If you have two mass_dataset, you can use cbind or rbind to merge them as one mass_dataset.

cbind()

cbind() means that two mass_dataset have same variabels and totally different samples.

library(massdataset)
library(tidyverse)

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: 1.0.12 
#> -------------------- 
#> 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())
#> 1 processings in total
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:33
object1 = 
  object[,1:4]

object2 = 
  object[,5:8]

colnames(object1)
#> [1] "Blank_3" "Blank_4" "QC_1"    "QC_2"
colnames(object2)
#> [1] "PS4P1" "PS4P2" "PS4P3" "PS4P4"
cbind(object1, object2)
#> -------------------- 
#> massdataset version: 1.0.12 
#> -------------------- 
#> 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())
#> 4 processings in total
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:33
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2022-08-07 19:21:33
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:33
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2022-08-07 19:21:33

rbind()

rbind() means that two mass_dataset have same samples and totally different variables

object1 = 
  object[1:10,]

object2 = 
  object[11:15,]

rownames(object1)
#>  [1] "M136T55_2_POS" "M79T35_POS"    "M307T548_POS"  "M183T224_POS" 
#>  [5] "M349T47_POS"   "M182T828_POS"  "M299T359_POS"  "M348T844_POS" 
#>  [9] "M344T471_POS"  "M181T436_POS"
rownames(object2)
#> [1] "M345T195_POS" "M304T825_POS" "M137T196_POS" "M359T638_POS" "M270T507_POS"
rbind(object1, object2)
#> -------------------- 
#> massdataset version: 1.0.12 
#> -------------------- 
#> 1.expression_data:[ 15 x 8 data.frame]
#> 2.sample_info:[ 8 x 7 data.frame]
#> 3.variable_info:[ 15 x 3 data.frame]
#> 4.sample_info_note:[ 7 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())
#> 4 processings in total
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:33
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2022-08-07 19:21:34
#> create_mass_dataset ---------- 
#>       Package         Function.used                Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:33
#> subset ---------- 
#>       Package Function.used                Time
#> 1 massdataset             [ 2022-08-07 19:21:34

Session information

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] masstools_1.0.2    forcats_0.5.1.9000 stringr_1.4.0      dplyr_1.0.9       
#>  [5] purrr_0.3.4        readr_2.1.2        tidyr_1.2.0        tibble_3.1.7      
#>  [9] ggplot2_3.3.6      tidyverse_1.3.1    magrittr_2.0.3     tinytools_0.9.1   
#> [13] massdataset_1.0.12
#> 
#> loaded via a namespace (and not attached):
#>   [1] readxl_1.4.0                backports_1.4.1            
#>   [3] circlize_0.4.15             systemfonts_1.0.4          
#>   [5] plyr_1.8.7                  lazyeval_0.2.2             
#>   [7] BiocParallel_1.30.3         GenomeInfoDb_1.32.2        
#>   [9] Rdisop_1.56.0               digest_0.6.29              
#>  [11] foreach_1.5.2               yulab.utils_0.0.5          
#>  [13] htmltools_0.5.2             fansi_1.0.3                
#>  [15] memoise_2.0.1               cluster_2.1.3              
#>  [17] doParallel_1.0.17           tzdb_0.3.0                 
#>  [19] openxlsx_4.2.5              limma_3.52.2               
#>  [21] ComplexHeatmap_2.12.0       modelr_0.1.8               
#>  [23] matrixStats_0.62.0          pkgdown_2.0.5              
#>  [25] colorspace_2.0-3            rvest_1.0.2                
#>  [27] textshaping_0.3.6           haven_2.5.0                
#>  [29] xfun_0.31                   crayon_1.5.1               
#>  [31] RCurl_1.98-1.7              jsonlite_1.8.0             
#>  [33] impute_1.70.0               iterators_1.0.14           
#>  [35] glue_1.6.2                  gtable_0.3.0               
#>  [37] zlibbioc_1.42.0             XVector_0.36.0             
#>  [39] GetoptLong_1.0.5            DelayedArray_0.22.0        
#>  [41] shape_1.4.6                 BiocGenerics_0.42.0        
#>  [43] scales_1.2.0                vsn_3.64.0                 
#>  [45] DBI_1.1.3                   Rcpp_1.0.8.3               
#>  [47] mzR_2.30.0                  viridisLite_0.4.0          
#>  [49] clue_0.3-61                 gridGraphics_0.5-1         
#>  [51] preprocessCore_1.58.0       stats4_4.2.1               
#>  [53] MsCoreUtils_1.8.0           htmlwidgets_1.5.4          
#>  [55] httr_1.4.3                  RColorBrewer_1.1-3         
#>  [57] ellipsis_0.3.2              pkgconfig_2.0.3            
#>  [59] XML_3.99-0.10               sass_0.4.1                 
#>  [61] dbplyr_2.2.1                utf8_1.2.2                 
#>  [63] ggplotify_0.1.0             tidyselect_1.1.2           
#>  [65] rlang_1.0.3                 munsell_0.5.0              
#>  [67] cellranger_1.1.0            tools_4.2.1                
#>  [69] cachem_1.0.6                cli_3.3.0                  
#>  [71] generics_0.1.3              broom_1.0.0                
#>  [73] evaluate_0.15               fastmap_1.1.0              
#>  [75] mzID_1.34.0                 yaml_2.3.5                 
#>  [77] ragg_1.2.2                  knitr_1.39                 
#>  [79] fs_1.5.2                    zip_2.2.0                  
#>  [81] ncdf4_1.19                  pbapply_1.5-0              
#>  [83] xml2_1.3.3                  compiler_4.2.1             
#>  [85] rstudioapi_0.13             plotly_4.10.0              
#>  [87] png_0.1-7                   affyio_1.66.0              
#>  [89] reprex_2.0.1                bslib_0.3.1                
#>  [91] stringi_1.7.6               desc_1.4.1                 
#>  [93] MSnbase_2.22.0              lattice_0.20-45            
#>  [95] ProtGenerics_1.28.0         Matrix_1.4-1               
#>  [97] ggsci_2.9                   vctrs_0.4.1                
#>  [99] pillar_1.7.0                lifecycle_1.0.1            
#> [101] BiocManager_1.30.18         jquerylib_0.1.4            
#> [103] MALDIquant_1.21             GlobalOptions_0.1.2        
#> [105] data.table_1.14.2           bitops_1.0-7               
#> [107] GenomicRanges_1.48.0        R6_2.5.1                   
#> [109] pcaMethods_1.88.0           affy_1.74.0                
#> [111] IRanges_2.30.0              codetools_0.2-18           
#> [113] MASS_7.3-57                 assertthat_0.2.1           
#> [115] SummarizedExperiment_1.26.1 rprojroot_2.0.3            
#> [117] rjson_0.2.21                withr_2.5.0                
#> [119] S4Vectors_0.34.0            GenomeInfoDbData_1.2.8     
#> [121] parallel_4.2.1              hms_1.1.1                  
#> [123] grid_4.2.1                  rmarkdown_2.14             
#> [125] MatrixGenerics_1.8.1        Biobase_2.56.0             
#> [127] lubridate_1.8.0