Hi, I have a Reactome enrichment result, obtained from the reactome website. it is an excel with all the categories, ratios, p-values.. how could I visualize the enrichment results? ggplot2? enrichplot? How should I convert the data to let them readable with a code like the ones below? sorry but I'm very new in R Thank you!

Code should be placed in three backticks as shown below

library(DOSE)
data(geneList)
de <- names(geneList)[1:100]
x <- enrichDO(de)
barplot(x)


library(ggplot2)
library(forcats)
ggplot(result_reactome_121, showCategory = 10,
       aes(richFactor,
           fct_reorder(Description, richFactor))) +
  geom_segment(aes(xend=0, yend = Description)) +
  geom_point(aes(color=p.adjust, size = Count)) +
  scale_color_gradientn
(colours=c("#f7ca64", "#46bac2",
           "#7e62a3"),
  trans = "log10",
  guide=guide_colorbar(reverse=TRUE,
                       order=1)) +
  scale_size_continuous(range=c(2, 10)) +
  theme_dose(12) +
  xlab("Rich Factor") +
  lab(NULL) +
  ggtitle("Biological Processes")

# Error in barplot.default(x) : 'height' must be a vector or a matrix


sessionInfo( )

R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.0.1

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Rome
tzcode source: internal

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

other attached packages:
[1] ggplot2_3.4.2 DOSE_3.26.1  

loaded via a namespace (and not attached):
 [1] KEGGREST_1.40.0         fastmatch_1.1-3         gtable_0.3.3            GOSemSim_2.26.0        
 [5] Biobase_2.60.0          lattice_0.21-8          vctrs_0.6.3             tools_4.3.1            
 [9] bitops_1.0-7            generics_0.1.3          stats4_4.3.1            parallel_4.3.1         
[13] tibble_3.2.1            fansi_1.0.4             AnnotationDbi_1.62.1    RSQLite_2.3.1          
[17] blob_1.2.4              pkgconfig_2.0.3         Matrix_1.5-4.1          data.table_1.14.8      
[21] S4Vectors_0.38.1        lifecycle_1.0.3         GenomeInfoDbData_1.2.10 stringr_1.5.0          
[25] HDO.db_0.99.1           compiler_4.3.1          Biostrings_2.68.1       munsell_0.5.0          
[29] fgsea_1.26.0            codetools_0.2-19        GenomeInfoDb_1.36.1     RCurl_1.98-1.12        
[33] pillar_1.9.0            crayon_1.5.2            GO.db_3.17.0            BiocParallel_1.34.2    
[37] cachem_1.0.8            tidyselect_1.2.0        stringi_1.7.12          dplyr_1.1.2            
[41] reshape2_1.4.4          splines_4.3.1           cowplot_1.1.1           fastmap_1.1.1          
[45] grid_4.3.1              colorspace_2.1-0        cli_3.6.1               magrittr_2.0.3         
[49] utf8_1.2.3              withr_2.5.0             scales_1.2.1            bit64_4.0.5            
[53] XVector_0.40.0          httr_1.4.6              bit_4.0.5               qvalue_2.32.0          
[57] png_0.1-8               memoise_2.0.1           IRanges_2.34.0          rlang_1.1.1            
[61] Rcpp_1.0.10             glue_1.6.2              DBI_1.1.3               BiocManager_1.30.21    
[65] BiocGenerics_0.46.0     R6_2.5.1                plyr_1.8.8              zlibbioc_1.46.0        

If you want to directly use ggplot2 you will need to convert the enrich result to a data.frame with as.data.frame(result_reactome_121) and then you can use ggplot()