3

有谁知道使用 dplyr 计算some_var == TRUE和的平均值之间的差异some_var == FALSE,并按第三个变量分组?

例如,给定以下示例数据框:

library('dplyr')

dat <- iris %>% 
     mutate(wide=Sepal.Width > 3) %>% 
     group_by(Species, wide) %>% 
     summarize(mean_width=mean(Sepal.Width))

dat

# A tibble: 6 x 3
# Groups:   Species [?]
     Species  wide mean_width
      <fctr> <lgl>      <dbl>
1     setosa FALSE   2.900000
2     setosa  TRUE   3.528571
3 versicolor FALSE   2.688095
4 versicolor  TRUE   3.200000
5  virginica FALSE   2.800000
6  virginica  TRUE   3.311765

有谁知道一种方法来导出一个新的数据框,其中 和 的差异wide == TRUEwide == FALSE按物种?

这可以使用几个语句来完成:

false_vals <- dat %>% filter(wide==FALSE)
true_vals <- dat %>% filter(wide==TRUE)

diff <- data.frame(Species=unique(dat$Species), diff=true_vals$mean_width - false_vals$mean_width)

> diff
     Species      diff
1     setosa 0.6285714
2 versicolor 0.5119048
3  virginica 0.5117647

但是,这似乎应该可以通过 dplyr 直接实现。

有任何想法吗?

4

2 回答 2

4

spread()tidyr包中使用:

library(tidyr)

iris %>% mutate(wide=Sepal.Width > 3) %>% 
        group_by(Species, wide) %>% 
        summarize(mean_width=mean(Sepal.Width)) %>%
        spread(wide, mean_width) %>%
        summarise(diff = `TRUE` - `FALSE`)
#     Species      diff
#1     setosa 0.6285714
#2 versicolor 0.5119048
#3  virginica 0.5117647
于 2017-12-09T15:45:33.547 回答
2

对于新版本的 Tidyr 包 (>1.0.0),现在最好使用pivot_wider命令而不是spread。它更直观,并且传播命令将来可能会被弃用。

library(tidyr)

    iris %>% mutate(wide=Sepal.Width > 3) %>% 
            group_by(Species, wide) %>% 
            summarize(mean_width=mean(Sepal.Width)) %>%
            pivot_wider(names_from = wide, values_from = mean_width) %>%
            summarise(diff = `TRUE` - `FALSE`)

    #     Species      diff
    #1     setosa 0.6285714
    #2 versicolor 0.5119048
    #3  virginica 0.5117647
于 2020-04-23T10:35:38.187 回答