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()

Google charts

This is how you can make fancy Google motion or map charts. See documentation for R package googleVis and Google's help.

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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))

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.

Help:Drawing graphs

Contents

Question

Answer

R-tools

rlnorm

Google charts

Maps and GIS-based data

GoogleDocs

See also

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