Interesting high contrast plots in R

I was inspired by this blog post and thought I could do the same thing in R.  Well I posted the code in Google+


Here are my results.  Not bad.

National Registry of Exonerations charts with R

According to recent news (dallasnews.com) there is a new release of a public national database for wrongful convictions.  There are plenty of details in the public list including Age, Race, and how the conviction was overturned.  According to the database it seems that most of the convictions were overturned due to DNA evidence.

I thought it would be interesting to plot summaries of the details using the open source statistical computing environment R Project.  The following are the plots from the National Registry of Exonerations database.

 

Here is the R code used to create the above pie charts.


# National Registry of Exonerations
# pie charts

library(XML)

u <- "http://www.law.umich.edu/special/exoneration/Pages/detaillist.aspx"

listu <- readHTMLTable(u)

exondf <- listu[[7]]
data <- exondf[24:nrow(exondf),]
names(data) <- as.character(unlist(exondf[4,]))

# transform data
data$Age <- droplevels(data$Age)
data$Race <- droplevels(data$Race)
data$State <- droplevels(data$State)
data$Crime <- droplevels(data$Crime)
data$Sentence <- droplevels(data$Sentence)
data$Convicted <- droplevels(data$Convicted)
data$Exonerated <- droplevels(data$Exonerated)

data$AgeCNV <- as.numeric(as.character(data$Age))
data$ConvictedCNV <- as.numeric(as.character(data$Convicted))
data$ExoneratedCNV <- as.numeric(as.character(data$Exonerated))

data$AgeCNV_floor <- floor(data$AgeCNV/10)*10
data$ConfinedYrs <- data$ExoneratedCNV - data$ConvictedCNV
data$ConfinedYrs_floor <- floor(data$ConfinedYrs/5)*5

# plot pie charts

LABELS <- c("10-19","20-29","30-39","40-49","50-59","60-69","")
pie(table(data$AgeCNV_floor), labels=LABELS, main="Age Exonerated")

pie(table(data$Race), main="Race")

pie(tail(sort(table(data$State)),10), main="Top 10 States")

LABELS <- c("0-4","5-9","10-14","15-19","20-24","25-29","30-34","35+")
pie(table(data$ConfinedYrs_floor), labels=LABELS, main="Years Confined")

R graphic used for Facebook IPO

Apparently former Facebook intern, Paul Butler,  graphic of the Facebook social network graph is being used for Facebook's IPO.  The social network graphic is featured on Page 7 of the IPO filing.  His graphic was featured on mashable and R-bloggers not too long ago.  The graphic is of Facebook connections between city centers around the world.  Paul used an ingenious method of color transparency and great circle arcs to display the social network graph.

This is just one of the really cool things you can do with R.  Not only is R used as a visual medium but also to calculate the great circle paths.  This is really neat to see R in such a high profile setting.  If you want to learn more about R you can read an IEORTools post about R links for beginners on World Statistics Day.  Also there are many books that you can buy on R programming at the IEORTools Online Store.

Visualizing categorical data in R

I came across an interesting SAS macro that was used for visualizing log odds relationships of data.  This type of chart is helpful for visualizing the relationship between a binary dependent variable and a continuous independent variable.  I don't use SAS on a daily basis as I prefer to use R.  So I got to thinking that I could recreate this macro using only R.  I thought this would be a good tutorial for R on developing functions, using different plot techniques, and overlapping chart types.

The following picture is the result of the logodds function in R.  The chart is really close but not quite exact.  For the histogram points I decided to use the default squares of the stripchart plot and used a grey color to make it look a little faded.

The following is the R script.

logoddsFnc <- function(data_ind, data_dep, ind_varname, min.count=1){

  # Assumptions: x & y are numeric vectors of the same
  # length, y is 0/1 varible.  This returns a vector
  # of breaks of the x variable where each bin has at
  # least min.countnumber of y's
  bin.by.other.count <- function(x, other, min.cnt=1) {
    csum <- cumsum(tapply(other, x, sum))
    breaks <- numeric(0)
 
    i <- 1
    breaks[i] <- as.numeric(names(csum)[1])
    cursum <- csum[1]
 
    for ( a in names(csum) ) {
      if ( csum[a] - cursum >= min.cnt ) {
        i <- i + 1
        breaks[i] <- as.numeric(a)
        cursum <- csum[a]
      }
    }
 
    breaks
  }
 
  brks <- bin.by.other.count(data_ind, data_dep, min.cnt=min.count)
 
  # Visualizing binary categorical data
  var_cut <- cut(data_ind, breaks=brks, include.lowest=T)
  var_mean <- tapply(data_dep, var_cut, mean)
  var_median <- tapply(data_ind, var_cut, median)
 
  mydf <- data.frame(ind=data_ind, dep=data_dep)
  fit <- glm(dep ~ ind, data=mydf, family=binomial())
  pred <- predict(fit, data.frame(ind=min(data_ind):max(data_ind)),
          type="response", se.fit=T)
 
  # Plot
  plot(x=var_median, y=var_mean, ylim=c(0,1.15),
       xlab=ind_varname, ylab="Exp Prob", pch=21, bg="black")
  stripchart(data_ind[data_dep==0], method="stack",
             at=0, add=T, col="grey")
  stripchart(data_ind[data_dep==1], method="stack",
             at=1, add=T, col="grey")
 
  lines(x=min(data_ind):max(data_ind),
        y=pred$fit, col="blue", lwd=2)
  lines(lowess(x=var_median,
               y=var_mean, f=.30), col="red")
 
  lines(x=min(data_ind):max(data_ind),
        y=pred$fit - 1.96*pred$se.fit, lty=2, col="blue")
  lines(x=min(data_ind):max(data_ind),
        y=pred$fit + 1.96*pred$se.fit, lty=2, col="blue")
}



 

logoddsFnc(icu$age, icu$died, "age", min.count=3)



The ICU data  for this example can be found in the R package "vcdExtra".  Special thanks to David of Univ. of Dallas for providing me with a way to develop breaks in the independent variable as seen by the bin.by.other.count function. 

The author of the SAS macro is also the author of Visualizing Categorical Data by M. Friendly which is a great reference for analyzing and visualizing data in factored groups.