Adding a sequential value using a loop-CodePudding

I have a large dataframe which is effectively combined output from a nested list using do.call(rbind, nested_list)

The output has the same number of rows for each list element (e.g. 5 rows per list) and I need to add a column which has a unique numeric code for each list (or group). How can I write a loop to reproduce the group column I have included in the example below, e.g. the five rows have a group value == 1, rows 6 to 10 have a group value == 2, rows 11 to 15 have a group value == 3

df <- data.frame("ID" = 1:15)
df$Var_A <- c(1,3,5,7,9,11,13,15,17,19,21,23,25,27,29)
df$Var_B <- c(10,0,0,0,12,12,12,12,0,14,NA_real_,14,16,16,16)
df$Var_C <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$Var_D <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$New_A <- c(2,5,5,8,11,14,15,17,20,21,22,23,25,25,27)
df$New_B <- c(10,0,0,0,12,12,12,12,0,14,NA_real_,14,16,16,16)
df$New_C <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$New_D <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$Group <- c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3)

   ID Var_A Var_B Var_C Var_D New_A New_B New_C New_D Group
1   1     1    10    10    10     2    10    10    10     1
2   2     3     0    12    12     5     0    12    12     1
3   3     5     0    14    14     5     0    14    14     1
4   4     7     0    16    16     8     0    16    16     1
5   5     9    12    10    10    11    12    10    10     1
6   6    11    12    12    12    14    12    12    12     2
7   7    13    12    14    14    15    12    14    14     2
8   8    15    12    16    16    17    12    16    16     2
9   9    17     0    10    10    20     0    10    10     2
10 10    19    14    12    12    21    14    12    12     2
11 11    21    NA    14    14    22    NA    14    14     3
12 12    23    14    16    16    23    14    16    16     3
13 13    25    16    10    10    25    16    10    10     3
14 14    27    16    12    12    25    16    12    12     3
15 15    29    16    14    14    27    16    14    14     3

CodePudding user response：

You can use the ceiling function:

df <- data.frame("ID" = 1:15)
df$Var_A <- c(1,3,5,7,9,11,13,15,17,19,21,23,25,27,29)
df$Var_B <- c(10,0,0,0,12,12,12,12,0,14,NA_real_,14,16,16,16)
df$Var_C <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$Var_D <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$New_A <- c(2,5,5,8,11,14,15,17,20,21,22,23,25,25,27)
df$New_B <- c(10,0,0,0,12,12,12,12,0,14,NA_real_,14,16,16,16)
df$New_C <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$New_D <- c(10,12,14,16,10,12,14,16,10,12,14,16,10,12,14)
df$Group <- ceiling(as.numeric(df$ID)/5)

df
#    ID Var_A Var_B Var_C Var_D New_A New_B New_C New_D Group
# 1   1     1    10    10    10     2    10    10    10     1
# 2   2     3     0    12    12     5     0    12    12     1
# 3   3     5     0    14    14     5     0    14    14     1
# 4   4     7     0    16    16     8     0    16    16     1
# 5   5     9    12    10    10    11    12    10    10     1
# 6   6    11    12    12    12    14    12    12    12     2
# 7   7    13    12    14    14    15    12    14    14     2
# 8   8    15    12    16    16    17    12    16    16     2
# 9   9    17     0    10    10    20     0    10    10     2
# 10 10    19    14    12    12    21    14    12    12     2
# 11 11    21    NA    14    14    22    NA    14    14     3
# 12 12    23    14    16    16    23    14    16    16     3
# 13 13    25    16    10    10    25    16    10    10     3
# 14 14    27    16    12    12    25    16    12    12     3
# 15 15    29    16    14    14    27    16    14    14     3

CodePudding user response：

Without adding an ID or rownums we can do this using nrow and knowledge of the group length.

group_len <- 5
groups <- nrow(df)/group_len
df$group <- rep(1:groups, each = group_len)

# Example: 
# rep(1:3, each = 5)
# 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

CodePudding user response：

I'd use dplyr::mutate with dplyr::row_number:

library(dplyr)

df %>% 
   mutate(Group=ceiling(row_number() / 5))

Output:

   ID Var_A Var_B Var_C Var_D New_A New_B New_C New_D Group
1   1     1    10    10    10     2    10    10    10     1
2   2     3     0    12    12     5     0    12    12     1
3   3     5     0    14    14     5     0    14    14     1
4   4     7     0    16    16     8     0    16    16     1
5   5     9    12    10    10    11    12    10    10     1
6   6    11    12    12    12    14    12    12    12     2
7   7    13    12    14    14    15    12    14    14     2
8   8    15    12    16    16    17    12    16    16     2
9   9    17     0    10    10    20     0    10    10     2
10 10    19    14    12    12    21    14    12    12     2
11 11    21    NA    14    14    22    NA    14    14     3
12 12    23    14    16    16    23    14    16    16     3
13 13    25    16    10    10    25    16    10    10     3
14 14    27    16    12    12    25    16    12    12     3
15 15    29    16    14    14    27    16    14    14     3