+LazyData: true
+Depends: R (>= 3.4.1)
+Imports: dplyr (>= 0.7.6),
+         tidyr (>= 0.8.1),
+         ggplot2 (>= 3.0.0),
+	 colorRamps (>= 2.3)
+RoxygenNote: 7.2.3
+Suggests: knitr,
+    rmarkdown
+VignetteBuilder: knitr

MFAssignR/NAMESPACE

@@ -1 +1,32 @@
-exportPattern("^[[:alpha:]]+")
+# Generated by roxygen2: do not edit by hand
+
+export(CompFactorToInt)
+export(CompFactorToInt2)
+export(CompIntToFactor)
+export(CompIntToFactor2)
+export(EM)
+export(EM2)
+export(Even)
+export(FindCoreFormulae2)
+export(FindCoreFormulae2_Halo)
+export(HighMoles)
+export(HighMoles2)
+export(HistNoise)
+export(IsoFiltR)
+export(KMDNoise)
+export(LowMoles)
+export(LowMoles2)
+export(MFAssign)
+export(MFAssignCHO)
+export(MaxMoles)
+export(MinMoles)
+export(NM)
+export(NM2)
+export(Recal)
+export(RecalList)
+export(SNplot)
+export(Valence)
+export(Valence2)
+export(ValidFormula)
+export(ValidFormula2)
+import(ggplot2)

MFAssignR/R/HistNoise.R

@@ -76,44 +76,3 @@ Freq2 <- ggplot2::ggplot(df, ggplot2::aes(x = log(intensity))) +
 Output <- list(Noise = SNout, Hist = Freq2)
 Output
 }
-##########################################
-#'Plot of Mass Spectrum with highlighted S/N cut
-#'
-#'SNplot which plots the mass spectrum with the S/N cut denoted by different colors
-#'for the mass spectrum peaks. This is useful for a qualitative look at the effectiveness
-#'of the S/N cut being used.
-#'
-#'@param df - dataframe of intensity and ion mass, column 1 should be intensity, column 2 should be mass
-#'@param cut - numeric value of the intensity cut value being investigated
-#'@param mass - numeric value setting a centerpoint to look at the mass spectrum
-#'@param window.x - numeric value setting the +/- range around the mass centerpoint, default is 0.5
-#'@param window.y - numeric value setting the y axis value for the plot, determined by multiplying the cut by this value
-#'
-#' @return S/N cut colored mass spectrum
-#'
-#' Zoomed Mass spectrum which shows where the cut is being applied
-#'
-#' @examples
-#' SNplot(df, cut = 1000, mass = 300, window.x = 1, window.y = 10)
-#'
-#' @export
-
-
-
-SNplot <- function(df, cut, mass, window.x = 0.5, window.y = 10){ #plots a data set displaying the SN cut around a specific mass
-
-  df <- df[c(2,1)]
-  names(df)[2] <- "mass"
-  names(df)[1] <- "Abundance"
-  df$Index <- "Bad"
-  df$Index <- replace(df$Index, df$Abundance > cut, "Good")
- SNplot <- ggplot2::ggplot(df, ggplot2::aes_string(x = "mass", xend = "mass", y = 0, yend = "Abundance")) +
-    ggplot2::geom_segment(ggplot2::aes(color = Index), size = 0.65, alpha = 1) +
-    ggplot2::geom_hline(yintercept = cut, linetype = "solid", size = 0.1) +
-    ggplot2::coord_cartesian(xlim = c(mass-window.x, mass+window.x), ylim = c(0, cut*window.y))
-  print(SNplot)
-}
-
-
-
-

MFAssignR/R/KMDNoise.R

@@ -150,21 +150,17 @@ KMDNoise <- function(df, upper.y = 0.2, lower.y = 0.05, upper.x = NA, lower.x =
 #'
 #' @examples
 #' SNplot(df, cut = 1000, mass = 300, window.x = 1, window.y = 10)
-#'
+#' @import ggplot2
 #' @export
-
-
-
 SNplot <- function(df, cut, mass, window.x = 0.5, window.y = 10) { # plots a data set displaying the SN cut around a specific mass
-
   df <- df[c(2, 1)]
   names(df)[2] <- "mass"
   names(df)[1] <- "Abundance"
   df$Index <- "Bad"
   df$Index <- replace(df$Index, df$Abundance > cut, "Good")
-  SNplot <- ggplot2::ggplot(df, ggplot2::aes_string(x = "mass", xend = "mass", y = 0, yend = "Abundance")) +
-    ggplot2::geom_segment(ggplot2::aes(color = Index), size = 0.65, alpha = 1) +
-    ggplot2::geom_hline(yintercept = cut, linetype = "solid", size = 0.1) +
-    ggplot2::coord_cartesian(xlim = c(mass - window.x, mass + window.x), ylim = c(0, cut * window.y))
+  SNplot <- ggplot(df, aes_string(x = "mass", xend = "mass", y = 0, yend = "Abundance")) +
+    geom_segment(aes(color = Index), size = 0.65, alpha = 1) +
+    geom_hline(yintercept = cut, linetype = "solid", size = 0.1) +
+    coord_cartesian(xlim = c(mass - window.x, mass + window.x), ylim = c(0, cut * window.y))
   print(SNplot)
 }