title: how to speed up ggwithinplot for large dataset?
body: <p><strong>The problem</strong></p>
  
  <p>I am using ggwithinplot in ggstatsplot to plot 1755 data points and it turns out to be very slow.</p>
  
  <p>I confirmed this problem by testing the consumed time to plot different number of data points:</p>
  
  <p>1.N=120, 5.576425 secs.</p>
  
  <p>2.N=360, 11.90681 secs. </p>
  
  <p>3.N=1755, 6.699168 <strong>mins</strong> but not secs.</p>
  
  <p><strong>What I've tried</strong></p>
  
  <ol>
  <li>I transformed the data.frame into data.table. It helped.</li>
  <li>I set point.path = FALSE in ggwithinplot. It helped. </li>
  <li>I set nboot=1 in ggwithinplot to reduce the time to calculate the CI of the effect size. It helped. </li>
  </ol>
  
  <p><strong>Code</strong></p>
  
  <pre><code>  ggwithinstats(long_general_disBU_dt, x = condition, y = disBU,
                           point.path = FALSE,
                           mean.ci = TRUE,
                           pairwise.comparisons = TRUE,
                           pairwise.annotation = "asterisk",
                           pairwise.display = "s",
                           effsize.type = 'partial_eta',
                           p.adjust.method = "fdr",
                           ggtheme = theme_classic(),
                           palette = "Darjeeling2",
                           package = "wesanderson",
                           ggstatsplot.layer = FALSE,
                           bf.message= FALSE,
                           nboot=1,
                           xlab = "targets", 
                           ylab = "general_disBU_dt",
                           title = "general disBU across 20 news")
  </code></pre>
  
  <p><strong>session info:</strong></p>
  
  <pre><code>R version 3.6.3 (2020-02-29)
  Platform: x86_64-w64-mingw32/x64 (64-bit)
  Running under: Windows 10 x64 (build 15063)
  
  Matrix products: default
  
  locale:
  [1] LC_COLLATE=Chinese (Simplified)_China.936  LC_CTYPE=Chinese (Simplified)_China.936   
  [3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C                              
  [5] LC_TIME=Chinese (Simplified)_China.936    
  
  attached base packages:
  [1] splines   stats4    stats     graphics  grDevices utils     datasets  methods   base     
  
  other attached packages:
   [1] reshape_0.8.8      gginnards_0.0.3    VGAM_1.1-2         parameters_0.6.0   nnet_7.3-13        openxlsx_4.1.4    
   [7] summarytools_0.9.6 ggcorrplot_0.1.3   bruceR_0.4.0       performance_0.4.4  lubridate_1.7.4    psych_1.9.12.31   
  [13] data.table_1.12.8  rio_0.5.16         ggstatsplot_0.3.1  forcats_0.5.0      stringr_1.4.0      dplyr_0.8.5       
  [19] purrr_0.3.3        readr_1.3.1        tidyr_1.0.2        tibble_2.1.3       ggplot2_3.3.0      tidyverse_1.3.0   
  [25] drawMap_0.1.0     
  
  loaded via a namespace (and not attached):
    [1] estimability_1.3          GGally_1.4.0              lavaan_0.6-5              coda_0.19-3              
    [5] acepack_1.4.1             knitr_1.28                multcomp_1.4-12           rpart_4.1-15             
    [9] inline_0.3.15             generics_0.0.2            callr_3.4.2               cowplot_1.0.0            
   [13] TH.data_1.0-10            xml2_1.2.5                httpuv_1.5.2              StanHeaders_2.21.0-1     
   [17] assertthat_0.2.1          d3Network_0.5.2.1         WRS2_1.0-0                xfun_0.12                
   [21] hms_0.5.3                 promises_1.1.0            fansi_0.4.1               dbplyr_1.4.2             
   [25] readxl_1.3.1              igraph_1.2.4.2            htmlwidgets_1.5.1         DBI_1.1.0                
   [29] Rsolnp_1.16               paletteer_1.1.0           rcompanion_2.3.25         backports_1.1.5          
   [33] pbivnorm_0.6.0            insight_0.8.2             rapportools_1.0           libcoin_1.0-5            
   [37] jmvcore_1.2.5             vctrs_0.2.4               sjlabelled_1.1.3          abind_1.4-5              
   [41] withr_2.1.2               pryr_0.1.4                metaBMA_0.6.2             checkmate_2.0.0          
   [45] bdsmatrix_1.3-4           emmeans_1.4.5             fdrtool_1.2.15            prettyunits_1.1.1        
   [49] fastGHQuad_1.0            mnormt_1.5-6              cluster_2.1.0             mi_1.0                   
   [53] crayon_1.3.4              pkgconfig_2.0.3           nlme_3.1-145              statsExpressions_0.3.1   
   [57] palr_0.2.0                pals_1.6                  rlang_0.4.5               lifecycle_0.2.0          
   [61] miniUI_0.1.1.1            groupedstats_0.2.0        skimr_2.1                 LaplacesDemon_16.1.4     
   [65] MatrixModels_0.4-1        sandwich_2.5-1            kutils_1.69               EMT_1.1                  
   [69] modelr_0.1.6              dichromat_2.0-0           tcltk_3.6.3               cellranger_1.1.0         
   [73] matrixStats_0.56.0        broomExtra_2.5.0          lmtest_0.9-37             Matrix_1.2-18            
   [77] regsem_1.5.2              loo_2.2.0                 mc2d_0.1-18               carData_3.0-3            
   [81] boot_1.3-24               zoo_1.8-7                 reprex_0.3.0              base64enc_0.1-3          
   [85] whisker_0.4               processx_3.4.2            png_0.1-7                 viridisLite_0.3.0        
   [89] rjson_0.2.20              oompaBase_3.2.9           pander_0.6.3              ggExtra_0.9              
   [93] afex_0.26-0               multcompView_0.1-8        coin_1.3-1                arm_1.10-1               
   [97] jpeg_0.1-8.1              rockchalk_1.8.144         ggsignif_0.6.0            scales_1.1.0             
  [101] magrittr_1.5              plyr_1.8.6                compiler_3.6.3            rstantools_2.0.0         
  [105] bbmle_1.0.23.1            RColorBrewer_1.1-2        lme4_1.1-21               cli_2.0.2                
  [109] lmerTest_3.1-1            pbapply_1.4-2             ps_1.3.2                  TMB_1.7.16               
  [113] Brobdingnag_1.2-6         htmlTable_1.13.3          Formula_1.2-3             MASS_7.3-51.5            
  [117] mgcv_1.8-31               tidyselect_1.0.0          stringi_1.4.6             lisrelToR_0.1.4          
  [121] sem_3.1-9                 jtools_2.0.2              OpenMx_2.17.3             latticeExtra_0.6-29      
  [125] ggrepel_0.8.2             bridgesampling_1.0-0      grid_3.6.3                tools_3.6.3              
  [129] parallel_3.6.3            matrixcalc_1.0-3          rstudioapi_0.11           foreign_0.8-76           
  [133] gridExtra_2.3             ipmisc_1.2.0              pairwiseComparisons_0.2.5 BDgraph_2.62             
  [137] digest_0.6.25             shiny_1.4.0.2             nortest_1.0-4             jmv_1.2.5                
  [141] Rcpp_1.0.3                car_3.0-7                 broom_0.5.5               metafor_2.1-0            
  [145] ez_4.4-0                  BayesFactor_0.9.12-4.2    metaplus_0.7-11           later_1.0.0              
  [149] httr_1.4.1                effectsize_0.2.0          sjstats_0.17.9            colorspace_1.4-1         
  [153] rvest_0.3.5               XML_3.99-0.3              fs_1.3.2                  truncnorm_1.0-8          
  [157] rematch2_2.1.0            expm_0.999-4              mapproj_1.2.7             jcolors_0.0.4            
  [161] MuMIn_1.43.15             xtable_1.8-4              jsonlite_1.6.1            nloptr_1.2.2.1           
  [165] corpcor_1.6.9             rstan_2.19.3              glasso_1.11               zeallot_0.1.0            
  [169] modeltools_0.2-23         scico_1.1.0               R6_2.4.1                  Hmisc_4.3-1              
  [173] broom.mixed_0.2.4         pillar_1.4.3              htmltools_0.4.0           mime_0.9                 
  [177] glue_1.3.2                fastmap_1.0.1             minqa_1.2.4               codetools_0.2-16         
  [181] maps_3.3.0                pkgbuild_1.0.6            mvtnorm_1.1-0             lattice_0.20-40          
  [185] numDeriv_2016.8-1.1       huge_1.3.4                curl_4.3                  DescTools_0.99.34        
  [189] gtools_3.8.1              magick_2.3                logspline_2.1.15          zip_2.0.4                
  [193] survival_3.1-11           qgraph_1.6.5              repr_1.1.0                munsell_0.5.0            
  [197] semPlot_1.1.2             sjmisc_2.8.3              haven_2.2.0               reshape2_1.4.3           
  [201] gtable_0.3.0              bayestestR_0.5.2 
  </code></pre>
  
  <p>Is there anything I can do to speed up the ggwithinplot function?</p>