# Recent adventures with lazyeval

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The lazyeval package is a tool-set for performing nonstandard evaluation in R. Nonstandard evaluation refers to any situation where something special happens with how user input or code is evaluated.

For example, the `library`

function doesn’t evaluate variables. In the
example below, I try to trick `library`

into loading a fake package called
`evil_package`

by assigning to the package name `lazyeval`

. In other words, we
have the expression `lazyeval`

and its value is `"evil_package"`

.

```
print(.packages())
#> [1] "stringr" "dplyr" "knitr" "stats" "graphics" "grDevices"
#> [7] "utils" "datasets" "base"
lazyeval <- "evil_package"
library(lazyeval)
# The lazyeval package is loaded now.
print(.packages())
#> [1] "lazyeval" "stringr" "dplyr" "knitr" "stats"
#> [6] "graphics" "grDevices" "utils" "datasets" "base"
```

But this gambit doesn’t work because `library`

did something special: It didn’t
evaluate the expression `lazyeval`

. In the source code of `library`

, there is a
line `package <- as.character(substitute(package))`

which replaces the value of
`package`

with a character version of the expression that the user wrote.

That’s a simple example of nonstandard evaluation, but it’s pervasive. It’s why you never have to quote column names in dplyr or ggplot2. In this post, I present some recent examples where I decided to use the lazyeval package to do something nonstandard. These examples are straight out of the lazyeval vignette in terms of complexity, but that’s fine. We all have to start somewhere.

## Capturing expressions

**Plot titles**. While reading the book *Machine Learning For Hackers*, I wanted
to plot random numbers generated by probability distributions discussed by the
book. I used the `lazyeval::expr_text`

function to capture the command used to
generate the numbers and write it as the title of the plot.

```
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
plot_dist <- function(xs) {
data <- data_frame(x = xs)
ggplot(data) +
aes(x = x) +
geom_density() +
ggtitle(lazyeval::expr_text(xs))
}
plot_dist(rcauchy(n = 250, location = 0, scale = 1))
```

```
plot_dist(rgamma(n = 25000, shape = 5, rate = .5))
```

```
plot_dist(rexp(n = 25000, rate = .5))
```

**Less fussy warning messages**. I recently inherited some code where there were
custom warning messages based on the input. The code threw a warning whenever a
duplicate participant ID was found in a survey. It went something like this:

```
# some dummy data
study1 <- data_frame(
id = c(1, 2, 3, 4),
response = c("b", "c", "a", "b"))
study2 <- data_frame(
id = c(1, 2, 2, 3, 1),
response = c("a", "a", "a", "b", "c"))
if (anyDuplicated(study1$id)) {
warning("Duplicate IDs found in Study1", call. = FALSE)
}
if (anyDuplicated(study2$id)) {
warning("Duplicate IDs found in Study2", call. = FALSE)
}
#> Warning: Duplicate IDs found in Study2
```

To extend this code to a new study, one would just copy-and-paste and update the
`if`

statement’s condition and warning messages. Like so:

```
study3 <- data_frame(
id = c(1, 2, 3, 2),
response = c("b", "c", "a", "b"))
if (anyDuplicated(study3$id)) {
warning("Duplicate IDs found in Study2", call. = FALSE)
}
#> Warning: Duplicate IDs found in Study2
```

Wait, that’s not right! I forgot to update the warning message…

This setup is too brittle for me, so I abstracted the procedure into a function. First, I wrote a helper function to print out duplicates elements in a vector. This helper will let us make the warning message a little more informative.

```
# Print out duplicated elements in a vector
print_duplicates <- function(xs) {
duplicated <- xs[duplicated(xs)]
duplicated %>% sort %>% unique %>% paste0(collapse = ", ")
}
print_duplicates(study2$id)
#> [1] "1, 2"
```

Next, I wrote a function to issue the warnings. I used `lazyeval::expr_label`

convert the user-inputted expression into a string wrapped in backticks.

```
# Print a warning if duplicates are found in a vector
warn_duplicates <- function(xs) {
if (anyDuplicated(xs)) {
# Get what the user wrote for the xs argument
actual_xs <- lazyeval::expr_label(xs)
msg <- paste0("Duplicate entries in ", actual_xs, ": ",
print_duplicates(xs))
warning(msg, call. = FALSE)
}
invisible(NULL)
}
warn_duplicates(study1$id)
warn_duplicates(study2$id)
#> Warning: Duplicate entries in `study2$id`: 1, 2
warn_duplicates(study3$id)
#> Warning: Duplicate entries in `study3$id`: 2
```

The advantage of this approach is that the warning is a generic message that can work on any input. But in a funny way, the warning is also customized for the input because it includes the input printed verbatim.

An aside: In plotting, I used `lazyeval::expr_text`

, but here I used
`lazyeval::expr_label`

. The two differ slighty. Namely, `expr_label`

surrounds
the captured expression with backticks to indicate that expression is code:

```
lazyeval::expr_text(stop())
#> [1] "stop()"
lazyeval::expr_label(stop())
#> [1] "`stop()`"
```

## Asking questions about a posterior distribution

I fit regression models with RStanARM. It lets me fit Bayesian models in Stan by writing conventional R modeling code. (Btw, I’m giving a tutorial on RStanARM in a month.)

Here’s a model about some famous flowers.

```
library(rstanarm)
model <- stan_glm(
Petal.Width ~ Petal.Length * Species,
data = iris,
family = gaussian(),
prior = normal(0, 1))
```

Here’s the essential relationship being explored.

```
ggplot(iris) +
aes(x = Petal.Length, y = Petal.Width, color = Species) +
geom_point() + stat_smooth(method = "lm")
```

The model gives me 4000 samples from the posterior distribution of the model.

```
summary(model)
#> stan_glm(formula = Petal.Width ~ Petal.Length * Species, family = gaussian(),
#> data = iris, prior = normal(0, 1))
#>
#> Family: gaussian (identity)
#> Algorithm: sampling
#> Posterior sample size: 4000
#> Observations: 150
#>
#> Estimates:
#> mean sd 2.5% 25% 50% 75%
#> (Intercept) 0.0 0.2 -0.4 -0.1 0.0 0.1
#> Petal.Length 0.2 0.1 0.0 0.1 0.2 0.3
#> Speciesversicolor -0.1 0.3 -0.6 -0.3 -0.1 0.1
#> Speciesvirginica 1.1 0.3 0.5 0.9 1.1 1.3
#> Petal.Length:Speciesversicolor 0.1 0.1 -0.1 0.1 0.1 0.2
#> Petal.Length:Speciesvirginica 0.0 0.1 -0.2 -0.1 0.0 0.1
#> sigma 0.2 0.0 0.2 0.2 0.2 0.2
#> mean_PPD 1.2 0.0 1.2 1.2 1.2 1.2
#> log-posterior 32.9 1.9 28.2 31.8 33.2 34.3
#> 97.5%
#> (Intercept) 0.3
#> Petal.Length 0.4
#> Speciesversicolor 0.5
#> Speciesvirginica 1.7
#> Petal.Length:Speciesversicolor 0.4
#> Petal.Length:Speciesvirginica 0.2
#> sigma 0.2
#> mean_PPD 1.2
#> log-posterior 35.7
#>
#> Diagnostics:
#> mcse Rhat n_eff
#> (Intercept) 0.0 1.0 704
#> Petal.Length 0.0 1.0 711
#> Speciesversicolor 0.0 1.0 983
#> Speciesvirginica 0.0 1.0 960
#> Petal.Length:Speciesversicolor 0.0 1.0 699
#> Petal.Length:Speciesvirginica 0.0 1.0 638
#> sigma 0.0 1.0 2593
#> mean_PPD 0.0 1.0 3365
#> log-posterior 0.1 1.0 1204
#>
#> For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).
```

At the 2.5% quantile, the `Petal.Length`

effect looks like zero or less than
zero. What proportion of the `Petal.Length`

effects (for `setosa`

flowers) is
positive?

To answer questions like this one in a convenient way, I wrote a function that
takes a boolean expression about a model’s parameters and evaluates it inside of
the data-frame summary of the model posterior distribution. `lazyeval::f_eval`

does the nonstandard evaluation: It evaluates an expression captured by a
formula like `~ 0 < Petal.Length`

inside of a list or data-frame. (Note that the
mean of a logical vector is the proportion of the elements that are true.)

```
# Get proportion of posterior samples satisfying some inequality
posterior_proportion_ <- function(model, inequality) {
draws <- as.data.frame(model)
mean(lazyeval::f_eval(inequality, data = draws))
}
posterior_proportion_(model, ~ 0 < Petal.Length)
#> [1] 0.9325
```

**But all those tildes**… The final underscore in `posterior_proportion_`

follows a convention for distinguishing between nonstandard evaluation functions
that require formulas and those that do not. In the dplyr package, for example,
there is `select_`

/`select`

/, `mutate_`

/`mutate`

, and so on. We can do the
formula-free form of this function by using `lazyeval::f_capture`

to capture the
input expression as a formula. We then hand that off to the version of the
function that takes a formula.

```
posterior_proportion <- function(model, expr) {
posterior_proportion_(model, lazyeval::f_capture(expr))
}
posterior_proportion(model, 0 < Petal.Length)
#> [1] 0.9325
```

Here’s another question: What proportion of the posterior of the `Petal.Length`

effect for `virginica`

flowers is positive? In classical models, we would change
the reference level for the categorical variable, refit the model, and check the
significance. But I don’t want to refit this model because that would repeat the
MCMC sampling. (It takes about 30 seconds to fit this model after all!) I’ll
just ask the model for the sum of `Petal.Length`

and
`Petal.Length:Speciesversicolor`

effects. That will give me the estimated
`Petal.Length`

effect but adjusted for the `versicolor`

species.

```
posterior_proportion(model, 0 < Petal.Length + `Petal.Length:Speciesversicolor`)
#> [1] 1
posterior_proportion(model, 0 < Petal.Length + `Petal.Length:Speciesvirginica`)
#> [1] 0.99975
```

(The backticks around `Petal.Length:Speciesversicolor`

here prevent the `:`

symbol from being evaluated as an operator.)

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