From the head() snapshot of your data this is the best I can do to reconstruct a portion (six observation). Normally I'd want to just run something from your example and get a good chunk of data to work with and recreate your issue without having to put in this extra effort.
You could do something like this:
# your data encoded as a character string using `deparse()` and pasted in here.
x_ <- c(
"structure(list(ID = c(2486L, 2574L, 1675L, 2565L, 1910L, 4237L",
"), AREA = c(49L, 96L, 431L, 337L, 846L, 135L), GCPNT_LAT = c(57.0410899168, ",
"5.10886858483, 5.34956326151, 9.06189891144, 5.62108507064, 37.0034193536",
"), GCPNT_LON = c(9.92426287095, 7.35086200977, -4.00269599617, ",
"7.43349518867, -0.215586874558, 35.2831339317), COUNTRY = structure(c(2L, ",
"4L, 1L, 4L, 3L, 5L), .Label = c(\" Côte d'Ivoire\", \" Denmark\", ",
"\" Ghana\", \" Nigeria\", \" Turkey\"), class = \"factor\"), GR = structure(c(1L, ",
"2L, 2L, 2L, 2L, 3L), .Label = c(\" Northern Europe\", \" Western Africa\", ",
"\" Western Asia\"), class = \"factor\"), UC_NAME = structure(1:6, .Label = c(\" Aalborg\", ",
"\" Aba\", \" Abidjan\", \" Abuja\", \" Accra\", \" Adana\"), class = \"factor\"), ",
" H00_NBR = c(1L, 1L, 1L, 1L, 2L, 1L), H00_AREA = c(3.89182, ",
" 4.418841, 5.894403, 4.718499, 6.51323, 4.836282), B15 = c(3.333719, ",
" 3.569795, 5.441951, 4.90637, 6.204348, 4.174412), BUCAP15 = c(5.635509, ",
" 3.39093, 3.926418, 4.534689, 4.71988, 4.478802), E_GR_AV14 = c(-0.7241529, ",
" -1.1212652, -1.301848, -1.0133702, -1.2254961, -1.4558008",
" ), E_GR_AH14 = c(3.498985, 3.019277, 3.628108, 4.246865, ",
" 2.680334, 2.454523), E_GR_AT14 = c(3.890039, 4.553592, 6.056978, ",
" 5.811337, 6.732362, 4.910071), SDG_LUE9015 = c(-0.8076205, ",
" -2.2733364, -1.3642453, -1.033672, -1.4974861, 6.3459896), ",
" SDG_OS15MX = c(4.212276, 4.143293, 3.837946, 4.092677, 3.725693, ",
" 3.948355), POP_DEN_15 = c(5.60433, 7.37086, 5.818301, 5.786284, ",
" 7.675128, 5.071417)), class = \"data.frame\", row.names = c(2486L, ",
"2574L, 1675L, 2565L, 1910L, 4237L))"
)
# parse the string to get the data frame `x`
x <- eval(parse(text= x_))
# use the ID column as rownames
rownames(x) <- x$ID
# OK, this next bit won't be reproducible on your side, but you have to put in some
# work to include your reproducible code here. e.g. I'd have to extract a minimal
# working version of the code from my functions `T1` and `plot.T1`.
# If you get used to using `reprex` this becomes a breeze, a joy.
x[-1] %>% T1() %>% plot(type. = "a")
Here's what I'm trying to illustrate: this is an example of what you could get with one-hot encoding of your categorical variables, but of course this hardly shows it here with only six observations.
Is this the sort of thing you're looking for?

Created on 2019-07-23 by the reprex package (v0.3.0)