按组计算高于阈值百分比的标准差

【问题标题】：Compute standard deviation of a percent above a threshold by group按组计算高于阈值百分比的标准差
【发布时间】：2017-12-21 20:24:48
【问题描述】：

我有几十年的降水数据的每日时间序列，包括年、月和季度的列（1 = 1-3 月，2 = 4-6 月等）。我正在为每个季度的每日降水量设置不同的最小阈值，并计算每个月超过季度阈值的天数百分比。我已经使用 dplyr 计算了高于阈值的百分比。现在，我想围绕该百分比计算每个月的标准偏差（以百分比为单位），但我没有找到一种简洁的方法。这是我到目前为止所做的。

数据：

weath_k <- structure(list(Year = c(2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2001L, 2001L), Qu = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4), Mo = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L), 
    Day = c("01", "02", "03", "04", "05", "06", "07", "08", "09", 
    "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", 
    "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", 
    "30", "31", "01", "02", "03", "04", "05", "06", "07", "08", 
    "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", 
    "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", 
    "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", 
    "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", 
    "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", 
    "31", "01", "02", "03", "04", "05", "06", "07", "08", "09", 
    "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", 
    "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", 
    "30", "01", "02", "03", "04", "05", "06", "07", "08", "09", 
    "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", 
    "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", 
    "30", "31", "01", "02", "03", "04", "05", "06", "07", "08", 
    "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", 
    "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", 
    "29", "30", "01", "02", "03", "04", "05", "06", "07", "08", 
    "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", 
    "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", 
    "29", "30", "31", "01", "02", "03", "04", "05", "06", "07", 
    "08", "09", "10", "11", "12", "13", "14", "15", "16", "17", 
    "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", 
    "28", "29", "30", "31", "01", "02", "03", "04", "05", "06", 
    "07", "08", "09", "10", "11", "12", "13", "14", "15", "16", 
    "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", 
    "27", "28", "29", "30", "01", "02", "03", "04", "05", "06", 
    "07", "08", "09", "10", "11", "12", "13", "14", "15", "16", 
    "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", 
    "27", "28", "29", "30", "31", "01", "02", "03", "04", "05", 
    "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", 
    "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", 
    "26", "27", "28", "29", "30", "01", "02", "03", "04", "05", 
    "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", 
    "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", 
    "26", "27", "28", "29", "30", "31", "01", "02", "03", "04", 
    "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", 
    "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", 
    "25", "26", "27", "28", "29", "30", "31", "01", "02", "03", 
    "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", 
    "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", 
    "24", "25", "26", "27", "28", "01", "02", "03", "04", "05", 
    "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", 
    "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", 
    "26", "27", "28", "29", "30", "31", "01", "02", "03", "04", 
    "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", 
    "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", 
    "25", "26", "27", "28", "29", "30", "01", "02", "03", "04", 
    "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", 
    "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", 
    "25", "26", "27", "28", "29", "30", "31", "01", "02", "03", 
    "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", 
    "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", 
    "24", "25", "26", "27", "28", "29", "30", "01", "02", "03", 
    "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", 
    "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", 
    "24", "25", "26", "27", "28", "29", "30", "31", "01", "02", 
    "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", 
    "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", 
    "23", "24", "25", "26", "27", "28", "29", "30", "31", "01", 
    "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", 
    "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", 
    "22", "23", "24", "25", "26", "27", "28", "29", "30", "01", 
    "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", 
    "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", 
    "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", 
    "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", 
    "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", 
    "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", 
    "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", 
    "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", 
    "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", 
    "31"), PRCP = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    4, 0, 0, 4, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 0, 0, 0, 
    0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 34, 18, 0, 0, 0, 
    0, 0, 0, 7, 1, 16, 8, 0, 0, 0, 13, 0, 0, 3, 11, 0, 0, 0, 
    0, 1, 5, 4, 0, 0, 0, 0, 0, 0, 0, 0, 6, 2, 21, 0, 1, 0, 0, 
    0, 3, 46, 4, 38, 30, 0, 0, 0, 4, 16, 36, 2, 3, 0, 0, 7, 10, 
    5, 0, 0, 3, 1, 20, 1, 0, 5, 7, 18, 19, 18, 0, 0, 12, 0, 5, 
    0, 0, 3, 0, 0, 0, 6, 4, 4, 1, 12, 2, 20, 7, 1, 6, 5, 23, 
    8, 20, 53, 10, 95, 117, 1, 1, 5, 0, 1, 0, 13, 32, 15, 0, 
    0, 0, 0, 0, 3, 3, 11, 29, 5, 25, 2, 28, 6, 6, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 9, 0, 14, 93, 82, 2, 1, 0, 25, 3, 2, 7, 0, 
    2, 2, 1, 0, 6, 3, 1, 2, 1, 84, 97, 7, 4, 4, 0, 0, 0, 0, 0, 
    0, 0, 0, 54, 3, 18, 2, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 72, 55, 1, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 8, 0, 0, 0, 7, 
    0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 7, 6, 0, 0, 0, 0, 0, 1, 2, 0, 9, 33, 0, 0, 0, 
    1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 1, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 20, 0, 5, 0, 2, 18, 0, 58, 41, 11, 10, 41, 
    0, 0, 0, 0, 0, 0, 0, 10, 5, 1, 0, 0, 0, 0, 22, 0, 0, 0, 0, 
    12, 1, 0, 27, 1, 42, 12, 3, 0, 0, 1, 1, 2, 24, 82, 15, 3, 
    7, 27, 27, 0, 5, 5, 13, 16, 0, 14, 1, 4, 10, 31, 18, 22, 
    10, 4, 0, 0, 3, 0, 9, 24, 15, 30, 8, 1, 13, 14, 3, 1, 3, 
    15, 9, 1, 3, 1, 7, 3, 1, 2, 13, 8, 4, 0, 8, 0, 1, 20, 2, 
    0, 1, 1, 0, 2, 0, 2, 6, 12, 39, 1, 2, 1, 11, 0, 0, 0, 14, 
    0, 32, 1, 6, 18, 4, 2, 0, 0, 1, 4, 12, 29, 9, 24, 16, 4, 
    1, 2, 18, 0, 0, 0, 80, 3, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 
    6, 2, 51, 15, 3, 5, 3, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 24, 
    78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 10, 16, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Year", 
"Qu", "Mo", "Day", "PRCP"), class = "data.frame", row.names = 10586:11315)

代码：

library(dplyr)

q1_t <- 2.5
q2_t <- 10
q3_t <- 20
q4_t <- 15

perc_test <- weath_k %>%
                filter(!is.na(PRCP)) %>%
                group_by(Qu, Mo) %>%
                summarize(n_days = n(),
                          n_q1t = sum(PRCP > q1_t),
                          p_q1t = round((n_q1t / n_days)*100, 2),
                          n_q2t = sum(PRCP > q2_t),
                          p_q2t = round((n_q2t / n_days)*100, 2),
                          n_q3t = sum(PRCP > q3_t),
                          p_q3t = round((n_q3t / n_days)*100, 2),
                          n_q4t = sum(PRCP > q4_t),
                          p_q4t = round((n_q4t / n_days)*100, 2))
perc_test$p_qs <- c(perc_test$p_q1t[1:3], perc_test$p_q2t[4:6], 
                    perc_test$p_q3t[7:9], perc_test$p_q4t[10:12])

“p_qs”列是感兴趣的列，因为它包含高于每个季度阈值的百分比。理想情况下，我会有另一列“sd_qs”来表示每个月和每个季度阈值以上阈值百分比的变化。最终目标是在下图中放置置信区间：

我已经阅读了许多函数来计算阈值以上的百分比以及平均值和标准差等，但没有一个可以证明这些函数的组合。任何帮助计算高于阈值的百分比的 SD？

【问题讨论】：

    标签： r dplyr


    【解决方案1】：

    我使用中心极限定理来计算下限和上限。如果你想要的只是标准错误，你可以修改我的函数。

    我没有试图找到一种方法来避免每季度重复一次计算。

    q_se <- function(q,n,p){
  # Return th quartile for the standard error of a probability assumining the Central Limit Theorem.
  qnorm(q,p,sqrt((p/100)*(1-p/100)/n))

}

library(dplyr)

q1_t <- 2.5
q2_t <- 10
q3_t <- 20
q4_t <- 15

perc_test <- weath_k %>%
                filter(!is.na(PRCP)) %>%
                group_by(Qu, Mo) %>%
                summarize(n_days = n(),
                          n_q1t = sum(PRCP > q1_t),
                          p_q1t = round((n_q1t / n_days)*100, 2),
                          p_q1t_L = q_se(0.05,n_q1t,p_q1t),
                          p_q1t_U = q_se(0.95,n_q1t,p_q1t),
                          n_q2t = sum(PRCP > q2_t),
                          p_q2t = round((n_q2t / n_days)*100, 2),
                          p_q2t_L = q_se(0.05,n_q2t,p_q2t),
                          p_q2t_U = q_se(0.95,n_q2t,p_q2t),
                          n_q3t = sum(PRCP > q3_t),
                          p_q3t = round((n_q3t / n_days)*100, 2),
                          p_q3t_L = q_se(0.05,n_q3t,p_q3t),
                          p_q3t_U = q_se(0.95,n_q3t,p_q3t),
                          n_q4t = sum(PRCP > q4_t),
                          p_q4t = round((n_q4t / n_days)*100, 2) ,
                          p_q4t_L = q_se(0.05,n_q4t,p_q4t),
                          p_q4t_U = q_se(0.95,n_q4t,p_q4t)
                )

perc_test$p_qs <- c(perc_test$p_q1t[1:3], perc_test$p_q2t[4:6], 
                    perc_test$p_q3t[7:9], perc_test$p_q4t[10:12])

    【讨论】：

    • 虽然我很欣赏这项工作，但我正在寻找以下形式的标准差计算：sd_qs
    • 在上述评论中，“平均每月百分比”表示 p_qs，“每月百分比”表示时间序列的每个年月中高于季度阈值的百分比。
    • 您可能想了解您所做工作背后的统计理论。看来你在计算什么和你认为你在计算什么是两个不同的东西。
    • 您能详细说明一下吗？谢谢。
    • 如果您尝试评估百分比 (p_q1t) 的准确性，那么您需要使用访问该数字可变性的公式。您的可变性是否解释了 p_q1t 的公式在分母中有 n_days 的事实？
    【解决方案2】：

    经过大量试验和错误后，我相信我已经得出了自己的答案，尽管其中的一些巧妙的代码肯定可以改进。

    q1_t <- 2.5
q2_t <- 10
q3_t <- 20
q4_t <- 10

year_test <- weath_k %>%
                filter(!is.na(PRCP)) %>%
                group_by(Year, Qu, Mo) %>%
                summarize(n_days = n(),
                          n_q1t = sum(PRCP > q1_t),
                          p_q1t = round((n_q1t / n_days)*100, 2),
                          n_q2t = sum(PRCP > q2_t),
                          p_q2t = round((n_q2t / n_days)*100, 2),
                          n_q3t = sum(PRCP > q3_t),
                          p_q3t = round((n_q3t / n_days)*100, 2),
                          n_q4t = sum(PRCP > q4_t),
                          p_q4t = round((n_q4t / n_days)*100, 2))

year_test$p_qs <- ifelse(year_test$Qu==1, year_test$p_q1t, 
                         ifelse(year_test$Qu==2, year_test$p_q2t,
                                ifelse(year_test$Qu==3, year_test$p_q3t,
                                       year_test$p_q4t)))

p_test <- year_test %>%
                group_by(Qu, Mo) %>%
                summarize(m.qs = mean(p_qs),
                          sd.qs = sd(p_qs))
p_test
# A tibble: 12 x 4
# Groups:   Qu [?]
      Qu    Mo       m.qs      sd.qs
   <dbl> <int>      <dbl>      <dbl>
 1     1     1  4.0327273  4.8255089
 2     1     2  8.4911111  7.9238986
 3     1     3  8.8893333  7.6625199
 4     2     4  7.4636957  5.8455865
 5     2     5 18.1893478  7.1200800
 6     2     6 27.4613333  8.8789374
 7     3     7 17.1231818 10.1258237
 8     3     8 16.7724444  8.2073313
 9     3     9 13.1262222  8.0349977
10     4    10 12.5718605  8.2473945
11     4    11  2.3306818  3.3184428
12     4    12  0.2153333  0.8148078

    【讨论】：

      猜你喜欢
      • 2015-04-25
      • 2020-05-28
      • 2020-11-27
      • 1970-01-01
      • 1970-01-01
      • 1970-01-01
      • 1970-01-01
      • 1970-01-01
      • 2020-11-08
      相关资源
      最近更新 更多
      热门标签
      Java Python linux javascript Mysql C# Docker 算法 前端 SpringBoot Redis Vue spring 设计模式 .net core .net kubernetes c++ 数据库 数据结构 大数据 js 机器学习 微服务 Android Go 程序员 面试 JVM ASP.net core 云原生 人工智能 后端 PHP git CSS golang k8s Nginx Django mybatis 深度学习 多线程 React 架构 devops 爬虫 云计算 Spring Boot LeetCode