How to separate repeated measures that are in lines to columns

[Shelsea]

How to organize my database for analysis
I have a database that I want to study the effect of a treatment, but I have two weights on two lines measuring for a single patient, pre and post the visit.
I want a code to separate the weights into columns, in preweight and post weight

[FJCC]

This seems like a job for the pivot_wider() function from the tidyr package. Can you post some data so a more detailed answer can be provided? If your data frame is named DF, post the output of

dput(head(DF, 10))

Place a line with three back ticks just before and after the pasted output in your response, like this:
```
output of dput()
```

[Shelsea]

> dput(head(dados,10))
structure(list(...1 = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), ID = c(1, 
1, 2, 2, 3, 3, 4, 4, 5, 5), TREAT = c("CONTROL", "CONTROL", "CONTROL", 
"CONTROL", "CONTROL", "CONTROL", "CONTROL", "CONTROL", "CONTROL", 
"CONTROL"), AGE = c(55, 55, 32, 32, 24, 24, 32, 32, 42, 42), 
    WEIGHT = c(84.5761825495586, 79.3926584958957, 75.266228456406, 
    75.1019421280918, 77.4664984122051, 75.2932810841592, 79.6836860743261, 
    75.7596164670626, 76.2523902602365, 76.1172594017774), LIVINGSTATUS = c("ALONE (SINGLE)", 
    "ALONE (SINGLE)", "ALONE (SINGLE)", "ALONE (SINGLE)", "WITH FRIENDS", 
    "WITH FRIENDS", "WITH PARTNER", "WITH PARTNER", "ALONE (SINGLE)", 
    "ALONE (SINGLE)"), SMOKESTATUS = c("SMOKER", "SMOKER", "QUIT-SMOKER", 
    "QUIT-SMOKER", "NON-SMOKER", "NON-SMOKER", "SMOKER", "SMOKER", 
    "SMOKER", "SMOKER"), GENDER = c("FEMALE", "FEMALE", "FEMALE", 
    "FEMALE", "MALE", "MALE", "FEMALE", "FEMALE", "MALE", "MALE"
    ), VISIT = c("PRE", "POST", "PRE", "POST", "PRE", "POST", 
    "PRE", "POST", "PRE", "POST")), row.names = c(NA, -10L), class = c("tbl_df", 
"tbl", "data.frame"))

[FJCC]

Your first column named ...1 doesn't seem to be helpful, so I dropped it, then I used pivot_wider() to rearrange the data.

dados <- structure(list(...1 = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 
                        ID = c(1, 1, 2, 2, 3, 3, 4, 4, 5, 5), 
                        TREAT = c("CONTROL", "CONTROL", "CONTROL", 
                                  "CONTROL", "CONTROL", "CONTROL", 
                                  "CONTROL", "CONTROL", "CONTROL", 
                                  "CONTROL"), 
                        AGE = c(55, 55, 32, 32, 24, 24, 32, 32, 42, 42), 
                        WEIGHT = c(84.5761825495586, 79.3926584958957, 
                                   75.266228456406, 75.1019421280918, 
                                   77.4664984122051, 75.2932810841592, 79.6836860743261, 
                                   75.7596164670626, 76.2523902602365, 76.1172594017774), 
                        LIVINGSTATUS = c("ALONE (SINGLE)", "ALONE (SINGLE)", "ALONE (SINGLE)", 
                                         "ALONE (SINGLE)", "WITH FRIENDS", 
                                         "WITH FRIENDS", "WITH PARTNER", "WITH PARTNER", 
                                         "ALONE (SINGLE)", "ALONE (SINGLE)"), 
                        SMOKESTATUS = c("SMOKER", "SMOKER", "QUIT-SMOKER", 
                                        "QUIT-SMOKER", "NON-SMOKER", "NON-SMOKER", "SMOKER", "SMOKER", 
                                        "SMOKER", "SMOKER"), 
                        GENDER = c("FEMALE", "FEMALE", "FEMALE", 
                                   "FEMALE", "MALE", "MALE", "FEMALE", "FEMALE", "MALE", "MALE"
                        ), 
                        VISIT = c("PRE", "POST", "PRE", "POST", "PRE", "POST", 
                                  "PRE", "POST", "PRE", "POST")), 
                   row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"))
#######
library(tidyr)
dados$...1 <- NULL
dados_wide <- dados |> 
  pivot_wider(names_from = VISIT, values_from = WEIGHT)
dados_wide
#> # A tibble: 5 × 8
#>      ID TREAT     AGE LIVINGSTATUS   SMOKESTATUS GENDER   PRE  POST
#>   <dbl> <chr>   <dbl> <chr>          <chr>       <chr>  <dbl> <dbl>
#> 1     1 CONTROL    55 ALONE (SINGLE) SMOKER      FEMALE  84.6  79.4
#> 2     2 CONTROL    32 ALONE (SINGLE) QUIT-SMOKER FEMALE  75.3  75.1
#> 3     3 CONTROL    24 WITH FRIENDS   NON-SMOKER  MALE    77.5  75.3
#> 4     4 CONTROL    32 WITH PARTNER   SMOKER      FEMALE  79.7  75.8
#> 5     5 CONTROL    42 ALONE (SINGLE) SMOKER      MALE    76.3  76.1

^{Created on 2023-09-03 with reprex v2.0.2}

[Shelsea]

thank you, I am really gratefull,