Cumulative / Cumulative Plot (or "Visualizing a Lorenz Curve") I don't know what such plots are called and thus I just gave this question a stupid title.
Let's say I have an ordered dataset as follows
4253  4262  4270  4383  4394  4476  4635  ...

Each number corresponds to the amount of postings a certain user contributed to a website. I am empirically investigating the "participation inequality" phenomenon as defined here.
In order to make it easy to grasp I would like to produce a plot which allows the reader to quickly deduce statements such as "10% of the users contribute 50% of the data". It should probably look similar to this admittedly pretty lousy paint sketch:

I have no clue how this is called thus I don't know where to look for. Also, if somebody had an implementation in R, that would be awesome.
 A: If you want to do it simply with the basic R commands, then following codes may help. 
At first you read the data.
person<-rep(1:7)
data<-c(4253, 4262, 4270, 4383, 4394, 4476, 4635)

Then you can see the contribution of each user.
plot(person,data)
lines(person,data)


You can also see how much the first two, three, four, ... , seven persons contribute.
cdata<-cumsum(data)    
plot(person,cdata)
lines(person,cdata)


Finally you can get your desired plot (in proportions in both axes) by the following commands:
plot(person/max(person),cdata/max(cdata),xlab="Top-contributing users",ylab="Data",col="red")
lines(person/max(person),cdata/max(cdata),col="red")


I have labelled the axes as you wanted. It can give you a clear view about how much percentage of data are being contributed by a certain proportion of persons. 
A: Two more ways to do this as I was recently working on this for vaccine clinical trials:
1.Use Hmisc Ecdf. This is straight forward and plots it out though bit difficult to figure out details on changing different elements of the graph. 
2.Calculate cumulative distribution and then 1-cumulative is reverse cumulative. Plot the reverse using ggplot2 using geom_step if you like a step function in the graph. The function below would use ecdf from base r to give you cumulative distribution and then 1-cumulative:
     rcdf <- function (x) {
     cdf <- ecdf(x)
     y <- cdf(x)
    xrcdf <- 1-y
      }

in the above rcdf is a user-defined function defined using ecdf. 
