Interesting tip about multicolor title of a plot

I'd like to suggest to take a look at this interesting post about creating a title with multi-coloured words.

Radar chart

I thank David for the following example of radar chart:

corelations <- c(1:97)
corelation.names <- names(corelations) <- c("Alp12Mn",
"AvrROE", "DivToP", "GrowAPS", "GrowAsst", "GrowBPS", "GrowCFPS",
"GrowDPS", "GrowEPS", "GrowSPS", "HistAlp", "HistSigm", "InvVsSal",
"LevGrow", "Payout5", "PredSigm", "RecVsSal", "Ret12Mn", "Ret3Mn",
"Ret1Mn", "ROE", "_CshPlow", "_DDM", "_EarnMom", "_EstChgs",
"_EstRvMd", "_Neglect", "_NrmEToP", "_PredEToP", "_RelStMd", "_ResRev",
"_SectMom", "AssetToP", "ARM_Pref_Earnings", "AvrCFtoP", "AvrDtoP",
"AvrEtoP", "ARM_Sec_Earnings", "BondSens", "BookToP", "Capt",
"CaptAdj", "CashToP", "CshFlToP", "CurrSen", "DivCuts5", "EarnToP",
"Earnvar", "Earnyld", "Growth", "HistBeta", "IndConc", "Leveflag",
"Leverag", "Leverage", "Lncap", "Momentum", "Payoflag", "PredBeta",
"Ret_11M_Momentum", "PotDilu", "Price", "ProjEgro", "RecEPSGr",
"SalesToP", "Size", "SizeNonl", "Tradactv", "TradVol", "Value",
"VarDPS", "Volatility", "Yield", "CFROI", "ADJUST", "ERC", "RC", "SPX",
"R1000", "MarketCap", "TotalRisk", "Value_AX", "truncate_ret_1mo",
"truncate_PredSigma", "Residual_Returns", "ARM_Revenue",
"ARM_Rec_Comp", "ARM_Revisions_Comp", "ARM_Global_Rank", "ARM_Score",
"TEMP", "EQ_Raw", "EQ_Region_Rank", "EQ_Acc_Comp", "EQ_CF_Comp",
"EQ_Oper_Eff_Comp", "EQ_Exc_Comp")
corelations <- c(0.223, 0.1884, -0.131, 0.1287, 0.0307,
0.2003, 0.2280, 0.1599, 0.2680, 0.2596, 0.3399, 0.0324, 0.0382, -0.173,
-0.177, -0.056, -0.063, 0.2211, 0.0674, -0.023, 0.2641, 0.2369, 0.1652,
-0.023, 0.1070, 0.0791, -0.023, 0.0434, -0.002, -0.001, -0.000, -0.108,
-0.288, 0.1504, -0.127, -0.142, 0.0852, 0, -0.031, -0.320, 0.0785,
0.0465, -0.166, 0.1416, 0.0945, -0.063, 0.1461, -0.305, 0.1215, 0.0776,
0.0449, 0.0823, -0.018, -0.261, -0.318, 0.1194, 0.3151, -0.124, 0.1037,
0.2240, -0.115, 0.1543, 0, 0.1775, -0.153, 0.1194, 0.1407, 0.1047,
0.0926, -0.403, 0.0067, -0.048, -0.136, 0.1068, 0.0381, 0.1878, -0.035,
0.0761, 0.0784, 0, 0, 0, -0.018, 0.1602, 0.0543, 0, -0.013, 0.1439, 0,
0, -0.054, 0.7426, 0.7510, 0.1657, 0.1657, 0.4949, 1.0000)
require(plotrix)
par(ps=6)
radial.plot(corelations, labels=corelation.names,rp.type="p",main="Correlation Radar", radial.lim=c(-1,1),line.col="blue")

Map coordinates to actual pixel locations on a PNG device

Jason emailed me a new tip. Enjoy it!

Use grconvertX and grconvertY to map the X,Y coordinates for an entity on a graphics device to user coordinates. For example if you plotted points to an image and wanted to map those X,Y coordinates to the actual pixel locations on the PNG you would use this family of functions.

#
# Sample R Code for grconvertX and grconvertY
#

# make fake data
tDat <- cbind(rnorm(10), rnorm(10));

#
# Example #1 -- plot them to an X11 window
#
x11();
plot(tDat);
print(paste(grconvertX(tDat[, 1], "user", "device"), grconvertY(tDat[, 2], "user", "device")));

# turn off the x11 device
#dev.off()

#
# Example 2-- Get the pixel coordinates of the data on a PNG image
#
# plot to a PNG
png(file="RTip_coordinates.png", height=1000, width=1000);
plot(tDat);
print( paste(grconvertX(tDat[, 1], "user", "device"), grconvertY(tDat[, 2], "user", "device")));
dev.off()

# Now, go into GIMP or photoshop. At each data point should be at the
# X,Y coordinate listed.

The New York Times article about R

The New York Times published an article regarding the importance of R for data analysts.

Statistical Visualizations - Part 2

Other 2 plots inspired by this post.

>original
Europe Asia Americas Africa Oceania
1820-30 106487 36 11951 17 33333
1831-40 495681 53 33424 54 69911
1841-50 1597442 141 62469 55 53144
1851-60 2452577 41538 74720 210 29169
1861-70 2065141 64759 166607 312 18005
1871-80 2271925 124160 404044 358 11704
1881-90 4735484 69942 426967 857 13363
1891-00 3555352 74862 38972 350 18028
1901-10 8056040 323543 361888 7368 46547
1911-20 4321887 247236 1143671 8443 14574
1921-30 2463194 112059 1516716 6286 8954
1931-40 347566 16595 160037 1750 2483
1941-50 621147 37028 354804 7367 14693
1951-60 1325727 153249 996944 14092 25467
1961-70 1123492 427642 1716374 28954 25215
1971-80 800368 1588178 1982735 80779 41254
1981-90 761550 2738157 3615225 176893 46237
1991-00 1359737 2795672 4486806 354939 98263
2001-06 1073726 2265696 3037122 446792 185986

png("immigration_barplot_me.png", width = 1419, height = 736)
library(RColorBrewer) # take a look at http://www.personal.psu.edu/cab38/ColorBrewer/ColorBrewer_intro.html
# display.brewer.all()
FD.palette <- c("#984EA3","#377EB8","#4DAF4A","#FF7F00","#E41A1C")
options(scipen=10)
par(mar=c(6, 6, 3, 3), las=2)
data4bp <- t(original[,c(5,4,2,3,1)])
barplot( data4bp, beside=F,col=FD.palette, border=FD.palette, space=1, legend=F, ylab="Number of People", main="Migration to the United States by Source Region (1820 - 2006)", mgp=c(4.5,1,0) )
legend( "topleft", legend=rev(rownames(data4bp)), fill=rev(FD.palette) )
box()
dev.off()

I find this 'bubbleplot' visualization quite interesting; unfortunately the R code I was capable to produce is quite poor and unsatisfactory. Any improvement or suggestion is more than welcome!
Anyway, this is the code:

png("immigration_bubbleplot_me.png", width=1400, height=400)
par(mar=c(3, 6, 3, 2), col="grey85")
mag = 0.9
original.vec <- as.matrix(original)
dim(original.vec) <- NULL
symbols( rep(1:nrow(original),ncol(original)), rep(5:1, each=nrow(original)), circles = original.vec, inches=mag, ylim=c(1,6),fg="grey85", bg="grey20", ylab="", xlab="", xlim =range(1:nrow(original)), xaxt="n", yaxt="n", main="Immigration to the USA - 1821 to 2006", panel.first = grid())
axis(1, 1:nrow(original), labels=rownames(original), las=1, col="grey85")
axis(2, 1:ncol(original), labels=rev(colnames(original)), las=1, col="grey85")
dev.off()

You can find the first part of this 'series' with Yihui contributed code (Thanks again!) here.