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add cex examples

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hyginn 2020-10-12 09:18:10 +10:00
parent 0c395a1c83
commit 3bee83495f
1 changed files with 107 additions and 25 deletions
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116
plottingReference.R
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@ -49,17 +49,17 @@
#TOC> 08 LEGENDS 1054
#TOC> 08.1 basic legends 1057
#TOC> 08.2 Color bars 1061
#TOC> 09 LAYOUT 1168
#TOC> 10 TEXT 1203
#TOC> 11 DRAWING ON PLOTS 1224
#TOC> 12 IMAGES 1291
#TOC> 13 CONTOUR LINES 1296
#TOC> 14 3D PLOTS 1301
#TOC> 15 GRAPHS AND NETWORKS 1306
#TOC> 16 OTHER GRPAHICS PACKAGES 1311
#TOC> 17 INTERACTIVE PLOTS 1335
#TOC> 17.1 locator() 1339
#TOC> 17.2 plotly:: 1342
#TOC> 09 LAYOUT 1180
#TOC> 10 TEXT 1215
#TOC> 11 DRAWING ON PLOTS 1243
#TOC> 12 IMAGES 1310
#TOC> 13 CONTOUR LINES 1315
#TOC> 14 3D PLOTS 1320
#TOC> 15 GRAPHS AND NETWORKS 1325
#TOC> 16 OTHER GRPAHICS PACKAGES 1330
#TOC> 17 INTERACTIVE PLOTS 1354
#TOC> 17.1 locator() 1358
#TOC> 17.2 plotly:: 1361
#TOC>
#TOC> ==========================================================================
@ -498,7 +498,7 @@ points(t, SC$xpr[169, ], type = "b", cex = 0.7, col = "#EE5500CC")
plot(SC$xpr[169, ], SC$xpr[1124, ]) # these two profiles are anticorrelated
# = 05 ENCODING INFORMATION: SYMBOL, SIZE, COLOR ==========================
# = 05 ENCODING INFORMATION: SYMBOL, SIZE, COLOuR =========================
# We have many options to encode information in scatter plots (and others), in order to be able to visualize and discover patterns and relationships. The most important ones are the type of symbol to use, its size, and its color.
@ -701,6 +701,68 @@ for (i in 1:10) {
text(6, y, paste("lwd = ", (0.3*i)^2), col="grey60", adj=0, cex=0.75)
}
# == 05.1 cex ("character expansion" size) =============================
# The size of characters can be controlled with the cex parameter. cex takes a
# vector of numbers, mapped to the vecors of plotted elements. The usual R
# conventions of vector recycling apply, so if cex s only a single number, it is
# applied to all plotted elements. Here is an example plotting expression
# amplitudes in a comparison of pre-replication vs. replication phase
# expression.
tPre <- c("t.0", "t.5", "t.10") # Pre replication-phase time points
tMid <- c("t.25", "t.30", "t.35") # Midway between replication and duplication
x <- rowMeans(SC$xpr[ , tPre]) # row means average out fluctuations
y <- rowMeans(SC$xpr[ , tMid])
plot(x, y)
myAmps <- SC$mdl$A # fetch the correlations of the fitted
# low correlations are not well modeled
# with a periodic function
hist(myAmps, breaks = 40) # examine the distribution
hist(log(myAmps), breaks = 40) # sometimes log() is be better suited
# to map to symbol size
# useful cex sizes are approximately between 0.5 and 5
# Example:
N <- 10
cexMin <- 0.5
cexMax <- 5.0
myCex <- seq(cexMax, cexMin, length.out=N) # Vector of cex values of length N
# We plot these points from largest to smallest, so we don't overplot
x1 <- runif(N)
y1 <- runif(N)
plot(x1, y1, xlim = 0:1, ylim = 0:1, xlab = "", ylab = "", pch = 16,
cex = myCex, col= colorRampPalette(c("#F4F0F8", "#3355DD"))(N))
# Note that the smallest cex at 0.5 is _really_ small.
points(x1[N], y1[N], cex = 3.0, col = "#FF000077") # Here it is!
# To rescale a vector into the desired interval (lo, up) we write a little
# function. First we normalize the vector into the interval (0, 1), then we
# multiply it by up-lo, and add lo.
#
reScale <- function(x, lo = 0, up = 1) {
return((((x-min(x)) / (max(x)-min(x))) * (up-lo)) + lo )
}
# with this, our coding amplitudes as plot character size becomes quite easy:
plot(x, y, pch=16,
cex = reScale(myAmps, lo=0.5, up=5), # <<<---- That's all we need
col="#4499BB22")
abline(a = 0, b = 1, col = "#DD333344")
abline(h = 0, col = "#33333344")
abline(v = 0, col = "#33333344")
# ToDo: add titles, scale legend, and interpretation
# = 06 COLOUR =============================================================
# Colour is immensely important for data visualization, and colours in R can be
@ -942,7 +1004,7 @@ plotPal(myOutliers)
# crowded plots, or for creating overlays.
x <- SC$xpr[, "t.10"] # towards the begiining of the cycle
x <- SC$xpr[, "t.10"] # towards the beginning of the cycle
y <- SC$xpr[, "t.50"] # towards the end of the cycle
# compare:
@ -979,7 +1041,6 @@ abline(v = 0, col = "#FFFFFF", lwd = 0.5)
# ToDo: expand to "real" density plots (cf. )
# == 06.4 abline(), lines() and segments() ================================
# lines() draws an arbitrary line from the supplied points
@ -1065,8 +1126,8 @@ grid()
# Example: plotting expression levels in two cell-cycle phases against each other, and applying a divergent color spectrum that displays the model correlations: genes with low correlations have expression profiles that can not be well approximated with a periodic function.
tRep <- c("t.15", "t.20", "t.25", "t.30") # Replication-phase time points
tDup <- c("t.40", "t.45", "t.50", "t.55") # Duplication-phase time points
tRep <- c("t.15", "t.20", "t.25") # Replication-phase time points
tDup <- c("t.40", "t.45", "t.50") # Duplication-phase time points
x <- rowMeans(SC$xpr[ , tRep])
y <- rowMeans(SC$xpr[ , tDup])
plot(x, y)
@ -1090,6 +1151,8 @@ hist(myCors,
N <- 10 # We'll map into N intervals
#$$$
# The usual way to scale a vector x to the [0,1] interval
# is: x-min / max-min
idx <- (myCors - min(myCors)) / (max(myCors) - min(myCors))
@ -1119,7 +1182,9 @@ basePlot <- function() {
main = m,
xlab = "mean Expression in Replication phase",
ylab = "mean Expression in Duplication phase",
sub = "Colour by Correlation to Fitted Periodic Function",
sub = paste("Coloured by Correlation of",
"Expression Profile (xpr) to",
"Fitted Periodic Function (mdl)."),
cex.main = 0.9,
cex.lab = 0.7,
cex.sub = 0.7,
@ -1144,6 +1209,7 @@ corLabels <- sprintf("%4.2f", # craft the labels to plot
length.out = length(corCols)))
oPar <- par(mar=c(5,4,4,7)) # more space on the margin
par("xpd" = FALSE)
basePlot()
# we need to figure out where to put the legend. par("usr") gives us the coordinates of the last plot window. We'll space the legend between 1.05 and 1.1 times the plot width, and centred along the y-axis with a height of 0.8 times the plot height.
@ -1162,8 +1228,17 @@ plotrix::color.legend(xl, yb, xr, yt,
cex = 0.8,
gradient = "y") # vertical gradient
# Almost perfect. Except we need a title for the color bar.
# in this case the title is "r:" ... but the "r" should be in italics.
par("xpd" = TRUE) # enable plotting into the margin
text(xr + (0.01 * dx), yt + 0.015 * dy,
expression(italic(r)["xpr,mdl"]),
adj = c(0, 0),
cex = 0.9)
par(oPar)
# ToDo - this is a common plot prototype - cast it into a function
# = 09 LAYOUT =============================================================
@ -1203,6 +1278,7 @@ plot(x,y) # confirm reset
# = 10 TEXT ===============================================================
# ToDo: sprintf()
# Plotting arbitrary text
# use the text() function to plot characters and strings to coordinates
@ -1218,6 +1294,12 @@ text(x3-0.4, y3, extra, col="slateblue", cex=0.75, vfont=c("serif", "plain"))
# ToDo: writing into the margin ...
# par("mar")
# par("xpd")
# par("srt")
#
# ToDo: plotmath() # demo(plotmath)
# ToDo: expression()