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.

+ Show code- Hide code 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

Size of base font: + Show code- Hide code 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

• Cookbook for colours
• Default colour palette

+ Show code- Hide code 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.

+ Show code- Hide code 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.

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.

• Harvard tagteam: rCharts R bloggers
  • Sankey from scratch using rCharts
• Aaronberdanier: SankeyR function
  • Biological posteriors: Sankey diagrams
• Riverplot (and SankeyR?)
• General Sankey diagram website

See also

• Double dots in a ggplot
• Visualising distributions
• Plotting means and error bars
• Cookbook for R: Scatterplots
• Scales of x and y axes
• Add a smoothed conditional mean
• theme_grey
• Different histograms
• Scale and flip graphs with coord_cartesian. Note that coord_flip() can take coord_cartesian() parameters.
• Parameters for geom_density (adjust, fill,...)