r - 对列执行分组以计算 R 中另一列的出现次数

Question

我有一个类似于下面提供的示例数据集的数据集:

| Name | Response_days | state |
|------|---------------|-------|
| John | 0             | NY    |
| John | 6             | NY    |
| John | 9             | NY    |
| Mike | 3             | CA    |
| Mike | 7             | CA    |

同样表示为:

Name = c("John","John", "John", "Mike", "Mike")
Response_days = c(0,6,9,3,7)
state= c("NY","NY","NY", "CA","CA")
df= data.frame(Name, Response_days, state, stringsAsFactors = TRUE)
df$Response_days= as.integer(df$Response_days)

我想对数据进行子集化并只查看 Response_days>5。之后我想按“名称”分组并计算“Response_days”的出现次数。我已经尝试了下面提到的代码，但它会引发错误。

df1=subset(df, df$Response_days>5) %>%  group_by(Name) %>%
  summarise(count= count(Response_days))

我得到的错误是错误:

Problem with `summarise()` input `count`.
x no applicable method for 'count' applied to an object of class "c('double', 'numeric')"
i Input `count` is `count(Response_days)`.
i The error occurred in group 1: Name = "John".

有人可以解释一下我哪里出错了吗？此外，我的最终输出应如下所示:

| Name | Response_days |
|------|---------------|
| John | 2             |
| Mike | 1             |

Answer 1

在 dplyr 中有几种方法可以做到这一点 -

library(dplyr)
#1.
df %>% filter(Response_days>5) %>% count(Name, name = 'Count')

#2.
df %>% group_by(Name) %>% summarise(count = sum(Response_days > 5))

在基础 R 中:

#1.
aggregate(Response_days~Name, subset(df, Response_days>5), length)

#2.
aggregate(Response_days~Name, df, function(x) sum(x > 5))