# Flexibility of R Graphics

**Econometric Sense**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

(note scroll all the way down to see ‘old code’ and ‘new more flexible code’

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"))

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"))

See graph below:

**leave a comment**for the author, please follow the link and comment on their blog:

**Econometric Sense**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.