r - 基于阈值定义循环

Question

我有一个包含 30,000 行不均匀数据的数据集，这些行由三个变量组成:时间、腔室和温度。两个腔室以不同的速率加热和冷却，每次超过 75 度的正斜率时，我都需要分配一个循环计数。我正在尝试将 x 轴从时间转换为加热和冷却循环。 This would classify as one cycle

我在这里发现了两个类似的问题:How to count the amount of times (frequency) that data passes through a certain threshold in R?和 time between max and min of cycles但两者都不能让我达到最终目标。利用data.table::rleid((lead(Chamber_temperature, order_by = Chamber_ID) > threshold)在正斜率和负斜率处都计为阈值的两个不同交叉点。如果我包括 data.table::rleid((Chamber_temperature <= threshold | lead(Chamber_temperature, order_by = Chamber_ID) > threshold)我得到一个单行循环，另一个循环包含多行，高于阈值。最终，这似乎也是对循环次数的重复计算。

在这个示例数据集中有两个完整的循环:

Time=c(100674751, 100674851, 100674951, 100675051, 100675151, 100675251, 100675351, 100675451, 100675551, 100675651, 100675751, 100675851, 100675951, 100676051, 100676151, 100676251, 100676351, 100676451, 100676551, 100676651, 100676751, 100676851, 100676951, 100677051, 100677151, 100677251, 100677351, 100677451, 100677551, 100677651, 100677751, 100677851, 100677951, 100678051, 100678151, 100678251, 100678351, 100678451, 100678551, 100678651, 100678751, 100678851, 100678951, 100679051, 100679151, 100679251, 100679351, 100679451, 100679551, 100679651, 100679751, 100679851, 100679951, 100680051, 100680151, 100680251, 100680351, 100680451, 100680551, 100680651, 100680751, 100680851, 100680951, 100681051, 100681151, 100681251, 100681351, 100681451, 100681551, 100681651, 100681751, 100681851, 100681951, 100682051, 100682151, 100682251, 100682351, 100682451, 100682551, 100682651, 100682751, 100682851, 100682951, 100683051, 100683151, 100683251, 100683351, 100683451, 100683551, 100683651, 100683751, 100683851, 100683951, 100684051, 100684151, 100684251, 100684351, 100684451, 100684551, 100684651, 100684751, 100684851, 100684951, 100685051, 100685151, 100685251, 100685351, 100685451, 100685551, 100685651, 100685751, 100685851, 100685951, 100686051, 100686151, 100686251, 100686351, 100686451, 100686551, 100686651, 100686751, 100686851, 100686951, 100687051, 100687151, 100687251, 100687351, 100687451, 100687551, 100687651, 100687751, 100687851, 100687951, 100688051, 100688151, 100688251, 100688351, 100688451, 100688551, 100688651, 100688751, 100688851, 100688951, 100689051, 100689151, 100689251, 100689351, 100689451, 100689551, 100689651, 100689751, 100689851, 100689951, 100690051, 100690151, 100690251, 100690351, 100690451, 100690551, 100690651, 100690751, 100690851, 100690951, 100691051, 100691151, 100691251, 100691351, 100691451, 100691551, 100691651, 100691751, 100691851, 100691951, 100692051, 100692151, 100692251, 100692351, 100692451, 100692551, 100692651, 100692751, 100692851, 100692951, 100693051, 100693151, 100693251)  
Chamber_ID=c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1) 
Temp=c(56.8, 58.2, 59.7, 59.7, 61.4, 63.2, 63.2, 65.0, 66.8, 66.8, 68.7, 70.5, 70.5, 72.3, 74.1, 74.1, 75.9, 77.6, 77.6, 79.3, 81.0, 81.0, 82.7, 84.4, 84.4, 87.9, 86.6, 86.6, 85.4, 84.1, 84.1, 82.9, 81.8, 81.8, 80.6, 79.5, 79.5, 78.3, 77.2, 77.2, 76.2, 75.1, 75.1, 74.1, 73.0, 73.0, 72.0, 71.1, 71.1, 70.1, 69.1, 69.1, 68.2, 67.2, 67.2, 66.3, 65.4, 65.4, 64.6, 63.7, 63.7, 62.8, 62.0, 62.0, 61.2, 60.4, 60.4, 59.6, 58.8, 58.8, 58.0, 57.3, 57.3, 56.8, 58.2, 58.2, 59.7, 61.4, 61.4, 63.2, 65.0, 65.0, 66.9, 68.7, 68.7, 70.5, 72.3, 72.3, 74.1, 75.9, 75.9, 77.6, 79.3, 79.3, 81.0, 82.7, 82.7, 84.4, 86.0, 86.0, 87.7, 89.3, 86.6, 85.3, 85.3, 84.1, 82.9, 82.9, 81.7, 80.6, 80.6, 79.4, 78.3, 78.3, 77.2, 76.1, 76.1, 75.1, 74.1, 74.1, 73.0, 72.0, 72.0, 71.0, 70.1, 70.1, 69.1, 68.1, 68.1, 67.2, 66.3, 66.3, 65.4, 64.5, 64.5, 63.7, 62.8, 62.8, 62.0, 61.2, 61.2, 60.4, 59.6, 59.6, 58.8, 58.0, 58.0, 57.3, 56.5, 56.5, 56.9, 56.9, 58.2, 59.7, 59.7, 61.5, 63.2, 63.2, 65.0, 66.9, 66.9, 68.7, 70.5, 70.5, 72.3, 74.1, 74.1, 75.9, 77.6, 77.6, 79.3, 81.0, 81.0, 82.7, 84.4, 84.4, 86.0, 87.7, 87.7, 89.3, 90.9, 90.9, 92.5, 94.0, 94.0, 95.6)

这将是预期的输出，只有当温度升高到阈值以上时，循环才会增加，而不是低于阈值。

Answer 1

周期变化可以通过以下算法识别:

1. 按 Time 对数据进行排序
2. 查找 Temp > 75 的行返回 TRUE
3. 在该行子集中，找到 Temp <= 75 所在的行返回 TRUE对于前行。

一旦我们确定了周期变化，我们就可以使用cumsum() 获得周期的运行记录。 .

我已经在下面的代码中实现了它。因为你想用 ChamberID 做什么不明确，我假设您想跟踪由 ChamberID 的唯一值定义的每个子组分隔的周期。 .

创建数据

# Create your original data, stored in a data.frame
df <- data.frame(Time=c(100674751, 100674851, 100674951, 100675051, 100675151, 100675251, 100675351, 100675451, 100675551, 100675651, 100675751, 100675851, 100675951, 100676051, 100676151, 100676251, 100676351, 100676451, 100676551, 100676651, 100676751, 100676851, 100676951, 100677051, 100677151, 100677251, 100677351, 100677451, 100677551, 100677651, 100677751, 100677851, 100677951, 100678051, 100678151, 100678251, 100678351, 100678451, 100678551, 100678651, 100678751, 100678851, 100678951, 100679051, 100679151, 100679251, 100679351, 100679451, 100679551, 100679651, 100679751, 100679851, 100679951, 100680051, 100680151, 100680251, 100680351, 100680451, 100680551, 100680651, 100680751, 100680851, 100680951, 100681051, 100681151, 100681251, 100681351, 100681451, 100681551, 100681651, 100681751, 100681851, 100681951, 100682051, 100682151, 100682251, 100682351, 100682451, 100682551, 100682651, 100682751, 100682851, 100682951, 100683051, 100683151, 100683251, 100683351, 100683451, 100683551, 100683651, 100683751, 100683851, 100683951, 100684051, 100684151, 100684251, 100684351, 100684451, 100684551, 100684651, 100684751, 100684851, 100684951, 100685051, 100685151, 100685251, 100685351, 100685451, 100685551, 100685651, 100685751, 100685851, 100685951, 100686051, 100686151, 100686251, 100686351, 100686451, 100686551, 100686651, 100686751, 100686851, 100686951, 100687051, 100687151, 100687251, 100687351, 100687451, 100687551, 100687651, 100687751, 100687851, 100687951, 100688051, 100688151, 100688251, 100688351, 100688451, 100688551, 100688651, 100688751, 100688851, 100688951, 100689051, 100689151, 100689251, 100689351, 100689451, 100689551, 100689651, 100689751, 100689851, 100689951, 100690051, 100690151, 100690251, 100690351, 100690451, 100690551, 100690651, 100690751, 100690851, 100690951, 100691051, 100691151, 100691251, 100691351, 100691451, 100691551, 100691651, 100691751, 100691851, 100691951, 100692051, 100692151, 100692251, 100692351, 100692451, 100692551, 100692651, 100692751, 100692851, 100692951, 100693051, 100693151, 100693251),  
Chamber_ID=c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1),
Temp=c(56.8, 58.2, 59.7, 59.7, 61.4, 63.2, 63.2, 65.0, 66.8, 66.8, 68.7, 70.5, 70.5, 72.3, 74.1, 74.1, 75.9, 77.6, 77.6, 79.3, 81.0, 81.0, 82.7, 84.4, 84.4, 87.9, 86.6, 86.6, 85.4, 84.1, 84.1, 82.9, 81.8, 81.8, 80.6, 79.5, 79.5, 78.3, 77.2, 77.2, 76.2, 75.1, 75.1, 74.1, 73.0, 73.0, 72.0, 71.1, 71.1, 70.1, 69.1, 69.1, 68.2, 67.2, 67.2, 66.3, 65.4, 65.4, 64.6, 63.7, 63.7, 62.8, 62.0, 62.0, 61.2, 60.4, 60.4, 59.6, 58.8, 58.8, 58.0, 57.3, 57.3, 56.8, 58.2, 58.2, 59.7, 61.4, 61.4, 63.2, 65.0, 65.0, 66.9, 68.7, 68.7, 70.5, 72.3, 72.3, 74.1, 75.9, 75.9, 77.6, 79.3, 79.3, 81.0, 82.7, 82.7, 84.4, 86.0, 86.0, 87.7, 89.3, 86.6, 85.3, 85.3, 84.1, 82.9, 82.9, 81.7, 80.6, 80.6, 79.4, 78.3, 78.3, 77.2, 76.1, 76.1, 75.1, 74.1, 74.1, 73.0, 72.0, 72.0, 71.0, 70.1, 70.1, 69.1, 68.1, 68.1, 67.2, 66.3, 66.3, 65.4, 64.5, 64.5, 63.7, 62.8, 62.8, 62.0, 61.2, 61.2, 60.4, 59.6, 59.6, 58.8, 58.0, 58.0, 57.3, 56.5, 56.5, 56.9, 56.9, 58.2, 59.7, 59.7, 61.5, 63.2, 63.2, 65.0, 66.9, 66.9, 68.7, 70.5, 70.5, 72.3, 74.1, 74.1, 75.9, 77.6, 77.6, 79.3, 81.0, 81.0, 82.7, 84.4, 84.4, 86.0, 87.7, 87.7, 89.3, 90.9, 90.9, 92.5, 94.0, 94.0, 95.6))

data.table解决方案

# Convert to data.table
dt <- as.data.table(df)

# Add a 1-period lagged temperature column
dt[order(Time), Temp_lag1 := shift(Temp, 1), by = Chamber_ID]

# Add indicator for whether a cycle change occurs
dt[order(Time), 
   cycle_change := ifelse(Temp > 75 & Temp_lag1 <= 75, 1, 0),
   by = Chamber_ID]

# Add cycle tracker (add 1 so first cycle is 1 not 0)
dt[order(Time), 
   cycle_id := cumsum(cycle_change) + 1, 
   by = Chamber_ID]

# Print distribution of cycles across chambers
table(chamber_id = dt$Chamber_ID, cycle_id = dt$cycle_id)
#>           cycle_id
#> chamber_id  1  2  3  4
#>          0  8 37 39  9
#>          1  8 36 39 10

dplyr解决方案

result <- df %>%
  group_by(Chamber_ID) %>%
  arrange(Time) %>%
  mutate(
    Temp_lag1 = lag(Temp, 1),
    cycle_change = if_else(Temp > 75 & Temp_lag1 <= 75, 1, 0),
    cycle_id = cumsum(cycle_change) + 1
  )

table(result$Chamber_ID, result$cycle_id)
#>    
#>      1  2  3  4
#>   0  8 37 39  9
#>   1  8 36 39 10

根据上述，每个室有4个循环，持续时间最长的是第二个和第三个循环。