Add fixed number of rows for each group with values based on another column

Question

I have a large dataframe containing IDs and a start date of intervention for each ID:

  ID Date
1 1  17228
2 2  17226
3 3  17230

And I would like to add 2 rows to each ID with subsequent dates as the values in those rows:

  ID Date
1 1  17228
2 1  17229
3 1  17230
4 2  17226
5 2  17227
6 2  17228
7 3  17230
8 3  17231
9 3  17232

Is there any way using dplyr if possible? Other ways are also fine!

Answer 1

We expand the data by uncounting, then grouped by 'ID', get the sequence from the first 'Date' to the number of rows (n()) while incrementing by 1

library(tidyverse)
df1 %>%
  uncount(3) %>% 
  group_by(ID) %>% 
  mutate(Date = seq(Date[1], length.out = n(), by = 1))
# A tibble: 9 x 2
# Groups:   ID [3]
#     ID  Date
#  <int> <dbl>
#1     1 17228
#2     1 17229
#3     1 17230
#4     2 17226
#5     2 17227
#6     2 17228
#7     3 17230
#8     3 17231
#9     3 17232

---

Or another option is unnest a list column

df1 %>%
   group_by(ID) %>% 
   mutate(Date = list(Date[1] + 0:2)) %>% 
   unnest

---

Or with complete

df1 %>%
   group_by(ID) %>%
   complete(Date = first(Date) + 0:2)

---

Or using base R (pasteing from the comments)

within(df1[rep(seq_len(nrow(df1)), each = 3),], Date <- Date + 0:2)

---

Or more compactly in data.table

library(data.table)
setDT(df1)[, .(Date = Date  + 0:2), ID]

Answer 2

do.call(rbind, lapply(split(d, d$ID), function(x){
    rbind(x, data.frame(ID = rep(tail(x$ID, 1), 2),
                        Date = tail(x$Date, 1) + 1:2))
}))
#     ID  Date
#1.1   1 17228
#1.11  1 17229
#1.2   1 17230
#2.2   2 17226
#2.1   2 17227
#2.21  2 17228
#3.3   3 17230
#3.1   3 17231
#3.2   3 17232

Data

d = structure(list(ID = 1:3, Date = c(17228L, 17226L, 17230L)),
              class = "data.frame",
              row.names = c("1", "2", "3"))

Answer 3

Using dplyr, we can repeat every row 3 times, group_by ID and increment every date from 0 to n() - 1 for each ID.

library(dplyr)

df %>%
  slice(rep(seq_len(n()), each = 3)) %>%
  group_by(ID) %>%
  mutate(Date = Date + 0: (n() - 1))

#    ID  Date
#  <int> <int>
#1     1 17228
#2     1 17229
#3     1 17230
#4     2 17226
#5     2 17227
#6     2 17228
#7     3 17230
#8     3 17231
#9     3 17232

---

A base R one-liner using the same logic above would be

transform(df[rep(seq_len(nrow(df)), each = 3),], Date = Date + 0:2)