1

Here is my dataframe:

structure(list(Dispensary = c("A", "A", "A", "A", "A", "A", "A", 
"A", "A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "C", "C", 
"C", "C", "C", "C", "C", "C", "C"), cohort = c(1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L), t0 = c(100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100, 100, 100, 100, 100, 100), t1 = c(46.1, 41.7, 
37.5, 36.2, 35.9, 34.4, 39.8, 38.3, 0, 34.6, 37.4, 31.4, 29.5, 
25.5, 33.2, 30.6, 30.8, 0, 28.6, 30.2, 28.1, 28.8, 30.7, 29.2, 
33.5, 30.3, 0), t2 = c(41.4, 34.6, 38.6, 27.9, 30, 32.8, 35.2, 
0, 0, 35.2, 31.4, 23.9, 24.2, 23.1, 30.1, 24, 0, 0, 26.9, 24.8, 
21, 25.3, 25.8, 25.9, 23.3, 0, 0), t3 = c(29.6, 32.3, 31.7, 25.8, 
29.5, 23.8, 0, 0, 0, 25.2, 28.9, 23, 23.8, 20.5, 22.6, 0, 0, 
0, 20.7, 24, 21.5, 24.9, 23, 23.7, 0, 0, 0), t4 = c(30.9, 28, 
30.4, 24.9, 29.5, 0, 0, 0, 0, 23.1, 23.1, 20.7, 21.8, 19.2, 0, 
0, 0, 0, 21, 20.8, 21.3, 23.7, 20.5, 0, 0, 0, 0), t5 = c(30.3, 
25.6, 23.5, 24.9, 0, 0, 0, 0, 0, 19.4, 22.1, 20, 19.5, 0, 0, 
0, 0, 0, 17.2, 18.5, 20.4, 14.7, 0, 0, 0, 0, 0), t6 = c(30.9, 
24, 23.2, 0, 0, 0, 0, 0, 0, 19.6, 20.3, 17.2, 0, 0, 0, 0, 0, 
0, 20.3, 17.7, 18.2, 0, 0, 0, 0, 0, 0), t7 = c(27.6, 18.5, 0, 
0, 0, 0, 0, 0, 0, 18, 16.1, 0, 0, 0, 0, 0, 0, 0, 14.2, 14.8, 
0, 0, 0, 0, 0, 0, 0), t8 = c(17.8, 0, 0, 0, 0, 0, 0, 0, 0, 17.2, 
0, 0, 0, 0, 0, 0, 0, 0, 11.4, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, 
-27L), class = c("tbl_df", "tbl", "data.frame"))

I'm trying to insert a blank row after every 9th row, in between where cohort == 9 and cohort == 1

The dataframe is long and I'm hoping to not have to do it one a time

Here is a link to a similar question, but I'm looking for an R method, not a VBA method

hachiko
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2 Answers2

0

Assuming you are trying to add a row after every Dispensary you could do:

library(dplyr)
library(purrr)

df %>% 
  group_split(Dispensary) %>% 
  map_dfr(add_row)

If you do actually need to insert after every 9th row you can do:

df %>% 
  group_split(group = rep(1:(nrow(.) / 9), each = 9)) %>% 
  map_dfr(add_row) %>% 
  select(-group)
LMc
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0

Here is an option:

library(tidyverse)

df |>
  group_split(Dispensary) |>
  map_dfr(~add_row(.x))
#> # A tibble: 30 x 11
#>    Dispensary cohort    t0    t1    t2    t3    t4    t5    t6    t7    t8
#>    <chr>       <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 A               1   100  46.1  41.4  29.6  30.9  30.3  30.9  27.6  17.8
#>  2 A               2   100  41.7  34.6  32.3  28    25.6  24    18.5   0  
#>  3 A               3   100  37.5  38.6  31.7  30.4  23.5  23.2   0     0  
#>  4 A               4   100  36.2  27.9  25.8  24.9  24.9   0     0     0  
#>  5 A               5   100  35.9  30    29.5  29.5   0     0     0     0  
#>  6 A               6   100  34.4  32.8  23.8   0     0     0     0     0  
#>  7 A               7   100  39.8  35.2   0     0     0     0     0     0  
#>  8 A               8   100  38.3   0     0     0     0     0     0     0  
#>  9 A               9   100   0     0     0     0     0     0     0     0  
#> 10 <NA>           NA    NA  NA    NA    NA    NA    NA    NA    NA    NA  
#> # ... with 20 more rows
AndS.
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