Hi,

I'm noticing something odd when I combine my 2 column allelic microsatellite dataset in R to 1 column with a separator (i.e. from locus1a: 100, locus1b: 90 to locus1: 100/90).

I've tried pegas::alleles2loci() and genetics::makeGenotypes() and noticed with both functions that for come loci in some rows, the order of the alleles switches when applying the functions, but it is not consistent (i.e. from locus1a: 100, locus1b: 90 to locus1: 90/10). I can't find any other help or documentation on why this occurs, can anyone help?

Code below:

library(strataG)
library(pegas)
library(genetics)

msat <- readGenData("./R_tarandus_Kluetsch_2017_microsats.csv")

str(msat)

'data.frame':   591 obs. of  20 variables:
 $ indiv_id: chr  "30554" "30555" "30559" "30560" ...
 $ pop     : chr  "BAN" "BAN" "BAN" "BAN" ...
 $ BM848a  : chr  "364" "362" "372" "386" ...
 $ BM848b  : chr  "386" "372" "386" "386" ...

etc

head(msat)

indiv_id pop BM848a BM848b BM888a BM888b MAP2Ca MAP2Cb RT24a RT24b RT30a RT30b RT5a RT5b RT6a RT6b RT7a RT7b RT9a RT9b
1    30554 BAN    364    386    164    174     89    105   209   209   187   193   98  114  110  124  218  218  116  124

msat_loci <- pegas::alleles2loci(msat, ploidy = 2, rownames = 1, population = 2)
msat_gen <- genetics::makeGenotypes(msat, convert=list(3:4,5:6,7:8,9:10,11:12,13:14,15:16,17:18,19:20))
head(msat_gen)

  indiv_id pop BM848a/BM848b BM888a/BM888b MAP2Ca/MAP2Cb RT24a/RT24b RT30a/RT30b RT5a/RT5b RT6a/RT6b RT7a/RT7b RT9a/RT9b
1    30554 BAN       386/364       174/164        105/89     209/209     193/187    114/98   110/124   218/218   116/124


sessionInfo()

 R version 3.5.1 (2018-07-02)
 Platform: x86_64-w64-mingw32/x64 (64-bit)
 Running under: Windows 10 x64 (build 14393)

 Matrix products: default

 locale:
   [1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United Kingdom.1252    LC_MONETARY=English_United Kingdom.1252
 [4] LC_NUMERIC=C                            LC_TIME=English_United Kingdom.1252    

 attached base packages:
   [1] stats     graphics  grDevices utils     datasets  methods   base     

 other attached packages:
  [1] genetics_1.3.8.1.2  mvtnorm_1.1-0       MASS_7.3-50         gtools_3.8.2        gdata_2.18.0        combinat_0.0-8     
 [7] HardyWeinberg_1.6.3 Rsolnp_1.16         mice_3.8.0          genepop_1.1.7       tidyr_1.0.2         purrr_0.3.4        
 [13] stringr_1.4.0       reshape_0.8.8       diffdf_1.0.4        kableExtra_1.1.0    dplyr_0.8.5         haplotypes_1.1.2   
[19] pegas_0.13          adegenet_2.1.2      ade4_1.7-15         ape_5.3             strataG_2.4.905    

 loaded via a namespace (and not attached):
   [1] colorspace_1.4-1      seqinr_3.6-1          deldir_0.1-25         ellipsis_0.3.0        class_7.3-14         
 [6] PopGenome_2.7.5       rstudioapi_0.11       spatstat.data_1.4-3   fansi_0.4.1           xml2_1.3.2           
 [11] codetools_0.2-15      splines_3.5.1         knitr_1.28            polyclip_1.10-0       swfscMisc_1.3        
 [16] broom_0.5.6           cluster_2.0.7-1       apex_1.0.4            shiny_1.4.0.2         readr_1.3.1          
 [21] compiler_3.5.1        httr_1.4.1            backports_1.1.6       assertthat_0.2.1      Matrix_1.2-14        
 [26] fastmap_1.0.1         cli_2.0.2             later_1.0.0           htmltools_0.4.0       tools_3.5.1          
 [31] igraph_1.2.5          coda_0.19-3           gtable_0.3.0          glue_1.4.0            reshape2_1.4.4       
 [36] maps_3.3.0            gmodels_2.18.1        tinytex_0.23          fastmatch_1.1-0       Rcpp_1.0.4.6         
 [41] spatstat_1.63-3       statnet.common_4.3.0  raster_3.1-5          vctrs_0.2.4           spdep_1.1-3          
 [46] nlme_3.1-137          xfun_0.14             network_1.16.0        rvest_0.3.5           mime_0.9             
 [51] mapdata_2.3.0         lifecycle_0.2.0       phangorn_2.5.5        goftest_1.2-2         LearnBayes_2.15.1    
 [56] scales_1.1.1          hms_0.5.3             promises_1.1.0        spatstat.utils_1.17-0 parallel_3.5.1       
 [61] expm_0.999-4          yaml_2.2.1            ggplot2_3.3.1         rpart_4.1-13          stringi_1.4.6        
 [66] plotrix_3.7-8         e1071_1.7-3           permute_0.9-5         boot_1.3-20           truncnorm_1.0-8      
 [71] spData_0.3.5          rlang_0.4.5           pkgconfig_2.0.3       evaluate_0.14         lattice_0.20-35      
 [76] tensor_1.5            sf_0.9-2              bit_1.1-15.2          tidyselect_1.0.0      plyr_1.8.6           
 [81] magrittr_1.5          R6_2.4.1              generics_0.0.2        sna_2.5               DBI_1.1.0            
 [86] pillar_1.4.4          mgcv_1.8-24           units_0.6-6           abind_1.4-5           sp_1.4-1             
 [91] tibble_3.0.1          crayon_1.3.4          KernSmooth_2.23-15    rmarkdown_2.2         grid_3.5.1           
 [96] data.table_1.12.8     vegan_2.5-6           digest_0.6.25         classInt_0.4-3        webshot_0.5.2        
 [101] xtable_1.8-4          ff_2.2-14.2           httpuv_1.5.2          munsell_0.5.0         viridisLite_0.3.0    
 [106] quadprog_1.5-8