Help:Drawing graphs: Difference between revisions

Revision as of 03:55, 28 September 2013

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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)

----#: . Code seems to work, but gives the yellow errorbox saying "Removed 745 rows containing missing values (geom_point)" --Heta 12:56, 27 August 2013 (EEST) (type: truth; paradigms: science: comment)

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, CH", "Old, CZ", "Old, GR", "New, all cities", "Renovated, all cities")),
	Mean = c(0.83, 0.7, 1.29, 0.65, 0.6),
	Sd = c(0.46, 0.43, 1.09, 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) + 
scale_x_continuous(limits = c(0, 3)) + 
theme_grey(base_size = 24) + 
labs(x = "Air exchange rate (1/h)", y = "Cumulative probability") + 
theme(legend.position=c(.7, .2))

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

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.