变异多个变量以创建多个新变量

本文介绍了变异多个变量以创建多个新变量的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

假设我有一个 tibble，我需要在其中获取多个变量并将它们变异为新的多个新变量.

举个例子，这是一个简单的小标题:

tb <- tribble(~x, ~y1, ~y2, ~y3, ~z,1,2,4,6,2,2,1,2,3,3,3,6,4,2,1)

我想从名称以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y"变量.我希望该解决方案非常适合 tidyverse/dplyr 工作流程.

本质上，我不明白如何将多个变量变异为多个新变量.我不确定您是否可以在这种情况下使用 mutate ?我已经尝试过 mutate_if，但我认为我没有正确使用它(并且出现错误):

tb %>% mutate_if(starts_with("y"), funs(.-z))#Error: 没有注册 tidyselect 变量

提前致谢！

解决方案

因为操作的是列名，所以需要使用 mutate_at 而不是 mutate_if列内的值

tb %>% mutate_at(vars(starts_with(y")), funs(. - z))#># 小块: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，我们可以为 funs

命名

#添加后缀tb %>% mutate_at(vars(starts_with(y")), funs(mod = . - z))#># 小块: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# 去除后缀，添加前缀tb%＞%mutate_at(vars(starts_with(y")), funs(mod = . - z)) %>%rename_at(vars(ends_with("_mod")), funs(paste("mod", gsub("_mod", "", .), sep = "_")))#># 小块: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，funs() 将被弃用(source1 & source2)，需要改用list()

tb %>% mutate_at(vars(starts_with(y")), list(~ . - z))#># 小块: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 1tb %>% mutate_at(vars(starts_with(y")), list(mod = ~ . - z))#># 小块: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 1tb%＞%mutate_at(vars(starts_with("y")), list(mod = ~ . - z)) %>%rename_at(vars(ends_with("_mod")), list(~ paste("mod", gsub("_mod", "", .), sep = "_")))#># 小块: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+ 有 across() 函数进一步简化了这个任务

基本用法

across() 有两个主要参数:

• 第一个参数 .cols 选择要操作的列.它使用整洁的选择(如 select())，因此您可以通过以下方式选择变量位置、名称和类型.

• 第二个参数 .fns 是一个函数或要应用的函数列表每列.这也可以是 purrr 风格的公式(或公式列表)像~.x/2.(这个参数是可选的，如果你只是想要，你可以省略它获取底层数据；你会看到该技术用于vignette(rowwise").)

# 控制如何使用 `.names` 参数创建名称# 采用 [glue](glue.tidyverse/) 规范:tb%＞%变异(跨越(starts_with(y")，〜.x - z，.names =mod_{col}"))#># 小块: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 1tb%＞%变异(跨越(num_range(前缀 = y"，范围 = 1:3)，~ .x - z，.names = mod_{col}"))#># 小块: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### 多种功能tb%＞%变异(跨越(c(匹配(x")，包含(z"))，〜max(.x，na.rm = TRUE)，.names =max_{col}")，跨越(c(y1:y3)，〜.x - z，.names =mod_{col}"))#># 小费: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

由 reprex 包 (v0.2.1) 于 2018 年 10 月 29 日创建

Let's say I have a tibble where I need to take multiple variables and mutate them into new multiple new variables.

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 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.

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

Thanks in advance!

解决方案

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

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

---

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

---

Edit 2: dplyr 1.0.0+ has across() function which simplifies this task even further

Basic usage

across() has two primary arguments:

• 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.

• 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

Created on 2018-10-29 by the reprex package (v0.2.1)