# help(let, package=’replyr’)

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A bit more on our replyr R package.

```
``` library("replyr")

help(let, package='replyr')

let {replyr} | R Documentation |

## Prepare expr for execution with name substitutions specified in alias.

### Description

`replyr::let`

implements a mapping from desired names (names used directly in the expr code) to names used in the data. Mnemonic: “expr code symbols are on the left, external data and function argument names are on the right.”

### Usage

let(alias, expr)

### Arguments

`alias` |
mapping from free names in expr to target names to use. |

`expr` |
block to prepare for execution |

### Details

Code adapted from `gtools::strmacro`

by Gregory R. Warnes (License: GPL-2, this portion also available GPL-2 to respect gtools license). Please see the `replyr`

`vignette`

for some discussion of let and crossing function call boundaries: `vignette('replyr','replyr')`

. Transformation is performed by substitution on the expression parse tree, so be wary of name collisions or aliasing.

Something like `replyr::let`

is only useful to get control of a function that is parameterized (in the sense it take column names) but non-standard (in that it takes column names from non-standard evaluation argument name capture, and not as simple variables or parameters). So `replyr:let`

is not useful for non-parameterized functions (functions that work only over values such as `base::sum`

), and not useful for functions take parameters in straightforward way (such as `base::merge`

‘s “`by`

” argument). `dplyr::mutate`

is an example where we can use a `replyr::let`

helper. `dplyr::mutate`

is parameterized (in the sense it can work over user supplied columns and expressions), but column names are captured through non-standard evaluation (and it rapidly becomes unwieldy to use complex formulas with the standard evaluation equivalent `dplyr::mutate_`

).

### Value

item ready to evaluate, need to apply with “()” to perform the evaluation in own environment.

### See Also

`replyr_mapRestrictCols`

### Examples

library('dplyr') d <- data.frame(Sepal_Length=c(5.8,5.7), Sepal_Width=c(4.0,4.4), Species='setosa', rank=c(1,2)) mapping = list(RankColumn='rank',GroupColumn='Species') let(alias=mapping, expr={ # Notice code here can be written in # terms of known or concrete # names "RankColumn" and "GroupColumn", # but executes as if we # had written mapping specified # columns "rank" and "Species". # # restart ranks at zero. d %>% mutate(RankColumn=RankColumn-1) -> dres # confirm set of groups. unique(d$GroupColumn) -> groups })() print(groups) print(length(groups)) print(dres) # It is also possible to pipe into let-blocks, # but it takes some extra notation # (notice the extra ". %>%" at the beginning # and the extra "()" at the end). d %>% let(alias=mapping, expr={ . %>% mutate(RankColumn=RankColumn-1) })()() # Or: f <- let(alias=mapping, expr={ . %>% mutate(RankColumn=RankColumn-1) })() d %>% f # Be wary of using any assignment to attempt # side-effects in these "delayed pipelines", # as the assignment tends to happen during the # let dereference and not (as one would hope) # during the later pipeline application. Example: g <- let(alias=mapping, expr={ . %>% mutate(RankColumn=RankColumn-1) -> ZZZ })() print(ZZZ) # Notice ZZZ has captured a copy of the # sub-pipeline and not waited for application of g. # Applying g performs a calculation, # but does not overwrite ZZZ. g(d) print(ZZZ) # Notice ZZZ is not a copy of g(d), # but instead still the pipeline fragment. # let works by string substitution # aligning on word boundaries, # so it does (unfortunately) # also re-write strings. let(list(x='y'),'x')()

*replyr*version 0.1.1 Index]

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