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157 lines
5 KiB
Markdown
157 lines
5 KiB
Markdown
# Alternative pivoting method for easily pivoting based on name pattern
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This function requires and assumes a systematic naming of variables. For
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now only supports one level pivoting. Adding more levels would require
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an added "ignore" string pattern or similarly. Example 2.
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## Usage
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``` r
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wide2long(
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data,
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pattern,
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type = c("prefix", "infix", "suffix"),
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id.col = 1,
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instance.name = "instance"
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)
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```
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## Arguments
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- data:
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data
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- pattern:
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pattern(s) to match. Character vector of length 1 or more.
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- type:
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type of match. can be one of "prefix","infix" or "suffix".
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- id.col:
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ID column. Will fill ID for all. Column name or numeric index. Default
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is "1", first column.
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- instance.name:
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## Value
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data.frame
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## Examples
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``` r
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data.frame(
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1:20, sample(70:80, 20, TRUE),
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sample(70:100, 20, TRUE),
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sample(70:100, 20, TRUE),
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sample(170:200, 20, TRUE)
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) |>
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setNames(c("id", "age", "weight_0", "weight_1", "height_1")) |>
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wide2long(pattern = c("_0", "_1"), type = "suffix")
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#> id age instance weight height
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#> 1 1 70 0 73 NA
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#> 2 1 NA 1 77 176
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#> 3 2 77 0 97 NA
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#> 4 2 NA 1 75 180
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#> 5 3 78 0 70 NA
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#> 6 3 NA 1 99 181
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#> 7 4 78 0 87 NA
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#> 8 4 NA 1 74 198
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#> 9 5 73 0 81 NA
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#> 10 5 NA 1 98 198
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#> 11 6 72 0 90 NA
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#> 12 6 NA 1 73 178
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#> 13 7 72 0 71 NA
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#> 14 7 NA 1 100 179
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#> 15 8 79 0 96 NA
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#> 16 8 NA 1 74 177
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#> 17 9 77 0 88 NA
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#> 18 9 NA 1 98 189
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#> 19 10 78 0 88 NA
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#> 20 10 NA 1 85 181
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#> 21 11 79 0 83 NA
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#> 22 11 NA 1 97 197
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#> 23 12 76 0 79 NA
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#> 24 12 NA 1 94 186
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#> 25 13 74 0 77 NA
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#> 26 13 NA 1 77 200
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#> 27 14 70 0 88 NA
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#> 28 14 NA 1 100 181
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#> 29 15 72 0 95 NA
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#> 30 15 NA 1 73 175
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#> 31 16 70 0 99 NA
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#> 32 16 NA 1 83 185
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#> 33 17 80 0 71 NA
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#> 34 17 NA 1 84 174
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#> 35 18 79 0 77 NA
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#> 36 18 NA 1 94 184
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#> 37 19 72 0 70 NA
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#> 38 19 NA 1 93 195
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#> 39 20 77 0 77 NA
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#> 40 20 NA 1 77 187
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data.frame(
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1:20, sample(70:80, 20, TRUE),
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sample(70:100, 20, TRUE),
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sample(70:100, 20, TRUE),
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sample(170:200, 20, TRUE)
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) |>
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setNames(c("id", "age", "weight_0", "weight_a_1", "height_b_1")) |>
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wide2long(pattern = c("_0", "_1"), type = "suffix")
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#> id age instance weight weight_a height_b
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#> 1 1 77 0 84 NA NA
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#> 2 1 NA 1 NA 78 171
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#> 3 2 70 0 89 NA NA
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#> 4 2 NA 1 NA 72 182
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#> 5 3 72 0 98 NA NA
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#> 6 3 NA 1 NA 93 170
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#> 7 4 73 0 100 NA NA
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#> 8 4 NA 1 NA 98 171
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#> 9 5 73 0 83 NA NA
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#> 10 5 NA 1 NA 91 173
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#> 11 6 72 0 81 NA NA
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#> 12 6 NA 1 NA 81 170
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#> 13 7 76 0 88 NA NA
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#> 14 7 NA 1 NA 75 174
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#> 15 8 74 0 70 NA NA
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#> 16 8 NA 1 NA 82 173
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#> 17 9 76 0 87 NA NA
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#> 18 9 NA 1 NA 84 176
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#> 19 10 71 0 85 NA NA
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#> 20 10 NA 1 NA 70 189
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#> 21 11 77 0 79 NA NA
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#> 22 11 NA 1 NA 83 185
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#> 23 12 74 0 87 NA NA
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#> 24 12 NA 1 NA 77 187
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#> 25 13 71 0 75 NA NA
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#> 26 13 NA 1 NA 89 173
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#> 27 14 75 0 70 NA NA
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#> 28 14 NA 1 NA 84 178
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#> 29 15 80 0 93 NA NA
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#> 30 15 NA 1 NA 87 182
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#> 31 16 76 0 96 NA NA
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#> 32 16 NA 1 NA 72 187
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#> 33 17 71 0 88 NA NA
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#> 34 17 NA 1 NA 80 173
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#> 35 18 73 0 86 NA NA
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#> 36 18 NA 1 NA 91 184
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#> 37 19 76 0 91 NA NA
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#> 38 19 NA 1 NA 89 189
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#> 39 20 71 0 79 NA NA
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#> 40 20 NA 1 NA 84 197
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# Optional filling of missing values by last observation carried forward
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# Needed for mmrm analyses
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long_missings |>
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# Fills record ID assuming none are missing
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tidyr::fill(record_id) |>
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# Grouping by ID for the last step
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dplyr::group_by(record_id) |>
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# Filling missing data by ID
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tidyr::fill(names(long_missings)[!names(long_missings) %in% new_names]) |>
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# Remove grouping
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dplyr::ungroup()
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#> Error: object 'long_missings' not found
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```
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