I've created a heatplot using enrichPlot, from GSEA results obtained by clusterProfiler. However, it's plotted a lot of genes on the x axis and it's far too busy (pictured). Is there a way to cut down the amount of genes plotted to make the graph more readable? Thanks for your time!

GSEA_results <- GSEA(ranked_gene_list, 
                         TERM2GENE = m_t2g)

heatplot(GSEA_results,
         showCategory = 10,
         foldChange = ranked_gene_list)

sessionInfo( )
R version 4.0.1 (2020-06-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

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

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

other attached packages:
 [1] DOSE_3.14.0            enrichplot_1.8.1       pathview_1.28.1       
 [4] forcats_0.5.0          stringr_1.4.0          dplyr_1.0.2           
 [7] purrr_0.3.4            readr_1.4.0            tidyr_1.1.2           
[10] tibble_3.0.3           ggplot2_3.3.2          tidyverse_1.3.0       
[13] clusterProfiler_3.16.1 limma_3.44.3           biomaRt_2.44.4        

loaded via a namespace (and not attached):
  [1] snow_0.4-3                  readxl_1.3.1                backports_1.1.10           
  [4] fastmatch_1.1-0             BiocFileCache_1.12.1        plyr_1.8.6                 
  [7] igraph_1.2.6                splines_4.0.1               BiocParallel_1.22.0        
 [10] GenomeInfoDb_1.24.2         urltools_1.7.3              digest_0.6.25              
 [13] htmltools_0.5.0             GOSemSim_2.14.2             viridis_0.5.1              
 [16] GO.db_3.11.4                fansi_0.4.1                 magrittr_1.5               
 [19] memoise_1.1.0               Biostrings_2.56.0           annotate_1.66.0            
 [22] graphlayouts_0.7.1          modelr_0.1.8                matrixStats_0.57.0         
 [25] askpass_1.1                 prettyunits_1.1.1           colorspace_1.4-1           
 [28] blob_1.2.1                  rvest_0.3.6                 rappdirs_0.3.1             
 [31] ggrepel_0.8.2               haven_2.3.1                 xfun_0.18                  
 [34] crayon_1.3.4                RCurl_1.98-1.2              jsonlite_1.7.1             
 [37] org.Mm.eg.db_3.11.4         graph_1.66.0                scatterpie_0.1.5           
 [40] genefilter_1.70.0           survival_3.1-12             glue_1.4.2                 
 [43] polyclip_1.10-0             gtable_0.3.0                zlibbioc_1.34.0            
 [46] XVector_0.28.0              DelayedArray_0.14.1         Rgraphviz_2.32.0           
 [49] BiocGenerics_0.34.0         scales_1.1.1                DBI_1.1.0                  
 [52] Rcpp_1.0.5                  viridisLite_0.3.0           xtable_1.8-4               
 [55] progress_1.2.2              gridGraphics_0.5-0          bit_4.0.4                  
 [58] europepmc_0.4               stats4_4.0.1                DT_0.16                    
 [61] htmlwidgets_1.5.2           httr_1.4.2                  fgsea_1.14.0               
 [64] RColorBrewer_1.1-2          ellipsis_0.3.1              pkgconfig_2.0.3            
 [67] XML_3.99-0.5                farver_2.0.3                dbplyr_1.4.4               
 [70] locfit_1.5-9.4              ggplotify_0.0.5             tidyselect_1.1.0           
 [73] labeling_0.4.2              rlang_0.4.7                 reshape2_1.4.4             
 [76] AnnotationDbi_1.50.3        munsell_0.5.0               cellranger_1.1.0           
 [79] tools_4.0.1                 downloader_0.4              cli_2.1.0                  
 [82] generics_0.0.2              RSQLite_2.2.1               broom_0.7.2                
 [85] ggridges_0.5.2              org.Hs.eg.db_3.11.4         bit64_4.0.5                
 [88] fs_1.5.0                    tidygraph_1.2.0             KEGGREST_1.28.0            
 [91] ggraph_2.0.3                KEGGgraph_1.48.0            DO.db_2.9                  
 [94] xml2_1.3.2                  compiler_4.0.1              rstudioapi_0.11            
 [97] png_0.1-7                   curl_4.3                    reprex_0.3.0               
[100] tweenr_1.0.1                geneplotter_1.66.0          stringi_1.5.3              
[103] lattice_0.20-41             Matrix_1.2-18               vctrs_0.3.4                
[106] pillar_1.4.6                lifecycle_0.2.0             BiocManager_1.30.10        
[109] triebeard_0.3.0             data.table_1.13.0           cowplot_1.1.0              
[112] bitops_1.0-6                GenomicRanges_1.40.0        qvalue_2.20.0              
[115] R6_2.5.0                    gridExtra_2.3               IRanges_2.22.2             
[118] MASS_7.3-51.6               assertthat_0.2.1            SummarizedExperiment_1.18.2
[121] openssl_1.4.3               DESeq2_1.28.1               withr_2.3.0                
[124] S4Vectors_0.26.1            GenomeInfoDbData_1.2.3      parallel_4.0.1             
[127] hms_0.5.3                   grid_4.0.1                  rvcheck_0.1.8              
[130] ggforce_0.3.2               Biobase_2.48.0              lubridate_1.7.9            
[133] tinytex_0.26