# Alternative pivoting method for easily pivoting based on name pattern — wide2long • FreesearchR
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.

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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  71        0     95     NA
    #> 2   1  NA        1     98    179
    #> 3   2  72        0     77     NA
    #> 4   2  NA        1     98    182
    #> 5   3  70        0     85     NA
    #> 6   3  NA        1     74    197
    #> 7   4  71        0     83     NA
    #> 8   4  NA        1     82    199
    #> 9   5  72        0     90     NA
    #> 10  5  NA        1     88    179
    #> 11  6  75        0     94     NA
    #> 12  6  NA        1     94    175
    #> 13  7  74        0     75     NA
    #> 14  7  NA        1    100    179
    #> 15  8  71        0     91     NA
    #> 16  8  NA        1     83    195
    #> 17  9  78        0     77     NA
    #> 18  9  NA        1     96    176
    #> 19 10  79        0     90     NA
    #> 20 10  NA        1     92    187
    #> 21 11  78        0     93     NA
    #> 22 11  NA        1     96    188
    #> 23 12  71        0     72     NA
    #> 24 12  NA        1     73    193
    #> 25 13  71        0     77     NA
    #> 26 13  NA        1     89    187
    #> 27 14  76        0     74     NA
    #> 28 14  NA        1    100    192
    #> 29 15  73        0     83     NA
    #> 30 15  NA        1     96    190
    #> 31 16  75        0     96     NA
    #> 32 16  NA        1     82    183
    #> 33 17  71        0     82     NA
    #> 34 17  NA        1     72    192
    #> 35 18  76        0     88     NA
    #> 36 18  NA        1     77    195
    #> 37 19  78        0     72     NA
    #> 38 19  NA        1     93    184
    #> 39 20  75        0     97     NA
    #> 40 20  NA        1     98    198
    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  79        0     75       NA       NA
    #> 2   1  NA        1     NA       71      171
    #> 3   2  74        0     93       NA       NA
    #> 4   2  NA        1     NA       97      199
    #> 5   3  75        0     95       NA       NA
    #> 6   3  NA        1     NA       81      192
    #> 7   4  70        0     97       NA       NA
    #> 8   4  NA        1     NA       75      195
    #> 9   5  70        0     77       NA       NA
    #> 10  5  NA        1     NA       83      191
    #> 11  6  75        0     90       NA       NA
    #> 12  6  NA        1     NA       95      178
    #> 13  7  75        0     76       NA       NA
    #> 14  7  NA        1     NA       78      200
    #> 15  8  79        0     72       NA       NA
    #> 16  8  NA        1     NA       76      185
    #> 17  9  75        0     79       NA       NA
    #> 18  9  NA        1     NA       88      195
    #> 19 10  78        0     70       NA       NA
    #> 20 10  NA        1     NA       74      199
    #> 21 11  70        0     99       NA       NA
    #> 22 11  NA        1     NA       86      176
    #> 23 12  77        0     74       NA       NA
    #> 24 12  NA        1     NA       78      174
    #> 25 13  75        0     74       NA       NA
    #> 26 13  NA        1     NA       91      192
    #> 27 14  70        0     73       NA       NA
    #> 28 14  NA        1     NA       81      176
    #> 29 15  79        0     80       NA       NA
    #> 30 15  NA        1     NA       91      176
    #> 31 16  72        0     78       NA       NA
    #> 32 16  NA        1     NA       94      181
    #> 33 17  77        0     71       NA       NA
    #> 34 17  NA        1     NA       82      181
    #> 35 18  80        0     97       NA       NA
    #> 36 18  NA        1     NA       81      200
    #> 37 19  80        0     79       NA       NA
    #> 38 19  NA        1     NA       72      195
    #> 39 20  74        0     71       NA       NA
    #> 40 20  NA        1     NA       77      171
    # 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