将列名传递给用R编写的函数，该函数导入到单独的R Markdown/R文件中

Question

好吧，问题来了：我在R中写了一个函数，它接受一个数据帧，然后使用dplyr和tidyr来操作和返回另一个数据框。

问题是：我传递了用引号、双引号或反引号(``(包装的列名，但R抛出了一条错误消息，说"错误：找不到对象"对象名称"，在回溯的情况下；错误：必须按.data中的变量分组。*找不到列"Column_variable">

第一块：我通过source((导入的函数

cat_perc_by_dept <- function(data, group, category) {
# Returns the percentage of each category for a specific group.  
# Inputs: 
#   data: the dataset with multiple groups and multiple categories for each group
#   group: the specific group, eg department, that we want to focus on within group_col
#   category: the category we want to focus on within a particular group
data %>% 
select(DeptSpecialty, category) %>% 
filter(DeptSpecialty == group) %>% 
group_by(DeptSpecialty) %>% 
mutate(Total = n(), Instance = 1) %>% 
group_by(category) %>% 
summarise(Perc = sum(Instance) / Total) %>% 
distinct()
}

第二：我在主文件中用引号调用的函数

cat_perc_by_dept(data = visit_data, group = "Bariatrics", category = "ChargeDiagnosisCode")

还有反悔：

cat_perc_by_dept(data = visit_data, group = "Bariatrics", category = `ChargeDiagnosisCode`)

如何以不引发上述错误的方式将列名传递给函数？

Answer 1

如果输入是字符串，我们可以在group_by和select中添加across

cat_perc_by_dept <- function(data, group, category) {
# Returns the percentage of each category for a specific group.  
# Inputs: 
#   data: the dataset with multiple groups and multiple categories for each group
#   group: the specific group, eg department, that we want to focus on within group_col
#   category: the category we want to focus on within a particular group
data %>% 
select(DeptSpecialty, across(all_of(category))) %>% 
filter(DeptSpecialty == group) %>% 
group_by(DeptSpecialty) %>% 
mutate(Total = n(), Instance = 1) %>% 
group_by(across(all_of(category))) %>% 
summarise(Perc = sum(Instance) / Total) %>% 
distinct()
}

---

或者另一个选项是用ensym转换为symbol并求值(!!(，这可以是灵活的，因为未引用和引用的参数都可以传递

cat_perc_by_dept <- function(data, group, category) {
# Returns the percentage of each category for a specific group.  
# Inputs: 
#   data: the dataset with multiple groups and multiple categories for each group
#   group: the specific group, eg department, that we want to focus on within group_col
#   category: the category we want to focus on within a particular group

category <- rlang::ensym(category)
data %>% 
select(DeptSpecialty, !!category) %>% 
filter(DeptSpecialty == group) %>% 
group_by(DeptSpecialty) %>% 
mutate(Total = n(), Instance = 1) %>% 
group_by(!!category) %>% 
summarise(Perc = sum(Instance) / Total) %>% 
distinct()
}