I was about to say the same. loading the code line per line gave the error a
few lines after that one. Which explains...
Cheers
Joris
On Wed, May 26, 2010 at 9:27 PM, Erik Iverson <er...@ccbr.umn.edu> wrote:
> Is the '`' character supposed to be there before the ## Add error bars
> comment?
>
> If that is the problem, let it be a good lesson to use an editor with
> syntax highlighting. :)
>
>
>
>
> Brian Frizzelle wrote:
>
>> All,
>>
>> I think there may be some misunderstanding about my problem. In my code,
>> which is written as an R function, all of my variable names are short. I
>> get
>> that error when I try to source the function so I can call it. I just do
>> not
>> know why I'm getting the error, especially since I have written other very
>> similar functions that all work.
>>
>> So I have chosen to post the code below. I welcome any ideas about where
>> in
>> this code the error is occurring.
>>
>> ##########################################################
>> ### SCRIPT: graph_hh_wealth_function.R
>> ### DATE: April 22, 2010
>> ### AUTHOR: Brian Frizzelle
>> ### ### This function draws two line
>> graphs of household-level wealth from a
>> variable ### in a dataset output from an agent-based model run.
>> ###
>> ### The two graphs are:
>> ### 1) a line graph showing change in Wealth over time for each
>> individual
>> ### household
>> ### 2) a line graph of summary statistics of Wealth for the
>> households
>> (min,
>> ### max, mean, and error bars)
>> ###
>> ### Required Arguments:
>> ### * inpath - The path to the directory containing the
>> village-level
>> statistics
>> ### file
>> ### NOTE: Use the UNIX forward slash (/) convention when
>> entering the path
>> ### and do not include a slash at the end of the path.
>> ### OK: "D:/data"
>> ### Not OK: "D:\data"
>> ### Not OK: "D:/data/"
>> ### * infile - The name of the village-level statistics file
>> ### * outpath - The path to the directory where you want the
>> output graphics
>> to
>> ### be saved
>> ### * outpref - The prefix that you want for the output PNG graphics
>> files
>> ### NOTE: Do not include underscores (_) or spaces in the
>> output prefix.
>> ### Instead, please use dashes (-). Your prefix will
>> be separated
>> ### from the remainder of the file names by a dash.
>> ###
>> ### Optional Arguments:
>> ### * log.plot - Logical. Default is FALSE.
>> ### If TRUE, then the individual household wealth
>> graph is plotted
>> ### with a logarithmic Y-axis.
>> ### If FALSE, then the individual household wealth
>> graph is plotted
>> ### with a standard Y-axis.
>> ### * err.bar - Logical. Default is TRUE.
>> ### If TRUE, then error bars of 1 standard deviation
>> will be drawn
>> ### around the mean.
>> ### If FALSE, then no error bars will be drawn.
>> ### * n.quantiles - Integer. Value between 3 and 10. Default is 0,
>> meaning
>> no
>> ### quantile lines will be drawn.
>> ### This is the number of bins into which you would
>> like the variable
>> ### separated. One line for each will be drawn, with
>> the exception of
>> ### the min and max, which are already drawn.
>> ##########################################################
>>
>> graph.hh.wealth <- function(inpath, infile, outpath, outpref,
>> log.plot=FALSE, err.bar=TRUE,
>> n.quantiles=0)
>>
>> {
>>
>> ##*************************************************
>> ## Set the path and name of the input file
>> ## - The 'paste' command concatenates the two
>> ## - The 'skiplines' var sets the number of lines
>> ## to skip when reading in the dataset.
>> ##*************************************************
>> if (substr(inpath, nchar(inpath), nchar(inpath)) == "/")
>> inpath <- substr(inpath, 0, nchar(inpath)-1)
>> if (substr(outpath, nchar(outpath), nchar(outpath)) == "/")
>> outpath <- substr(outpath, 0, nchar(outpath)-1)
>> pathfile <- paste(inpath, infile, sep="/")
>> skiplines <- 1
>>
>> ##*************************************************
>> ## Set the names of the output file graphics
>> ##*************************************************
>> fnout.wlth <- "hhwealth.png"
>> fnout.wlthss <- "hhwealth-sumstats.png"
>> output.png.wlth <- paste(outpath, "/", outpref, fnout.wlth, sep="")
>> output.png.wlthss <- paste(outpath, "/", outpref, fnout.wlthss,
>> sep="")
>>
>> ##*************************************************
>> ## Read in the household-level output dataset
>> ##*************************************************
>> hhstats <- read.delim(file=pathfile, header=TRUE, sep ="\t",
>> dec=".",
>> skip=skiplines)
>>
>> ##*************************************************
>> ## Get some information from the household-level
>> ## dataset for use in plotting.
>> ## - the village number
>> ## - a vector of the unique HH IDs
>> ## - the maximum number of model run years
>> ## - the maximum wealth among all households
>> ##*************************************************
>> villnum <- mean(hhstats$V84ID)
>> uniq.hh.ids <- unique(hhstats$HHID00)
>> max.yr <- max(hhstats$Year)
>> max.wlth <- max(hhstats$Wealth)
>>
>> ##*************************************************
>> ## Extract out the vars needed for the individual
>> ## household line graph
>> ## Vars: Year, HHID00, Wealth, Status
>> ##*************************************************
>> hh.wealth <- hhstats[,c("Year","HHID00","Wealth","Status")]
>> hh.wealth.pos <- hh.wealth[hh.wealth$Wealth >= 0,]
>> hh.wealth.plot <- hh.wealth.pos[hh.wealth.pos$Status == 0 |
>> hh.wealth.pos$Status == 1,]
>>
>> ##*************************************************
>> ## Get the summary statistics by year for Wealth
>> ##*************************************************
>> ## Get a list of unique years for iterating
>> unqyrlist <- unique(hh.wealth.plot$Year)
>>
>> ## Determine the divisor for the quantiles
>> div.q <- 1.0 / n.quantiles
>>
>> ## Loop through years to extract summary statistics
>> for (yr in min(unqyrlist):max(unqyrlist)) {
>> # Get records for current year
>> this.yr <- hh.wealth.plot[hh.wealth.plot$Year == yr,]
>> # Calc summary statistics by statistic
>> this.n <- dim(this.yr)[1]
>> this.min <- min(this.yr$Wealth)
>> this.max <- max(this.yr$Wealth)
>> this.mean <- mean(this.yr$Wealth)
>> this.med <- median(this.yr$Wealth)
>> this.sd <- sd(this.yr$Wealth)
>> this.ebneg <- this.mean-this.sd
>> this.ebpos <- this.mean+this.sd
>> this.sumstats <- c(yr, this.n, this.min, this.max,
>> this.mean, this.med, this.sd,
>> this.ebneg, this.ebpos)
>> # Convert vector to 1-row data frame
>> this.df <- as.data.frame(t(as.matrix(this.sumstats)))
>> colnames(this.df) <- c("Year", "Count", "Min", "Max",
>> "Mean", "Median", "SD",
>> "EB.Low", "EB.High")
>> # Now, calculate the specified quantiles for this year
>> names(yr) <- "Year"
>> this.q <- as.data.frame(t(as.matrix(c(
>> quantile(this.yr$Wealth, probs=seq(0, 1, div.q)), yr))))
>> # Append the data frame to the output data frame
>> if (yr == min(unqyrlist)) {
>> hh.wealth.plot.ss <- this.df
>> hh.wealth.quantiles <- this.q
>> } else {
>> hh.wealth.plot.ss <-
>> rbind.data.frame(hh.wealth.plot.ss, this.df)
>> hh.wealth.quantiles <-
>> rbind.data.frame(hh.wealth.quantiles, this.q)
>> }
>> }
>>
>> ##*************************************************
>> ## Set up parameters for quantile plots
>> ##*************************************************
>> q.names <- c("", "", "Terciles", "Quantiles", "Quintiles",
>> "Sextiles", "Septiles", "Octiles", "Noniles",
>> "Deciles")
>> q.labs <- c("1st", "2nd", "3rd", "4th", "5th", "6th", "7th", "8th",
>> "9th")
>> # Determine if median will be plotted
>> if (n.quantiles %% 2) plot.med <- FALSE else plot.med <- TRUE
>> # Determine which non-median quantile columns will be plotted
>> if (n.quantiles %% 2) { # If the number of
>> quantiles is odd
>> cols.q <- c(2:n.quantiles)
>> } else { # If the number of
>> quantiles is even
>> cols.q <- c(2:(n.quantiles/2),
>> (2+(n.quantiles/2)):n.quantiles)
>> col.med <- (n.quantiles/2) + 1
>> }
>>
>> ##*************************************************
>> ## Set up plotting parameters for the summary
>> ## statistics graph
>> ##*************************************************
>> # Set y axis limits
>> ylim.min <- min(hh.wealth.plot.ss)
>> ylim.max <- max(hh.wealth.plot.ss)
>> # Set title
>> ttl.ss <- paste("Summary Statistics of Household Wealth Change Over
>> Time\n
>> (Village", villnum, ")", sep="")
>> # Set legend based on arguments
>> leg.txt.ss <- c("Mean", "Min", "Max", "Error Bars", "# of HHs")
>> if (err.bars == TRUE & n.quantiles == 0) {
>> leg.txt.ss <- c("Mean", "Min", "Max", "Error Bars", "# of
>> HHs")
>> leg.lty.ss <- c(1,2,4,5,0)
>> leg.lwd.ss <- c(2,1,1,1,0)
>> leg.pch.ss <- c(-1,-1,-1,-1,16)
>> leg.col.ss <-
>> c("black","darkolivegreen4","chartreuse4","red","blue")
>> } else if (err.bars == FALSE & n.quantiles >= 3) {
>> if (n.quantiles %% 2) { # Odd
>> number of quantiles
>> leg.txt.ss <- c("Mean", "Min", "Max",
>> q.names[n.quantiles], "# of HHs")
>> leg.lty.ss <- c(1,2,4,3,0)
>> leg.lwd.ss <- c(2,1,1,1,0)
>> leg.pch.ss <- c(-1,-1,-1,-1,16)
>> leg.col.ss <-
>> c("black","darkolivegreen4","chartreuse4","orange","blue")
>> } else { #
>> Even number of quantiles
>> leg.txt.ss <- c("Mean", "Min", "Max", "Median",
>> q.names[n.quantiles], "# of HHs")
>> leg.lty.ss <- c(1,2,4,3,3,0)
>> leg.lwd.ss <- c(2,1,1,2,1,0)
>> leg.pch.ss <- c(-1,-1,-1,-1,-1,16)
>> leg.col.ss <- c("black", "darkolivegreen4",
>> "chartreuse4", "orange",
>> "orange", "blue")
>> }
>> } else if (err.bars == TRUE & n.quantiles >= 3) {
>> if (n.quantiles %% 2) { # Odd
>> number of quantiles
>> leg.txt.ss <- c("Mean", "Min", "Max", "Error Bars",
>> q.names[n.quantiles], "# of
>> HHs")
>> leg.lty.ss <- c(1,2,4,5,3,0)
>> leg.lwd.ss <- c(2,1,1,1,1,0)
>> leg.pch.ss <- c(-1,-1,-1,-1,-1,16)
>> leg.col.ss <- c("black", "darkolivegreen4",
>> "chartreuse4", "red",
>> "orange",
>> "blue")
>> } else { #
>> Even number of quantiles
>> leg.txt.ss <- c("Mean", "Min", "Max", "Error Bars",
>> "Median", q.names[n.quantiles], "#
>> of HHs")
>> leg.lty.ss <- c(1,2,4,5,3,3,0)
>> leg.lwd.ss <- c(2,1,1,1,2,1,0)
>> leg.pch.ss <- c(-1,-1,-1,-1,-1,-1,16)
>> leg.col.ss <- c("black", "darkolivegreen4",
>> "chartreuse4", "red",
>> "orange",
>> "orange","blue")
>> }
>> } else {
>> leg.txt.ss <- c("Mean", "Min", "Max", "# of HHs")
>> leg.lty.ss <- c(1,2,4,0)
>> leg.lwd.ss <- c(2,1,1,0)
>> leg.pch.ss <- c(-1,-1,-1,16)
>> leg.col.ss <- c("black", "darkolivegreen4", "chartreuse4",
>> "blue")
>> }
>>
>>
>> ##*************************************************
>> ## Set up vector of possible colors for the lines
>> ##*************************************************
>> colorset <- c(3:12, 26:62, 67:79, 81, 83:137, 139, 142:151)
>>
>> ##*************************************************
>> ## Set up plotting parameters for the individual
>> ## household graph
>> ##*************************************************
>> ## Create a vector of colors from the colorset
>> ## above, one for every HHID00
>> colors.id <- sample(colorset, length(uniq.hh.ids), replace=TRUE)
>> ## Other parameters
>> ttl.hh <- paste("Change in Household Wealth Over Time\n (Village",
>> villnum, ")", sep="")
>> leg.txt.hh <- c("Old Households", "Split Households")
>>
>> ##*************************************************
>> ## Plot household-level summary statistics
>> ##*************************************************
>> png(filename=output.png.wlthss, width=10, height=7, units="in",
>> res=300)
>>
>> ## Set initial plot with mean
>> plot(hh.wealth.plot.ss$Year, # x var
>> hh.wealth.plot.ss$Mean, # y var (MEAN)
>> type="l", # line graph
>> xlim=c(0,max.yr), # x-axis limits
>> ylim=c(ylim.min, ylim.max), # y-axis limits
>> lty=1, # solid line
>> lwd=2, # line thickness
>> xlab="",
>> ylab="Wealth", # y-axis label
>> main=ttl.ss)
>> ## Add MIN
>> lines(hh.wealth.plot.ss$Year, # x var
>> hh.wealth.plot.ss$Min, # y var (MIN)
>> type="l", # line graph
>> col="darkolivegreen4",
>> lty=2)
>> ## Add MAX
>> lines(hh.wealth.plot.ss$Year, # x var
>> hh.wealth.plot.ss$Max, # y var (MAX)
>> type="l", # line graph
>> col="chartreuse4",
>> lty=4)
>>
>> ` ## Add ERROR BARS if Argument 'err.bars' is TRUE
>> if (err.bars == TRUE) {
>> ## Add LOW ERROR BAR
>> lines(hh.wealth.plot.ss$Year, # x var
>> hh.wealth.plot.ss$EB.Low, # y var (EB
>> LOW)
>> type="l", #
>> line graph
>> col="red",
>> lty=3)
>> ## Add HIGH ERROR BAR
>> lines(hh.wealth.plot.ss$Year, # x var
>> hh.wealth.plot.ss$EB.High, # y var (EB
>> HIGH)
>> type="l", #
>> line graph
>> col="red",
>> lty=3)
>> }
>> ## Add QUANTILES if Argument 'n.quantiles' is >= 3
>> if (n.quantiles >= 3) {
>> # Cycle through column numbers and draw quantile lines
>> for (qcol in 1:length(cols.q)) {
>> lines(hh.wealth.quantiles$Year, # Plot quantile
>> lines
>> hh.wealth.quantiles[,cols.q[qcol]],
>> type="l",
>> col="orange",
>> lty=3)
>> }
>> # Add MEDIAN line
>> if (plot.med == TRUE) {
>> lines(hh.wealth.quantiles$Year, # Plot median
>> hh.wealth.quantiles[,col.med],
>> type="l",
>> col="orange",
>> lty=3,
>> lwd=2)
>> }
>> }
>> ## Add COUNT
>> points(hh.wealth.plot.ss$Year, # x var
>> hh.wealth.plot.ss$Count, # y var (COUNT)
>> type="p", # line graph
>> pch=16,
>> col="blue",
>> cex=0.5)
>> ## Add LEGEND
>> legend(x="topright",
>> leg.txt.ss,
>> lty=leg.lty.ss,
>> lwd=leg.lwd.ss,
>> pch=leg.pch.ss,
>> col=leg.col.ss,
>> cex=1)
>>
>> dev.off()
>>
>>
>> ##*************************************************
>> ## Plot wealth for individual households
>> ##*************************************************
>> png(filename=output.png.wlth, width=10, height=7, units="in",
>> res=300)
>>
>> ## Create an empty plot
>> if (log.plot == FALSE) {
>> plot(hh.wealth.plot$Year, hh.wealth.plot$Wealth,
>> type="n",
>> xlim=c(0,max.yr),
>> main=ttl.hh,
>> xlab="",
>> ylab="Wealth")
>> } else {
>> plot(hh.wealth.plot$Year, hh.wealth.plot$Wealth,
>> log="y",
>> type="n",
>> xlim=c(0,max.yr),
>> main=ttl.hh,
>> xlab="",
>> ylab="Wealth (log scale)")
>> }
>>
>> #legend(x=leg.x.coord, y=leg.y.coord, # Sets the location for the
>> legend
>> legend(x="topright",
>> leg.txt.hh, # text in the
>> legent
>> col=c("red", "red"), # sets the line
>> colors in the legend
>> lty=c(1,3), # draws lines
>> lwd=c(1,1), # sets line
>> thickness
>> # bty="n", # no border
>> on the legend
>> ncol=2, # makes it
>> a 2-column legend
>> cex=0.8) # sets the
>> legend text size
>>
>> ## Loop through IDs and add a line for each
>> for (id in 1:length(uniq.hh.ids)) {
>> ## Get the current HH ID
>> this.id <- uniq.hh.ids[id]
>>
>> ## Extract the records for the current ID
>> this.sub <- hh.wealth.plot[hh.wealth.plot$HHID00 ==
>> this.id,]
>>
>> if (dim(this.sub)[1] > 0) {
>>
>> ## Set line type
>> if (mean(this.sub$Status) == 0) {
>> ltype <- 1
>> } else {
>> ltype <- 3
>> }
>>
>> ## Add the line for this ID
>> lines(this.sub$Year, this.sub$Wealth,
>> type="l",
>> col= colors.id[id],
>> lwd=1,
>> lty=ltype)
>> }
>>
>> }
>>
>> dev.off()
>>
>> }
>>
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Joris Meys
Statistical Consultant
Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control
Coupure Links 653
B-9000 Gent
tel : +32 9 264 59 87
joris.m...@ugent.be
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