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").)
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 registeredThanks 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 1To 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)
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