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# Alternative pivoting method for easily pivoting based on name pattern

Source: `R/wide2long.R`
`wide2long.Rd`

This function requires and assumes a systematic naming of variables. For now only supports one level pivoting. Adding more levels would require an added "ignore" string pattern or similarly. Example 2.

## Usage

    wide2long(
      data,
      pattern,
      type = c("prefix", "infix", "suffix"),
      id.col = 1,
      instance.name = "instance"
    )

## Arguments

data

  data

pattern

  pattern(s) to match. Character vector of length 1 or more.

type

  type of match. can be one of "prefix","infix" or "suffix".

id.col

  ID column. Will fill ID for all. Column name or numeric index. Default is "1", first column.

instance.name

## Value

data.frame

## Examples

    data.frame(
      1:20, sample(70:80, 20, TRUE),
      sample(70:100, 20, TRUE),
      sample(70:100, 20, TRUE),
      sample(170:200, 20, TRUE)
    ) |>
      setNames(c("id", "age", "weight_0", "weight_1", "height_1")) |>
      wide2long(pattern = c("_0", "_1"), type = "suffix")
    #>    id age instance weight height
    #> 1   1  70        0     73     NA
    #> 2   1  NA        1     77    176
    #> 3   2  77        0     97     NA
    #> 4   2  NA        1     75    180
    #> 5   3  78        0     70     NA
    #> 6   3  NA        1     99    181
    #> 7   4  78        0     87     NA
    #> 8   4  NA        1     74    198
    #> 9   5  73        0     81     NA
    #> 10  5  NA        1     98    198
    #> 11  6  72        0     90     NA
    #> 12  6  NA        1     73    178
    #> 13  7  72        0     71     NA
    #> 14  7  NA        1    100    179
    #> 15  8  79        0     96     NA
    #> 16  8  NA        1     74    177
    #> 17  9  77        0     88     NA
    #> 18  9  NA        1     98    189
    #> 19 10  78        0     88     NA
    #> 20 10  NA        1     85    181
    #> 21 11  79        0     83     NA
    #> 22 11  NA        1     97    197
    #> 23 12  76        0     79     NA
    #> 24 12  NA        1     94    186
    #> 25 13  74        0     77     NA
    #> 26 13  NA        1     77    200
    #> 27 14  70        0     88     NA
    #> 28 14  NA        1    100    181
    #> 29 15  72        0     95     NA
    #> 30 15  NA        1     73    175
    #> 31 16  70        0     99     NA
    #> 32 16  NA        1     83    185
    #> 33 17  80        0     71     NA
    #> 34 17  NA        1     84    174
    #> 35 18  79        0     77     NA
    #> 36 18  NA        1     94    184
    #> 37 19  72        0     70     NA
    #> 38 19  NA        1     93    195
    #> 39 20  77        0     77     NA
    #> 40 20  NA        1     77    187
    data.frame(
      1:20, sample(70:80, 20, TRUE),
      sample(70:100, 20, TRUE),
      sample(70:100, 20, TRUE),
      sample(170:200, 20, TRUE)
    ) |>
      setNames(c("id", "age", "weight_0", "weight_a_1", "height_b_1")) |>
      wide2long(pattern = c("_0", "_1"), type = "suffix")
    #>    id age instance weight weight_a height_b
    #> 1   1  77        0     84       NA       NA
    #> 2   1  NA        1     NA       78      171
    #> 3   2  70        0     89       NA       NA
    #> 4   2  NA        1     NA       72      182
    #> 5   3  72        0     98       NA       NA
    #> 6   3  NA        1     NA       93      170
    #> 7   4  73        0    100       NA       NA
    #> 8   4  NA        1     NA       98      171
    #> 9   5  73        0     83       NA       NA
    #> 10  5  NA        1     NA       91      173
    #> 11  6  72        0     81       NA       NA
    #> 12  6  NA        1     NA       81      170
    #> 13  7  76        0     88       NA       NA
    #> 14  7  NA        1     NA       75      174
    #> 15  8  74        0     70       NA       NA
    #> 16  8  NA        1     NA       82      173
    #> 17  9  76        0     87       NA       NA
    #> 18  9  NA        1     NA       84      176
    #> 19 10  71        0     85       NA       NA
    #> 20 10  NA        1     NA       70      189
    #> 21 11  77        0     79       NA       NA
    #> 22 11  NA        1     NA       83      185
    #> 23 12  74        0     87       NA       NA
    #> 24 12  NA        1     NA       77      187
    #> 25 13  71        0     75       NA       NA
    #> 26 13  NA        1     NA       89      173
    #> 27 14  75        0     70       NA       NA
    #> 28 14  NA        1     NA       84      178
    #> 29 15  80        0     93       NA       NA
    #> 30 15  NA        1     NA       87      182
    #> 31 16  76        0     96       NA       NA
    #> 32 16  NA        1     NA       72      187
    #> 33 17  71        0     88       NA       NA
    #> 34 17  NA        1     NA       80      173
    #> 35 18  73        0     86       NA       NA
    #> 36 18  NA        1     NA       91      184
    #> 37 19  76        0     91       NA       NA
    #> 38 19  NA        1     NA       89      189
    #> 39 20  71        0     79       NA       NA
    #> 40 20  NA        1     NA       84      197
    # Optional filling of missing values by last observation carried forward
    # Needed for mmrm analyses
    long_missings |>
      # Fills record ID assuming none are missing
      tidyr::fill(record_id) |>
      # Grouping by ID for the last step
      dplyr::group_by(record_id) |>
      # Filling missing data by ID
      tidyr::fill(names(long_missings)[!names(long_missings) %in% new_names]) |>
      # Remove grouping
      dplyr::ungroup()
    #> Error: object 'long_missings' not found