subjects observed in every level of x. This is the listwise deletion step that long_to_wide_converter() applies when paired = TRUE, and thus the data that two_sample_test(), oneway_anova(), and pairwise_comparisons() analyze for within-subjects designs. The following steps are carried out: Rows with a missing x or subject.id are dropped, since they cannot be paired. An error is thrown if any subject has more than one row in a level of x. Subjects with a missing y value, or without a row, in any level of x are removed entirely. "> Skip to contents

Keep complete subjects in repeated measures data

Source: R/complete-subjects.R
complete_subjects.Rd

Prepares long/tidy repeated measures data for analysis by keeping only the subjects observed in every level of x. This is the listwise deletion step that long_to_wide_converter() applies when paired = TRUE, and thus the data that two_sample_test(), oneway_anova(), and pairwise_comparisons() analyze for within-subjects designs.

The following steps are carried out:

  • Rows with a missing x or subject.id are dropped, since they cannot be paired.

  • An error is thrown if any subject has more than one row in a level of x.

  • Subjects with a missing y value, or without a row, in any level of x are removed entirely.

Usage

complete_subjects(data, x, y, subject.id = NULL)

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. For repeated measures designs, see the note on ordering in subject.id.

y

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

subject.id

Relevant in case of a repeated measures or within-subjects design (i.e., paired = TRUE), it specifies the subject or repeated measures identifier. Important: If this argument is NULL (which is the default), observations are paired by their row order within each level of x (i.e., the data is assumed to be sorted in a subject-1, subject-2, ... pattern within every level). If the data is not sorted this way, the paired results will be silently incorrect, so it is safest to always specify subject.id. For paired designs, each subject must have at most one row per level of x (an error is thrown otherwise), and rows with a missing subject.id are dropped because they cannot be paired (see complete_subjects()).

Value

A tibble in long format with columns .rowid (the subject identifier, or an internal row identifier when subject.id is NULL), x (as a factor with unused levels dropped), and y, sorted by .rowid.

Citation

Patil, I., (2021). statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details. Journal of Open Source Software, 6(61), 3236, https://doi.org/10.21105/joss.03236

Examples

library(statsExpressions)

# subjects with a missing rating in any condition are removed
complete_subjects(bugs_long, condition, desire, subject.id = subject)
#> # A tibble: 352 × 3
#>    .rowid condition desire
#>     <int> <fct>      <dbl>
#>  1      1 HDHF          10
#>  2      1 HDLF           9
#>  3      1 LDHF           6
#>  4      1 LDLF           6
#>  5      3 HDHF          10
#>  6      3 HDLF          10
#>  7      3 LDHF          10
#>  8      3 LDLF           5
#>  9      4 HDHF           9
#> 10      4 HDLF           6
#> # ℹ 342 more rows