如何计算每个类别中的非零值

Question

这是我的数据

df <- structure(list(team_3_F = c("team ", "team ", "site", "site", 
"team ", "team ", "newyorkish", "newyorkish", "team ", "team ", 
"newyorkish", "newyorkish", "browingal ", "browingal ", "site", 
"site", "browingal ", "browingal ", "browingal ", "browingal ", 
"team ", "team ", "team ", "team ", "team ", "team ", "team ", 
"team ", "team ", "team ", "site", "site", "browingal ", "browingal ", 
"browingal ", "browingal ", "browingal ", "browingal ", "browingal ", 
"browingal ", "browingal ", "browingal ", "team ", "team ", "team ", 
"team ", "newyorkish", "newyorkish", "browingal ", "browingal ", 
"newyorkish", "newyorkish", "browingal ", "browingal ", "team ", 
"team ", "browingal ", "browingal ", "team "), name = c("AAA_US", 
"BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", 
"AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", 
"BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", 
"AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", 
"BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", 
"AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", 
"BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", 
"AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", "BBB_US", "AAA_US", 
"BBB_US", "AAA_US"), value = c(0L, 0L, 0L, 8L, 1L, 0L, 11L, 0L, 
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 45L, 
0L, 0L, 0L, 18L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 0L, 3L, 
0L, 2L, 0L, 2L, 1L, 0L, 4L, 0L, 88L, 0L, 0L, 1L, 5L, 0L, 0L, 
0L, 0L, 0L, 0L, 1L, 19L)), row.names = c(NA, -59L), class = "data.frame")

我正在尝试识别每个组的非零值，因此我应该得到这样的输出

browingal   AAA_US  1
browingal   BBB_US  7
newyorkish  AAA_US  4
newyorkish  BBB_US  0
site        AAA_US  0
site        BBB_US  1
team        AAA_US  6
team        BBB_US  0

我试着归类，但我想不通

df %>% group_by(name) %>% summarise_each(function(x) min(x[x != 0]),value)

Answer 1

您可以使用以下解决方案。根据文档：

mutate_each() and summarise_each are deprecated in favor of new across function that works with summarize() & mutate().

library(dplyr)

df %>%
  group_by(team_3_F, name) %>%
  summarise(across(value, ~ sum(.x != 0)))

# A tibble: 8 x 3
# Groups:   team_3_F [4]
  team_3_F     name   value
  <chr>        <chr>  <int>
1 "browingal " AAA_US     1
2 "browingal " BBB_US     7
3 "newyorkish" AAA_US     4
4 "newyorkish" BBB_US     0
5 "site"       AAA_US     0
6 "site"       BBB_US     1
7 "team "      AAA_US     6
8 "team "      BBB_US     0

Answer 2

data.table的另一个选项：

library(data.table)
dt <- data.table(df)

dt[, sum(value != 0), by = c("team_3_F", "name")]

     team_3_F   name V1
1:      team  AAA_US  6
2:      team  BBB_US  0
3:       site AAA_US  0
4:       site BBB_US  1
5: newyorkish AAA_US  4
6: newyorkish BBB_US  0
7: browingal  AAA_US  1
8: browingal  BBB_US  7

如何计算每个类别中的非零值

how to count non zero values in each category

r

dplyr

data.table

across