In the last question I did they pointed out that less data would be easy to read and understand as part of the reproducible example. On the way to asking again I tried to sh
Another way to shorten it up would be to convert the columns to character before dput. The data can then be read back in with as.data.frame and factor levels are preserved.
First subset
> data2 <- data[sample(nrow(data), 4), ]
Then dput as characters
> d <- dput(lapply(data2, as.character))
structure(list(GOterm = c("GO:0000746", "GO:0070647", "GO:0006914",
"GO:0007010"), GOdesc = c("conjugation", NA, NA, "cytoskeleton organization and biogenesis"
), GSA_p33_SC = c(NA_character_, NA_character_, NA_character_,
NA_character_), GSA_p33_X33 = c(NA, NA, "1", "1"), GSA_p38_SC = c(NA_character_,
NA_character_, NA_character_, NA_character_), GSA_p38_X33 = c(NA_character_,
NA_character_, NA_character_, NA_character_), GSA_p52_SC = c(NA,
"-1", NA, NA), GSA_p52_X33 = c(NA, NA, NA, "1"), GSA_p64_SC = c(NA,
NA, NA, "1"), GSA_p64_X33 = c("1", NA, NA, NA), GSA_SC_X33 = c(NA,
NA, NA, "1")), .Names = c("GOterm", "GOdesc", "GSA_p33_SC", "GSA_p33_X33",
"GSA_p38_SC", "GSA_p38_X33", "GSA_p52_SC", "GSA_p52_X33", "GSA_p64_SC",
"GSA_p64_X33", "GSA_SC_X33"))
And read back in
> as.data.frame(d)