diff dfhlp.R @ 0:e66bb061af06 draft

planemo upload for repository https://github.com/workflow4metabolomics/lcmsmatching.git commit 3529b25417f8e1a5836474c9adec4b696d35099d-dirty

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dfhlp.R	Tue Jul 12 12:02:37 2016 -0400
@@ -0,0 +1,116 @@
+if ( ! exists('remove.na.rows')) { # Do not load again if already loaded
+
+	source('strhlp.R')
+
+	#################
+	# RENAME COLUMN #
+	#################
+
+	rename.col <- function(df, cur, new) {
+
+		for (k in seq(cur)) {
+			i <- which(cur[k] == colnames(df)) 
+			if (length(i) == 1)
+				colnames(df)[i] <- new[k]
+		}
+
+		return(df)
+	}
+
+	##################
+	# REMOVE NA ROWS #	
+	##################
+
+	remove.na.rows <- function(df) {
+		na.rows <- apply(is.na(df), MARGIN = 1, all)
+		return(df[ ! na.rows, , drop = FALSE])
+	}
+
+	######################
+	# MOVE COLUMNS FIRST #
+	######################
+
+	df.move.col.first <- function(df, cols) {
+		not.cols <- setdiff(names(df), cols)
+		df[c(cols, not.cols)]
+	}
+
+	#####################
+	# MOVE COLUMNS LAST #
+	#####################
+
+	df.move.col.last <- function(df, cols) {
+		not.cols <- setdiff(names(df), cols)
+		df[c(not.cols, cols)]
+	}
+
+	##############
+	# READ TABLE #
+	##############
+
+	df.read.table <- function(file, sep = "", header = TRUE, remove.na.rows = TRUE, check.names = TRUE, stringsAsFactors = TRUE, trim.header = FALSE, trim.values = FALSE, fileEncoding = "") {
+
+		# Call built-in read.table()
+		df <- read.table(file, sep = sep, header = header, check.names = check.names, stringsAsFactors = stringsAsFactors, fileEncoding = fileEncoding)
+
+		# Clean data frame
+		df <- df.clean(df, trim.colnames = trim.header, trim.values = trim.values, remove.na.rows = remove.na.rows)
+
+		return(df)
+	}
+
+	#################
+	# READ CSV FILE #
+	#################
+
+	# Read CSV file and return a data.frame.
+	# file      The path to the CSV file.
+	# header                If TRUE, use first line as header line.
+	# check.names           If TRUE, correct header (column) names in the data frame, by replacing non-ASCII characters by dot.
+	# stringsAsFactors      If TRUE, replace string values by factors.
+	# trim.header           If TRUE, remove whitespaces at beginning and of header titles.
+	# trim.values           If TRUE, remove whitespaces at beginning and of string values.
+	# remove.na.rows        If TRUE, remove all lines that contain only NA values.
+	df.read.csv <- function(file, header = TRUE, remove.na.rows = TRUE, check.names = TRUE, stringsAsFactors = TRUE, trim.header = FALSE, trim.values = FALSE) {
+	
+		# Call built-in read.csv()
+		df <- read.csv(file, header = header, check.names = check.names, stringsAsFactors = stringsAsFactors)
+
+		# Clean data frame
+		df <- df.clean(df, trim.colnames = trim.header, trim.values = trim.values, remove.na.rows = remove.na.rows)
+
+		return(df)
+	}
+
+	##################
+	# WRITE TSV FILE #
+	##################
+
+	df.write.tsv <- function(df, file, row.names = FALSE, col.names = TRUE) {
+		write.table(df, file = file, row.names = row.names, col.names = col.names, sep = "\t")
+	}
+
+	####################
+	# CLEAN DATA FRAME #
+	####################
+
+	df.clean <- function(df, trim.colnames = FALSE, trim.values = FALSE, remove.na.rows = FALSE) {
+
+		# Remove NA lines
+		if (remove.na.rows)
+			df <- remove.na.rows(df)
+
+		# Trim header
+		if (trim.colnames)
+			colnames(df) <- trim(colnames(df))
+
+		# Trim values
+		if (trim.values)
+			for (c in 1:ncol(df))
+				if (typeof(df[[c]]) == 'character')
+					df[[c]] <- trim(df[[c]])
+		
+		return(df)
+	}
+
+} # end of load safe guard