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templates / r / graph-functions.r
Mark on 8 Dec 2016 7 KB Fixed a few issues with r diagrams.
```library(TeachingDemos)

# define some colors
dCol <- rgb(1.0,0,0,alpha=0.2)
dBorder <- rgb(1.0,0,0,alpha=1.0)

# function for producing a histogram
histogram <- function(data, width=5, step=10, min=0, max=100, xlab=columnName, ylab=NULL, overridelabels=NULL, showcurve=FALSE, stats=TRUE, showbinwidth=FALSE, ylimit=NULL, curveAdjust=0.6, centresRight=TRUE, statsRight=FALSE, values=TRUE, ...) {

# get number of results of data in specified column
n <- length(data)

# create histogram data
h <- hist(data, breaks=seq(min, max, by=width), include.lowest=TRUE, right=centresRight, plot=FALSE)

if(showcurve) {
# calculate density for plotting a fitted curve
den <- density(data, curveAdjust, kernel="cosine", na.rm=TRUE)
}

# calculate range for y-axis unless it is specified as a parameter
if(is.null(ylimit)) {
ylimit <- max(h\$count)
ylimit <- ceiling(ylimit / step) * step

if(showcurve) {
ylimit <- max(c(ylimit,width*n*den\$y))
}
}

subText = ""
if(showbinwidth) {
subText = paste("(bin width of ", width,")",sep="")
}

# create initial plot

if(is.null(ylab)) {
ylab <- "Number of Students"
}

plot(h,ylim=c(0, ylimit), xlab=xlab, ylab=ylab, sub=subText, axes=FALSE, ...)

# plot some hrizontal grid lines
abline(h=seq(0, ylimit, by=step), col="grey", lty="solid", lwd=0.5)

# plot the histogram
lines(h, ylim=c(0, ylimit), border="darkblue", col="light blue")

mtext("Histogram", side=3, line=0.25)

## render the axis labels  ##

# cache the original graphics params and shrink the text a bit
op <- par(cex=0.7, font.lab=2)

# y-axis
LEFT <- 2
axis(LEFT, at=seq(0, ylimit, by=step), las=1)

# x-axis

# are we overriding x labels?
if (!is.null(overridelabels)) {
axis(1, at=seq(min+0.5*width, max-0.5*width, by=width), labels=overridelabels)
} else {
axis(1, at=seq(min, max, by=width), labels=seq(min, max, by=width))
}

# restore graphics params
par(op)

# plot density curve if required
if(showcurve) {
# scale the height of the curve and render it
polygon(x=den\$x, y=width*n*den\$y, col=dCol, border=dBorder)
}

# render values over bars
if(values) {
op <- par(cex=0.7)  # cache the graphics params
plot(h, labels=TRUE, col="transparent", border="transparent", add=TRUE)
par(op) # restore graphics params
}

if(stats) {
op <- par(cex=0.7)
avg <- mean(na.omit(data), trim=0)
med <- median(na.omit(data))
mi <- min(na.omit(data))
ma <- max(na.omit(data))

maxCount <- length(which(round(data,3) == max))

subsStr <- paste("Submissions: ", length(na.omit(data)), sep="")
avgStr <- paste("Mean: ", round(avg,1), " (", round((avg/max)*100,1),"%)", sep="")
medStr <- paste("Median: ", round(med,1), " (", round((med/max)*100,1),"%)", sep="")
minStr <- paste("Minimum: ", round(mi,1), " (",round((mi/max)*100,1),"%)", sep="")
maxStr <- paste("Maximum: ", round(ma,1), " (",round((ma/max)*100,1),"%)", sep="")
fullMarksStr <- paste("Full Marks: ", maxCount," (", round(maxCount/length(na.omit(data))*100,1),"%)", sep="")

combinedStr <- paste(subsStr, avgStr, medStr, minStr, maxStr, fullMarksStr, sep="\n")

strW <- strwidth(combinedStr)
strH <- strheight(combinedStr)

margin <- 2
xmargin <- margin * max / 100
ymargin <- margin * ylimit / 100

if(!statsRight) {
rect(xleft=0, xright=strW+xmargin*2, ytop=ylimit, ybottom=ylimit-strH-ymargin*2, col="white")
text(x=xmargin, y=ylimit-(strH / 2) - ymargin, combinedStr, adj=0)
} else {
rect(xleft=max-strW-(xmargin*2), max, ytop=ylimit, ybottom=ylimit-strH-ymargin*2, col="white")
text(x=max-strW-xmargin, y=ylimit-(strH / 2) - ymargin, combinedStr, adj=0)
}
par(op)
}

}

# function for producing a scatter plot

#pointlabels are labels to be rendered beside each point on the plot
scatter <- function(pointlabels=NULL, x, y, xlab, ylab, maxx=100, maxy=100, fit=FALSE, ...) {

# plot x vs y as solid points (thats what the pch=19 means) points
plot(x, y, xlim=c(0,maxx), ylim=c(0,maxy), col="light blue", pch=19, xlab=xlab, ylab=ylab, xaxt='n',yaxt='n', ...)

# plot some grid lines
abline(h=seq(0, maxy, 10), col="light grey", lty="solid", lwd=0.5)
abline(v=seq(0, maxx, 10), col="light grey", lty="solid", lwd=0.5)
#	abline(a=0, b=1, col="light grey", lwd=0.5)

# plot the graph
points(x, y, xlim=c(0,maxx), ylim=c(0,maxy), col="light blue", pch=19, xlab=xlab, ylab=ylab, ...)

# cache the original graphics params and shrink the text a bit
op <- par(cex=0.7)

# y-axis
LEFT <- 2
axis(LEFT, at=seq(0, maxy, by=10), las=1)
axis(1, at=seq(0, maxx, by=10))

par(op)

mtext("Scatter Plot", side=3, line=0.25)

# replot the points with darker outline to create a border around the points
points(x,y, col="dark blue")

# display point labels next to points if they were provided
if(!is.null(pointlabels)) {
# calculate a normalised offset for the x position of the labels
xlabelpos <- x-3.8*(maxx/100)

# render the labels
text(xlabelpos,y,pointlabels, cex=0.5)
}

# change plot color
op <- par(col="red")

co <- list()
co\$x <- x
co\$y <- y

valid <- is.na(co\$x) | is.na(co\$y)
valid <- !valid

co\$x <- co\$x[valid]
co\$y <- co\$y[valid]

if(fit) {
# render a lowess regression
lines(lowess(co))
#abline(reggie)
}

# restore original plot params
par(op)
}

# Sums two vectors in a coalescing fashion.
# If either value in x1 or x2 is NA that the result is the non-NA value.
# If both values in x1 or x2 are NA then the result is NA.
# If neither x1 or x2 is NA then the result is x1 + x2.
# coalescing_sum <- function (x1, x2) {
#
# # Iterative version.  Keeping around as an example.
#
# 	results <- 1:length(x1)
#
# 	for(i in 1:length(x1)) {
#
# 		if(xor(is.na(x1[i]),is.na(x2[i]))) {
# 			results[i] <- ifelse(is.na(x1[i]), 0, x1[i]) + ifelse(is.na(x2[i]), 0, x2[i])
# 		} else {
# 			results[i] <- x1[i] + x2[i]
# 		}
#
# 	}
#
# 	return(results)
# }

# Sums two vectors in a coalescing fashion.
# If either value in x1 or x2 is NA that the result is the non-NA value.
# If both values in x1 or x2 are NA then the result is NA.
# If neither x1 or x2 is NA then the result is x1 + x2.
coalescing_sum <- function (x, y) {

# keep track of which positions have both values as na
both.na <- is.na(x) & is.na(y)

# zero nas
x[is.na(x)] <- 0
y[is.na(y)] <- 0

# do the sum
z <- x + y;

# replace positions with both na as na
z[both.na] <- NA

return(z)
}

summaryboxplot <- function(data, formula, drawOutlierLabels=F, extras=NULL, labs=NULL, ...) {

op <-if(!is.null(extras)) {
par(mar=c(4+length(extras), 4, 4, 2) + 0.15)
} else {
par()
}

boxplot(formula=formula, data=data, axes=F, ylim=c(0,100), ...)
abline(h=seq(0,100, by=5), col="grey", lty="solid", lwd=0.5)
axis(2, at=seq(0,100, by=10), las=1)

axis(1, labels=levels(labs), at=seq(1, length(levels(labs))), font=2, ...)

if(!is.null(extras)) {
for(i in 1:length(extras)) {
mtext(line=i-1, text=names(extras[i]), side=1, padj=4, adj=1, at=0.55, cex=0.8, font=2);
axis(line=i-1, side=1, padj=2, at=seq(1,nrow(extras[i])), labels=t(extras[i]), cex.axis=0.8, tick=F, ...)
}
}

b <- boxplot(formula=formula, data=data, col="light blue", yaxt="n", xaxt="n", add=TRUE)

for (g in unique(b\$group)) { # for every group that has at least one outlier

# get assessment label
l <- b\$name[g]

# get outliers
o <- b\$out[which(b\$group == g)]

# get x,y coords of outliers and ID labels
x0 <- rep(g, length(o));
x1 <- rep(g+0.1, length(o))
y0 <- o;
y1 <- spread.labs(o, 1.25*strheight('A'), maxiter=6000, stepsize = 0.05)

# plots IDs
if(drawOutlierLabels) {
# get IDs of outliers
i <- data\$ID[data\$Assessment == l & data\$Mark %in% o]

text(x1, y1, i, adj=c(-0.1,0.5), col="black", cex=0.7)

# plot lines linking IDs to points (swap y0 with y1 if the lines pointing to outliers are backwards)
segments(x0+0.5*strwidth("X")+strwidth(" "), y0, x1-strwidth(""), y1, col="black", lty=1)
}

# replot outliers with coloured background
points(x=x0, y=y0, bg="light blue", pch=21)
}
par(op)
}
```