我有一个长格式的数据集。数据集中的每个对象都被观察了很多次（从1到3）。根据受试者的可用性，从周一到周五每天进行一次观察。如果病例观察少于3天，我需要为缺失的观察生成空行（s）如果一个事例在星期一和星期五被观察到，那么第三个观察应该随机出现在星期二、星期三或星期四。下面的代码运行良好，除了一个基本缺陷：当日期是随机产生的，它们可能与受试者已经被观察的日期重叠。2这不应该发生。3如果我的解决方案太复杂而无法编辑，请随意建议完全不同的代码。4谢谢！

#Toy dataset
dataset_long <- data.frame(
  id = c(1, 1, 2, 2, 2, 3, 3, 4, 5, 5),
  observation = c(1, 2, 1, 2, 3, 1, 2, 1, 1, 2),
  day_name = c("Monday", "Tuesday", "Monday", "Wednesday", "Thursday", "Tuesday", "Thursday", "Wednesday", "Monday", "Friday"),
  scores = sample(20:60, 10)
)

#   case observation       day scores
#1     1           1    Monday     32
#2     1           2   Tuesday     31
#3     2           1    Monday     29
#4     2           2 Wednesday     28
#5     2           3  Thursday     22
#6     3           1   Tuesday     45
#7     3           2  Thursday     30
#8     4           1 Wednesday     36
#9     5           1    Monday     58
#10    5           2    Friday     37

#Identify cases with fewer than 3 observations
cases_to_fill <- dataset_long %>%
  group_by(case) %>%
  summarize(days_observed = n()) %>%
  filter(days_observed < 3) %>%
  ungroup()

#Create vector with day names
all_days <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")

#Fill the missing observation day randomly
cases_to_fill_long <- cases_to_fill %>%
  mutate(missing_day = map(days_observed, ~sample(all_days, 3 - .x))) %>%
  unnest(missing_day) %>%
  mutate(observation = 3)

#Join the filled cases with the original dataset
dataset_long_filled <- dataset_long %>%
  full_join(cases_to_fill_long, by = c("case", "observation")) %>%
  arrange(case, observation)

#Coalesce the two columns of day into one
dataset_long_filled |> 
  mutate(day = coalesce(day, missing_day)) |> 
  select(-days_observed, -missing_day)

#   case observation       day scores
#1     1           1    Monday     32
#2     1           2   Tuesday     31
#3     1           3    Friday     NA
#4     2           1    Monday     29
#5     2           2 Wednesday     28
#6     2           3  Thursday     22
#7     3           1   Tuesday     45
#8     3           2  Thursday     30
#9     3           3   Tuesday     NA Tuesday is repeated for this subject
#10    4           1 Wednesday     36
#11    4           3 Wednesday     NA Wednesday is repeated for this subject
#12    4           3    Monday     NA
#13    5           1    Monday     58
#14    5           2    Friday     37
#15    5           3    Friday     NA Friday is repeated for this subject