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.
# 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")