Last updated: 2020-09-22

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Knit directory: R_gene_analysis/

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library(tidyverse)
-- Attaching packages ------------------------------------------------------------------------------------------------------------------ tidyverse 1.3.0 --
v ggplot2 3.3.2     v purrr   0.3.4
v tibble  3.0.2     v dplyr   1.0.0
v tidyr   1.1.0     v stringr 1.4.0
v readr   1.3.1     v forcats 0.5.0
-- Conflicts --------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(knitr)

Introduction

Here i make some of the tables for the manuscript.

files_to_make_tables_for <- list.files(path = './data', pattern = '*lst')
files_to_make_tables_for
[1] "Lee.NBS.candidates.lst"             "Lee.preRGA.candidates.by.Blast.lst"
[3] "Lee.RGA.candidates.lst"             "Lee.RLK.candidates.lst"            
[5] "Lee.RLP.candidates.lst"             "Lee.TMCC.candidates.lst"           

Let’s print the counts of R-gene classes for each type, that will be table 1:

for(i in seq_along(files_to_make_tables_for)) {
  f <- files_to_make_tables_for[i]
  fh <- read_tsv(paste('./data/', f, sep=''), col_names = c('Name','Class','Type'))
  fh <- fh %>% unite(United, Class, Type)
  # remove NAs in files with 2 columns
  fh$United <- gsub(pattern = '_NA', replacement = '', fh$United)
  
  # now split into pangenome genes and reference genome genes
  pan_fh <- fh %>% filter(str_detect(Name, 'UWA'))
  ref_fh <- fh %>% filter(!str_detect(Name, 'UWA'))
  print(f)
  print(kable(table(fh$United), 'html'))
  print('Reference')
  print(kable(table(ref_fh$United), 'html'))
  print('Pangenome')
  print(kable(table(pan_fh$United), 'html'))
}
[1] “Lee.NBS.candidates.lst”
Var1 Freq
CN 13
CNL 123
NBS 52
NL 95
OTHER 20
TN 22
TNL 99
TX 62
[1] “Reference”
Var1 Freq
CN 8
CNL 123
NBS 29
NL 92
OTHER 20
TN 22
TNL 99
TX 49
[1] “Pangenome”
Var1 Freq
CN 5
NBS 23
NL 3
TX 13
[1] “Lee.preRGA.candidates.by.Blast.lst”
Var1 Freq
NA 14358
[1] “Reference”
Var1 Freq
NA 13448
[1] “Pangenome”
Var1 Freq
NA 910
[1] “Lee.RGA.candidates.lst”
Var1 Freq
CN 13
CNL 123
NBS 52
NL 95
OTHER 20
RLK 1173
RLP 180
TM-CC 280
TN 22
TNL 99
TX 62
[1] “Reference”
Var1 Freq
CN 8
CNL 123
NBS 29
NL 92
OTHER 20
RLK 1164
RLP 170
TM-CC 273
TN 22
TNL 99
TX 49
[1] “Pangenome”
Var1 Freq
CN 5
NBS 23
NL 3
RLK 9
RLP 10
TM-CC 7
TX 13
[1] “Lee.RLK.candidates.lst”
Var1 Freq
RLK_lrr 470
RLK_lysm 19
RLK_other_receptor 684
[1] “Reference”
Var1 Freq
RLK_lrr 469
RLK_lysm 19
RLK_other_receptor 676
[1] “Pangenome”
Var1 Freq
RLK_lrr 1
RLK_other_receptor 8
[1] “Lee.RLP.candidates.lst”
Var1 Freq
RLP_lrr 177
RLP_lysm 3
[1] “Reference”
Var1 Freq
RLP_lrr 167
RLP_lysm 3
[1] “Pangenome”
Var1 Freq
RLP_lrr 10
[1] “Lee.TMCC.candidates.lst”
Var1 Freq
TM-CC 280
[1] “Reference”
Var1 Freq
TM-CC 273
[1] “Pangenome”
Var1 Freq
TM-CC 7

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)

Matrix products: default

locale:
[1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
[3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
[5] LC_TIME=English_Australia.1252    

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

other attached packages:
 [1] knitr_1.29           forcats_0.5.0        stringr_1.4.0       
 [4] dplyr_1.0.0          purrr_0.3.4          readr_1.3.1         
 [7] tidyr_1.1.0          tibble_3.0.2         ggplot2_3.3.2       
[10] tidyverse_1.3.0      workflowr_1.6.2.9000

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.17        haven_2.3.1      lattice_0.20-41 
 [5] colorspace_1.4-1 vctrs_0.3.1      generics_0.0.2   htmltools_0.5.0 
 [9] yaml_2.2.1       blob_1.2.1       rlang_0.4.7      later_1.1.0.1   
[13] pillar_1.4.4     withr_2.2.0      glue_1.4.2       DBI_1.1.0       
[17] dbplyr_1.4.4     readxl_1.3.1     modelr_0.1.8     lifecycle_0.2.0 
[21] cellranger_1.1.0 munsell_0.5.0    gtable_0.3.0     rvest_0.3.5     
[25] evaluate_0.14    callr_3.4.4      httpuv_1.5.4     ps_1.3.4        
[29] fansi_0.4.1      highr_0.8        broom_0.5.6      Rcpp_1.0.5      
[33] promises_1.1.1   backports_1.1.10 scales_1.1.1     jsonlite_1.7.1  
[37] fs_1.5.0.9000    hms_0.5.3        digest_0.6.25    stringi_1.5.3   
[41] processx_3.4.4   getPass_0.2-2    rprojroot_1.3-2  grid_3.6.3      
[45] cli_2.0.2        tools_3.6.3      magrittr_1.5     crayon_1.3.4    
[49] whisker_0.4      pkgconfig_2.0.3  ellipsis_0.3.1   xml2_1.3.2      
[53] reprex_0.3.0     lubridate_1.7.9  assertthat_0.2.1 rmarkdown_2.3   
[57] httr_1.4.2       rstudioapi_0.11  R6_2.4.1         nlme_3.1-148    
[61] git2r_0.27.1     compiler_3.6.3