(This article was first published on

(note scroll all the way down to see 'old code' and 'new more flexible code' **Econometric Sense**, and kindly contributed to R-bloggers)Recall and older post that presented overlapping density plots using R (Visualizing Agricultural Subsidies by KY County) see image below.

The code I used to produce this plot makes use of the rbind and data.frame functions (see below)

library(colorspace) # package for rainbow_hcl function

ds <- rbind(data.frame(dat=KyCropsAndSubsidies[,][,"LogAcres"], grp="All"),

data.frame(dat=KyCropsAndSubsidies[,][KyCropsAndSubsidies$subsidy_in_millions > 2.76,"LogAcres"], grp=">median"),

data.frame(dat=KyCropsAndSubsidies[,][KyCropsAndSubsidies$subsidy_in_millions <= 2.76,"LogAcres"], grp="<=median"))

# histogram and density for all ears

hs <- hist(ds[ds$grp=="All",1], main="", xlab="LogAcres", col="grey90", ylim=c(0, 25), breaks="fd", border=TRUE)

dens <- density(ds[ds$grp=="All",1], na.rm=TRUE)

rs <- max(hs$counts)/max(dens$y)

lines(dens$x, dens$y*rs, type="l", col=rainbow_hcl(3)[1])

# density for above median subsidies

dens <- density(ds[ds$grp==">median",1], na.rm=TRUE)

rs <- max(hs$counts)/max(dens$y)

lines(dens$x, dens$y*rs, type="l", col=rainbow_hcl(3)[2])

# density for below median subsidies

dens <- density(ds[ds$grp=="<=median",1], na.rm=TRUE)

rs <- max(hs$counts)/max(dens$y)

lines(dens$x, dens$y*rs, type="l", col=rainbow_hcl(3)[3])

# Add a rug to illustrate density.

rug(ds[ds$grp==">median", 1], col=rainbow_hcl(3)[2])

rug(ds[ds$grp=="<=median", 1], col=rainbow_hcl(3)[3])

# Add a legend to the plot.

legend("topright", c("All", ">median", "<=media"), bty="n", fill=rainbow_hcl(3))

# Add a title to the plot.

title(main="Distribution of Acres Planted by Subsidies Recieved Above or Below Median", sub=paste("Created Using R Statistical Package"))

Created by Pretty R at inside-R.org

I really don't understand the ins and outs of the rbind or data.frame functions, and in another project, when I tried to repeat a similar analysis, it wouldn't work. I could not figure out what my error was, but I new enough about R to create the plots with an alternative implementation. It is not as compact, but more general, and it worked. (see code below, although it references a new data set with new vars and produces 4 density curves vs. 3)

# histogram and density estimates for all data

hs <- hist(trade_by_yr$logTrade,main="", xlab="trade", col="grey90", ylim=c(0, 95), breaks="fd", border=TRUE) # histogram

dens <- density(trade_by_yr$logTrade) # density

rs <- max(hs$counts)/max(dens$y) # rescale/mormalize density

lines(dens$x, dens$y*rs, type="l", col=rainbow_hcl(4)[1]) # plot densiy

# density estimates for year 2000 trade data

y2000 <- trade_by_yr[trade_by_yr$year==2000,] # subset data for year

dens <- density(y2000$logTrade) # density

rs <- max(hs$counts)/max(dens$y) # rescale/mormalize density

lines(dens$x, dens$y*rs, type="l", col=rainbow_hcl(4)[2]) # plot densiy

# density estimates for year 2004 trade data

y2004 <- trade_by_yr[trade_by_yr$year==2004,] # subset data for year

dens <- density(y2004$logTrade) # density

rs <- max(hs$counts)/max(dens$y) # rescale/mormalize density

lines(dens$x, dens$y*rs, type="l", col=rainbow_hcl(4)[3]) # plot densiy

# densty estimates for year 2008 trade data

y2008 <- trade_by_yr[trade_by_yr$year==2008,] # subset data for year

dens <- density(y2008$logTrade) # density

rs <- max(hs$counts)/max(dens$y) # rescale/mormalize density

lines(dens$x, dens$y*rs, type="l", col=rainbow_hcl(4)[4]) # plot densiy

# Add a legend to the plot.

legend("topright", c("All", "2000", "2004", "2008"), bty="n", fill=rainbow_hcl(4))

# Add a title to the plot.

title(main="Distribution of Total World Trade Volume by Country by Year", sub=paste("Created Using R Statistical Package"))

Created by Pretty R at inside-R.org

See graph below:

To

**leave a comment**for the author, please follow the link and comment on his blog:**Econometric Sense**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...