Posted on 9 Comments on Wanted: A Perfect Scatterplot (with Marginals)

## Wanted: A Perfect Scatterplot (with Marginals)

We saw this scatterplot with marginal densities the other day, in a blog post by Thomas Wiecki:

The graph was produced in Python, using the seaborn package. Seaborn calls it a “jointplot;” it’s called a “scatterhist” in Matlab, apparently. The seaborn version also shows the strength of the linear relationship between the x and y variables. Nice.

I like this plot a lot, but we’re mostly an R shop here at Win-Vector. So we asked: can we make this plot in ggplot2? Natively, ggplot2 can add rugs to a scatterplot, but doesn’t immediately offer marginals, as above.

However, you can use Dean Attali’s ggExtra package. Here’s an example using the same data as the seaborn jointplot above; you can download the dataset here.

``` library(ggplot2) library(ggExtra) frm = read.csv("tips.csv") plot_center = ggplot(frm, aes(x=total_bill,y=tip)) + geom_point() + geom_smooth(method="lm") # default: type="density" ggMarginal(plot_center, type="histogram") ```

I didn’t bother to add the internal annotation for the goodness of the linear fit, though I could.

The `ggMarginal()` function goes to heroic effort to line up the coordinate axes of all the graphs, and is probably the best way to do a scatterplot-plus-marginals in ggplot (you can also do it in base graphics, of course). Still, we were curious how close we could get to the seaborn version: marginal density and histograms together, along with annotations. Below is our version of the graph; we report the linear fit’s R-squared, rather than the Pearson correlation.

``` # our own (very beta) plot package: details later library(WVPlots) frm = read.csv("tips.csv") ScatterHist(frm, "total_bill", "tip", smoothmethod="lm", annot_size=3, title="Tips vs. Total Bill") ```

You can see that (at the moment) we’ve resorted to padding the axis labels with underbars to force the x-coordinates of the top marginal plot and the scatterplot to align; white space gets trimmed. This is profoundly unsatisfying, and less robust than the `ggMarginal` version. If you’re curious, the code is here. It relies on some functions in the file `sharedFunctions.R` in the same repository. Our more general version will do either a linear or lowess/spline smooth, and you can also adjust the histogram and density plot parameters.

Thanks to Slawa Rokicki’s excellent ggplot2: Cheatsheet for Visualizing Distributions for our basic approach. Check out the graph at the bottom of her post — and while you’re at it, check out the rest of her blog too.

Posted on Categories Coding, math programming, Statistics, Tutorials4 Comments on The Extra Step: Graphs for Communication versus Exploration

## The Extra Step: Graphs for Communication versus Exploration

Visualization is a useful tool for data exploration and statistical analysis, and it’s an important method for communicating your discoveries to others. While those two uses of visualization are related, they aren’t identical.

One of the reasons that I like ggplot so much is that it excels at layering together multiple views and summaries of data in ways that improve both data exploration and communication. Of course, getting at the right graph can be a bit of work, and often I will stop when I get to a visualization that tells me what I need to know — even if no one can read that graph but me. In this post I’ll look at a couple of ggplot graphs that take the extra step: communicating effectively to others.

For my examples I’ll use a pre-treated sample from the 2011 U.S. Census American Community Survey. The dataset is available as an R object in the file `phsample.RData`; the data dictionary and additional information can be found here. Information about getting the original source data from the U.S. Census site is at the bottom of this post.

The file `phsample.RData` contains two data frames: `dhus` (household information), and `dpus` (information about individuals; they are joined to households using the column `SERIALNO`). We will only use the `dhus` data frame.

```library(ggplot2)

# Restrict to non-institutional households
# (No jails, schools, convalescent homes, vacant residences)
hhonly = subset(dhus, (dhus\$TYPE==1) &(dhus\$NP > 0))```
Posted on 6 Comments on My Favorite Graphs

## My Favorite Graphs

The important criterion for a graph is not simply how fast we can see a result; rather it is whether through the use of the graph we can see something that would have been harder to see otherwise or that could not have been seen at all.

— William Cleveland, The Elements of Graphing Data, Chapter 2

In this article, I will discuss some graphs that I find extremely useful in my day-to-day work as a data scientist. While all of them are helpful (to me) for statistical visualization during the analysis process, not all of them will necessarily be useful for presentation of final results, especially to non-technical audiences.

I tend to follow Cleveland’s philosophy, quoted above; these graphs show me — and hopefully you — aspects of data and models that I might not otherwise see. Some of them, however, are non-standard, and tend to require explanation. My purpose here is to share with our readers some ideas for graphical analysis that are either useful to you directly, or will give you some ideas of your own.