One of the nice features in the recent versions of GCkit is that it can be ran without any graphical user interface, just by typing commands in the console.
What good is it, will you say ? Well:
All the commands that are found in the menus can be used from the command line (and, in fact, even more or at least with more flexibility). So you may, for instance, run the following scripts (borrowed from the latest workshop in Catania).
In all examples, you may either copy the commands to a text file, that you save as "something.r", and then drag and drop the r-file to your console; or type each command in succession into the console.
Also, we assume in each example that you have the relevant data files, stored in a directory that happens to be, in this case, C:\\Users\\admin\\Documents\\R code\\.The datafiles are from the book.
groupsByLabel("Intrusion")
What good is it, will you say ? Well:
- It saves time. After a while it gets a bit annoying to have to go to "plot", "binary", select X variable, select Y variable "ok", "ok"... Isn't it easier, and faster, to just type binary("SiO2","Rb/Sr") ? I think so.
- It is reproducible. You can copy a string of commands to a file, and re-run the file every time you need it, perhaps because you've updated the dataset, or you're doing the same thing with all of your data anyway.
- Since it does not rely on the GUI, it is platform-independent (ha-ha !). So this allows to use GCDkit on platforms other than windows.
All the commands that are found in the menus can be used from the command line (and, in fact, even more or at least with more flexibility). So you may, for instance, run the following scripts (borrowed from the latest workshop in Catania).
In all examples, you may either copy the commands to a text file, that you save as "something.r", and then drag and drop the r-file to your console; or type each command in succession into the console.
Also, we assume in each example that you have the relevant data files, stored in a directory that happens to be, in this case, C:\\Users\\admin\\Documents\\R code\\.The datafiles are from the book.
Example 1 (loading and grouping data; simple calculations)
setwd("C:\\Users\\admin\\Documents\\R
code\\")
loadData("sazava.data",sep="\t")
print(WR[,"A/CNK"])
Sa-1 Sa-2 Sa-3 Sa-4 Sa-7 SaD-1 Gbs-1 Gbs-20 Gbs-2 Gbs-3
0.8355344 0.7618697 0.8078703 0.7193632 0.9617544 0.8418038 0.4370269 0.5549208 0.7506752 0.8598510
Po-1 Po-3 Po-4 Po-5
0.9716144 1.0248678 1.0129914 1.169989
groupsByLabel("Intrusion")
summaryByGroup(LILE,show.boxplot=TRUE)
print(results)
results<-CIPW(WR)
spider(WR,"Boynton",plot=FALSE)
# Calculation only
print(results)
Example 2 (simple diagrams)
setwd("C:\\Users\\admin\\Documents\\R
code\\")
loadData("dolerites_test.data",sep="\t")
#
Binary plot
binary(x="SiO2",y="Y",ymin=0,ymax=50,main="My
Binary plot")
figCex(1.8)
#
A single classification diagram, zoom in
plotDiagram("LarochePlut",F,F)
figXlim(c(1000,2700))
figYlim(c(1000,2500))
figRemove()
#
Harker plot (a simple plate)
multipleMjr("SiO2")
plateCex(1.7)
plateCexLab(1.2)
plateXLim(c(45,60))
plateBW()
Example 3 (Custom plates)
setwd("C:\\Users\\admin\\Documents\\R
code\\")
loadData("dolerites_test.data",sep="\t")
#
Custom plate
multiplePerPage(4,nrow=2,ncol=2,dummy=F,title="Geotectonic")
plotDiagram("Meschede",F,F,main="
")
plateExtract("Wood",3,main="
")
plotDiagram("PearceNbThYb",F,F,main="
")
plotDiagram("PearceNbTiYb",F,F,main="
")
plateLabelSlots(text=letters,cex=1.5,pos="topleft")
plateCex(1.7)
plateCexLab(1.2)
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)
(this example involves still a lot of clicking in dialogs, so it is not too likely to work in non-windows environments)
Example 4 (spiderplots)
setwd("C:\\Users\\admin\\Documents\\R
code\\")
loadData("dolerites_test.data",sep="\t")
groupsByLabel("region")
#
REE spider
spider(WR,"Boynton",1,100,pch="*",col="blue",cex=2)
spider.contour("Boynton","MgO","jet.colors",ymin=1,ymax=100,
pch=15)
pch=15)
#
NMORB Sun and McDonough
spider(WR,"^NMORB..Sun",0.1,500,offset=TRUE)
figMulti(shaded.col="khaki",plot.symb=FALSE,nrow=2,ncol=2)
plateLabelSlots(text=letters,cex=1.5,pos="bottomright")
plateCex(2)
plateCexLab(2)
plateBW()