07-additional-material.Rmd

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# Additional Material

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# Alternatives to `{{ · }}`?

* `{{ · }}` is a shortcut for `!!enquo(·)`

* Use the `!!` and `rlang::enquo(·)` combination when you need to pass `var1` and `var2` around before it's injected.

* `!!` is a part of `{rlang}`

```{r, eval = FALSE}
my_mean <- function(data, var1, var2) {
  var1_quo <- rlang::enquo(var1)
  var2_quo <- rlang::enquo(var2)
  
  dplyr::summarise(data, mean(!!var1_quo + !!var2_quo))
}

my_mean(mtcars, cyl, am)
#> # A tibble: 1 x 1
#>   `mean(cyl + am)`
#>              <dbl>
#> 1             6.59
```

---
# Can I avoid `{{ · }}` and `!!enquo(·)`?

* YES! (mostly)

* Immediately convert inputs into column name **strings**, and utilize all the what you learned in the first portion of this presentation.

```{r, eval = FALSE}
my_mean <- function(data, var1, var2) {
  var1 <- dplyr::select(data, {{ var1 }}) |> names()
  var2 <- dplyr::select(data, {{ var2 }}) |> names()
  
  dplyr::summarise(data, mean(.data[[var1]] + .data[[var2]]))
}

my_mean(mtcars, cyl, am)
#> # A tibble: 1 x 1
#>   `mean(.data[["cyl"]] + .data[["am"]])`
#>                                    <dbl>
#> 1                                   6.59
```

---

# Naming new variables created within your function

* Cue, the walrus operator `:=`

* Part of the `rlang` package

* `{glue}` syntax is automatically recognized on the left-hand side of the  
walrus operator

```{r, eval=FALSE}
my_naming_function <- function(variable){
  gtsummary::trial %>% 
    dplyr::mutate("mean_{variable}" := mean(.data[[variable]], na.rm = TRUE))
}

my_naming_function(variable = "age")
# A tibble: 200 x 9
#  trt      age marker stage grade response death ttdeath mean_age
#  <chr>  <dbl>  <dbl> <fct> <fct>    <int> <int>   <dbl>    <dbl>
# 1 Drug A    23  0.16  T1    II           0     0    24       47.2
# 2 Drug B     9  1.11  T2    I            1     0    24       47.2
```

---

# rlang

* Examples for when you can't use the walrus operator and have to use
`rlang::sym()` or `rlang::syms()`

* `rlang::sym()` is used when you have a string and you want it to be unquoted

* `rlang::syms()` is used when you have a vector and you want it to be unquoted

```{r, eval=FALSE}
# e.g., a vector of c("A", "B", "C")

vec <- c("mpg", "hp")

# Error: Can't subset columns that don't exist.
# x Column `vec` doesn't exist.
mtcars %>% 
  dplyr::select(vec)

# now works
# similar to `all_of()` but there are times when you need the below
mtcars %>% 
  dplyr::select(!!!rlang::syms(vec))
```

* Can also be used to name variables within functions

---

# rlang

* Can use `rlang::expr()` to see what `rlang::sym()` is evaluating to

```{r, eval=FALSE}
vec <- c("mpg", "hp")

rlang::expr(mtcars %>% 
  dplyr::select(!!!rlang::syms(vec)))
# mtcars %>% dplyr::select(mpg, hp)
```

---

# rlang: unquoted expresssions

* You can also write functions that accept unquoted expressions as arguments

```r
my_filter <- function(data, condition) {
  condition <- rlang::enquo(condition)
  
  dplyr::filter(data, !!condition)
}

my_filter(gtsummary::trial, age < 30) |> head()
#> # A tibble: 6 x 8
#>   trt      age marker stage grade response death ttdeath
#>   <chr>  <dbl>  <dbl> <fct> <fct>    <int> <int>   <dbl>
#> 1 Drug A    23  0.16  T1    II           0     0    24  
#> 2 Drug B     9  1.11  T2    I            1     0    24  
#> 3 Drug B    21  0.258 T4    I            0     1    12.9
#> 4 Drug B    28  0.803 T4    II           0     1    18  
#> 5 Drug B    25  2.45  T1    I            1     0    24  
#> 6 Drug B    25  0.531 T4    III          0     1    23.2

```