I am using pipes in R (the magrittr package) a lot recently. It reduces the number of errors due to nested functions, among other things. I’ve given a simple example before, and here’s another. Continue reading

# Category Archives: Statistics

# PMean: When differing versions of R packages matter

When you use R, you are using a program that is constantly evolving. The user-contributed packages are also evolving as well. Normally this is not that big a deal. But sometimes it is. Continue reading

# Why secondary data analysis takes a lot longer

Someone posted a question noting that most of the statistical consulting projects that they worked on finished in a reasonable time frame, a few were outliers. They took a lot longer and required a lot more effort by the statisticians. Were there any common features to these outliers they wondered. So they asked if anyone else had identified methodological features of projects that went overtime. I only had a subjective impression, but thought it was still worth sharing. Continue reading

# PMean: About those “awful” election predictions

If you were on Mars for the past few days, you may not have noticed that Donald Trump has won the election. There has been a lot of commentary lately about how badly the predictions about the U.S. election have been and someone mentioned that even Nate Silver at the fivethirtyeight website had a predicted probability of a Clinton win at 71%. I wrote a brief comment that predicting an event with 71% probability does not mean that your prediction was “wrong” if the other event occurs. Continue reading

# PMean: A simple example of pipes in R

At the Joint Statistical Meetings this year, I learned a lot about recent developments in R, and not so recent developments that I was totally clueless about. One of those developments was the use of pipes in R. I wanted to show a simple example of how pipes can simplify your code. Continue reading

# PMean: Small group presentations using screen sharing tools

I received a suggestion for the Kansas City R Users Group to use screen sharing tools. I am going to experiment with this a bit. Here are two tools worth trying. Continue reading

# PMean: Measuring pixels in an R graph

I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates unit conversions. If you want to measure the length of a diagonal line segment in an R graph, you need to calculate the size of the plotting region in pixels, compare that to the range of the plotting region in the x and y directions, and then apply the Pythagorean Theorem. Continue reading

# PMean: Rotating text in an R graph

I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates how you can use some of the commands described in that cheat sheet to rotate text to match a diagonal line in an R graph. It’s trickier than it seems. Continue reading

# PMean: Drawing the perfect circle

I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates how you can use some of the commands described in that cheat sheet to draw a perfect circle. Continue reading

# PMean: Independent consulting and the cold call

There’s been some more discussion about getting started as an independent statistical consultant. One person is ready to hang their shingle and proposes to “find a niche I can serve, contact companies in that niche, etc.” but didn’t know what that niche might be. I had one cautionary comment and then discussed finding your niche. Continue reading