Help:Drawing graphs

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Question

How to draw graphs in Opasnet?

Answer

R-tools

In R-tools, you have the functionalities of R available. We recommend that you use the package ggplot2 whenever possible. It is very powerful, and borrowing good ideas from others is easier if we all use the same approach. Of course, it is also possible to use plot' (a kind of basic graph) as well, but the limits come sooner. This is an example code that contains all kinds of examples with comments.

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library(OpasnetUtils)
library(ggplot2)
library(xtable)

objects.get("QWYxSEJltnuTn0Xh") # data.time object from 
# http://fi.opasnet.org/fi_wiki/index.php?title=Talvivaaran_kaivoksen_mittauksia&oldid=16176
# data.time
altiste <- "Ni"

head(data.time)

ggplot(data.time, aes(x = Paikka, y = pitoisuusResult, colour = pitoisuusResult)) + 
	geom_point(size = 5) + # point size
	theme_grey(base_size = 24) + # font etc. http://sape.inf.usi.ch/quick-reference/ggplot2/themes
	labs( # label names
		title 	= paste(altiste, "Talvivaaran läheisyydessä"), 
		y 	= paste("Pitoisuus,", data.time$pitoisuusUnit[1]),
		x 	= "Mittauspaikka"
	) +
	theme(axis.text.x = element_text(angle = 90, hjust = 1)) # Turn text and adjust to right

########### Concentrations in time

ggplot(data.time, aes(x = Aika, y = pitoisuusResult, colour = Paikka)) + 
	geom_point(size = 5) + 
	theme_grey(base_size = 24) +
	labs(
		title 	= paste(altiste, "Talvivaaran läheisyydessä"), 
		y 	= paste("Pitoisuus,", data.time$pitoisuusUnit[1]),
		x 	= "Aika"
	) 

# data from http://fi.opasnet.org/fi_wiki/index.php?title=Kivilajien_kemiallinen_koostumus&oldid=16183
objects.get("yRxoBZmZBL5GOP6D")

head(data)
nrow(data)
ggplot(data[data$Observation == "Mean", ], aes(x = Element, y = Result, colour = Sequence)) + 
	geom_point() + 
	theme_grey(base_size = 20)

rlnorm

Graph for cumulative probability distributions

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library(ggplot2)

dat <- data.frame(
	Group = factor(1:5, levels = 1:5, 
		labels = c("Old, GR", "Old, CH", "Old, CZ", "New, all locations", "Renovated, all locations"), ordered = TRUE),
	Mean = c(1.29, 0.83, 0.7, 0.65, 0.6),
	Sd = c(1.09, 0.46, 0.43, 0.1, 0.1)
)

out <- data.frame()
for(i in 1:nrow(dat)) {
	temp <- data.frame(Group = dat$Group[i], Iter = 1:10000, 
 Result = rlnorm(10000, 
meanlog = log(dat$Mean[i]^2 / sqrt(dat$Sd[i]^2 + dat$Mean[i]^2)),
sdlog = sqrt(log(1 + dat$Sd[i]^2 / dat$Mean[i]^2))
))
	out <- rbind(out, temp)
}
head(out)

tapply(out$Result, out$Group, mean)
tapply(out$Result, out$Group, sd)


p <- ggplot(out, aes(x = Result, linetype = Group)) + 
stat_ecdf(size = 1.2) + 
coord_cartesian(xlim = c(0, 3))  + 
theme_grey(base_size = basesize) + 
labs(x = "Air exchange rate (1/h)", y = "Cumulative probability") + 
theme(legend.position=c(.7, .2)) + 
theme(legend.title=element_blank()) +
guides(fill = guide_legend(keywidth = 3, keyheight = 1))

# png(filename = "//cesium/jtue$/_Documents/Alexairexchange.png", width = 800, height = 800)
print(p)
# dev.off()

Colours and ordering of bars

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library(OpasnetUtils)
library(ggplot2)
library(RColorBrewer) # Makes color palettes http://www.cookbook-r.com/Graphs/Colors_%28ggplot2%29/
# http://stackoverflow.com/questions/8197559/emulate-ggplot2-default-color-palette

a <- data.frame(
	X = c("A", "B", "C", "D"),
	Y = c(1, 3, 2, 4),
	FILL = c("Up", "Middle", "Down", "Up")
)

ggplot(a, aes(x = reorder(X, Y, sum), weight = Y, fill = FILL)) + # X is reordered to decreasing order of Y.
	geom_bar() + coord_flip() + # Flip x and y axes
	theme_gray(base_size = 24) + # increase font
	labs( # Define labels
		title = "Environmental burden of disease in Finland", 
		x = "",
		y = "Impact (DALY/year)"
	) + 
	scale_fill_manual(values = brewer.pal(4, "Set2")) + # Use palette Set2 to define colours. 
	# Always use 4 colours, as the palette depends on the amount of colours used.
	theme(legend.position = c(0.8, 0.2)) # Adjust the legend position.

Google charts

This is how you can make fancy Google motion or map charts. See documentation for R package googleVis and Google's help. Note that Google has copyright in its maps, but the license to use them is very flexible and in practice free [1].

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library(OpasnetUtilsExt)
library(googleVis) # needed only for the example data, otherwise is included automagically


# Check all the examples at
# http://code.google.com/p/google-motion-charts-with-r/wiki/GadgetExamples

# Opasnet wrapper for gvis-functions, plain and simple as that
google.gvis('Motion Chart', Fruits, idvar="Fruit", timevar="Year", options=list(height=350, width=400))


google.gvis('Geo Map', Exports, locationvar="Country", numvar="Profit",options=list(dataMode='regions',height=480,width=640))

Export a graph to EPS or PDF file

This code only works on your own computer, because you cannot save files when running code in Opasnet. [2]

# Saving an .eps file
setEPS()
postscript("whatever.eps")
plot(rnorm(100), main="Hey Some Data")
dev.off()

# Saving a .pdf file
pdf("whatever.pdf")
plot(rnorm(100), main="Hey Some Data")
dev.off()

If you are using ggplot2 to generate a figure, then a

ggsave(file="name.eps", width = 7, height = 7)

will also work. It will save the last ggplot with the width and height you give (in inches).

Cumulative graphs

With ggplot, stat_ecdf() gives an empirical cumulative distribution function that sums up to 1. But if you want to get a cumulative sum of counts (that sum up to the number of observations), you need to do something else. For example, see [3].

ggplot(x,aes(x=X,color=A)) +
  stat_bin(data=subset(x,A=="a"),aes(y=cumsum(..count..)),geom="step")+
  stat_bin(data=subset(x,A=="b"),aes(y=cumsum(..count..)),geom="step")+
  stat_bin(data=subset(x,A=="c"),aes(y=cumsum(..count..)),geom="step")

Using positions

ggplot2 Quick Reference: position[4]

Position adjustments are used to adjust the position of each geom. The following position adjustments are available:

position_identity - default of most geoms
position_jitter - default of geom_jitter
position_dodge - default of geom_boxplot
position_stack - default of geom_bar==geom_histogram and geom_area
position_fill - useful for geom_bar==geom_histogram and geom_area

Setting the Position Adjustment: To set the position adjustment of a geom, use the position parameter of the layer() function:

layer(geom="point", ..., position="jitter")

Or use the position parameter of the geom_...() function:

geom_point(..., position="jitter")

Double dots in ggplot

What are double dots eg. ..density.. in ggplot?[5]

Unlike many other languages, in R, the dot is perfectly valid in identifiers. In this case, ..count.. is an identifier. However, there is special code in ggplot2 to detect this pattern, and to strip the dots. It feels unlikely that real code would use identifiers formatted like that, and so this is a neat way to distinguish between defined and calculated aesthetics.

It is used further up above in the map_statistic function. If a calculated aesthetic is present, another data frame (one that contains e.g. the count column) is used for the plot.

The single dot . is just another identifier, defined in the plyr package. As you can see, it is a function.

Maps and GIS-based data

There are several methods to produce maps. These are described on Opasnet map.

GoogleDocs

GoogleDocs is the method of choice for drawing causal diagrams.

Make a drawing.
Share it with everyone with open editing.
Download is in png or svg format.
Upload the file to Opasnet and copy a link to the original Google document to the image page.
Use like any image.

Sankey diagrams

There is no established approach to Sankey diagrams. A few packages provide with functionalities, but the usebility and user-friendliness has not been tested.

Help:Drawing graphs

Contents

Question

Answer

R-tools

rlnorm

Colours and ordering of bars

Google charts

Export a graph to EPS or PDF file

Cumulative graphs

Using positions

Double dots in ggplot

Maps and GIS-based data

GoogleDocs

Sankey diagrams

See also

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