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5 KiB
5 KiB
Alternative pivoting method for easily pivoting based on name pattern
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 74 0 98 NA
#> 2 1 NA 1 91 173
#> 3 2 78 0 77 NA
#> 4 2 NA 1 91 189
#> 5 3 78 0 80 NA
#> 6 3 NA 1 73 184
#> 7 4 70 0 88 NA
#> 8 4 NA 1 95 199
#> 9 5 72 0 85 NA
#> 10 5 NA 1 83 179
#> 11 6 73 0 90 NA
#> 12 6 NA 1 75 192
#> 13 7 71 0 85 NA
#> 14 7 NA 1 78 195
#> 15 8 78 0 83 NA
#> 16 8 NA 1 99 198
#> 17 9 80 0 85 NA
#> 18 9 NA 1 70 175
#> 19 10 77 0 74 NA
#> 20 10 NA 1 79 186
#> 21 11 76 0 92 NA
#> 22 11 NA 1 80 193
#> 23 12 73 0 71 NA
#> 24 12 NA 1 84 194
#> 25 13 77 0 94 NA
#> 26 13 NA 1 72 198
#> 27 14 75 0 96 NA
#> 28 14 NA 1 89 183
#> 29 15 79 0 77 NA
#> 30 15 NA 1 94 178
#> 31 16 75 0 99 NA
#> 32 16 NA 1 73 189
#> 33 17 73 0 99 NA
#> 34 17 NA 1 75 185
#> 35 18 72 0 70 NA
#> 36 18 NA 1 83 172
#> 37 19 80 0 70 NA
#> 38 19 NA 1 79 172
#> 39 20 74 0 89 NA
#> 40 20 NA 1 97 179
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 83 NA NA
#> 2 1 NA 1 NA 97 197
#> 3 2 78 0 79 NA NA
#> 4 2 NA 1 NA 94 186
#> 5 3 79 0 77 NA NA
#> 6 3 NA 1 NA 77 200
#> 7 4 76 0 88 NA NA
#> 8 4 NA 1 NA 100 181
#> 9 5 74 0 95 NA NA
#> 10 5 NA 1 NA 73 175
#> 11 6 70 0 99 NA NA
#> 12 6 NA 1 NA 83 185
#> 13 7 72 0 71 NA NA
#> 14 7 NA 1 NA 84 174
#> 15 8 70 0 77 NA NA
#> 16 8 NA 1 NA 94 184
#> 17 9 80 0 70 NA NA
#> 18 9 NA 1 NA 93 195
#> 19 10 79 0 77 NA NA
#> 20 10 NA 1 NA 77 187
#> 21 11 72 0 77 NA NA
#> 22 11 NA 1 NA 76 177
#> 23 12 77 0 75 NA NA
#> 24 12 NA 1 NA 80 170
#> 25 13 73 0 99 NA NA
#> 26 13 NA 1 NA 81 172
#> 27 14 70 0 74 NA NA
#> 28 14 NA 1 NA 98 189
#> 29 15 71 0 98 NA NA
#> 30 15 NA 1 NA 98 184
#> 31 16 74 0 73 NA NA
#> 32 16 NA 1 NA 78 173
#> 33 17 71 0 100 NA NA
#> 34 17 NA 1 NA 79 172
#> 35 18 80 0 74 NA NA
#> 36 18 NA 1 NA 77 181
#> 37 19 72 0 98 NA NA
#> 38 19 NA 1 NA 89 176
#> 39 20 72 0 85 NA NA
#> 40 20 NA 1 NA 81 174
# 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