Question

我创建了一个包含数据的数据框：

idCol <- c('1','1','2','2')
stepCol <- c('step1' , 'step2' , 'step1' , 'step2')
timestampCol <- c('01-01-2017:09.00', '01-01-2017:10.00', '01-01-2017:09:00', '01-01-2017:14.00')
mydata <- data.frame(idCol , stepCol , timestampCol)
colnames(mydata) <- c('id' , 'steps' , 'timestamp')

stepCol是给定id的开始时间，当step2开始时，这意味着step1已经结束。我尝试根据步骤开始时间生成包含每个ID持续时间平均值的tibble。

所以我试图生成：

step , averagetime
step1 , 1 hour
step2 , 5 hours

最近我得到的是：

diffTime <- c(0, difftime(ymd_hms(mydata$timestamp[-1]), ymd_hms(mydata$timestamp[-nrow(mydata)]), units="hours"))
diffTime %>% group_by(id, steps) %>% summarize(mean(diffTime))

但是返回错误：

Error in UseMethod("group_by_") : 
  no applicable method for 'group_by_' applied to an object of class "c('double', 'numeric')"

Answer 1

我对您的代码进行了一些小修改，但基本上您需要将ymd_hms的结果与您的mydata相关联：

mydata$diffTime <- c(0, difftime(lubridate::ymd_hms(mydata$timestamp[-1]), 
                          lubridate::ymd_hms(mydata$timestamp[-nrow(mydata)]), units="hours"))
diffTime <- mydata %>% group_by(id) %>% summarize(mean(diffTime))

返回：

R> diffTime
# A tibble: 2 x 2
     id `mean(diffTime)`
  <chr>            <dbl>
1     1         0.008333
2     2         0.033333

Answer 2

请注意，时间命名法的示例数据timestamp列中存在不一致

timestampCol <- c('01-01-2017:09.00', '01-01-2017:10.00', '01-01-2017:09.00', '01-01-2017:14.00')

将字符串转换为时间值（考虑因素）

mydata$timestamp <- as.POSIXct(strptime(levels(mydata$timestamp)[mydata$timestamp], format="%m-%d-%Y:%H.%M"))

library(dplyr)
mydata %>%
  group_by(id) %>%
  mutate(diff = difftime(timestamp, lag(timestamp))) %>%
  summarise(na.omit(diff))

# A tibble: 2 x 2
      id `na.omit(diff)`
  <fctr>          <time>
1      1         1 hours
2      2         5 hours

使用group_by和difftime

2 个答案: