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Convert tidymatrix to long-format data.frame

Source: R/flatten.R
to_long.Rd

Converts a tidymatrix into a long-format data.frame where each row represents a single matrix cell with its associated row and column metadata. This is useful for plotting individual data points or performing analyses that require long-format data.

Usage

to_long(.data, return_tibble = TRUE)

Arguments

.data

A tidymatrix object

return_tibble

Logical. If TRUE (default), returns tibble. If FALSE, returns data.frame.

Value

A data.frame/tibble with m*n rows (where m and n are matrix dimensions) containing:

  • All row_data columns (with "row." prefix if name conflicts exist)

  • All col_data columns (with "col." prefix if name conflicts exist)

  • A "value" column containing the matrix values

Details

The conversion always processes the entire matrix regardless of which component is active. If column names conflict between row_data and col_data, all row metadata columns are prefixed with "row." and all column metadata columns are prefixed with "col." to avoid ambiguity.

Matrix values are unwrapped in column-major order (R's default), meaning all values from column 1, then all values from column 2, etc.

Examples

# Basic conversion to long format
mat <- matrix(1:12, nrow = 4, ncol = 3)
row_data <- data.frame(
  gene_id = paste0("Gene", 1:4),
  gene_type = c("A", "A", "B", "B")
)
col_data <- data.frame(
  sample_id = paste0("Sample", 1:3),
  condition = c("Control", "Treatment", "Control")
)
tm <- tidymatrix(mat, row_data, col_data)

long <- to_long(tm)
head(long)
#> # A tibble: 6 × 5
#>   gene_id gene_type sample_id condition value
#>   <chr>   <chr>     <chr>     <chr>     <int>
#> 1 Gene1   A         Sample1   Control       1
#> 2 Gene2   A         Sample1   Control       2
#> 3 Gene3   B         Sample1   Control       3
#> 4 Gene4   B         Sample1   Control       4
#> 5 Gene1   A         Sample2   Treatment     5
#> 6 Gene2   A         Sample2   Treatment     6

# Use in ggplot2 workflow
if (requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggplot2)
  tm |>
    to_long() |>
    ggplot(aes(x = sample_id, y = value, color = condition)) +
    geom_point() +
    facet_wrap(~gene_id)
}


# Statistics added to the metadata carry over to the long format
big5 <- tidymatrix(big5_responses, big5_respondents, big5_items) |>
  activate(columns) |>
  compute_ttest(
    group_col = "gender", control = "Female", treatment = "Male",
    add_to_data = TRUE
  )
long_big5 <- to_long(big5)
head(long_big5[long_big5$p.adj < 0.05, ])
#> # A tibble: 6 × 17
#>   respondent_id   age gender education occupation     country life_satisfaction
#>   <chr>         <int> <chr>  <fct>     <chr>          <chr>               <int>
#> 1 R001             52 Female Secondary Service/Manual EE                      8
#> 2 R002             32 Female Master    Office         FI                      4
#> 3 R003             66 Male   Bachelor  Office         EE                      6
#> 4 R004             42 Male   Bachelor  Professional   EE                      9
#> 5 R005             48 Female Secondary Office         FI                      8
#> 6 R006             62 Female Basic     Service/Manual EE                      4
#> # ℹ 10 more variables: completion_min <dbl>, item_id <chr>, trait <chr>,
#> #   reversed <lgl>, item_text <chr>, position <int>, p.value <dbl>,
#> #   log2fc <dbl>, p.adj <dbl>, value <int>