如何转换/重新采样/插值数据以在R中具有多个分组变量的整洁数据集中标准化变量长度？

Question

我的目标是规范化矢量的长度，以便在整洁的数据集中进行平均。使用约似乎是要走的路，但我不能让它在整齐的过程中有效地工作。一个问题可能与数据框中的大小调整有关。这是一个可重复的例子：

# create reproducible dataset
i = 80
I = 110
id = rep("AA", I+i)
event = rep("event1", I+i)
sub_event = NA
sub_event[1:i] = 1
sub_event[i+1:I] = 2
sub_event = as.factor(sub_event)
y1 = sin(seq(0, 5*pi, length.out = i))
y2 = sin(seq(0, 5*pi, length.out = I))
y3 = cos(seq(0, 5*pi, length.out = i))
y4 = cos(seq(0, 5*pi, length.out = I))
var1 = c(y1,y2)
var2 = c(y3,y4)

df1 <- data.frame(id, event, sub_event,var1, var2)
df2 <- df1
df2$event = "event2"
df <- rbind(df1, df2)
temp <- df
temp$id = "BB"
df <- rbind(df, temp)


# create a "time" vector for sub_event

df <- df %>% 
  group_by(id, event, sub_event) %>%
  mutate(sub_event_time = seq_along(var1)) %>%
  select(id, event, sub_event, sub_event_time, everything()) %>%
  ungroup()

绘制var1

# plot 
ggplot(df,
       aes(x=sub_event_time, y=var1, colour = sub_event)) + 
  geom_point() +
  geom_path() +
  facet_wrap(id~event)

我希望转换/重采样数据为每个sub_events获取var1的长度，使其成为每个id中每个事件中最长的sub_event的长度。

例如我们想要：事件1子事件的var1的长度1 =事件1子事件2的var1的长度（这是最长的）。

这是一次尝试：

# attempt for var1 only
aim.df <- df %>%
  ungroup() %>%
  select(-var2) %>%
  group_by(id, event) %>%
  mutate(max_sub_event_time = max(sub_event_time)) %>%
  mutate(var1 = approx(var1, n = max_sub_event_time)$y) 

这会返回以下错误：

    Error in mutate_impl(.data, dots) : 
    Column `var1` must be length 190 (the group size) or one, not 110
    In addition: Warning messages:
    1: In if (n <= 0) stop("'approx' requires n >= 1") :
    the condition has length > 1 and only the first element will be used
    2: In seq.int(x[1L], x[nx], length.out = n) :
    first element used of 'length.out' argument

有什么想法吗？

Answer 1

步骤......

1. group_by(id, event, sub_event)
2. 删除sub_event_time，因为添加观察后它将无关紧要
3. summarise approx函数的结果作为列表列（您必须将var1和max_sub_event_time转换为approx的适当输入<） / LI>
4. unnest生成的列表列
再次
5. group_by(id, event, sub_event)并添加新的sub_event_time

...代码

library(dplyr)
library(tidyr)

df %>%
  ungroup() %>%
  select(-var2) %>%
  group_by(id, event) %>%
  mutate(max_sub_event_time = max(sub_event_time)) %>% 
  group_by(id, event, sub_event) %>% 
  select(-sub_event_time) %>% 
  summarise(var1_int = list(approx(as.numeric(var1), n = first(max_sub_event_time))$y)) %>% 
  unnest() %>% 
  group_by(id, event, sub_event) %>% 
  mutate(sub_event_time = row_number())