试试这个：

library(dplyr)
start <- "2023-01-01"; end <- "2023-01-10"
df %>%
  group_by(group) %>%
  summarize(newdate1 = c(start, date2), newdate2 = c(date1, end)) %>%
  rename(date1 = newdate1, date2 = newdate2) %>%
  filter(date2 > date1) %>%
  ungroup() %>%
  bind_rows(df) %>%
  arrange(group, date1)
# # A tibble: 10 × 4
#    group date1      date2      value
#    <chr> <chr>      <chr>      <dbl>
#  1 A     2023-01-01 2023-01-04    NA
#  2 A     2023-01-04 2023-01-06     1
#  3 A     2023-01-06 2023-01-07     2
#  4 A     2023-01-07 2023-01-08    NA
#  5 A     2023-01-08 2023-01-09     3
#  6 A     2023-01-09 2023-01-10    NA
#  7 B     2023-01-01 2023-01-05    NA
#  8 B     2023-01-05 2023-01-06     3
#  9 B     2023-01-06 2023-01-08     2
# 10 B     2023-01-08 2023-01-10     1

(In dplyr_1.1.0或更高版本，它可能更喜欢reframe而不是summarize。）

另一种选择，虽然更长：

# Get start as numeric date
start <- as.numeric(as.Date("2023-01-01"))

# Get end as numeric date
end <- as.numeric(as.Date("2023-01-10"))

# Change character dates to numeric dates
df <- df %>%
    # Change dates
    mutate(date1 = as.numeric(as.Date(date1)),
           date2 = as.numeric(as.Date(date2)))

# For each group, fill in missing dates and bind together
df <- do.call("rbind", lapply(unique(df[["group"]]), \(x){
    # Get temporary data with group of interest
    df_tmp <- dplyr::filter(df, group == x)
    
    # First add start date if this is not present
    if(!(start %in% df_tmp[["date1"]])){
        # Add start date
        df_tmp <- 
            # Bind data together
            rbind(# Start date
                  data.frame(group = x,                   # Current group
                             date1 = start,               # Start date
                             date2 = df_tmp[1, "date1"],  # First date1 in data
                             value = NA),                 # No value
                  # Current data
                  df_tmp)
    }
    
    # Also add last date if this is not present
    if(!(end %in% df_tmp[["date2"]])){
        # Add end date
        df_tmp <- 
            # Bind data together
            rbind(# Start date
                data.frame(group = x,                               # Current group
                           date1 = df_tmp[nrow(df_tmp), "date2"],   # Last date2 in data
                           date2 = end,                             # First date1 in data
                           value = NA),                             # No value
                # Current data
                df_tmp) %>%
            # Rearrange data
            dplyr::arrange(date1, date2)
    }
    
    # Check each row of data to add all missing dates and bind together
    new_rows <- do.call("rbind", lapply(1:(nrow(df_tmp) - 1), \(i){
        # Check if current row date2 does not continue on next row date2
        if(df_tmp[i, "date2"] != df_tmp[i + 1, "date1"]){
            # Create new row
            new_row <- data.frame(group = x,                       # Current group
                                  date1 = df_tmp[i, "date2"],      # Start date
                                  date2 = df_tmp[i + 1, "date1"],  # First date1 in data
                                  value = NA)                      # No value
            
            # Return new row
            return(new_row)
        }}))
    
    # Add new rows to data
    df_tmp <- 
        # Bind data
        rbind(# Current data
              df_tmp,
              # New rows
              new_rows) %>%
        # Rearrange data
        dplyr::arrange(date1, date2) %>%
        # Change numeric dates back to character dates
        mutate(date1 = as.character(as.Date(date1, origin = "1970-01-01")),
               date2 = as.character(as.Date(date2, origin = "1970-01-01")))
}))

给出：

group      date1      date2 value
1      A 2023-01-01 2023-01-04    NA
2      A 2023-01-04 2023-01-06     1
3      A 2023-01-06 2023-01-07     2
4      A 2023-01-07 2023-01-08    NA
5      A 2023-01-08 2023-01-09     3
6      A 2023-01-09 2023-01-10    NA
7      B 2023-01-01 2023-01-05    NA
8      B 2023-01-05 2023-01-06     3
9      B 2023-01-06 2023-01-08     2
10     B 2023-01-08 2023-01-10     1

使用来自ivs的dplyr和iv_set_complement()，一个专用于处理间隔的软件包：

df <- df %>%
  as_tibble() %>%
  mutate(date1 = as.Date(date1), date2 = as.Date(date2)) %>%
  mutate(range = iv(date1, date2), .keep = "unused", .after = group)

df
#> # A tibble: 6 × 3
#>   group                    range value
#>   <chr>               <iv<date>> <dbl>
#> 1 A     [2023-01-04, 2023-01-06)     1
#> 2 A     [2023-01-06, 2023-01-07)     2
#> 3 A     [2023-01-08, 2023-01-09)     3
#> 4 B     [2023-01-05, 2023-01-06)     3
#> 5 B     [2023-01-06, 2023-01-08)     2
#> 6 B     [2023-01-08, 2023-01-10)     1

lower <- as.Date("2023-01-01")
upper <- as.Date("2023-01-10")

# Using `iv_set_complement()` with extended lower/upper bounds
missing <- df %>%
  reframe(
    range = iv_set_complement(range, lower = lower, upper = upper),
    .by = group
  )

missing
#> # A tibble: 4 × 2
#>   group                    range
#>   <chr>               <iv<date>>
#> 1 A     [2023-01-01, 2023-01-04)
#> 2 A     [2023-01-07, 2023-01-08)
#> 3 A     [2023-01-09, 2023-01-10)
#> 4 B     [2023-01-01, 2023-01-05)

# Let `rows_append()` generate `NA`s for unspecified columns
df %>%
  rows_append(missing) %>%
  arrange(group, range)
#> # A tibble: 10 × 3
#>    group                    range value
#>    <chr>               <iv<date>> <dbl>
#>  1 A     [2023-01-01, 2023-01-04)    NA
#>  2 A     [2023-01-04, 2023-01-06)     1
#>  3 A     [2023-01-06, 2023-01-07)     2
#>  4 A     [2023-01-07, 2023-01-08)    NA
#>  5 A     [2023-01-08, 2023-01-09)     3
#>  6 A     [2023-01-09, 2023-01-10)    NA
#>  7 B     [2023-01-01, 2023-01-05)    NA
#>  8 B     [2023-01-05, 2023-01-06)     3
#>  9 B     [2023-01-06, 2023-01-08)     2
#> 10 B     [2023-01-08, 2023-01-10)     1