How to process data for a cumulative percent frequency plot in R

Question

I have a large dataset of clusters with values for a parameter. Multiple clusters can have the same value.

I want to make a cumulative percent frequency distribution plot, with cumulative percentage of no. of clusters in y axis and the parameter values (which ranges from 0-1) on x axis.

I have sorted the data based on the values, but after that I am not sure how can I process it to get the cumulative plot using R (ecdf) or matplotlib. How can I approach this? Any help would be greatly appreciated.

My data looks like this

Cluster_20637   0.020
Cluster_20919   0.020
Cluster_9642    0.147
Cluster_10141   0.148
Cluster_21451   0.148
Cluster_30198   0.148
Cluster_55982   0.498
Cluster_10883   0.500
Cluster_16641   0.500
Cluster_20143   0.500
Cluster_57942   0.867
Cluster_32878   0.868
Cluster_26249   0.870
Cluster_46928   0.870
Cluster_41908   0.871
Cluster_28603   0.872
Cluster_1419    0.873

Answer 1

Here's a dump of the data as a data.frame called test:

test <- structure(list(cluster = structure(c(6L, 7L, 17L, 1L, 8L, 11L, 
15L, 2L, 4L, 5L, 16L, 12L, 9L, 14L, 13L, 10L, 3L), .Label = c("Cluster_10141", 
"Cluster_10883", "Cluster_1419", "Cluster_16641", "Cluster_20143", 
"Cluster_20637", "Cluster_20919", "Cluster_21451", "Cluster_26249", 
"Cluster_28603", "Cluster_30198", "Cluster_32878", "Cluster_41908", 
"Cluster_46928", "Cluster_55982", "Cluster_57942", "Cluster_9642"
), class = "factor"), value = c(0.02, 0.02, 0.147, 0.148, 0.148, 
0.148, 0.498, 0.5, 0.5, 0.5, 0.867, 0.868, 0.87, 0.87, 0.871, 
0.872, 0.873)), .Names = c("cluster", "value"), row.names = c(NA, 
-17L), class = "data.frame")

Which looks like:

         cluster value
1  Cluster_20637 0.020
2  Cluster_20919 0.020
3   Cluster_9642 0.147
<<snip>>
16 Cluster_28603 0.872
17  Cluster_1419 0.873

Generate a cumulative percentage variable

> test$cumperc <- (1:nrow(test))/nrow(test)
> test

         cluster value    cumperc
1  Cluster_20637 0.020 0.05882353
2  Cluster_20919 0.020 0.11764706
3   Cluster_9642 0.147 0.17647059
<<snip>>
14 Cluster_46928 0.870 0.82352941
15 Cluster_41908 0.871 0.88235294
16 Cluster_28603 0.872 0.94117647
17  Cluster_1419 0.873 1.00000000

Then plot the data

plot(test$value,test$cumperc,type="l",xlim=c(0,1))

Edit to address comment below:

Try this to group the clusters first:

tabvals <- table(test$value)
plot(names(tabvals),(1:length(tabvals))/length(tabvals),xlim=c(0,1),type="l")

Which gives this plot: