Has any study been done on what are the best set of colors to use for showing multiple series on the same plot? I've just been using the defaults in matplotlib, and they look a little childish since they're all bright, primary colors.
Solved – “Best” series of colors to use for differentiating series in publication-quality plots
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Python with MatplotLib. Python is pretty good at manipulating csv files.
but haven't seen any simple examples for plotting multiple columns of data onto the same X and Y axes as a spreadsheet scatterplot will do.
Have you explored ggplot2? You can keep adding series to a plot using ggplot2. It also has a very good facet plotting feature.
It isn't unreasonable at the onset to plot the line charts as a series of small multiples, with different scales for the Y axis but with the X axis (dates) aligned.
I think this is a good start, as it allows one to examine the raw data, and allows for comparison of trends between different line charts. IMO you should look at the raw data first, then think about conversions or ways to normalize the charts to be comparable after you examine the raw data.
As King has already mentioned, it appears that your variables have a natural ordering based on the names and numbers, and assuming it is appropriate, I created three new variables based on the percentage converted at each state. The new variables are;
% Carts Created = Carts_Created/Visits
% Orders Created = Orders_Created/Carts_Created
% Carts Converted = Carts_Converted/Orders_Created
Making percentages is a way to bring the series closer to a common scale, but even then placing all of the lines on one chart (as below) is still difficult to visualize the series effectively. The level and variation of the orders created and carts converted series dwarfs that of the other series. You can't see any variation in the carts created series on this scale (and I suspect that is the one you are most interested in).
So again, IMO a better way to examine this is to use different scales. Below is the Percentage chart using different scales.
With these graphics, there doesn't appear to me to be any real meaningful correlation to me between the series, but you do have plenty of interesting variation within each series (especially the proportion converted). What's up with 2011-11-13? You had a much lower proportion of order's created but every one of the order's created was a converted cart. Did you have any other interventions which might explain trends in either site visits or proportion or percentage carts created?
This is all just exploratory data analysis, and to take any more steps I would need more insight into the data (I hope this is a good start though). You could normalize the line charts in other ways to be able to plot them on a comparable scale, but that is a difficult task, and I think can be done as effectively choosing arbitrary scales based what is informative given the data as opposed to choosing some default normalization schemes. Another interesting application of viewing many line graphs simultaneously is horizon graphs, but that is more for viewing many different line charts at once.
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Best Answer
A common reference for choosing a color palette is the work of Cynthia Brewer on ColorBrewer. The colors were chosen based on perceptual patterns in choropleth maps, but most of the same advice applies to using color in any type of plot to distinguish data patterns. If color is solely to distinguish between the different lines, then a qualitative palette is in order.
Often color is not needed in line plots with only a few lines, and different point symbols and/or dash patterns are effective enough. A more common problem with line plots is that if the lines frequently overlap it will be difficult to distinguish different patterns no matter what symbols or color you use. Stephen Kosslyn recommends a general rule of thumb for only having 4 lines in a plot. If you have more consider splitting the lines into a series of small multiple plots. Here is an example showing the recommendation
No color needed and the labels are more than sufficient.