how to reshape my data frame in R (transpose, select, remove row)?

Question

Here is data frame(ttt) I have:

 .id     dn     mavg    up      pctB
AA.1    18.8    21.1    23.4    0.8 
AA.2    18.7    21.1    23.5    0.8 
AA.3    18.7    21.2    23.7    0.8 
AAN.1   23.1    24.6    26.1    0.5 
AAN.2   23.1    24.6    26.0    0.4 
AAN.3   23.1    24.5    26.0    0.5 
AAP.1   145.5   179.2   212.9   0.3 
AAP.2   144.2   177.4   210.7   0.3 
AAP.3   143.4   175.6   207.7   0.3 

The shape that I want to have is following:

    pctB.1  pctB.2  pctB.3
AA  0.8     0.8     0.8 
AAN 0.5     0.4     0.5 
AAP 0.3     0.3     0.3 

Only column that I need is pctB. I tried by writing:

ttt <- ttt %>% select(1,5)
ttt <- do.call(cbind, split(ttt, ttt$`.id`))
ttt <- t(ttt)

it gives a result that I don't want. What should I do?

<error/rlang_error>
`n()` must only be used inside dplyr verbs.
Backtrace:
  1. plyr::mutate(., .id = sub("\\..*", "", .id))
  1. dplyr::group_by(., .id)
  8. plyr::mutate(., col = paste0("pctB.", row_number()))
  9. [ base::eval(...) ] with 1 more call
 12. dplyr::row_number()
 13. dplyr::n()
 14. dplyr:::peek_mask("n()")
 15. dplyr:::context_peek("mask", fun)
 16. context_peek_bare(name) %||% abort(glue("`{fun}` must only be used inside {location}."))

Answer 1

You can remove additional characters from it, create a unique id column and get the data in wide format selecting only the interested columns.

library(dplyr)
library(tibble)

ttt %>%
  mutate(.id = sub('\\..*', '', .id)) %>%
  group_by(.id) %>%
  mutate(col = paste0('pctB.', row_number())) %>%
  select(-(dn:up)) %>%
  tidyr::pivot_wider(names_from = col, values_from = pctB)

#  .id   pctB.1 pctB.2 pctB.3
#  <chr>  <dbl>  <dbl>  <dbl>
#1 AA       0.8    0.8    0.8
#2 AAN      0.5    0.4    0.5
#3 AAP      0.3    0.3    0.3

data

ttt <- structure(list(.id = c("AA.1", "AA.2", "AA.3", "AAN.1", "AAN.2", 
"AAN.3", "AAP.1", "AAP.2", "AAP.3"), dn = c(18.8, 18.7, 18.7, 
23.1, 23.1, 23.1, 145.5, 144.2, 143.4), mavg = c(21.1, 21.1, 
21.2, 24.6, 24.6, 24.5, 179.2, 177.4, 175.6), up = c(23.4, 23.5, 
23.7, 26.1, 26, 26, 212.9, 210.7, 207.7), pctB = c(0.8, 0.8, 
0.8, 0.5, 0.4, 0.5, 0.3, 0.3, 0.3)),class = "data.frame", row.names = c(NA, -9L))

Answer 2

Split .id into two separate columns (eg. "AA.1" -> "AA", "1" ), then pivot it by these two columns.

library(tidyverse)

ttt %>%
  mutate(.id.1 = str_split(.id, "\\.") %>% map(~ .[[1]]) %>% unlist,
         .id.2 = str_split(.id, "\\.") %>% map(~ paste0("pctB.", .[[2]])) %>% unlist) %>%
  pivot_wider(id_cols = .id.1,
              names_from = .id.2,
              values_from = pctB) %>%
  column_to_rownames(".id.1")