comparison src/breadcrumbs/scripts/scriptBiplotTSV.R @ 0:2f4f6f08c8c4 draft

Uploaded
author george-weingart
date Tue, 13 May 2014 21:58:57 -0400
parents
children
comparison
equal deleted inserted replaced
-1:000000000000 0:2f4f6f08c8c4
1 #!/usr/bin/env Rscript
2
3 library(vegan)
4 library(optparse)
5
6 funcGetCentroidForMetadatum <- function(
7 ### Given a binary metadatum, calculate the centroid of the samples associated with the metadata value of 1
8 # 1. Get all samples that have the metadata value of 1
9 # 2. Get the x and y coordinates of the selected samples
10 # 3. Get the median value for the x and ys
11 # 4. Return those coordinates as the centroid's X and Y value
12 vfMetadata,
13 ### Logical or integer (0,1) vector, TRUE or 1 values indicate correspoinding samples in the
14 ### mSamplePoints which will be used to define the centroid
15 mSamplePoints
16 ### Coordinates (columns;n=2) of samples (rows) corresponding to the vfMetadata
17 ){
18 # Check the lengths which should be equal
19 if(length(vfMetadata)!=nrow(mSamplePoints))
20 {
21 print(paste("funcGetCentroidForMetadata::Error: Should have received metadata and samples of the same length, received metadata length ",length(vfMetadata)," and sample ",nrow(mSamplePoints)," length.",sep=""))
22 return( FALSE )
23 }
24
25 # Get all the samples that have the metadata value of 1
26 viMetadataSamples = which(as.integer(vfMetadata)==1)
27
28 # Get the x and y coordinates for the selected samples
29 mSelectedPoints = mSamplePoints[viMetadataSamples,]
30
31 # Get the median value for the x and the ys
32 if(!is.null(nrow(mSelectedPoints)))
33 {
34 return( list(x=median(mSelectedPoints[,1],na.rm = TRUE),y=median(mSelectedPoints[,2],na.rm = TRUE)) )
35 } else {
36 return( list(x=mSelectedPoints[1],y=mSelectedPoints[2]) )
37 }
38 }
39
40 funcGetMaximumForMetadatum <- function(
41 ### Given a continuous metadata
42 ### 1. Use the x and ys from mSamplePoints for coordinates and the metadata value as a height (z)
43 ### 2. Use lowess to smooth the landscape
44 ### 3. Take the maximum of the landscape
45 ### 4. Return the coordiantes for the maximum as the centroid
46 vdMetadata,
47 ### Continuous (numeric or integer) metadata
48 mSamplePoints
49 ### Coordinates (columns;n=2) of samples (rows) corresponding to the vfMetadata
50 ){
51 # Work with data frame
52 if(class(mSamplePoints)=="matrix")
53 {
54 mSamplePoints = data.frame(mSamplePoints)
55 }
56 # Check the lengths of the dataframes and the metadata
57 if(length(vdMetadata)!=nrow(mSamplePoints))
58 {
59 print(paste("funcGetMaximumForMetadatum::Error: Should have received metadata and samples of the same length, received metadata length ",length(vdMetadata)," and sample ",nrow(mSamplePoints)," length.",sep=""))
60 return( FALSE )
61 }
62
63 # Add the metadata value to the points
64 mSamplePoints[3] = vdMetadata
65 names(mSamplePoints) = c("x","y","z")
66
67 # Create lowess to smooth the surface
68 # And calculate the fitted heights
69 # x = sample coordinate 1
70 # y = sample coordinate 2
71 # z = metadata value
72 loessSamples = loess(z~x*y, data=mSamplePoints, degree = 1, normalize = FALSE, na.action=na.omit)
73
74 # Naively get the max
75 vdCoordinates = loessSamples$x[which(loessSamples$y==max(loessSamples$y)),]
76 return(list(lsmod = loessSamples, x=vdCoordinates[1],y=vdCoordinates[2]))
77 }
78
79 funcMakeShapes <- function(
80 ### Takes care of defining shapes for the plot
81 dfInput,
82 ### Data frame of metadata measurements
83 sShapeBy,
84 ### The metadata to shape by
85 sShapes,
86 ### List of custom metadata (per level if factor).
87 ### Should correspond to the number of levels in shapeBy; the format is level:shape,level:shape for example HighLuminosity:14,LowLuminosity:2,HighPH:10,LowPH:18
88 cDefaultShape
89 ### Shape to default to if custom shapes are not used
90 ){
91 lShapes = list()
92 vsShapeValues = c()
93 vsShapeShapes = c()
94 vsShapes = c()
95 sMetadataId = sShapeBy
96
97 # Set default shape, color, and color ranges
98 if(!is.null(cDefaultShape))
99 {
100 # Default shape should be an int for the int pch options
101 if(!is.na(as.integer(cDefaultShape)))
102 {
103 cDefaultShape = as.integer(cDefaultShape)
104 }
105 } else {
106 cDefaultShape = 16
107 }
108
109 # Make shapes
110 vsShapes = rep(cDefaultShape,nrow(dfInput))
111
112 if(!is.null(sMetadataId))
113 {
114 if(is.null(sShapes))
115 {
116 vsShapeValues = unique(dfInput[[sMetadataId]])
117 vsShapeShapes = 1:length(vsShapeValues)
118 } else {
119 # Put the markers in the order of the values)
120 vsShapeBy = unlist(strsplit(sShapes,","))
121 for(sShapeBy in vsShapeBy)
122 {
123 vsShapeByPieces = unlist(strsplit(sShapeBy,":"))
124 lShapes[vsShapeByPieces[1]] = as.integer(vsShapeByPieces[2])
125 }
126 vsShapeValues = names(lShapes)
127 }
128
129 # Shapes in the correct order
130 if(!is.null(sShapes))
131 {
132 vsShapeShapes = unlist(lapply(vsShapeValues,function(x) lShapes[[x]]))
133 }
134 vsShapeValues = paste(vsShapeValues)
135
136 # Make the list of shapes
137 for(iShape in 1:length(vsShapeValues))
138 {
139 vsShapes[which(paste(dfInput[[sMetadataId]])==vsShapeValues[iShape])]=vsShapeShapes[iShape]
140 }
141
142 # If they are all numeric characters, make numeric
143 viIntNas = which(is.na(as.integer(vsShapes)))
144 viNas = which(is.na(vsShapes))
145 if(length(setdiff(viIntNas,viNas))==0)
146 {
147 vsShapes = as.integer(vsShapes)
148 } else {
149 print("funcMakeShapes::Error: Please supply numbers 1-25 for shape in the -y,--shapeBy option")
150 vsShapeValues = c()
151 vsShapeShapes = c()
152 }
153 }
154 return(list(PlotShapes=vsShapes,Values=vsShapeValues,Shapes=vsShapeShapes,ID=sMetadataId,DefaultShape=cDefaultShape))
155 }
156
157 ### Global defaults
158 c_sDefaultColorBy = NULL
159 c_sDefaultColorRange = "orange,cyan"
160 c_sDefaultTextColor = "black"
161 c_sDefaultArrowColor = "cyan"
162 c_sDefaultArrowTextColor = "Blue"
163 c_sDefaultNAColor = NULL
164 c_sDefaultShapeBy = NULL
165 c_sDefaultShapes = NULL
166 c_sDefaultMarker = "16"
167 c_fDefaultPlotArrows = TRUE
168 c_sDefaultRotateByMetadata = NULL
169 c_sDefaultResizeArrow = 1
170 c_sDefaultTitle = "Custom Biplot of Bugs and Samples - Metadata Plotted with Centroids"
171 c_sDefaultOutputFile = NULL
172
173 ### Create command line argument parser
174 pArgs <- OptionParser( usage = "%prog last_metadata input.tsv" )
175
176 # Selecting features to plot
177 pArgs <- add_option( pArgs, c("-b", "--bugs"), type="character", action="store", default=NULL, dest="sBugs", metavar="BugsToPlot", help="Comma delimited list of data to plot as text. To not plot metadata pass a blank or empty space. Bug|1,Bug|2")
178 # The default needs to stay null for metadata or code needs to be changed in the plotting for a check to see if the metadata was default. Search for "#!# metadata default dependent"
179 pArgs <- add_option( pArgs, c("-m", "--metadata"), type="character", action="store", default=NULL, dest="sMetadata", metavar="MetadataToPlot", help="Comma delimited list of metadata to plot as arrows. To not plot metadata pass a blank or empty space. metadata1,metadata2,metadata3")
180
181 # Colors
182 pArgs <- add_option( pArgs, c("-c", "--colorBy"), type="character", action="store", default=c_sDefaultColorBy, dest="sColorBy", metavar="MetadataToColorBy", help="The id of the metadatum to use to make the marker colors. Expected to be a continuous metadata.")
183 pArgs <- add_option( pArgs, c("-r", "--colorRange"), type="character", action="store", default=c_sDefaultColorRange, dest="sColorRange", metavar="ColorRange", help=paste("Colors used to color the samples; a gradient will be formed between the color.Default=", c_sDefaultColorRange))
184 pArgs <- add_option( pArgs, c("-t", "--textColor"), type="character", action="store", default=c_sDefaultTextColor, dest="sTextColor", metavar="TextColor", help=paste("The color bug features will be plotted with as text. Default =", c_sDefaultTextColor))
185 pArgs <- add_option( pArgs, c("-a", "--arrowColor"), type="character", action="store", default=c_sDefaultArrowColor, dest="sArrowColor", metavar="ArrowColor", help=paste("The color metadata features will be plotted with as an arrow and text. Default", c_sDefaultArrowColor))
186 pArgs <- add_option( pArgs, c("-w", "--arrowTextColor"), type="character", action="store", default=c_sDefaultArrowTextColor, dest="sArrowTextColor", metavar="ArrowTextColor", help=paste("The color for the metadata text ploted by the head of the metadata arrow. Default", c_sDefaultArrowTextColor))
187 pArgs <- add_option(pArgs, c("-n","--plotNAColor"), type="character", action="store", default=c_sDefaultNAColor, dest="sPlotNAColor", metavar="PlotNAColor", help=paste("Plot NA values as this color. Example -n", c_sDefaultNAColor))
188
189 # Shapes
190 pArgs <- add_option( pArgs, c("-y", "--shapeby"), type="character", action="store", default=c_sDefaultShapeBy, dest="sShapeBy", metavar="MetadataToShapeBy", help="The metadata to use to make marker shapes. Expected to be a discrete metadatum. An example would be -y Environment")
191 pArgs <- add_option( pArgs, c("-s", "--shapes"), type="character", action="store", default=c_sDefaultShapes, dest="sShapes", metavar="ShapesForPlotting", help="This is to be used to specify the shapes to use for plotting. Can use numbers recognized by R as shapes (see pch). Should correspond to the number of levels in shapeBy; the format is level:shape,level:shape for example HighLuminosity:14,LowLuminosity:2,HighPH:10,LowPH:18 . Need to specify -y/--shapeBy for this option to work.")
192 pArgs <- add_option( pArgs, c("-d", "--defaultMarker"), type="character", action="store", default=c_sDefaultMarker, dest="sDefaultMarker", metavar="DefaultColorMarker", help="Default shape for markers which are not otherwise indicated in --shapes, can be used for unspecified values or NA. Must not be a shape in --shapes.")
193
194 # Plot manipulations
195 pArgs <- add_option( pArgs, c("-e","--rotateByMetadata"), type="character", action="store", default=c_sDefaultRotateByMetadata, dest="sRotateByMetadata", metavar="RotateByMetadata", help="Rotate the ordination by a metadata. Give both the metadata and value to weight it by. The larger the weight, the more the ordination is influenced by the metadata. If the metadata is continuous, use the metadata id; if the metadata is discrete, the ordination will be by one of the levels so use the metadata ID and level seperated by a '_'. Discrete example -e Environment_HighLumninosity,100 ; Continuous example -e Environment,100 .")
196 pArgs <- add_option( pArgs, c("-z","--resizeArrow"), type="numeric", action="store", default=c_sDefaultResizeArrow, dest="dResizeArrow", metavar="ArrowScaleFactor", help="A constant to multiple the length of the arrow to expand or shorten all arrows together. This will not change the angle of the arrow nor the relative length of arrows to each other.")
197 pArgs <- add_option( pArgs, c("-A", "--noArrows"), type="logical", action="store_true", default=FALSE, dest="fNoPlotMetadataArrows", metavar="DoNotPlotArrows", help="Adding this flag allows one to plot metadata labels without the arrows.")
198
199 # Misc
200 pArgs <- add_option( pArgs, c("-i", "--title"), type="character", action="store", default=c_sDefaultTitle, dest="sTitle", metavar="Title", help="This is the title text to add to the plot.")
201 pArgs <- add_option( pArgs, c("-o", "--outputfile"), type="character", action="store", default=c_sDefaultOutputFile, dest="sOutputFileName", metavar="OutputFile", help="This is the name for the output pdf file. If an output file is not given, an output file name is made based on the input file name.")
202
203 funcDoBiplot <- function(
204 ### Perform biplot. Samples are markers, bugs are text, and metadata are text with arrows. Markers and bugs are dtermined usiing NMDS and Bray-Curtis dissimilarity. Metadata are placed on the ordination in one of two ways: 1. Factor data - for each level take the ordination points for the samples that have that level and plot the metadata text at the average orindation point. 2. For continuous data - make a landscape (x and y form ordination of the points) and z (height) as the metadata value. Use a lowess line to get the fitted values for z and take the max of the landscape. Plot the metadata text at that smoothed max.
205 sBugs,
206 ### Comma delimited list of data to plot as text. Bug|1,Bug|2
207 sMetadata,
208 ### Comma delimited list of metadata to plot as arrows. metadata1,metadata2,metadata3.
209 sColorBy = c_sDefaultColorBy,
210 ### The id of the metadatum to use to make the marker colors. Expected to be a continuous metadata.
211 sColorRange = c_sDefaultColorRange,
212 ### Colors used to color the samples; a gradient will be formed between the color. Example orange,cyan
213 sTextColor = c_sDefaultTextColor,
214 ### The color bug features will be plotted with as text. Example black
215 sArrowColor = c_sDefaultArrowColor,
216 ### The color metadata features will be plotted with as an arrow and text. Example cyan
217 sArrowTextColor = c_sDefaultArrowTextColor,
218 ### The color for the metadata text ploted by the head of the metadat arrow. Example Blue
219 sPlotNAColor = c_sDefaultNAColor,
220 ### Plot NA values as this color. Example grey
221 sShapeBy = c_sDefaultShapeBy,
222 ### The metadata to use to make marker shapes. Expected to be a discrete metadatum.
223 sShapes = c_sDefaultShapes,
224 ### This is to be used to specify the shapes to use for plotting. Can use numbers recognized by R as shapes (see pch). Should correspond to the number of levels in shapeBy; the format is level:shape,level:shape for example HighLuminosity:14,LowLuminosity:2,HighPH:10,LowPH:18 . Works with sShapesBy.
225 sDefaultMarker = c_sDefaultMarker,
226 ### The default marker shape to use if shapes are not otherwise indicated.
227 sRotateByMetadata = c_sDefaultRotateByMetadata,
228 ### Metadata and value to rotate by. example Environment_HighLumninosity,100
229 dResizeArrow = c_sDefaultResizeArrow,
230 ### Scale factor to resize tthe metadata arrows
231 fPlotArrow = c_fDefaultPlotArrows,
232 ### A flag which can be used to turn off arrow plotting.
233 sTitle = c_sDefaultTitle,
234 ### The title for the figure.
235 sInputFileName,
236 ### File to input (tsv file: tab seperated, row = sample file)
237 sLastMetadata,
238 ### Last metadata that seperates data and metadata
239 sOutputFileName = c_sDefaultOutputFile
240 ### The file name to save the figure.
241 ){
242 # Define the colors
243 vsColorRange = c("blue","orange")
244 if(!is.null(sColorRange))
245 {
246 vsColorRange = unlist(strsplit(sColorRange,","))
247 }
248 cDefaultColor = "grey"
249 if(!is.null(vsColorRange) && length(vsColorRange)>0)
250 {
251 cDefaultColor = vsColorRange[1]
252 }
253
254 # List of bugs to plot
255 # If there is a list it needs to be more than one.
256 vsBugsToPlot = c()
257 if(!is.null(sBugs))
258 {
259 vsBugsToPlot = unlist(strsplit(sBugs,","))
260 }
261 # Metadata to plot
262 vsMetadata = c()
263 if(!is.null(sMetadata))
264 {
265 vsMetadata = unlist(strsplit(sMetadata,","))
266 }
267
268 ### Load table
269 dfInput = sInputFileName
270 if(class(sInputFileName)=="character")
271 {
272 dfInput = read.table(sInputFileName, sep = "\t", header=TRUE)
273 names(dfInput) = unlist(lapply(names(dfInput),function(x) gsub(".","|",x,fixed=TRUE)))
274 row.names(dfInput) = dfInput[,1]
275 dfInput = dfInput[-1]
276 }
277
278 iLastMetadata = which(names(dfInput)==sLastMetadata)
279 viMetadata = 1:iLastMetadata
280 viData = (iLastMetadata+1):ncol(dfInput)
281
282 ### Dummy the metadata if discontinuous
283 ### Leave the continous metadata alone but include
284 listMetadata = list()
285 vsRowNames = c()
286 viContinuousMetadata = c()
287 for(i in viMetadata)
288 {
289 vCurMetadata = unlist(dfInput[i])
290 if(is.numeric(vCurMetadata)||is.integer(vCurMetadata))
291 {
292 vCurMetadata[which(is.na(vCurMetadata))] = mean(vCurMetadata,na.rm=TRUE)
293 listMetadata[[length(listMetadata)+1]] = vCurMetadata
294 vsRowNames = c(vsRowNames,names(dfInput)[i])
295 viContinuousMetadata = c(viContinuousMetadata,length(listMetadata))
296 } else {
297 vCurMetadata = as.factor(vCurMetadata)
298 vsLevels = levels(vCurMetadata)
299 for(sLevel in vsLevels)
300 {
301 vNewMetadata = rep(0,length(vCurMetadata))
302 vNewMetadata[which(vCurMetadata == sLevel)] = 1
303 listMetadata[[length(listMetadata)+1]] = vNewMetadata
304 vsRowNames = c(vsRowNames,paste(names(dfInput)[i],sLevel,sep="_"))
305 }
306 }
307 }
308
309 # Convert to data frame
310 dfDummyMetadata = as.data.frame(sapply(listMetadata,rbind))
311 names(dfDummyMetadata) = vsRowNames
312 iNumberMetadata = ncol(dfDummyMetadata)
313
314 # Data to use in ordination in NMDS
315 dfData = dfInput[viData]
316
317 # If rotating the ordination by a metadata
318 # 1. Add in the metadata as a bug
319 # 2. Multiply the bug by the weight
320 # 3. Push this through the NMDS
321 if(!is.null(sRotateByMetadata))
322 {
323 vsRotateMetadata = unlist(strsplit(sRotateByMetadata,","))
324 sMetadata = vsRotateMetadata[1]
325 dWeight = as.numeric(vsRotateMetadata[2])
326 sOrdinationMetadata = dfDummyMetadata[sMetadata]*dWeight
327 dfData[sMetadata] = sOrdinationMetadata
328 }
329
330 # Run NMDS on bug data (Default B-C)
331 # Will have species and points because working off of raw data
332 mNMDSData = metaMDS(dfData,k=2)
333
334 ## Make shapes
335 # Defines thes shapes and the metadata they are based on
336 # Metadata to use as shapes
337 lShapeInfo = funcMakeShapes(dfInput=dfInput, sShapeBy=sShapeBy, sShapes=sShapes, cDefaultShape=sDefaultMarker)
338
339 sMetadataShape = lShapeInfo[["ID"]]
340 vsShapeValues = lShapeInfo[["Values"]]
341 vsShapeShapes = lShapeInfo[["Shapes"]]
342 vsShapes = lShapeInfo[["PlotShapes"]]
343 cDefaultShape = lShapeInfo[["DefaultShape"]]
344
345 # Colors
346 vsColors = rep(cDefaultColor,nrow(dfInput))
347 vsColorValues = c()
348 vsColorRBG = c()
349 if(!is.null(sColorBy))
350 {
351 vsColorValues = paste(sort(unique(unlist(dfInput[[sColorBy]])),na.last=TRUE))
352 iLengthColorValues = length(vsColorValues)
353
354 vsColorRBG = lapply(1:iLengthColorValues/iLengthColorValues,colorRamp(vsColorRange))
355 vsColorRBG = unlist(lapply(vsColorRBG, function(x) rgb(x[1]/255,x[2]/255,x[3]/255)))
356
357 for(iColor in 1:length(vsColorRBG))
358 {
359 vsColors[which(paste(dfInput[[sColorBy]])==vsColorValues[iColor])]=vsColorRBG[iColor]
360 }
361
362 #If NAs are seperately given color, then color here
363 if(!is.null(sPlotNAColor))
364 {
365 vsColors[which(is.na(dfInput[[sColorBy]]))] = sPlotNAColor
366 vsColorRBG[which(vsColorValues=="NA")] = sPlotNAColor
367 }
368 }
369
370 # Reduce the bugs down to the ones in the list to be plotted
371 viBugsToPlot = which(row.names(mNMDSData$species) %in% vsBugsToPlot)
372 viMetadataDummy = which(names(dfDummyMetadata) %in% vsMetadata)
373
374 # Build the matrix of metadata coordinates
375 mMetadataCoordinates = matrix(rep(NA, iNumberMetadata*2),nrow=iNumberMetadata)
376
377 for( i in 1:iNumberMetadata )
378 {
379 lxReturn = NA
380 if( i %in% viContinuousMetadata )
381 {
382 lxReturn = funcGetMaximumForMetadatum(dfDummyMetadata[[i]],mNMDSData$points)
383 } else {
384 lxReturn = funcGetCentroidForMetadatum(dfDummyMetadata[[i]],mNMDSData$points)
385 }
386 mMetadataCoordinates[i,] = c(lxReturn$x,lxReturn$y)
387 }
388 row.names(mMetadataCoordinates) = vsRowNames
389
390 #!# metadata default dependent
391 # Plot the biplot with the centroid constructed metadata coordinates
392 if( ( length( viMetadataDummy ) == 0 ) && ( is.null( sMetadata ) ) )
393 {
394 viMetadataDummy = 1:nrow(mMetadataCoordinates)
395 }
396
397 # Plot samples
398 # Make output name
399 if(is.null(sOutputFileName))
400 {
401 viPeriods = which(sInputFileName==".")
402 if(length(viPeriods)>0)
403 {
404 sOutputFileName = paste(OutputFileName[1:viPeriods[length(viPeriods)]],"pdf",sep=".")
405 } else {
406 sOutputFileName = paste(sInputFileName,"pdf",sep=".")
407 }
408 }
409
410 pdf(sOutputFileName,useDingbats=FALSE)
411 plot(mNMDSData$points, xlab=paste("NMDS1","Stress=",mNMDSData$stress), ylab="NMDS2", pch=vsShapes, col=vsColors)
412 title(sTitle,line=3)
413 # Plot Bugs
414 mPlotBugs = mNMDSData$species[viBugsToPlot,]
415 if(length(viBugsToPlot)==1)
416 {
417 text(x=mPlotBugs[1],y=mPlotBugs[2],labels=row.names(mNMDSData$species)[viBugsToPlot],col=sTextColor)
418 } else if(length(viBugsToPlot)>1){
419 text(x=mPlotBugs[,1],y=mPlotBugs[,2],labels=row.names(mNMDSData$species)[viBugsToPlot],col=sTextColor)
420 }
421
422 # Add alternative axes
423 axis(3, col=sArrowColor)
424 axis(4, col=sArrowColor)
425 box(col = "black")
426
427 # Plot Metadata
428 if(length(viMetadataDummy)>0)
429 {
430 if(fPlotArrow)
431 {
432 # Plot arrows
433 for(i in viMetadataDummy)
434 {
435 curCoordinates = mMetadataCoordinates[i,]
436 curCoordinates = curCoordinates * dResizeArrow
437 # Plot Arrow
438 arrows(0,0, curCoordinates[1] * 0.8, curCoordinates[2] * 0.8, col=sArrowColor, length=0.1 )
439 }
440 }
441 # Plot text
442 if(length(viMetadataDummy)==1)
443 {
444 text(x=mMetadataCoordinates[viMetadataDummy,][1]*dResizeArrow*0.8, y=mMetadataCoordinates[viMetadataDummy,][2]*dResizeArrow*0.8, labels=row.names(mMetadataCoordinates)[viMetadataDummy],col=sArrowTextColor)
445 } else {
446 text(x=mMetadataCoordinates[viMetadataDummy,1]*dResizeArrow*0.8, y=mMetadataCoordinates[viMetadataDummy,2]*dResizeArrow*0.8, labels=row.names(mMetadataCoordinates)[viMetadataDummy],col=sArrowTextColor)
447 }
448 }
449
450 sLegendText = c(paste(vsColorValues,sColorBy,sep="_"),paste(vsShapeValues,sMetadataShape,sep="_"))
451 sLegendShapes = c(rep(cDefaultShape,length(vsColorValues)),vsShapeShapes)
452 sLegendColors = c(vsColorRBG,rep(cDefaultColor,length(vsShapeValues)))
453 if(length(sLegendText)>0)
454 {
455 legend("topright",legend=sLegendText,pch=sLegendShapes,col=sLegendColors)
456 }
457
458 # Original biplot call if you want to check the custom ploting of the script
459 # There will be one difference where the biplot call scales an axis, this one does not. In relation to the axes, the points, text and arrows should still match.
460 # Axes to the top and right are for the arrow, otherse are for markers and bug names.
461 #biplot(mNMDSData$points,mMetadataCoordinates[viMetadataDummy,],xlabs=vsShapes,xlab=paste("MDS1","Stress=",mNMDSData$stress),main="Biplot function Bugs and Sampes - Metadata Plotted with Centroids")
462 dev.off()
463 }
464
465 # This is the equivalent of __name__ == "__main__" in Python.
466 # That is, if it's true we're being called as a command line script;
467 # if it's false, we're being sourced or otherwise included, such as for
468 # library or inlinedocs.
469 if( identical( environment( ), globalenv( ) ) &&
470 !length( grep( "^source\\(", sys.calls( ) ) ) )
471 {
472 lsArgs <- parse_args( pArgs, positional_arguments=TRUE )
473
474 print("lsArgs")
475 print(lsArgs)
476
477 funcDoBiplot(
478 sBugs = lsArgs$options$sBugs,
479 sMetadata = lsArgs$options$sMetadata,
480 sColorBy = lsArgs$options$sColorBy,
481 sColorRange = lsArgs$options$sColorRange,
482 sTextColor = lsArgs$options$sTextColor,
483 sArrowColor = lsArgs$options$sArrowColor,
484 sArrowTextColor = lsArgs$options$sArrowTextColor,
485 sPlotNAColor = lsArgs$options$sPlotNAColor,
486 sShapeBy = lsArgs$options$sShapeBy,
487 sShapes = lsArgs$options$sShapes,
488 sDefaultMarker = lsArgs$options$sDefaultMarker,
489 sRotateByMetadata = lsArgs$options$sRotateByMetadata,
490 dResizeArrow = lsArgs$options$dResizeArrow,
491 fPlotArrow = !lsArgs$options$fNoPlotMetadataArrows,
492 sTitle = lsArgs$options$sTitle,
493 sInputFileName = lsArgs$args[2],
494 sLastMetadata = lsArgs$args[1],
495 sOutputFileName = lsArgs$options$sOutputFileName)
496 }