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Refactor print.SummarizedExperiment function #29

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Merged
stemangiola merged 1 commit into from
Oct 25, 2025
Merged

Refactor print.SummarizedExperiment function #29

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219 changes: 99 additions & 120 deletions R/print_methods.R
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Original file line number Diff line number Diff line change
Expand Up @@ -44,130 +44,109 @@
print.SummarizedExperiment <- function(x, n = 10, ...) {

# SE_print_abstraction
print_tidyprint_1 <- function(x, n_print = n, ...){

onr <- nr <- nrow(x) %>% as.double()
onc <- nc <- ncol(x) %>% as.double()

if ( onc > 0 && onr > 0 && n_print / onc >= onr ) {
n_print <- onc*onr
separator_row_flag = FALSE
}else{
separator_row_flag = TRUE
}

top_n <- ceiling(n_print / 2)
bot_n <- floor(n_print / 2)

if (bot_n == 0) separator_row_flag = FALSE

row_slice <- if (nr < 2 * n_print) {
seq_len(nr)
} else {
c(seq_len(n_print), (nr - n_print + 1):nr)
}


col_slice <- if (nc < 2 * n_print) {
seq_len(nc)
} else {
c(seq_len(n_print), (nc - n_print + 1):nc)
}

x_ <- x[row_slice, col_slice]
nr <- nrow(x_)
nc <- ncol(x_)
.features <- rownames(x_) %||% seq_len(onr)[row_slice]
.samples <- colnames(x_) %||% seq_len(onc)[col_slice]

assays_ <- purrr::map(assays(x_), as_vec)
row_ <- purrr::map(rowData(x_), vec_rep, times = nc) |> purrr::map(maybe_phantom)
col_ <- purrr::map(colData(x_), vec_rep_each, times = nr) |> purrr::map(maybe_phantom)

nn <- nc * nr
out <- c(
list(
.feature = vctrs::vec_rep(.features, times = nc),
.sample = vctrs::vec_rep_each(.samples, times = nr)
),
list(`|` = sep_(nn)),
assays_,
list(`|` = sep_(nn)),
col_,
list(`|` = sep_(nn)),
row_
)
attr(out, "row.names") <- c(NA_integer_, -nn)
class(out) <- c("SE_abstraction", "tbl_df", "tbl", "data.frame")

sub_seq <- if (nn < 2 * top_n) {
seq_len(nn)
} else if (bot_n == 0){
seq_len(top_n)
} else {
c(seq_len(top_n), (nn - bot_n + 1):nn)
}

out_sub <- out[sub_seq, ]

# Compute the max character width for each column
separator_row <- vapply(out_sub %>% colnames(), function(col) {
max_width <- max(nchar(as.character(col)), na.rm = TRUE) # Get max width in the column
paste(rep("-", max_width), collapse = "") # Generate a separator of the same length
}, character(1))

if (separator_row_flag){

# Modify the entire tibble to include a separator row across all columns
## temporalily convert factor cols to char
fct_col = map(out_sub, is.factor) %>% keep(~{.x == TRUE}) %>% names
if (length(fct_col)) out_sub[, fct_col] = out_sub[, fct_col] %>% mutate(across(all_of(fct_col), as.character))


out_sub <- suppressWarnings(rbind(
out_sub[seq_len(top_n),],
as.list(separator_row), # Adaptive separator row
out_sub[(top_n+1):nrow(out_sub), ]
))
## reverse to factor cols
if (length(fct_col)) out_sub[, fct_col] = out_sub[, fct_col] %>% mutate(across(all_of(fct_col), as.factor))
}

# attr(out_sub, "n") <- n
# attr(out_sub, "total_rows") <- x %>% dim %>% {(.)[1] * (.)[2]}

# class(out_sub) <- c("SE_print_abstraction", "tbl_df", "tbl", "data.frame")

out_sub = out_sub %>%
vctrs::new_data_frame(class=c('SE_print_abstraction', "tbl_df", "tbl", "data.frame")) %>%
add_attr(n_print, 'n') %>%
add_attr(onc*onr, 'total_rows') %>%
add_attr(nrow(x), "number_of_features") %>%
add_attr(ncol(x), "number_of_samples") %>%
add_attr(assays(x) %>% names, "assay_names") %>%
add_attr(separator_row, "separator_row") |>
add_attr(names(col_), "covariate_names") |>
onr <- nr <- nrow(x) %>% as.double()
onc <- nc <- ncol(x) %>% as.double()

if ( onc > 0 && onr > 0 && n / onc >= onr ) {
n <- onc*onr
separator_row_flag = FALSE
}else{
separator_row_flag = TRUE
}

# add_attr(
# # Get the actual column names that will be printed on screen
# # This uses tibble's internal method to determine visible columns
# pillar::tbl_format_setup(out_sub, width = getOption("width", 80) + 4)$body[1] |> as.character(),
# "printed_colnames"
# ) %>%
add_attr(
'' %>%
setNames("A SummarizedExperiment-tibble abstraction"),
"named_header"
)
top_n <- ceiling(n / 2)
bot_n <- floor(n / 2)

if (bot_n == 0) separator_row_flag = FALSE

row_slice <- if (nr < 2 * n) {
seq_len(nr)
} else {
c(seq_len(n), (nr - n + 1):nr)
}

# print(attributes(out_sub))

out_sub %>% print(n = ifelse(separator_row_flag, n_print+1, n_print), ...)
invisible(x)
}

col_slice <- if (nc < 2 * n) {
seq_len(nc)
} else {
c(seq_len(n), (nc - n + 1):nc)
}

x_ <- x[row_slice, col_slice]
nr <- nrow(x_)
nc <- ncol(x_)
.features <- rownames(x_) %||% seq_len(onr)[row_slice]
.samples <- colnames(x_) %||% seq_len(onc)[col_slice]

assays_ <- purrr::map(assays(x_), as_vec)
row_ <- purrr::map(rowData(x_), vec_rep, times = nc) |> purrr::map(maybe_phantom)
col_ <- purrr::map(colData(x_), vec_rep_each, times = nr) |> purrr::map(maybe_phantom)

nn <- nc * nr
out <- c(
list(
.feature = vctrs::vec_rep(.features, times = nc),
.sample = vctrs::vec_rep_each(.samples, times = nr)
),
list(`|` = sep_(nn)),
assays_,
list(`|` = sep_(nn)),
col_,
list(`|` = sep_(nn)),
row_
)
attr(out, "row.names") <- c(NA_integer_, -nn)
class(out) <- c("SE_abstraction", "tbl_df", "tbl", "data.frame")

sub_seq <- if (nn < 2 * top_n) {
seq_len(nn)
} else if (bot_n == 0){
seq_len(top_n)
} else {
c(seq_len(top_n), (nn - bot_n + 1):nn)
}

out_sub <- out[sub_seq, ]

# Compute the max character width for each column
separator_row <- vapply(out_sub %>% colnames(), function(col) {
max_width <- max(nchar(as.character(col)), na.rm = TRUE) # Get max width in the column
paste(rep("-", max_width), collapse = "") # Generate a separator of the same length
}, character(1))

if (separator_row_flag){

# Modify the entire tibble to include a separator row across all columns
## temporalily convert factor cols to char
fct_col = map(out_sub, is.factor) %>% keep(~{.x == TRUE}) %>% names
if (length(fct_col)) out_sub[, fct_col] = out_sub[, fct_col] %>% mutate(across(all_of(fct_col), as.character))


out_sub <- suppressWarnings(rbind(
out_sub[seq_len(top_n),],
as.list(separator_row), # Adaptive separator row
out_sub[(top_n+1):nrow(out_sub), ]
))
## reverse to factor cols
if (length(fct_col)) out_sub[, fct_col] = out_sub[, fct_col] %>% mutate(across(all_of(fct_col), as.factor))
}

print_tidyprint_1(x, ...)
out_sub = out_sub %>%
vctrs::new_data_frame(class=c('SE_print_abstraction', "tbl_df", "tbl", "data.frame")) %>%
add_attr(n, 'n') %>%
add_attr(onc*onr, 'total_rows') %>%
add_attr(nrow(x), "number_of_features") %>%
add_attr(ncol(x), "number_of_samples") %>%
add_attr(assays(x) %>% names, "assay_names") %>%
add_attr(separator_row, "separator_row") |>
add_attr(names(col_), "covariate_names") |>
add_attr(
'' %>%
setNames("A SummarizedExperiment-tibble abstraction"),
"named_header"
)

out_sub %>% print(n = ifelse(separator_row_flag, n+1, n), ...)
invisible(x)
}

Expand Down