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，只查看 Response_days>5。之后我想按 'Name' 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，但它抛出了一个错误。

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".

有人能解释一下我哪里错了吗？另外，我的最终输出应该如下所示：

| 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))

在基数 R 中：

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

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

Answer 2

我们可以使用data.table

library(data.table)
setDT(df)[Response_days > 5, .(count = .N), Name]

或使用base R

table(subset(df, Response_days > 5)$Name)

对列执行分组以计算 R 中另一列的出现次数

Perform group by on a column to calculate count of occurrences of another column in R

group-by

r

count

summarize