ggformula: another option for teaching graphics in R to beginners

A previous entry (http://sas-and-r.blogspot.com/2017/07/options-for-teaching-r-to-beginners.html) describes an approach to teaching graphics in R that also “get[s] students doing powerful things quickly”, as David Robinson suggested. 

In this guest blog entry, Randall Pruim offers an alternative way based on a different formula interface. Here's Randall: 

For a number of years I and several of my colleagues have been teaching R to beginners using an approach that includes a combination of

• the lattice package for graphics,
• several functions from the stats package for modeling (e.g., lm(), t.test()), and
• the mosaic package for numerical summaries and for smoothing over edge cases and inconsistencies in the other two components.

Important in this approach is the syntactic similarity that the following “formula template” brings to all of these operations.  

goal ( y ~ x , data = mydata, ... )

Many data analysis operations can be executed by filling in four pieces of information (goal, y, x, and mydata) with the appropriate information for the desired task. This allows students to become fluent quickly with a powerful, coherent toolkit for data analysis.

Trouble in paradise
As the earlier post noted, the use of lattice has some drawbacks. While basic graphs like histograms, boxplots, scatterplots, and quantile-quantile plots are simple to make with lattice, it is challenging to combine these simple plots into more complex plots or to plot data from multiple data sources. Splitting data into subgroups and either overlaying with multiple colors or separating into sub-plots (facets) is easy, but the labeling of such plots is not as convenient (and takes more space) than the equivalent plots made with ggplot2. And in our experience, students generally find the look of ggplot2 graphics more appealing.

On the other hand, introducing ggplot2 into a first course is challenging. The syntax tends to be more verbose, so it takes up more of the limited space on projected images and course handouts. More importantly, the syntax is entirely unrelated to the syntax used for other aspects of the course. For those adopting a “Less Volume, More Creativity” approach, ggplot2 is tough to justify.

ggformula: The third-and-a half way

Danny Kaplan and I recently introduced ggformula, an R package that provides a formula interface to ggplot2 graphics. Our hope is that this provides the best aspects of lattice (the formula interface and lighter syntax) and ggplot2 (modularity, layering, and better visual aesthetics).

For simple plots, the only thing that changes is the name of the plotting function. Each of these functions begins with gf. Here are two examples, either of which could replace the side-by-side boxplots made with lattice in the previous post.

We can even overlay these two types of plots to see how they compare. To do so, we simply place what I call the "then" operator (%>%, also commonly called a pipe) between the two layers and adjust the transparency so we can see both where they overlap.

Comparing groups

Groups can be compared either by overlaying multiple groups distinguishable by some attribute (e.g., color)

or by creating multiple plots arranged in a grid rather than overlaying subgroups in the same space. The ggformula package provides two ways to create these facets. The first uses | very much like lattice does. Notice that the gf_lm() layer inherits information from the the gf_points() layer in these plots, saving some typing when the information is the same in multiple layers.

The second way adds facets with gf_facet_wrap() or gf_facet_grid() and can be more convenient for complex plots or when customization of facets is desired.

Fitting into the tidyverse work flow

ggformala also fits into a tidyverse-style workflow (arguably better than ggplot2 itself does). Data can be piped into the initial call to a ggformula function and there is no need to switch between %>% and + when moving from data transformations to plot operations.

Summary

The “Less Volume, More Creativity” approach is based on a common formula template that has served well for several years, but the arrival of ggformula strengthens this approach by bringing a richer graphical system into reach for beginners without introducing new syntactical structures. The full range of ggplot2 features and customizations remains available, and the  ggformula  package vignettes and tutorials describe these in more detail.

-- Randall Pruim

Options for teaching R to beginners: a false dichotomy?

I've been reading David Robinson's excellent blog entry "Teach the tidyverse to beginners" (http://varianceexplained.org/r/teach-tidyverse), which argues that a tidyverse approach is the best way to teach beginners.  He summarizes two competing curricula:

1) "Base R first": teach syntax such as $ and [[]], built in functions like ave() and tapply(), and use base graphics

2) "Tidyverse first": start from scratch with pipes (%>%) and leverage dplyr and use ggplot2 for graphics

If I had to choose one of these approaches, I'd also go with 2) ("Tidyverse first"), since it helps to move us closer to helping our students "think with data" using more powerful tools (see here for my sermon on this topic).

A third way

Of course, there’s a third option that addresses David’s imperative to "get students doing powerful things quickly".  The mosaic package was written to make R easier to use in introductory statistics courses.  The package is part of Project MOSAIC (http://mosaic-web.org), an NSF-funded initiative to integrate statistics, modeling, and computing. A paper outlining the mosaic package's "Less Volume, More Creativity" approach was recently published in the R Journal (https://journal.r-project.org/archive/2017/RJ-2017-024). To his credit, David mentions the mosaic package in a response to one of the comments on his blog.

Less Volume, More Creativity

One of the big ideas in the mosaic package is that students build on the existing formula interface in R as a mechanism to calculate summary statistics, generate graphical displays, and fit regression models. Randy Pruim has dubbed this approach "Less Volume, More Creativity".

While teaching this formula interface involves adding a new learning outcome (what is "Y ~ X"?), the mosaic approach simplifies calculation of summary statistics by groups and the generation of two or three dimensional displays on day one of an introductory statistics course (see for example Wang et al., "Data Viz on Day One: bringing big ideas into intro stats early and often" (2017), TISE).

The formula interface also prepares students for more complicated models in R (e.g., logistic regression, classification).

Here's a simple example using the diamonds data from the ggplot2 package.  We model the relationships between two colors (D and J), number of carats, and price.

I'll begin with a bit of data wrangling to generate an analytic dataset with just those two colors. (Early in a course I would either hide the next code chunk or make the recoded dataframe accessible to the students to avoid cognitive overload.)  Note that an R Markdown file with the following commands is available for download at https://nhorton.people.amherst.edu/mosaic-blog.Rmd.

library(mosaic)
recoded <- diamonds %>%
  filter(color=="D" | color=="J") %>%
  mutate(col = as.character(color))

We first calculate the mean price (in US$) for each of the two colors.

mean(price ~ col, data = recoded)

   D    J 
3170 5324 

This call is an example of how the formula interface facilitates calculation of a variable's mean for each of the levels of another variable. We see that D color diamonds tend to cost less than J color diamonds.

A useful function in mosaic is favstats() which provides a useful set of summary statistics (including sample size and missing values) by group.

favstats(price ~ col, data = recoded)

col	min	Q1	median	Q3	max	mean	sd	n	missing
D	357	911	1838	4214	18693	3170	3357	6775	0
J	335	1860	4234	7695	18710	5324	4438	2808	0

A similar command can be used to generate side by side boxplots. Here we illustrate the use of lattice graphics. (An alternative formula based graphics system (ggformula) will be the focus of a future post.)

bwplot(col ~ price, data = recoded)

The distributions are skewed to the right (not surprisingly since they are prices). If we wanted to formally compare these sample means we could do so with a two-sample t-test (or in a similar fashion, by fitting a linear model).

t.test(price ~ col, data = recoded)
Welch Two Sample t-test

data:  price by col
t = -20, df = 4000, p-value <2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -2336 -1971
sample estimates:
mean in group D mean in group J 
           3170            5324 


msummary(lm(price ~ col, data = recoded))
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3170.0       45.0    70.4   <2e-16 ***
colJ          2153.9       83.2    25.9   <2e-16 ***

Residual standard error: 3710 on 9581 degrees of freedom
Multiple R-squared:  0.0654, Adjusted R-squared:  0.0653 

F-statistic:  670 on 1 and 9581 DF,  p-value: <2e-16

The results from the two approaches are consistent: the group differences are highly statistically significant.  We could conclude that J diamonds tend to cost more than D diamonds, back in the population of all diamonds.

Let's do a quick review of the mosaic modeling syntax to date:
mean(price ~ col)
bwplot(price ~ col) t.test(price ~ col) lm(price ~ col) See the pattern? On a statistical note, it's important to remember that the diamonds were not randomized into colors: this is a found (observational dataset) so there may be other factors at play.  The revised GAISE College report reiterates the importance of multivariate thinking in intro stats. Moving to three dimensions Let's continue with the "Less Volume, More Creativity" approach to bring in a third variable: the number of carats in each diamond. xyplot(price ~ carat, groups=col, auto.key=TRUE, type=c("p", "r"), data = recoded)

We see that controlling for the number of carats, the D color diamonds tend to sell for more than the J color diamonds.  We can confirm this by fitting a regression model that controls for both variables (and then display the resulting predicted values from this parallel slopes model using plotModel()).

This is a great example of Simpson's paradox: accounting for the number of carats has yielded opposite results from a model that didn't include carats. If we were to move forward with such an analysis we'd need to be sure to undertake an assessment of our model and verify conditions and assumptions (but for the purpose of the blog entry I'll defer that).

Moving beyond mosaic

The revised GAISE College report enunciated the importance of technology when teaching statistics. Many courses still use calculators or web-based applets to incorporate technology into their classes. R is an excellent environment for teaching statistics, but many instructors feel uncomfortable using it (particularly if they feel compelled to teach the $ and [[]] syntax, which many find offputting).  The mosaic approach helps make the use of R feasible for many audiences by keeping things simple. It's unfortunately true that many introductory statistics courses don't move beyond bivariate relationships (so students may feel paralyzed about what to do about other factors). The mosaic approach has the advantage that it can bring multivariate thinking, modeling, and exploratory data tools together with a single interface (and modest degree of difficulty in terms of syntax). I've been teaching multiple regression as a descriptive method early in an intro stat course for the past ten years (and it helps to get students excited about material that they haven't seen before). The mosaic approach also scales well: it's straightforward to teach students dplyr/tidyverse data wrangling by adding in the pipe operator and some key data idioms. (So perhaps the third option should be labeled "mosaic and tidyverse".)  

See the following for an example of how favstats() can be replaced by dplyr idioms. 

recoded %>%
  group_by(col) %>%
  summarize(meanval = mean(price, na.rm = TRUE))

col	meanval
D	3170
J	5324

That being said, I suspect that many students (and instructors) will still use favstats() for simple tasks (e.g., to check sample sizes, check for missing data, etc).  I know that I do.  But the important thing is that unlike training wheels, mosaic doesn't hold them back when they want to learn new things. I'm a big fan of ggplot2, but even Hadley agrees that the existing syntax is not what he wants it to be.  While it's not hard to learn to use + to glue together multiple graphics commands and to get your head around aesthetics, teaching ggplot2 adds several additional learning outcomes to a course that's already overly pregnant with them.

Side note

I would argue that a lot of what is in mosaic should have been in base R (e.g., formula interface to mean(), data= option for mean()).  Other parts are more focused on teaching (e.g., plotModel(), xpnorm(), and resampling with the do() function).

Closing thoughts

In summary, I argue that the mosaic approach is consistent with the tidyverse. It dovetails nicely with David's "Teach tidyverse" as an intermediate step that may be more accessible for undergraduate audiences without a strong computing background.  I'd encourage people to check it out (and let Randy, Danny, and me know if there are ways to improve the package).

Want to learn more about mosaic?  In addition to the R Journal paper referenced above, you can see how we get students using R quickly in the package's "Less Volume, More Creativity" and "Minimal R" vignettes.  We also provide curated examples from commonly used textbooks in the “mosaic resources” vignette and a series of freely downloadable and remixable monographs including The Student’s Guide to R and Start Teaching with R.