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()
}
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