假设我有一个 tibble ,我需要在其中采用多个变量并将其变异为新的多个新变量。
Let's say I have a tibble where I need to take multiple variables and mutate them into new multiple new variables.
例如,下面是一个简单的小标题:
As an example, here is a simple tibble:
tb <- tribble( ~x, ~y1, ~y2, ~y3, ~z, 1,2,4,6,2, 2,1,2,3,3, 3,6,4,2,1 )I想要从名称以 y开头的每个变量中减去变量z,并将结果变异为tb的新变量。另外,假设我不知道我有多少个 y变量。我希望该解决方案很好地适合 tidyverse / dplyr 工作流程。
I want to subtract variable z from every variable with a name starting with "y", and mutate the results as new variables of tb. Also, suppose I don't know how many "y" variables I have. I want the solution to fit nicely within tidyverse / dplyr workflow.
本质上,我不了解如何将多个变量突变为多个新变量。我不确定在这种情况下是否可以使用 mutate ?我已经尝试过 mutate_if ,但是我认为我使用的方式不正确(并且出现错误):
In essence, I don't understand how to mutate multiple variables into multiple new variables. I'm not sure if you can use mutate in this instance? I've tried mutate_if, but I don't think I'm using it right (and I get an error):
tb %>% mutate_if(starts_with("y"), funs(.-z)) #Error: No tidyselect variables were registered提前谢谢!
推荐答案由于要对列名进行操作,因此需要使用 mutate_at 而不是 mutate_if 它使用列中的值
Because you are operating on column names, you need to use mutate_at rather than mutate_if which uses the values within columns
tb %>% mutate_at(vars(starts_with("y")), funs(. - z)) #> # A tibble: 3 x 5 #> x y1 y2 y3 z #> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 0 2 4 2 #> 2 2 -2 -1 0 3 #> 3 3 5 3 1 1要创建新列,而不是覆盖现有列,我们可以将名称命名为 funs
To create new columns, instead of overwriting existing ones, we can give name to funs
# add suffix tb %>% mutate_at(vars(starts_with("y")), funs(mod = . - z)) #> # A tibble: 3 x 8 #> x y1 y2 y3 z y1_mod y2_mod y3_mod #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 4 6 2 0 2 4 #> 2 2 1 2 3 3 -2 -1 0 #> 3 3 6 4 2 1 5 3 1 # remove suffix, add prefix tb %>% mutate_at(vars(starts_with("y")), funs(mod = . - z)) %>% rename_at(vars(ends_with("_mod")), funs(paste("mod", gsub("_mod", "", .), sep = "_"))) #> # A tibble: 3 x 8 #> x y1 y2 y3 z mod_y1 mod_y2 mod_y3 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 4 6 2 0 2 4 #> 2 2 1 2 3 3 -2 -1 0 #> 3 3 6 4 2 1 5 3 1
编辑 :在 dplyr 0.8.0 或更高版本中,不建议使用 funs()( source1 & 源2 ),需要改用 list()
Edit: In dplyr 0.8.0 or higher versions, funs() will be deprecated (source1 & source2), need to use list() instead
tb %>% mutate_at(vars(starts_with("y")), list(~ . - z)) #> # A tibble: 3 x 5 #> x y1 y2 y3 z #> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 0 2 4 2 #> 2 2 -2 -1 0 3 #> 3 3 5 3 1 1 tb %>% mutate_at(vars(starts_with("y")), list(mod = ~ . - z)) #> # A tibble: 3 x 8 #> x y1 y2 y3 z y1_mod y2_mod y3_mod #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 4 6 2 0 2 4 #> 2 2 1 2 3 3 -2 -1 0 #> 3 3 6 4 2 1 5 3 1 tb %>% mutate_at(vars(starts_with("y")), list(mod = ~ . - z)) %>% rename_at(vars(ends_with("_mod")), list(~ paste("mod", gsub("_mod", "", .), sep = "_"))) #> # A tibble: 3 x 8 #> x y1 y2 y3 z mod_y1 mod_y2 mod_y3 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 4 6 2 0 2 4 #> 2 2 1 2 3 3 -2 -1 0 #> 3 3 6 4 2 1 5 3 1
编辑2 : dplyr 1.0。 0+ 具有 across() 函数可进一步简化此任务
Edit 2: dplyr 1.0.0+ has across() function which simplifies this task even further
基本用法
across()有两个主要参数:
- 第一个参数 .cols 选择所需的列进行操作。 它使用整洁的选择(例如 select()),因此您可以按的位置,名称和类型来选择变量。
- The first argument, .cols, selects the columns you want to operate on. It uses tidy selection (like select()) so you can pick variables by position, name, and type.
- 第二个参数 .fns 是要应用于每列的一个函数或函数列表。这也可以是Purrr样式的公式(或公式列表),例如〜.x / 2 。 (该参数是可选的,如果只希望来获取基础数据,则可以将其忽略;您将看到 vignette( rowwise)中使用的技术。 。)
- The second argument, .fns, is a function or list of functions to apply to each column. This can also be a purrr style formula (or list of formulas) like ~ .x / 2. (This argument is optional, and you can omit it if you just want to get the underlying data; you'll see that technique used in vignette("rowwise").)
# Control how the names are created with the `.names` argument which # takes a [glue](glue.tidyverse/) spec: tb %>% mutate( across(starts_with("y"), ~ .x - z, .names = "mod_{col}") ) #> # A tibble: 3 x 8 #> x y1 y2 y3 z mod_y1 mod_y2 mod_y3 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 4 6 2 0 2 4 #> 2 2 1 2 3 3 -2 -1 0 #> 3 3 6 4 2 1 5 3 1 tb %>% mutate( across(num_range(prefix = "y", range = 1:3), ~ .x - z, .names = "mod_{col}") ) #> # A tibble: 3 x 8 #> x y1 y2 y3 z mod_y1 mod_y2 mod_y3 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 4 6 2 0 2 4 #> 2 2 1 2 3 3 -2 -1 0 #> 3 3 6 4 2 1 5 3 1 ### Multiple functions tb %>% mutate( across(c(matches("x"), contains("z")), ~ max(.x, na.rm = TRUE), .names = "max_{col}"), across(c(y1:y3), ~ .x - z, .names = "mod_{col}") ) #> # A tibble: 3 x 10 #> x y1 y2 y3 z max_x max_z mod_y1 mod_y2 mod_y3 #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1 2 4 6 2 3 3 0 2 4 #> 2 2 1 2 3 3 3 3 -2 -1 0 #> 3 3 6 4 2 1 3 3 5 3 1
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Created on 2018-10-29 by the reprex package (v0.2.1)
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