使用dplyr进行编程时，ensym和enquo有什么区别?

问题描述

相对于整洁的评估而言，这是一个相对较新的功能，而我正在使用这些功能时，我想知道为什么要使用不同的帮助程序功能.例如，enquo和ensym有什么区别?在下面我用来捕获每日平均和移动平均的函数中，它们是可以互换的:

Relatively new to tidy evaluation and while the functions I'm making work, I want to know why different helper functions are used. For example, what is the difference between enquo and ensym? In the function I made below to capture daily average and moving average they're interchangeable:

library(dplyr)
library(lubridate)
library(rlang)
library(zoo)

manipulate_for_ma <- function(data, group_var, da_col_name, summary_var, ma_col_name) {
  group_var <- ensym(group_var) 
  summary_var <- enquo(summary_var)
  da_col_name <- ensym(da_col_name) 
  ma_col_name <- enquo(ma_col_name)

  data %>% 
    group_by(!!group_var) %>%
    summarise(!!da_col_name := mean(!!summary_var, na.rm = TRUE)) %>% 
    mutate(!!ma_col_name := rollapply(!!da_col_name,
                                      30,
                                      mean,
                                      na.rm = TRUE,
                                      partial = TRUE,
                                      fill = NA)) %>% 
    rename(date = !!group_var)
}

lakers %>%
 mutate(date = ymd(date)) %>%
 manipulate_for_ma(group_var = date,
                   da_col_name = points_per_play_da,
                   summary_var = points,
                   points_per_play_ma)

# A tibble: 78 x 3
   date       points_per_play_da points_per_play_ma
   <date>                  <dbl>              <dbl>
 1 2008-10-28              0.413              0.458
 2 2008-10-29              0.431              0.459
 3 2008-11-01              0.408              0.456
 4 2008-11-05              0.386              0.457

我已经了解了enquo 此处和ensym(此处) [ https://adv-r.hadley.nz/quasiquotation.html] .区别在于ensym的限制更严格，只接受字符串或类似字符串的对象吗?

I've read about enquo here and ensym (here)[https://adv-r.hadley.nz/quasiquotation.html]. Is the difference that ensym is more restrictive and only takes strings or string-like objects?

推荐答案

另一种方法:

library(rlang)
library(dplyr, warn.conflicts = FALSE)

test <- function(x){
  Species <- "bar"
  cat("--- enquo builds a quosure from any expression\n")
  print(enquo(x))
  cat("--- ensym captures a symbol or a literal string as a symbol\n")
  print(ensym(x))
  cat("--- evaltidy will evaluate the quosure in its environment\n")
  print(eval_tidy(enquo(x)))
  cat("--- evaltidy will evaluate a symbol locally\n")
  print(eval_tidy(ensym(x)))
  cat("--- but both work fine where the environment doesn't matter\n")
  identical(select(iris,!!ensym(x)), select(iris,!!enquo(x)))
}

Species = "foo"
test(Species)
#> --- enquo builds a quosure from any expression
#> <quosure>
#> expr: ^Species
#> env:  global
#> --- ensym captures a symbol or a literal string as a symbol
#> Species
#> --- evaltidy will evaluate the quosure in its environment
#> [1] "foo"
#> --- evaltidy will evaluate a symbol locally
#> [1] "bar"
#> --- but both work fine where the environment doesn't matter
#> [1] TRUE

test("Species")
#> --- enquo builds a quosure from any expression
#> <quosure>
#> expr: ^"Species"
#> env:  empty
#> --- ensym captures a symbol or a literal string as a symbol
#> Species
#> --- evaltidy will evaluate the quosure in its environment
#> [1] "Species"
#> --- evaltidy will evaluate a symbol locally
#> [1] "bar"
#> --- but both work fine where the environment doesn't matter
#> [1] TRUE
test(paste0("Spec","ies"))
#> --- enquo builds a quosure from any expression
#> <quosure>
#> expr: ^paste0("Spec", "ies")
#> env:  global
#> --- ensym captures a symbol or a literal string as a symbol
#> Only strings can be converted to symbols

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