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While it makes little difference using it for quick and short operations, it certainly helps when you want to chain a series of operations:.

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That's more like it. As you can see, we have now a complete picture of traffic allocation, sales, conversion ratios and revenue totals and per-user. Pretty damn cool, right?

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But even though it's tidy, the data is not exactly easy to comprehend at a glance, especially for someone who hasn't worked with spreadsheets as much. Vanilla plots in R are a little underwhelming — visually and procedurally, but lucky for us, there's a bunch of packages that make our life easier when we want to visualize something.

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Here's when the second batch of packages that we've installed is going to come in handy. Still a little clunky, although you can already see the pattern. Let's make it into a more readable chart:. I'm not going to go over each of the cosmetics I've added — once you go over the ggplot cheatsheet and go through the official learning resources , you'll be able to do much more than we just did. While not catastrophic — in fact, this is kind of expected with the sources that have such a big share of traffic — this might be symptomatic of issues with SEO, so we may want to investigate data on organic traffic in a little more detail later.

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On the other hand, paid search and referrals seem to be working better — their values on the chart are above the line we've added. While such behavior is rather typical for paid search, the referral channel is working better than usual and it might make sense to go into the referral reports to learn why exactly is it performing that good.

Now it's your playground — replace the x and y values in the aes parameter with other fields, explore other relationships and see what happens. For example, you could compare sales share vs revenue share and see if any channels contribute unproportionally to the revenue. This post is part of my R series. In the next posts, we'll be trying to look at the landing page performance, automated device insights and explore the possibilities for split-test analysis straight through the API.

Stay tuned for the next posts! Make sure to do this before proceeding. Session share vs sales share vs revenue share A good way to assess the traffic on the website is to see how do the major traffic sources stack up against each other in terms of sessions, sales and revenue.

Of those activities, client or skeleton code generation and response verification require the most effort. Various tools are available to generate the client or skeleton code for a web service based on its WSDL file, however, the code generated might be proprietary and might need to be regenerated every time the WSDL changes. A solution to this problem is to eliminate the generation of the client or skeleton code and invoke the web service directly through a generic HTTP client.

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Similarly, response verification usually involves some kind of manual intervention, depending on the complexity of the response content. If the response contains simple elements, verification can be simple since it requires you to just check the value in the simple element.

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  4. For responses which contain complex elements and a huge list of such complex elements, manual verification can require much more effort. We'll describe the steps involved in creating a simple web service using IBM Rational Software Architect, which offers a sophisticated set of features in an integrated development platform. We will create a sample web service with a single method that returns a list of users.

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    Each user will have a name and an address. For the sake of simplicity, we will make the web service return a static list of users.

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    5. In the real scenario, this list might come from a data source such as a database or a file.