R repeat in column based on value in row

Using data.table you can create a long version by repeating the Weight value Days number of times for each row, then dcasting to a wide format with the rowidof the new variable as the column.

library(data.table)
setDT(df)

dcast(df[, .(rep(Weight, Days)), .(Name, School)], 
      Name + School ~ rowid(V1))

# Name   School    1    2    3    4    5    6    7
# 1: Antoine     Bach 0.03 0.03 0.03 0.03 0.03   NA   NA
# 2: Antoine      Ken 0.02 0.02 0.02 0.02 0.02 0.02 0.02
# 3: Barbara Franklin 0.04 0.04 0.04   NA   NA   NA   NA

You could also rep Weight the number of Days, then rep NA enough times to complete the row.

max_days <- max(df$Days) 

df[, as.list(rep(c(Weight, NA), c(Days, max_days - Days))), 
   .(Name, School)]

# Name   School   V1   V2   V3   V4   V5   V6   V7
# 1: Antoine     Bach 0.03 0.03 0.03 0.03 0.03   NA   NA
# 2: Antoine      Ken 0.02 0.02 0.02 0.02 0.02 0.02 0.02
# 3: Barbara Franklin 0.04 0.04 0.04   NA   NA   NA   NA

You can use pmap_dfr to apply a function across the rows and then row bind the resulting list into a tibble object. The function will match arguments to column names, the rest of the row values will be captured in the ellipsis ....

library(purrr)
library(dplyr)

pmap_dfr(df, function(Weight, Days, ...) c(..., setNames(rep(Weight, Days), 1:Days))) %>% 
  mutate(across(3:last_col(), as.numeric))

Because vectors are atomic in R c() will coerce everything in the row to be character. So the mutate converts the newly created columns back to numeric.

setNames is used to name the newly created columns, which is required to bind by row.

Output

  Name    School     `1`   `2`   `3`   `4`   `5`   `6`   `7`
  <chr>   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Antoine Bach      0.03  0.03  0.03  0.03  0.03 NA    NA   
2 Antoine Ken       0.02  0.02  0.02  0.02  0.02  0.02  0.02
3 Barbara Franklin  0.04  0.04  0.04 NA    NA    NA    NA   

Note: pmap_dfr is from the purrr package, and mutate, across, and last_col are all from dplyr.

How it works

When you use pmap in the way above the named function arguments will be matched to columns with the same name. So Weights and Days as function arguments are matched to those columns with the same name in each row.

The ... collects the remaining columns that are still passed to the function, but are unused (by name) in the function. Essentially, the ellipsis collects Name and School in your case.

Since Name and School already have names they are passed to c() first to maintain your column order. In addition we combine the other values and give them names as well. The output for a single row is then this:

     Name    School         1         2         3         4         5         6 
"Antoine"    "Bach"    "0.03"    "0.03"    "0.03"    "0.03"    "0.03"        NA 
        7 
       NA 

The output of pmap is a list. _dfr is a specific function to row bind (hence the r) these list elements into a dataframe/tibble (hence the df).

You can use the following code to get the required output:

library(dplyr)
library(tidyr)

df %>% 
  select(Weight, Days) %>%
  uncount(Days, .remove = FALSE) %>%
  group_by(Days) %>%
  mutate(id = row_number()) %>%
  pivot_wider(Days, names_from = id, values_from = Weight) %>%
  right_join(df, by = "Days") %>%
  relocate(Name, School) %>%
  ungroup() %>%
  select(-c(Weight, Days))

# A tibble: 3 x 9
  Name    School     `1`   `2`   `3`   `4`   `5`   `6`   `7`
  <chr>   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Antoine Bach      0.03  0.03  0.03  0.03  0.03 NA    NA   
2 Antoine Ken       0.02  0.02  0.02  0.02  0.02  0.02  0.02
3 Barbara Franklin  0.04  0.04  0.04 NA    NA    NA    NA 

Data:

df <- tribble(
  ~Name,    ~School,   ~Weight, ~Days,
  "Antoine", "Bach",     0.03,   5,
  "Antoine", "Ken",      0.02,   7,
  "Barbara", "Franklin", 0.04,   3
)

Updated Since our dear friends rightfully suggested using pmap and map from purrr package, Here is another variation, guess it would be cool to know:

library(purrr)

df %>%
  mutate(map2_dfr(Weight, Days, ~ set_names(rep(.x, .y), 1:.y))) %>%
  select(-c(Weight, Days))

# A tibble: 3 x 9
  Name    School     `1`   `2`   `3`   `4`   `5`   `6`   `7`
  <chr>   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Antoine Bach      0.03  0.03  0.03  0.03  0.03 NA    NA   
2 Antoine Ken       0.02  0.02  0.02  0.02  0.02  0.02  0.02
3 Barbara Franklin  0.04  0.04  0.04 NA    NA    NA    NA 

One tidyverse solution.

• First we tidyr::nest the two columns. Resulting column will be a list column named d for dummy.
• then we mutate d into a vector of weights upto days times, by using rep function. This iteration is done using purrr::map. Note: map_dbl isn't required at this stage as it will be unnested in next step.
• we also wrap this argument into setNames so that d is mutated into a named list (names as expected). For names, seq function is used.
• lastly we tidyr::unnest_wider the d column into the columns with names already saved in list in earlier step

library(dplyr)
library(tidyr)
library(purrr)

df %>% nest(d = c(Weight, Days)) %>%
  mutate(d = map(d, ~setNames( rep(.x$Weight, .x$Days), seq(1, .x$Days, 1)))) %>%
  unnest_wider(d)

# A tibble: 3 x 9
  Name    School     `1`   `2`   `3`   `4`   `5`   `6`   `7`
  <chr>   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Antoine Bach      0.03  0.03  0.03  0.03  0.03 NA    NA   
2 Antoine Ken       0.02  0.02  0.02  0.02  0.02  0.02  0.02
3 Barbara Franklin  0.04  0.04  0.04 NA    NA    NA    NA 

I like tidyr::uncount for making x number of copies of each row. We can pivot longer, uncount, and then pivot wider again.

library(tidyr)
my_data %>%
  pivot_longer(Weight) %>%
  uncount(Days, .id = "colnum") %>%
  dplyr::select(-name) %>%
  pivot_wider(names_from = colnum, values_from = value)


# A tibble: 3 x 9
  Name    School     `1`   `2`   `3`   `4`   `5`   `6`   `7`
  <chr>   <chr>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Antoine Bach      0.03  0.03  0.03  0.03  0.03 NA    NA   
2 Antoine Ken       0.02  0.02  0.02  0.02  0.02  0.02  0.02
3 Barbara Franklin  0.04  0.04  0.04 NA    NA    NA    NA