massdataset version 0.99.20
Now, massdataset supports slice_ family functions in tidyverse.
library(massdataset)
#> Warning in fun(libname, pkgname): mzR has been built against a different Rcpp version (1.0.7)
#> than is installed on your system (1.0.8). This might lead to errors
#> when loading mzR. If you encounter such issues, please send a report,
#> including the output of sessionInfo() to the Bioc support forum at
#> https://support.bioconductor.org/. For details see also
#> https://github.com/sneumann/mzR/wiki/mzR-Rcpp-compiler-linker-issue.
#> ── Attaching packages ─────────────────────────────────── massdataset 0.99.20 ──
#> ✓ masstools 0.99.5 ✓ ggplot2 3.3.5
#> ── Conflicts ──────────────────────────────────────── massdataset_conflicts() ──
#> x massdataset::apply() masks base::apply()
#> x methods::body<-() masks base::body<-()
#> x massdataset::colMeans() masks base::colMeans()
#> x massdataset::colSums() masks base::colSums()
#> x massdataset::filter() masks stats::filter()
#> x massdataset::intersect() masks base::intersect()
#> x methods::kronecker() masks base::kronecker()
#> x masstools::mz_rt_match() masks massdataset::mz_rt_match()
#> x massdataset::rowMeans() masks base::rowMeans()
#> x massdataset::rowSums() masks base::rowSums()
#>
#> Attaching package: 'massdataset'
#> The following object is masked from 'package:stats':
#>
#> filter
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✓ tibble 3.1.6 ✓ dplyr 1.0.8
#> ✓ tidyr 1.2.0 ✓ stringr 1.4.0
#> ✓ readr 2.1.2 ✓ forcats 0.5.1.9000
#> ✓ purrr 0.3.4
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x tidyr::extract() masks magrittr::extract()
#> x dplyr::filter() masks massdataset::filter(), stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x purrr::set_names() masks magrittr::set_names()
data("expression_data")
data("sample_info")
data("sample_info_note")
data("variable_info")
data("variable_info_note")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
sample_info_note = sample_info_note,
variable_info_note = variable_info_note
)
object
#> --------------------
#> massdataset version: 0.99.20
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-03-10 20:05:48
object %>%
activate_mass_dataset(what = "sample_info") %>%
slice_head(n = 3)
#> --------------------
#> massdataset version: 0.99.20
#> --------------------
#> 1.expression_data:[ 1000 x 3 data.frame]
#> 2.sample_info:[ 3 x 4 data.frame]
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-03-10 20:05:48
#> slice_head ----------
#> Package Function.used Time
#> 1 massdataset slice_head() 2022-03-10 20:05:48Session information
sessionInfo()
#> R version 4.1.2 (2021-11-01)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] dplyr_1.0.8 metid_1.2.4 metpath_0.99.4
#> [4] massstat_0.99.13 ggfortify_0.4.14 massqc_0.99.7
#> [7] masscleaner_0.99.7 xcms_3.16.1 MSnbase_2.20.4
#> [10] ProtGenerics_1.26.0 S4Vectors_0.32.3 mzR_2.28.0
#> [13] Rcpp_1.0.8 Biobase_2.54.0 BiocGenerics_0.40.0
#> [16] BiocParallel_1.28.3 massprocesser_0.99.3 ggplot2_3.3.5
#> [19] masstools_0.99.5 massdataset_0.99.20 tidymass_0.99.6
#> [22] magrittr_2.0.2
#>
#> loaded via a namespace (and not attached):
#> [1] blogdown_1.7 tidyr_1.2.0
#> [3] missForest_1.4 knitr_1.37
#> [5] DelayedArray_0.20.0 data.table_1.14.2
#> [7] rpart_4.1.16 KEGGREST_1.34.0
#> [9] RCurl_1.98-1.5 doParallel_1.0.17
#> [11] generics_0.1.2 snow_0.4-4
#> [13] leaflet_2.1.0 preprocessCore_1.56.0
#> [15] mixOmics_6.18.1 RANN_2.6.1
#> [17] proxy_0.4-26 future_1.23.0
#> [19] tzdb_0.2.0 xml2_1.3.3
#> [21] lubridate_1.8.0 ggsci_2.9
#> [23] SummarizedExperiment_1.24.0 assertthat_0.2.1
#> [25] tidyverse_1.3.1 viridis_0.6.2
#> [27] xfun_0.29 hms_1.1.1
#> [29] jquerylib_0.1.4 evaluate_0.15
#> [31] DEoptimR_1.0-10 fansi_1.0.2
#> [33] dbplyr_2.1.1 readxl_1.3.1
#> [35] igraph_1.2.11 DBI_1.1.2
#> [37] htmlwidgets_1.5.4 MsFeatures_1.3.0
#> [39] rARPACK_0.11-0 purrr_0.3.4
#> [41] ellipsis_0.3.2 RSpectra_0.16-0
#> [43] crosstalk_1.2.0 backports_1.4.1
#> [45] bookdown_0.24 ggcorrplot_0.1.3
#> [47] MatrixGenerics_1.6.0 vctrs_0.3.8
#> [49] remotes_2.4.2 withr_2.4.3
#> [51] ggforce_0.3.3 itertools_0.1-3
#> [53] robustbase_0.93-9 checkmate_2.0.0
#> [55] cluster_2.1.2 lazyeval_0.2.2
#> [57] crayon_1.5.0 ellipse_0.4.2
#> [59] pkgconfig_2.0.3 tweenr_1.0.2
#> [61] GenomeInfoDb_1.30.0 nnet_7.3-17
#> [63] rlang_1.0.1 globals_0.14.0
#> [65] lifecycle_1.0.1 affyio_1.64.0
#> [67] extrafontdb_1.0 fastDummies_1.6.3
#> [69] MassSpecWavelet_1.60.0 modelr_0.1.8
#> [71] cellranger_1.1.0 randomForest_4.7-1
#> [73] polyclip_1.10-0 matrixStats_0.61.0
#> [75] Matrix_1.4-0 reprex_2.0.1
#> [77] base64enc_0.1-3 GlobalOptions_0.1.2
#> [79] png_0.1-7 viridisLite_0.4.0
#> [81] rjson_0.2.21 clisymbols_1.2.0
#> [83] bitops_1.0-7 pander_0.6.4
#> [85] Biostrings_2.62.0 shape_1.4.6
#> [87] stringr_1.4.0 parallelly_1.30.0
#> [89] robust_0.7-0 readr_2.1.2
#> [91] jpeg_0.1-9 gridGraphics_0.5-1
#> [93] scales_1.1.1 plyr_1.8.6
#> [95] zlibbioc_1.40.0 compiler_4.1.2
#> [97] RColorBrewer_1.1-2 pcaMethods_1.86.0
#> [99] clue_0.3-60 rrcov_1.6-2
#> [101] cli_3.2.0 affy_1.72.0
#> [103] XVector_0.34.0 listenv_0.8.0
#> [105] patchwork_1.1.1 pbapply_1.5-0
#> [107] htmlTable_2.4.0 Formula_1.2-4
#> [109] MASS_7.3-55 tidyselect_1.1.1
#> [111] vsn_3.62.0 stringi_1.7.6
#> [113] forcats_0.5.1.9000 yaml_2.3.4
#> [115] latticeExtra_0.6-29 MALDIquant_1.21
#> [117] ggrepel_0.9.1 grid_4.1.2
#> [119] sass_0.4.0 tools_4.1.2
#> [121] parallel_4.1.2 circlize_0.4.14
#> [123] rstudioapi_0.13 MsCoreUtils_1.6.0
#> [125] foreach_1.5.2 foreign_0.8-82
#> [127] gridExtra_2.3 farver_2.1.0
#> [129] mzID_1.32.0 ggraph_2.0.5
#> [131] rvcheck_0.2.1 digest_0.6.29
#> [133] BiocManager_1.30.16 GenomicRanges_1.46.1
#> [135] broom_0.7.12 ncdf4_1.19
#> [137] httr_1.4.2 ComplexHeatmap_2.10.0
#> [139] colorspace_2.0-2 rvest_1.0.2
#> [141] XML_3.99-0.8 fs_1.5.2
#> [143] IRanges_2.28.0 splines_4.1.2
#> [145] yulab.utils_0.0.4 graphlayouts_0.8.0
#> [147] ggplotify_0.1.0 plotly_4.10.0
#> [149] fit.models_0.64 jsonlite_1.7.3
#> [151] tidygraph_1.2.0 corpcor_1.6.10
#> [153] R6_2.5.1 Hmisc_4.6-0
#> [155] pillar_1.7.0 htmltools_0.5.2
#> [157] glue_1.6.1 fastmap_1.1.0
#> [159] class_7.3-20 codetools_0.2-18
#> [161] pcaPP_1.9-74 mvtnorm_1.1-3
#> [163] furrr_0.2.3 utf8_1.2.2
#> [165] lattice_0.20-45 bslib_0.3.1
#> [167] tibble_3.1.6 zip_2.2.0
#> [169] openxlsx_4.2.5 Rttf2pt1_1.3.9
#> [171] survival_3.2-13 limma_3.50.0
#> [173] rmarkdown_2.11 munsell_0.5.0
#> [175] e1071_1.7-9 GetoptLong_1.0.5
#> [177] GenomeInfoDbData_1.2.7 iterators_1.0.14
#> [179] impute_1.68.0 haven_2.4.3
#> [181] reshape2_1.4.4 gtable_0.3.0
#> [183] extrafont_0.17