r计数df变量划分的值

Question

使用以下数据集：

set.seed(2)
origin <- rep(c("DEU", "GBR", "ITA", "NLD", "CAN", "MEX", "USA", "CHN", "JPN", "KOR","DEU", "GBR", "ITA", "NLD", "CAN", "MEX", "USA", "CHN", "JPN", "KOR"), 4)
year <- rep(c(rep(1998, 10), rep(2000, 10)), 2)
type <- sample(1:10, size=length(origin), replace=TRUE)
value <- sample(100:10000, size=length(origin), replace=TRUE)
test.df <- as.data.frame(cbind(origin, year, type, value))
rm(origin, year, type, value)
### add some (6) missing values
test.df$value[sample(1:length(test.df$value), 6, replace = FALSE)] <- NA

我想计算每 year

按国家/地区计数多少 type s（origin）

我尝试了：

count(trade.df, origin, year)

和

test.df %>% group_by(origin, year) %>% count()

，但我不确定如何解释这些结果。

当然，如果value == NA，R不应计算它...

Answer 1

删除 value为 NA的行，使用 filter：

test.df %>% group_by(origin,year) %>% 
  filter(!is.na(value)) %>% count()
# A tibble: 20 x 3
# Groups:   origin, year [20]
   origin year      n
   <fct>  <fct> <int>
 1 CAN    1998      4
 2 CAN    2000      3
 3 CHN    1998      3
 4 CHN    2000      4
 5 DEU    1998      4
 6 DEU    2000      4
 7 GBR    1998      4
 8 GBR    2000      4
 9 ITA    1998      3
10 ITA    2000      4
11 JPN    1998      3
12 JPN    2000      3
13 KOR    1998      4
14 KOR    2000      4
15 MEX    1998      4
16 MEX    2000      4
17 NLD    1998      3
18 NLD    2000      4
19 USA    1998      4
20 USA    2000      4

但是，请注意，这并不计算每个组中有多少个type S，而是有多少行。如果要计算唯一type s的数量，则可以执行此操作：

test.df %>% group_by(origin,year) %>% 
  filter(!is.na(value)) %>% 
  summarize(n_distinct(type)) #Merci, @Frank!
# A tibble: 20 x 3
# Groups:   origin [?]
   origin year  `length(unique(type))`
   <fct>  <fct>                  <int>
 1 CAN    1998                       3
 2 CAN    2000                       3
 3 CHN    1998                       2
 4 CHN    2000                       3
 5 DEU    1998                       4
 6 DEU    2000                       3
 7 GBR    1998                       4
 8 GBR    2000                       4
 9 ITA    1998                       3
10 ITA    2000                       4
11 JPN    1998                       3
12 JPN    2000                       2
13 KOR    1998                       4
14 KOR    2000                       4
15 MEX    1998                       3
16 MEX    2000                       3
17 NLD    1998                       2
18 NLD    2000                       3
19 USA    1998                       3
20 USA    2000                       4