> library(tidyverse)
> cell_markers <- vroom::vroom('http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Mouse_cell_markers.txt') %>%
+   tidyr::unite("cellMarker", tissueType, cancerType, cellName, sep=", ") %>% 
+   dplyr::select(cellMarker, geneID) %>%
+   dplyr::mutate(geneID = strsplit(geneID, ', '))
Error in open.connection(3L, "rb") : 
  Timeout was reached: [bio-bigdata.hrbmu.edu.cn] Connection timed out after 10001 milliseconds
In addition: Warning messages:
1: In for (i in seq_along(args)) { :
  closing unused connection 4 (http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Mouse_cell_markers.txt)
2: In for (i in seq_along(args)) { :
  closing unused connection 3 (http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Mouse_cell_markers.txt)
> 1+1
[1] 2

> .libPaths()
[1] "/home/rstudio/R/x86_64-pc-linux-gnu-library/4.1"
[2] "/usr/local/lib/R/site-library"                  
[3] "/usr/lib/R/site-library"                        
[4] "/usr/lib/R/library"                             
> cell_markers <- vroom::vroom('http://bio-bigdata.hrbmu.edu.cn/CellMarker/download/Mouse_cell_markers.txt') %>%
+   tidyr::unite("cellMarker", tissueType, cancerType, cellName, sep=", ") %>% 
+   dplyr::select(cellMarker, geneID) %>%
+   dplyr::mutate(geneID = strsplit(geneID, ', '))
Rows: 1,255                                                                            
Columns: 15
Delimiter: "\t"
chr [15]: speciesType, tissueType, UberonOntologyID, cancerType, cellType, cellName, CellOntologyID...

Use `spec()` to retrieve the guessed column specification
Pass a specification to the `col_types` argument to quiet this message
> cell_markers
# A tibble: 1,255 x 2
   cellMarker                                         geneID   
   <chr>                                              <list>   
 1 Bone marrow, Normal, Blastema cell                 <chr [3]>
 2 Taste bud, Normal, Type II taste bud cell          <chr [2]>
 3 Taste bud, Normal, Type III taste bud cell         <chr [5]>
 4 Adipose tissue, Normal, Adipose-derived stem cell  <chr [1]>
 5 Undefined, Normal, Regulatory T (Treg) cell        <chr [2]>
 6 Adipose tissue, Normal, White fat cell             <chr [1]>
 7 Meniscus, Normal, Meniscus-derived stem cell       <chr [6]>
 8 Meniscus, Normal, Meniscus-derived progenitor cell <chr [6]>
 9 Peyer patch, Normal, B cell                        <chr [1]>
10 Peyer patch, Normal, T cell                        <chr [3]>
# … with 1,245 more rows
> y <- enricher(gene.df$ENTREZID, TERM2GENE=cell_markers, minGSSize=1)