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)