Hi DN99,

I think this may be what you need.

First download this file. It is the most current gene ontology database file: http://current.geneontology.org/annotations/goa_human.gaf.gz

Then extract the gzip file onto your Desktop. The following script will give you a master list of all GENE IDS and their associated GO IDs in a dataframe. Then you can use the merge() function to merge the GO:IDS with your gene list data frame.

system("awk 'NR>=42' ~/Desktop/goa_human.gaf > ~/Desktop/goa_human_no_header.txt")
GO <-read.csv("~/Desktop/goa_human_no_header.txt", header=F, sep="\t")

GO$V4 <- NULL
GO$V7 <- NULL
GO$V8 <- NULL
GO$V1 <- NULL
GO$V6 <- NULL
GO$V10 <- NULL
GO$V13 <- NULL
GO$V14 <- NULL
GO$V16 <- NULL
GO$V17 <- NULL
GO$V12 <- NULL
GO$V15 <- NULL
GO$V2 <- NULL
GO$V9 <- NULL
GO$V11 <- NULL
colnames(GO) <- c("GENEID", "GOID")

If you want more information such as GO TERMS in a dataframe as well, you can use the follow script:

system("awk 'NR>=42' ~/Desktop/goa_human.gaf > ~/Desktop/goa_human_no_header.txt")
GO <-read.csv("~/Desktop/goa_human_no_header.txt", header=F, sep="\t")

BiocManager::install("GO.db")
library(GO.db)
GOdb <- as.data.frame(GOTERM)
GO$V4 <- NULL
GO$V7 <- NULL
GO$V8 <- NULL
GO$V1 <- NULL
GO$V6 <- NULL
GO$V10 <- NULL
GO$V13 <- NULL
GO$V14 <- NULL
GO$V16 <- NULL
GO$V17 <- NULL
GO$V12 <- NULL
GO$V15 <- NULL
GO$V2 <- NULL
GO$V9 <- NULL
GO$V11 <- NULL
colnames(GO) <- c("GENEID", "GOID")
colnames(GOdb)[1] <- c("GOID")
GOdb <- head(GOdb,-1)
GENESwithGO <- merge(GO, GOdb, by = "GOID")
rm(GOdb, GO)
GENESwithGO$go_id <- NULL

This should create a master data frame for you of GO IDs and their gene ontology terms. It might be overkill for your purposes, but just throwing this here.

Hope this helps!

GO analysis using clusterProfiler

head(summary(ego2), n=3)

##                    ID          Description GeneRatio   BgRatio
## GO:0005819 GO:0005819              spindle    24/197 222/11632
## GO:0005876 GO:0005876  spindle microtubule    11/197  45/11632
## GO:0000793 GO:0000793 condensed chromosome    17/197 150/11632
##                  pvalue     p.adjust       qvalue
## GO:0005819 3.810608e-13 1.276554e-10 1.139171e-10
## GO:0005876 1.527089e-10 2.557874e-08 2.282596e-08
## GO:0000793 5.838332e-10 6.519471e-08 5.817847e-08
##                                                                                                                                                   geneID
## GO:0005819 CDCA8/CDC20/KIF23/CENPE/ASPM/DLGAP5/SKA1/NUSAP1/TPX2/NEK2/CDK1/MAD2L1/KIF18A/BIRC5/KIF11/TTK/AURKB/PRC1/KIFC1/KIF18B/KIF20A/AURKA/CCNB1/KIF4A
## GO:0005876                                                                             SKA1/NUSAP1/CDK1/KIF18A/BIRC5/KIF11/AURKB/PRC1/KIF18B/AURKA/KIF4A
## GO:0000793                                         CENPE/NDC80/TOP2A/NCAPH/HJURP/SKA1/NEK2/CENPM/CENPN/ERCC6L/MAD2L1/BIRC5/NCAPG/AURKB/CHEK1/AURKA/CCNB1
##            Count
## GO:0005819    24
## GO:0005876    11
## GO:0000793    17

This is already an old post, but actually there is a function in GOfuncR to get the genes associated with GO-terms: get_anno_genes