使用dplyr计算两组出现的百分比和频率

Use dplyr to calculate percentage and frequency of occurrence of two groups

我正在学习 dplyr 并从类似的帖子中搜索解决方案,但发现 none 遇到了这些问题的组合。

这是一个示例数据框:

set.seed(1)
    df <- data.frame(sampleID = c(rep("sample1",2),
                                 rep("sample2",3),
                                 rep("sample3",4)),
                     species = c("clover","nettle",
                                 "clover","nettle","vine",
                                 "clover","clover","nettle","vine"),
                     type = c("vegetation","seed",
                              "vegetation","vegetation","vegetation",
                              "seed","vegetation","seed","vegetation"),
                     mass = sample(1:9))

    > df
  sampleID species       type mass
1  sample1  clover vegetation    9
2  sample1  nettle       seed    4
3  sample2  clover vegetation    7
4  sample2  nettle vegetation    1
5  sample2    vine vegetation    2
6  sample3  clover       seed    6
7  sample3  clover vegetation    3
8  sample3  nettle       seed    8
9  sample3    vine vegetation    5

我需要 return 一个数据框来计算每个唯一 species/type 组合的质量百分比,并且我需要在 sampleIDs

中出现 species/type 的频率百分比

所以在这个例子中 vine/vegetation 的 species/type 的解决方案是 质量百分比 = (5+2)/(总和(质量)) 并且百分比频率将是 2/3,因为样本 1 中没有出现该组合。

首先,我尝试了不同的组合,例如:

df %>%
  group_by(species,type) %>%
  summarize(totmass = sum(mass))  %>%
  mutate(percmass = totmass/sum(totmass))

但这给出了 vine/vegetation 的 100% 质量?我也不知道从那里去哪里得到基于 sampleID 的百分比频率。

不确定我是否理解正确,但也许这就是您要找的:

set.seed(1)
df <- data.frame(sampleID = c(rep("sample1",2),
                              rep("sample2",3),
                              rep("sample3",4)),
                 species = c("clover","nettle",
                             "clover","nettle","vine",
                             "clover","clover","nettle","vine"),
                 type = c("vegetation","seed",
                          "vegetation","vegetation","vegetation",
                          "seed","vegetation","seed","vegetation"),
                 mass = sample(1:9))

library(dplyr)

df %>%
  # Add total mass
  add_count(wt = mass, name = "sum_mass") %>%
  # Add total number of samples
  add_count(nsamples = n_distinct(sampleID)) %>%
  # Add sum_mass and nsamples to group_by
  group_by(species, type, sum_mass, nsamples) %>%
  summarize(nsample = n_distinct(sampleID), 
            totmass = sum(mass), .groups = "drop")  %>%
  mutate(percmass = totmass / sum_mass,
         percfreq = nsample / nsamples)
#> # A tibble: 5 x 8
#>   species type       sum_mass nsamples nsample totmass percmass percfreq
#>   <chr>   <chr>         <int>    <int>   <int>   <int>    <dbl>    <dbl>
#> 1 clover  seed             45        3       1       6   0.133     0.333
#> 2 clover  vegetation       45        3       3      19   0.422     1    
#> 3 nettle  seed             45        3       2      12   0.267     0.667
#> 4 nettle  vegetation       45        3       1       1   0.0222    0.333
#> 5 vine    vegetation       45        3       2       7   0.156     0.667