# Transforms list of REDCap data.frames to a single wide data.frame — redcap_wider • REDCapCAST
Converts a list of REDCap data.frames from long to wide format. In essence it is a wrapper for the pivot_wider function applied on a REDCap output (from read_redcap_tables) or manually split by REDCap_split.

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# Transforms list of REDCap data.frames to a single wide data.frame

Source: `R/redcap_wider.R`
`redcap_wider.Rd`

Converts a list of REDCap data.frames from long to wide format. In essence it is a wrapper for the pivot_wider function applied on a REDCap output (from read_redcap_tables) or manually split by REDCap_split.

## Usage

    redcap_wider(
      data,
      event.glue = "{.value}____{redcap_event_name}",
      inst.glue = "{.value}____{redcap_repeat_instance}"
    )

## Arguments

data

  A list of data frames

event.glue

  A glue string for repeated events naming

inst.glue

  A glue string for repeated instruments naming

## Value

data.frame in wide format

## Examples

    # Longitudinal
    list1 <- list(
      data.frame(
        record_id = c(1, 2, 1, 2),
        redcap_event_name = c("baseline", "baseline", "followup", "followup"),
        age = c(25, 26, 27, 28)
      ),
      data.frame(
        record_id = c(1, 2),
        redcap_event_name = c("baseline", "baseline"),
        gender = c("male", "female")
      )
    )
    redcap_wider(list1)
    #> Joining with `by = join_by(record_id)`
    #> # A tibble: 2 × 4
    #>   record_id age____baseline age____followup gender
    #>       <dbl>           <dbl>           <dbl> <chr>
    #> 1         1              25              27 male
    #> 2         2              26              28 female
    # Simpel with two instruments
    list2 <- list(
      data.frame(
        record_id = c(1, 2),
        age = c(25, 26)
      ),
      data.frame(
        record_id = c(1, 2),
        gender = c("male", "female")
      )
    )
    redcap_wider(list2)
    #> Joining with `by = join_by(record_id)`
    #>   record_id age gender
    #> 1         1  25   male
    #> 2         2  26 female
    # Simple with single instrument
    list3 <- list(data.frame(
      record_id = c(1, 2),
      age = c(25, 26)
    ))
    redcap_wider(list3)
    #>   record_id age
    #> 1         1  25
    #> 2         2  26
    # Longitudinal with repeatable instruments
    list4 <- list(
      data.frame(
        record_id = c(1, 2, 1, 2),
        redcap_event_name = c("baseline", "baseline", "followup", "followup"),
        age = c(25, 26, 27, 28)
      ),
      data.frame(
        record_id = c(1, 1, 1, 1, 2, 2, 2, 2),
        redcap_event_name = c(
          "baseline", "baseline", "followup", "followup",
          "baseline", "baseline", "followup", "followup"
        ),
        redcap_repeat_instrument = "walk",
        redcap_repeat_instance = c(1, 2, 1, 2, 1, 2, 1, 2),
        dist = c(40, 32, 25, 33, 28, 24, 23, 36)
      ),
      data.frame(
        record_id = c(1, 2),
        redcap_event_name = c("baseline", "baseline"),
        gender = c("male", "female")
      )
    )
    redcap_wider(list4)
    #> Joining with `by = join_by(record_id)`
    #> Joining with `by = join_by(record_id)`
    #> # A tibble: 2 × 8
    #>   record_id age____baseline age____followup dist____1____baseline
    #>       <dbl>           <dbl>           <dbl>                 <dbl>
    #> 1         1              25              27                    40
    #> 2         2              26              28                    28
    #> # ℹ 4 more variables: dist____1____followup <dbl>, dist____2____baseline <dbl>,
    #> #   dist____2____followup <dbl>, gender <chr>

    list5 <- list(
      data.frame(
        record_id = c(1, 2, 1, 2),
        redcap_event_name = c("baseline", "baseline", "followup", "followup")
      ),
      data.frame(
        record_id = c(1, 1, 1, 1, 2, 2, 2, 2),
        redcap_event_name = c(
          "baseline", "baseline", "followup", "followup",
          "baseline", "baseline", "followup", "followup"
        ),
        redcap_repeat_instrument = "walk",
        redcap_repeat_instance = c(1, 2, 1, 2, 1, 2, 1, 2),
        dist = c(40, 32, 25, 33, 28, 24, 23, 36)
      ),
      data.frame(
        record_id = c(1, 2),
        redcap_event_name = c("baseline", "baseline"),
        gender = c("male", "female")
      )
    )
    redcap_wider(list5)
    #> Joining with `by = join_by(record_id)`
    #> # A tibble: 2 × 6
    #>   record_id dist____1____baseline dist____1____followup dist____2____baseline
    #>       <dbl>                 <dbl>                 <dbl>                 <dbl>
    #> 1         1                    40                    25                    32
    #> 2         2                    28                    23                    24
    #> # ℹ 2 more variables: dist____2____followup <dbl>, gender <chr>