检查对象是否为 Null 或未定义
Check if object is Null or undefined
我有一个包含可选变量参数的函数。默认情况下,我将变量设置为 NULL
,但如果不是 NULL
,我希望我的函数做一些事情。我需要一种方法来检查变量是否不为空。这很复杂,因为我正在使用 tidyeval,而仅使用 is.null(var)
会引发找不到对象错误。我找到了一个使用 try
的 hacky 解决方案,但我希望有更好的方法。
library(dplyr)
dat <- data.frame(value = 1:8,
char1 = c(rep("a", 4), rep("b", 4)),
char2 = rep(c(rep("c", 2), rep("d", 2)), 2))
myfun <- function(dat, group = NULL, var = NULL) {
x <- dat %>%
group_by({{group}}, {{var}}) %>%
summarize(mean = mean(value),
.groups = "drop")
# if(!is.null(var)) { # Throws object not found error if not null
null_var <- try(is.null(var), silent = TRUE)
null_var <- null_var == TRUE
if(!null_var) {
print("do something with `var`")
}
x
}
myfun(dat)
myfun(dat, char1)
myfun(dat, char1, char2)
您可以将 is.null
应用于符号:
dat <- data.frame(value = 1:8,
char1 = c(rep("a", 4), rep("b", 4)),
char2 = rep(c(rep("c", 2), rep("d", 2)), 2))
myfun <- function(dat, group = NULL, var = NULL) {
if (is.null(substitute(var))) {
print("var was NULL")
} else {
print("var was not NULL")
}
}
myfun(dat)
#> [1] "var was NULL"
myfun(dat, char1)
#> [1] "var was NULL"
myfun(dat, char1, char2)
#> [1] "var was not NULL"
由 reprex package (v0.3.0)
于 2021 年 3 月 11 日创建
如果您只想知道是否使用了参数,missing
可能会有用:
dat <- data.frame(value = 1:8,
char1 = c(rep("a", 4), rep("b", 4)),
char2 = rep(c(rep("c", 2), rep("d", 2)), 2))
myfun2 <- function(dat, group = NULL, var = NULL) {
if (missing(var)) {
print("var was missing")
} else {
print("var was not missing")
}
}
myfun2(dat)
#> [1] "var was missing"
myfun2(dat, char1)
#> [1] "var was missing"
myfun2(dat, char1, char2)
#> [1] "var was not missing"
注意以下皱纹:
myfun(dat, char1, NULL)
#> [1] "var was NULL"
## yet
myfun2(dat, char1, NULL)
#> [1] "var was not missing"
z <- NULL
myfun(dat, char1, z)
#> [1] "var was not NULL"
{{
是 enquo()
和 !!
在一步中的组合。要检查var
的内容,需要分解这两个步骤。 enquo()
化解了论点,returns 一个你可以检查的问题。 !!
将 quosure 注入其他调用。
下面我们 enquo()
函数开头的参数。使用 quo_is_null()
检查它(您也可以使用 quo_get_expr()
来查看其中的内容)。然后将其注入 group_by()
和 !!
:
myfun <- function(dat, group = NULL, var = NULL) {
var <- enquo(var)
if (!quo_is_null(var)) {
print("It works!")
}
# Use `!!` instead of `{{` because we have decomposed the
# `enquo()` and `!!` steps that `{{` bundles
dat %>%
group_by({{ group }}, !!var) %>%
summarize(mean = mean(value), .groups = "drop")
}
我有一个包含可选变量参数的函数。默认情况下,我将变量设置为 NULL
,但如果不是 NULL
,我希望我的函数做一些事情。我需要一种方法来检查变量是否不为空。这很复杂,因为我正在使用 tidyeval,而仅使用 is.null(var)
会引发找不到对象错误。我找到了一个使用 try
的 hacky 解决方案,但我希望有更好的方法。
library(dplyr)
dat <- data.frame(value = 1:8,
char1 = c(rep("a", 4), rep("b", 4)),
char2 = rep(c(rep("c", 2), rep("d", 2)), 2))
myfun <- function(dat, group = NULL, var = NULL) {
x <- dat %>%
group_by({{group}}, {{var}}) %>%
summarize(mean = mean(value),
.groups = "drop")
# if(!is.null(var)) { # Throws object not found error if not null
null_var <- try(is.null(var), silent = TRUE)
null_var <- null_var == TRUE
if(!null_var) {
print("do something with `var`")
}
x
}
myfun(dat)
myfun(dat, char1)
myfun(dat, char1, char2)
您可以将 is.null
应用于符号:
dat <- data.frame(value = 1:8,
char1 = c(rep("a", 4), rep("b", 4)),
char2 = rep(c(rep("c", 2), rep("d", 2)), 2))
myfun <- function(dat, group = NULL, var = NULL) {
if (is.null(substitute(var))) {
print("var was NULL")
} else {
print("var was not NULL")
}
}
myfun(dat)
#> [1] "var was NULL"
myfun(dat, char1)
#> [1] "var was NULL"
myfun(dat, char1, char2)
#> [1] "var was not NULL"
由 reprex package (v0.3.0)
于 2021 年 3 月 11 日创建如果您只想知道是否使用了参数,missing
可能会有用:
dat <- data.frame(value = 1:8,
char1 = c(rep("a", 4), rep("b", 4)),
char2 = rep(c(rep("c", 2), rep("d", 2)), 2))
myfun2 <- function(dat, group = NULL, var = NULL) {
if (missing(var)) {
print("var was missing")
} else {
print("var was not missing")
}
}
myfun2(dat)
#> [1] "var was missing"
myfun2(dat, char1)
#> [1] "var was missing"
myfun2(dat, char1, char2)
#> [1] "var was not missing"
注意以下皱纹:
myfun(dat, char1, NULL)
#> [1] "var was NULL"
## yet
myfun2(dat, char1, NULL)
#> [1] "var was not missing"
z <- NULL
myfun(dat, char1, z)
#> [1] "var was not NULL"
{{
是 enquo()
和 !!
在一步中的组合。要检查var
的内容,需要分解这两个步骤。 enquo()
化解了论点,returns 一个你可以检查的问题。 !!
将 quosure 注入其他调用。
下面我们 enquo()
函数开头的参数。使用 quo_is_null()
检查它(您也可以使用 quo_get_expr()
来查看其中的内容)。然后将其注入 group_by()
和 !!
:
myfun <- function(dat, group = NULL, var = NULL) {
var <- enquo(var)
if (!quo_is_null(var)) {
print("It works!")
}
# Use `!!` instead of `{{` because we have decomposed the
# `enquo()` and `!!` steps that `{{` bundles
dat %>%
group_by({{ group }}, !!var) %>%
summarize(mean = mean(value), .groups = "drop")
}