## 20.1 Introduction

The user-facing inverse of quotation is unquotation: it gives the user the ability to selectively evaluate parts of an otherwise quoted argument. The developer-facing complement of quotation is evaluation: this gives the developer the ability to evaluate quoted expressions in custom environments to achieve specific goals.

This chapter begins with a discussion of evaluation in its purest form. You’ll learn how eval() evaluates an expression in an environment, and then how it can be used to implement a number of important base R functions. Once you have the basics under your belt, you’ll learn extensions to evaluation that are needed for robustness. There are two big new ideas:

• The quosure: a data structure that captures an expression along with its associated environment, as found in function arguments.

• The data mask, which makes it easier to evaluate an expression in the context of a data frame. This introduces potential evaluation ambiguity which we’ll then resolve with data pronouns.

Together, quasiquotation, quosures, and data masks form what we call tidy evaluation, or tidy eval for short. Tidy eval provides a principled approach to non-standard evaluation that makes it possible to use such functions both interactively and embedded with other functions. Tidy evaluation is the most important practical implication of all this theory so we’ll spend a little time exploring the implications. The chapter finishes off with a discussion of the closest related approaches in base R, and how you can program around their drawbacks.

### Outline

• Section 20.2 discusses the basics of evaluation using eval(), and shows how you can use it to implement key functions like local() and source().

• Section 20.3 introduces a new data structure, the quosure, which combines an expression with an environment. You’ll learn how to capture quosures from promises, and evaluate them using rlang::eval_tidy().

• Section 20.4 extends evaluation with the data mask, which makes it trivial to intermingle symbols bound in an environment with variables found in a data frame.

• Section 20.5 shows how to use tidy evaluation in practice, focussing on the common pattern of quoting and unquoting, and how to handle ambiguity with pronouns.

• Section 20.6 circles back to evaluation in base R, discusses some of the downsides, and shows how to use quasiquotation and evaluation to wrap functions that use NSE.

### Prerequisites

You’ll need to be familiar with the content of Chapter 18 and Chapter 19, as well as the environment data structure (Section 7.2) and the caller environment (Section 7.5).

We’ll continue to use rlang and purrr.

library(rlang)
library(purrr)