These plots were generated with R's native plotting functions. In a CDF, however, rather than being something to fix, it's telling you something about the data (that there's not very much of it). If we had fewer points it may have more angles. This curve is smooth because the data we're working with contains 10,000 points.
An outlier would push the curve to one side rather than being in the middle. If you begin using the CDF in your work, you'll begin to get a sense for what a normal distribution looks like as well. And you can tell how spread out the data is by looking at the slope of the line. You can gauge the mean by looking at where the curve would cross the 0.5 point from the vertical axis. There are three datasets present, each with their own distribution and mean. Legend('right', c('a', 'b', 'c'), fill=c(aCDFcolor, bCDFcolor, cCDFcolor), border=NA) # Create a single chart with all 3 CDF plots. If we decide to bring in more data, we have to start over with bin counts. Further, any solution that we find would be unique to this data set.
CDF FILES REDDIT TRIAL
We may be able to clean it up by trying different bin sizes, but that would require trial and error and still may not work. If we had used opaque colors then portions would be hidden. Since the plots are superimposed on one another the only way to make them all visible is to adjusted the opacity of the colors (with the alpha setting of rgb()), which makes the colors bleed together. You can at least see that plot b has a different mean, and that each of these appears to be normal. Because we generated this data, it's reasonably clean.
CDF FILES REDDIT CODE
This is the chart produced with the code above. Legend('right', c('a', 'b', 'c'), fill=c(aHistColor, bHistColor, cHistColor), border=NA) Plot(cHistogram, col=cHistColor, xlim=c(45,55), add=T) Plot(bHistogram, col=bHistColor, xlim=c(45,55), add=T) Plot(aHistogram, col=aHistColor, xlim=c(45,55), main=NA) # Create a single chart with all 3 histograms. First, we'll create a single chart that contains three histograms for comparison. # Since we're generating data, set the seed. Let's start by generating some data to work with. Our charts will be created using the ecdf() function. And because its a single line, you can show several datasets within the same plot.Īs you might expect, R already has a function to do this (no extra packages necessary). Your data is represented by a single line, which is much easier to work with. The point that I want to make is that they're superior to histograms for evaluating data sets, so it's much easier for me to just show you. There's plenty written about this so I'll let you do your own searching & reading. Introducing the cumulative distribution function (aka CDF). And finally, depending on the data you're working with you'll need to regenerate it a few times with different numbers of bins to get it to look right. Second, histograms aren't well suited to large (> 1,000 rows) datasets. Similarly, you can stagger the X axis, but then you're trying to mentally shift to find similar points. You can superimpose one on the other, or make one of them opaque. First, it isn't practical to plot 2 histograms on the same axes.
There are a couple of issues in working with histograms. The histogram quickly becomes more cumbersome as I begin viewing the data after each iterations of transformation. As an analysis project unfolds, I'll compare & contrast the data a number of times. For quite a while I worked with histograms, which are useful for seeing the spread of the data, as well as how closely it resembles a normal dataset. When working with new data, I find it helpful to start by plotting the several variables as I get more familiar with the data.
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