how to count non zero values in each category-CodePudding

this is my data

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")

I am trying to identify the non-zero per each group and so I should have an output like this

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

I tried to categorize it but I could not figure it out

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

CodePudding user response：

Another option with data.table:

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

CodePudding user response：

You can use the following solution. As per documentation:

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