1. mutate_when(.data, when, ..., by)
  2. mutate_vars(.data, .cols = NULL, .func, ..., by)

Arguments

.data data.frame
when An object which can be coerced to logical mode
… Name-value pairs of expressions for mutate_when. Additional parameters to be passed to parameter ‘.func’ in mutate_vars.
by (Optional) Mutate by what group?
.cols Any types that can be accepted by [select_dt](https://hope-data-science.github.io/tidyfst/reference/select.html).
.func Function to be run within each column, should return a value or vectors with same length. 
  1. #mutate_dt
  2. # 增添一列名为one，内容全为1；同时，让所有的Sepal.Length列数值加1
  3. iris %>% mutate_dt(one = 1,Sepal.Length = Sepal.Length + 1)
  5. #如果只想要新增列，可以使用transmute_dt函数
  6. iris %>% transmute_dt(one = 1,Sepal.Length = Sepal.Length + 1)
  9. #有条件的新增列
  10. iris[3:8,] %>%
  11.   mutate_when(Petal.Width == .2,   #当Petal.Width = .2时
  12.               one = 1,Sepal.Length=2)  #one列全是1，Sepal.Length=2
  15. #pe开头的字段全部标准化操作
  16. iris %>% mutate_vars("Pe",scale)
  18. #数值型字段全部标准化操作
  19. iris %>% mutate_vars(is.numeric,scale)
  20. iris %>% mutate_vars(-is.factor,scale)
  22. #1：2列标准化操作
  23. iris %>% mutate_vars(1:2,scale)
  25. #全部列
  26. iris %>% mutate_vars(.func = as.character)