Convert long/tidy data frame to wide format

This conversion is helpful mostly for repeated measures design, where removing NAs by participant can be a bit tedious.

Usage

long_to_wide_converter(
  data,
  x,
  y,
  subject.id = NULL,
  paired = TRUE,
  spread = TRUE,
  ...
)

Arguments

data

A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.

x

The grouping (or independent) variable from data. In case of a repeated measures or within-subjects design, if subject.id argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted, the results can be inaccurate when there are more than two levels in x and there are NAs present. The data is expected to be sorted by user in subject-1,subject-2, ..., pattern.

y

The response (or outcome or dependent) variable from data.

subject.id

Relevant in case of a repeated measures or within-subjects design (paired = TRUE, i.e.), it specifies the subject or repeated measures identifier. Important: Note that if this argument is NULL (which is the default), the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted and you leave this argument unspecified, the results can be inaccurate when there are more than two levels in x and there are NAs present.

paired

Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is FALSE.

spread

Logical that decides whether the data frame needs to be converted from long/tidy to wide (default: TRUE).

...

Currently ignored.

Value

A data frame with NAs removed while respecting the between-or-within-subjects nature of the dataset.

Examples

# for reproducibility
library(statsExpressions)
set.seed(123)

# repeated measures design
long_to_wide_converter(
  bugs_long,
  condition,
  desire,
  subject.id = subject,
  paired = TRUE
)
#> # A tibble: 88 × 5
#>    .rowid  HDHF  HDLF  LDHF  LDLF
#>     <int> <dbl> <dbl> <dbl> <dbl>
#>  1      1  10     9     6     6  
#>  2      3  10    10    10     5  
#>  3      4   9     6     9     6  
#>  4      5   8.5   5.5   6.5   3  
#>  5      6   3     7.5   0.5   2  
#>  6      7  10    10    10    10  
#>  7      8  10     9    10    10  
#>  8      9  10     6     9.5   9.5
#>  9     11   0     0     2.5   0  
#> 10     12  10     8.5   7.5   9.5
#> # ℹ 78 more rows

# independent measures design
long_to_wide_converter(mtcars, cyl, wt, paired = FALSE)
#> # A tibble: 32 × 4
#>    .rowid   `4`   `6`   `8`
#>     <int> <dbl> <dbl> <dbl>
#>  1      1  2.32    NA    NA
#>  2      2  3.19    NA    NA
#>  3      3  3.15    NA    NA
#>  4      4  2.2     NA    NA
#>  5      5  1.62    NA    NA
#>  6      6  1.84    NA    NA
#>  7      7  2.46    NA    NA
#>  8      8  1.94    NA    NA
#>  9      9  2.14    NA    NA
#> 10     10  1.51    NA    NA
#> # ℹ 22 more rows