tidyprint is an R package that provides a centralised tidy display
strategy for biological data (e.g. SummarizedExperiment), and
centralised messaging styles for the tidyomics packages. To facilitate
the discussion about data display, we compare here four data display:
-
SummarizedExperiment: Standard R/SummarizedExperiment printing style.
-
tidyprint_1: Newly designed tibble abstraction, combines styles from tidySummarizedExperiment and plyxp
-
tidySummarizedExperiment: Tidyverse-friendly tibble abstraction.
-
plyxp: Top/bottom row truncation for large datasets.
Depending on your workflow and desired console output, tidyprint makes
it easy to switch between these printing styles.
You need the remotes package to install from GitHub. If you don’t have
it, install via:
install.packages("remotes")Then install tidyprint from GitHub:
remotes::install_github("tidyomics/tidyprint")Below is an example demonstrating how to use tidyprint with a sample
SummarizedExperiment object.
library(dplyr)
library(tidyr)
# Now load tidyprint
library(tidyprint)
# Example SummarizedExperiment data from the airway package
data(se_airway)
se_airway
#> class: SummarizedExperiment
#> dim: 38694 8
#> metadata(0):
#> assays(1): counts
#> rownames(38694): ENSG00000000003 ENSG00000000005 ... ENSG00000283120
#> ENSG00000283123
#> rowData names(0):
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(3): dex celltype geo_idThe standard SummarizedExperiment display:
# The default style:
se_airway %>% print(design = "SummarizedExperiment")
#> class: SummarizedExperiment
#> dim: 38694 8
#> metadata(0):
#> assays(1): counts
#> rownames(38694): ENSG00000000003 ENSG00000000005 ... ENSG00000283120
#> ENSG00000283123
#> rowData names(0):
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(3): dex celltype geo_idFor a more compact view (top and bottom rows), similar to a plyxp/tidyverse style with tidySummarizedExperiment header:
se_airway %>% print(design = "tidyprint_1")
#> # A SummarizedExperiment-tibble abstraction:
#> # Features=38694 | Samples=8 | Assays=counts
#> .features .samples `|` counts `|` `|` dex celltype geo_id
#> <chr> <chr> <|> <chr> <|> <|> <chr> <chr> <chr>
#> 1 ENSG00000000003 SRR1039508 | 723 | | control N61311 GSM1275862
#> 2 ENSG00000000005 SRR1039508 | 0 | | control N61311 GSM1275862
#> 3 ENSG00000000419 SRR1039508 | 467 | | control N61311 GSM1275862
#> 4 ENSG00000000457 SRR1039508 | 347 | | control N61311 GSM1275862
#> 5 ENSG00000000460 SRR1039508 | 96 | | control N61311 GSM1275862
#> --------------- ---------- -- --- -- -- ------- ------- ----------
#> 309548 ENSG00000283115 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 309549 ENSG00000283116 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 309550 ENSG00000283119 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 309551 ENSG00000283120 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 309552 ENSG00000283123 SRR1039521 | 0 | | treated N061011 GSM1275875Use the “tidySummarizedExperiment” design to view your data in a tidy-friendly tibble format:
# Tidy SummarizedExperiment print:
se_airway %>% print(design = "tidySummarizedExperiment")
#> # A SummarizedExperiment-tibble abstraction: 309,552 × 6
#> # Features=38694 | Samples=8 | Assays=counts
#> .feature .sample counts dex celltype geo_id
#> <chr> <chr> <dbl> <chr> <chr> <chr>
#> 1 ENSG00000000003 SRR1039508 723 control N61311 GSM1275862
#> 2 ENSG00000000005 SRR1039508 0 control N61311 GSM1275862
#> 3 ENSG00000000419 SRR1039508 467 control N61311 GSM1275862
#> 4 ENSG00000000457 SRR1039508 347 control N61311 GSM1275862
#> 5 ENSG00000000460 SRR1039508 96 control N61311 GSM1275862
#> 6 ENSG00000000938 SRR1039508 0 control N61311 GSM1275862
#> 7 ENSG00000000971 SRR1039508 3413 control N61311 GSM1275862
#> 8 ENSG00000001036 SRR1039508 2328 control N61311 GSM1275862
#> 9 ENSG00000001084 SRR1039508 670 control N61311 GSM1275862
#> 10 ENSG00000001167 SRR1039508 426 control N61311 GSM1275862
#> # ℹ 40 more rowsFor a more compact view (top and bottom rows), similar to a plyxp/tidyverse style:
se_airway %>% print(design = "plyxp")
#> # A tibble: 10 × 9
#> .features .samples `|` counts `|` `|` dex celltype geo_id
#> <chr> <chr> <|> <dbl> <|> <|> <chr> <chr> <chr>
#> 1 ENSG00000000003 SRR1039508 | 723 | | control N61311 GSM1275862
#> 2 ENSG00000000005 SRR1039508 | 0 | | control N61311 GSM1275862
#> 3 ENSG00000000419 SRR1039508 | 467 | | control N61311 GSM1275862
#> 4 ENSG00000000457 SRR1039508 | 347 | | control N61311 GSM1275862
#> 5 ENSG00000000460 SRR1039508 | 96 | | control N61311 GSM1275862
#> 6 ENSG00000283115 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 7 ENSG00000283116 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 8 ENSG00000283119 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 9 ENSG00000283120 SRR1039521 | 0 | | treated N061011 GSM1275875
#> 10 ENSG00000283123 SRR1039521 | 0 | | treated N061011 GSM1275875We integrated a messaging function providing standardized, visually appealing messages for packages within the tidyomics ecosystem. It automatically detects the calling package to provide contextualized messaging, such as “tidyprint says” or “tidybulk says”, enhancing consistency and readability across projects.
To use the tidy_message function:
tidyprint::tidy_message('message to print')
#> ℹ Console says: message to printYou can specify the type of message as
-
info (default)
-
success
-
warning
-
danger
test_tidy_message()
#> ℹ tidyprint says: This is an informational message send within tidyprint package.
#> ✔ tidyprint says: Operation completed successfully!
#> ! tidyprint says: Potential issue detected.
#> ✖ tidyprint says: Operation failed.The above code demonstrates calling tidy_message within a package
function, showing the name of package.
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-apple-darwin20
#> Running under: macOS Sonoma 14.6.1
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: Australia/Adelaide
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] tidyprint_0.0.1 tidyr_1.3.1 dplyr_1.1.4
#>
#> loaded via a namespace (and not attached):
#> [1] utf8_1.2.4 sass_0.4.9
#> [3] generics_0.1.3 SparseArray_1.5.44
#> [5] stringi_1.8.4 lattice_0.22-6
#> [7] digest_0.6.37 magrittr_2.0.3
#> [9] evaluate_1.0.3 grid_4.4.1
#> [11] fastmap_1.2.0 rprojroot_2.0.4
#> [13] jsonlite_1.8.9 Matrix_1.7-0
#> [15] GenomeInfoDb_1.41.2 httr_1.4.7
#> [17] fansi_1.0.6 purrr_1.0.4
#> [19] UCSC.utils_1.1.0 jquerylib_0.1.4
#> [21] abind_1.4-8 cli_3.6.4
#> [23] rlang_1.1.5 crayon_1.5.3
#> [25] XVector_0.45.0 Biobase_2.65.1
#> [27] withr_3.0.2 cachem_1.1.0
#> [29] DelayedArray_0.31.14 yaml_2.3.10
#> [31] S4Arrays_1.5.11 tools_4.4.1
#> [33] GenomeInfoDbData_1.2.13 SummarizedExperiment_1.35.4
#> [35] BiocGenerics_0.51.3 vctrs_0.6.5
#> [37] R6_2.6.1 matrixStats_1.5.0
#> [39] stats4_4.4.1 lifecycle_1.0.4
#> [41] stringr_1.5.1 zlibbioc_1.51.1
#> [43] S4Vectors_0.43.2 IRanges_2.39.2
#> [45] pkgconfig_2.0.3 pillar_1.10.1
#> [47] bslib_0.9.0 glue_1.8.0
#> [49] xfun_0.50 tibble_3.2.1
#> [51] GenomicRanges_1.57.2 tidyselect_1.2.1
#> [53] rstudioapi_0.16.0 MatrixGenerics_1.17.0
#> [55] knitr_1.49 htmltools_0.5.8.1
#> [57] rmarkdown_2.29 compiler_4.4.1