removing rows from dataframe in two different columns R - r

I have a dataframe of results. There are multiple comparisons for Cruise_Strata. I have two columns of cruise_strata (Cruise1_Strata1 and Cruise2_Strata2). The problem I found is that there are "duplicate" records in the dataframe. For example one row will have
Cruise_Strata1 Cruise_Strata2
201501.35 201502.35
and another row will have
Cruise_Strata1 Cruise_Strata2
201502.35 201501.35
The rows have the same results for the remaining columns. I would like to be able to identify rows where this happens and remove one row from the dataset, but do not know how to go about it. I cant use duplicate because they are not duplicates.
Any help would be appreciated.
Here is the dataframe.
dput(result5)
structure(list(Cruise_Strata1 = structure(c(1L, 1L, 2L, 2L, 3L,
3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L,
11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L,
17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L, 23L,
24L, 24L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L, 29L, 29L, 30L,
30L, 31L, 31L, 32L, 32L, 33L, 33L, 34L, 34L, 35L, 35L, 36L, 36L,
37L, 37L, 38L, 38L, 39L, 39L, 40L, 40L, 41L, 41L, 42L, 42L, 43L,
43L, 44L, 44L, 45L, 45L, 46L, 46L, 47L, 47L, 48L, 48L, 49L, 49L,
50L, 50L, 51L, 51L, 52L, 52L, 53L, 53L, 54L, 54L, 55L, 55L, 56L,
56L, 57L, 57L, 58L, 58L, 59L, 59L, 60L, 60L, 61L, 61L, 62L, 62L,
63L, 63L, 64L, 64L, 65L, 65L, 66L, 66L), .Label = c("201501.10",
"201501.11", "201501.13", "201501.14", "201501.15", "201501.17",
"201501.18", "201501.19", "201501.21", "201501.22", "201501.23",
"201501.24", "201501.25", "201501.26", "201501.27", "201501.29",
"201501.30", "201501.31", "201501.33", "201501.34", "201501.35",
"201501.9", "201502.10", "201502.11", "201502.13", "201502.14",
"201502.15", "201502.17", "201502.18", "201502.19", "201502.21",
"201502.22", "201502.23", "201502.24", "201502.25", "201502.26",
"201502.27", "201502.29", "201502.30", "201502.31", "201502.33",
"201502.34", "201502.35", "201502.9", "201503.10", "201503.11",
"201503.13", "201503.14", "201503.15", "201503.17", "201503.18",
"201503.19", "201503.21", "201503.22", "201503.23", "201503.24",
"201503.25", "201503.26", "201503.27", "201503.29", "201503.30",
"201503.31", "201503.33", "201503.34", "201503.35", "201503.9"
), class = "factor"), Cruise_Strata2 = structure(c(23L, 45L,
24L, 46L, 25L, 47L, 26L, 48L, 27L, 49L, 28L, 50L, 29L, 51L, 30L,
52L, 31L, 53L, 32L, 54L, 33L, 55L, 34L, 56L, 35L, 57L, 36L, 58L,
37L, 59L, 38L, 60L, 39L, 61L, 40L, 62L, 41L, 63L, 42L, 64L, 43L,
65L, 44L, 66L, 1L, 45L, 2L, 46L, 3L, 47L, 4L, 48L, 5L, 49L, 6L,
50L, 7L, 51L, 8L, 52L, 9L, 53L, 10L, 54L, 11L, 55L, 12L, 56L,
13L, 57L, 14L, 58L, 15L, 59L, 16L, 60L, 17L, 61L, 18L, 62L, 19L,
63L, 20L, 64L, 21L, 65L, 22L, 66L, 1L, 23L, 2L, 24L, 3L, 25L,
4L, 26L, 5L, 27L, 6L, 28L, 7L, 29L, 8L, 30L, 9L, 31L, 10L, 32L,
11L, 33L, 12L, 34L, 13L, 35L, 14L, 36L, 15L, 37L, 16L, 38L, 17L,
39L, 18L, 40L, 19L, 41L, 20L, 42L, 21L, 43L, 22L, 44L), .Label = c("201501.10",
"201501.11", "201501.13", "201501.14", "201501.15", "201501.17",
"201501.18", "201501.19", "201501.21", "201501.22", "201501.23",
"201501.24", "201501.25", "201501.26", "201501.27", "201501.29",
"201501.30", "201501.31", "201501.33", "201501.34", "201501.35",
"201501.9", "201502.10", "201502.11", "201502.13", "201502.14",
"201502.15", "201502.17", "201502.18", "201502.19", "201502.21",
"201502.22", "201502.23", "201502.24", "201502.25", "201502.26",
"201502.27", "201502.29", "201502.30", "201502.31", "201502.33",
"201502.34", "201502.35", "201502.9", "201503.10", "201503.11",
"201503.13", "201503.14", "201503.15", "201503.17", "201503.18",
"201503.19", "201503.21", "201503.22", "201503.23", "201503.24",
"201503.25", "201503.26", "201503.27", "201503.29", "201503.30",
"201503.31", "201503.33", "201503.34", "201503.35", "201503.9"
), class = "factor"), P_value = c(0.63, 0.6793, 0.0319, 0.0289,
0.9516, 0.8128, 0.9967, 0.3071, 0.9641, 0.0246, 0.7967, 0.2551,
0.2329, 0.3725, 0.0269, 0.3796, 0.0245, 0.5562, 0.9952, 0.5176,
0.5596, 0.9966, 0.32, 0.6402, 0.7691, 0.9671, 0.9396, 0.9, 0.9024,
0.3624, 0.0433, 0.3402, 0.5302, 0.787, 0.0295, 0.3638, 0.006,
0.701, 0.6323, 0.0366, 2e-04, 0.0011, 0.8849, 0.3, 0.63, 0.9738,
0.0319, 0.5197, 0.9516, 0.7369, 0.9967, 0.2276, 0.9641, 0.0158,
0.7967, 0.6332, 0.2329, 0.0322, 0.0269, 0.3013, 0.0245, 0.0129,
0.9952, 0.795, 0.5596, 0.7277, 0.32, 0.747, 0.7691, 0.3817, 0.9396,
0.7961, 0.9024, 0.4164, 0.0433, 0.0028, 0.5302, 0.2864, 0.0295,
0.7036, 0.006, 0, 0.6323, 0.002, 2e-04, 0.9548, 0.8849, 0.0546,
0.6793, 0.9738, 0.0289, 0.5197, 0.8128, 0.7369, 0.3071, 0.2276,
0.0246, 0.0158, 0.2551, 0.6332, 0.3725, 0.0322, 0.3796, 0.3013,
0.5562, 0.0129, 0.5176, 0.795, 0.9966, 0.7277, 0.6402, 0.747,
0.9671, 0.3817, 0.9, 0.7961, 0.3624, 0.4164, 0.3402, 0.0028,
0.787, 0.2864, 0.3638, 0.7036, 0.701, 0, 0.0366, 0.002, 0.0011,
0.9548, 0.3, 0.0546), Cruise1 = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("201501",
"201502", "201503"), class = "factor"), Cruise1_Strata1 = structure(c(1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L,
9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L,
16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L,
22L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L,
8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L,
14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L,
21L, 21L, 22L, 22L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L,
6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L,
13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L,
20L, 20L, 21L, 21L, 22L, 22L), .Label = c("10", "11", "13", "14",
"15", "17", "18", "19", "21", "22", "23", "24", "25", "26", "27",
"29", "30", "31", "33", "34", "35", "9"), class = "factor"),
Cruise2 = structure(c(2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L,
2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L,
3L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L,
1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L,
3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L,
1L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L), .Label = c("201501", "201502", "201503"), class = "factor"),
Cruise2_Strata2 = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L,
4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L,
11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L,
17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 1L,
1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L,
9L, 9L, 10L, 10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L,
15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L,
21L, 21L, 22L, 22L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L,
6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L, 11L, 11L, 12L,
12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L,
18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L), .Label = c("10",
"11", "13", "14", "15", "17", "18", "19", "21", "22", "23",
"24", "25", "26", "27", "29", "30", "31", "33", "34", "35",
"9"), class = "factor"), adjuste_p = c(1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.792, 1, 1, 1, 0.0264,
0.1452, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.3696, 1,
1, 1, 1, 0.792, 0, 1, 0.264, 0.0264, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0.3696, 1, 1, 1, 1, 1, 0, 1, 0.264,
0.1452, 1, 1, 1)), .Names = c("Cruise_Strata1", "Cruise_Strata2",
"P_value", "Cruise1", "Cruise1_Strata1", "Cruise2", "Cruise2_Strata2",
"adjuste_p"), row.names = c(1453L, 2905L, 1520L, 2972L, 1587L,
3039L, 1654L, 3106L, 1721L, 3173L, 1788L, 3240L, 1855L, 3307L,
1922L, 3374L, 1989L, 3441L, 2056L, 3508L, 2123L, 3575L, 2190L,
3642L, 2257L, 3709L, 2324L, 3776L, 2391L, 3843L, 2458L, 3910L,
2525L, 3977L, 2592L, 4044L, 2659L, 4111L, 2726L, 4178L, 2793L,
4245L, 2860L, 4312L, 23L, 2927L, 90L, 2994L, 157L, 3061L, 224L,
3128L, 291L, 3195L, 358L, 3262L, 425L, 3329L, 492L, 3396L, 559L,
3463L, 626L, 3530L, 693L, 3597L, 760L, 3664L, 827L, 3731L, 894L,
3798L, 961L, 3865L, 1028L, 3932L, 1095L, 3999L, 1162L, 4066L,
1229L, 4133L, 1296L, 4200L, 1363L, 4267L, 1430L, 4334L, 45L,
1497L, 112L, 1564L, 179L, 1631L, 246L, 1698L, 313L, 1765L, 380L,
1832L, 447L, 1899L, 514L, 1966L, 581L, 2033L, 648L, 2100L, 715L,
2167L, 782L, 2234L, 849L, 2301L, 916L, 2368L, 983L, 2435L, 1050L,
2502L, 1117L, 2569L, 1184L, 2636L, 1251L, 2703L, 1318L, 2770L,
1385L, 2837L, 1452L, 2904L), class = "data.frame")
R Info
R version 3.2.1 (2015-06-18)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1


Does this give you your desired result?
duplicated(apply(cbind(result5$Cruise_Strata1, df$Cruise_Strata2), 1,
function(x) paste(min(x), max(x))))
You can use the resulting logical vector to subset your data.
First you create a vector pasting the values in Cruise_Strata1 and Cruise_Strata2. Doing this you move the smaller of the two to the front and the larger one to the end (or you could do it vice versa). This is just a trick so that you can apply the duplicated function and recognize the duplicates.
Note: this approach will remove duplicates of the form:
Cruise_Strata1 Cruise_Strata2
x y
y x
As well as (if this is not desired let me know):
Cruise_Strata1 Cruise_Strata2
x y
x y


For a generic data frame df with duplicated values in Cruise_Strata1 and Cruise_Strata2:
df$dupe <- 0
for(i in 1:(length(df$Cruise_Strata1)-1))
{
for(j in (i+1):length(df$Cruise_Strata1))
if(df$Cruise_Strata1[i]==df$Cruise_Strata2[j])
{print(df[c(i,j),]); df$dupe[i] = 1;break}
}
df[df$dupe != 1,]

Related

R plotly multiple plots only show last figure

I would like to make an interactive graphs based on user input. However I'm struggle to make more than one graphs using R plotly. Suppose I have following data and codes,
dput(norwd5)
structure(list(LENGTH_OF_STAY = c(57L, 28L, 15L, 28L, 14L, 49L,
15L, 22L, 17L, 81L, 34L, 24L, 31L, 38L, 33L, 22L, 21L, 49L, 188L,
21L, 21L, 36L, 24L, 23L, 48L, 54L, 42L, 62L, 13L, 139L, 29L,
49L, 15L, 7L, 43L, 28L, 31L, 22L, 23L, 26L, 33L, 30L, 127L, 22L,
22L, 15L, 28L, 26L, 15L, 31L, 22L, 89L, 28L, 60L, 54L, 37L, 20L,
135L, 155L, 51L, 15L, 8L, 38L, 16L, 16L, 22L, 30L, 14L, 16L,
18L, 14L, 272L, 25L, 22L, 18L, 21L, 188L, 264L, 34L, 34L, 136L,
23L, 142L, 25L, 32L, 58L, 163L, 16L, 35L, 23L, 50L, 71L, 10L,
19L, 22L, 24L, 45L, 29L, 15L, 82L), PRE_OPERATIVE_LOS = c(2L,
2L, 3L, 1L, 3L, 6L, 3L, 7L, 2L, 2L, 11L, 2L, 6L, 3L, 6L, 3L,
5L, 3L, 179L, 2L, 5L, 3L, 4L, 2L, 5L, 6L, 2L, 4L, 2L, 6L, 3L,
2L, 2L, 6L, 6L, 1L, 4L, 5L, 6L, 5L, 0L, 4L, 6L, 2L, 4L, 4L, 7L,
4L, 4L, 6L, 2L, 4L, 3L, 3L, 2L, 6L, 4L, 110L, 63L, 6L, 4L, 7L,
5L, 1L, 6L, 1L, 4L, 2L, 6L, 3L, 2L, 8L, 2L, 2L, 4L, 3L, 6L, 171L,
5L, 4L, 116L, 6L, 47L, 3L, 7L, 3L, 60L, 1L, 3L, 20L, 31L, 49L,
9L, 8L, 3L, 4L, 35L, 7L, 4L, 9L), POST_OPERATIVE_LOS = c(55L,
26L, 12L, 27L, 11L, 43L, 12L, 15L, 15L, 79L, 23L, 22L, 25L, 35L,
27L, 19L, 16L, 46L, 9L, 19L, 16L, 33L, 20L, 21L, 43L, 48L, 40L,
58L, 11L, 133L, 26L, 47L, 13L, 1L, 37L, 27L, 27L, 17L, 17L, 21L,
33L, 26L, 121L, 20L, 18L, 11L, 21L, 22L, 11L, 25L, 20L, 85L,
25L, 57L, 52L, 31L, 16L, 25L, 92L, 45L, 11L, 1L, 33L, 15L, 10L,
21L, 26L, 12L, 10L, 15L, 12L, 264L, 23L, 20L, 14L, 18L, 182L,
93L, 29L, 30L, 20L, 17L, 95L, 22L, 25L, 55L, 103L, 15L, 32L,
3L, 19L, 22L, 1L, 11L, 19L, 20L, 10L, 22L, 11L, 73L), digoxin_any = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 2L), .Label = c("0:No", "1.Yes"), class = "factor")), row.names = c(NA,
-100L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000012f36b61ef0>)
num <- c('PRE_OPERATIVE_LOS','POST_OPERATIVE_LOS')
plist <- scan(text=num,what = "",quiet = T)
groups <- 'digoxin_any'
bygrp <- scan(text=groups,what="",quiet=T)
norwd5[, (bygrp) := lapply(.SD, as.factor), .SDcols = bygrp]
plotList = list()
for(i in length(plist)){
gplot <- ggplot(norwd5,aes_string(x=plist[i],group=bygrp,color=bygrp))+geom_histogram(aes(y=..density..),position = "dodge")+geom_density(alpha=.5) +theme(legend.position = "left")
plotList[[i]] <- plotly_build(gplot)
}
for(i in length(plist)){
print(plotList[[i]])
}
The goal is to show both graphs for PRE_OPERATIVE_LOS and POST_OPERATIVE_LOS. However, the codes above only show histogram for POST_OPERATIVE_LOS.
I checked maybe subplot is the way to go but how to make subplot work in a loop? Any hints?
Thanks!

There is an error in your first loop and calling each subplot won't make both appear at the same time.
First-- the issue with your first for call- when you wrote
for(i in length(plist))
You wrote for i in 2 or i == 2, meaning that you never looped. If you modify it to a range of values, now it's written: for i in 1 to 2.
for(i in 1:length(plist))
So you're aware, if you had written for(i in plist) it would have done both loops, but instead of a value, i would be the strings.
Okay, so now there are two graphs. From the plotly library, you can use the function subplot. You will want to turn the legend off for one of them, though.
subplot(plotList[[1]],
style(plotList[[2]], showlegend = FALSE))
If you wanted the outline color, that's more than okay! However, if you wanted to bars to be filled, you need to assign fill instead of color.
If you change color = bygrp to fill = bygrp, this is how this would change:
If you leave the color assignment and add fill = bygrp (so you have both), this is how this would change:

How to fix jagged line from predict_gam in ggplot2?

Data:
structure(list(ID = c(19903L, 28185L, 28207L, 28429L, 28522L,
29092L, 29127L, 29219L, 29304L, 30981L, 31166L, 31411L, 32010L,
33231L, 33640L, 33714L, 34093L, 34193L, 34385L, 35054L, 35337L,
35377L, 35608L, 35881L, 35940L, 37112L, 37122L, 37125L, 37170L,
37198L, 37266L, 37378L, 37589L, 37725L, 37877L, 38519L, 38522L,
38605L, 38623L, 38806L, 39040L, 39083L, 39159L, 39218L, 39593L,
39636L, 39657L, 39686L, 39700L, 39819L, 39820L, 39951L, 40151L,
40152L, 40181L, 40226L, 40248L, 40286L, 40382L, 40556L, 40623L,
40628L, 40798L, 40800L, 40815L, 40915L, 43282L, 43299L, 43450L,
43466L, 43509L, 43677L, 43740L, 43762L, 43998L, 44068L, 44130L,
44131L, 44307L, 44408L, 50679L, 50848L, 51064L, 51455L, 51690L,
51726L, 51727L, 51796L, 52126L, 52183L, 52461L, 52500L, 52502L,
52577L, 52614L, 53202L, 53320L, 53390L, 53456L, 53473L, 53474L,
53475L, 53577L, 53626L, 53851L, 53873L, 54153L, 54206L, 54532L,
54581L, 54913L, 55122L, 55267L, 55332L, 55462L, 55542L, 55612L,
55728L, 55867L, 55903L, 55920L, 55991L, 56022L, 56098L, 56307L,
56420L, 56679L, 56703L, 56746L, 56919L, 57005L, 57035L, 57405L,
57445L, 57480L, 57725L, 57808L, 57809L, 57863L, 58004L, 58060L,
58130L, 58145L, 58215L, 58229L, 58503L, 58515L, 58667L, 58999L,
59326L, 59327L, 59344L, 59361L, 59428L, 59756L, 59865L, 60099L,
60100L, 60169L, 60252L, 60280L, 60306L, 60384L, 60429L, 60472L,
60493L, 60503L, 60575L, 60603L, 60662L, 60664L, 60806L, 60846L,
60925L, 61274L, 61415L, 61727L, 61749L, 61882L, 61883L, 62081L,
62144L, 62210L, 62285L, 62411L, 62809L, 62917L, 62934L, 62937L,
62983L, 62989L, 63327L, 63329L, 63383L, 63458L, 63470L, 63589L,
64081L, 64328L, 64418L, 64507L, 64596L, 65178L, 65250L, 65302L,
65478L, 65480L, 65487L, 65565L, 65572L, 65574L, 65617L, 65802L,
65865L, 65934L, 65935L, 65974L, 65975L, 65978L, 65991L, 65995L,
66013L, 66154L, 66232L, 66237L, 66245L, 66314L, 66389L, 66396L,
66460L, 66572L, 66589L, 66735L, 67174L, 73230L, 73525L, 73539L,
73677L, 73705L, 73942L, 73953L, 74034L, 74113L, 74114L, 74425L,
74427L, 74439L, 74607L, 74618L, 74641L, 74657L, 74794L, 74800L,
74836L, 74942L, 74952L, 74962L, 74969L, 74975L, 74977L, 74985L,
74989L, 75220L, 75229L, 75377L, 75407L, 75432L, 75653L, 75732L,
75735L, 75737L, 75757L, 75895L, 75898L, 76381L, 76559L, 76574L,
76594L, 76595L, 76746L, 76751L, 76755L, 76759L, 76775L, 77088L,
77091L, 77099L, 77109L, 77134L, 77182L, 77188L, 77203L, 77204L,
77252L, 77304L, 77453L, 77528L, 77556L, 77585L, 77668L, 77733L,
77758L, 78262L, 79724L, 79730L, 79747L, 79850L, 79977L, 80052L,
80819L, 80901L, 80932L, 81064L, 81065L, 81071L, 81098L, 81112L,
81142L, 81175L, 81727L, 81938L, 82554L, 83744L, 83949L), Age = c(83L,
26L, 26L, 20L, 84L, 20L, 23L, 77L, 32L, 14L, 21L, 9L, 76L, 18L,
21L, 15L, 75L, 27L, 34L, 81L, 81L, 15L, 24L, 24L, 16L, 35L, 27L,
7L, 30L, 31L, 24L, 24L, 79L, 30L, 19L, 78L, 25L, 20L, 42L, 62L,
83L, 79L, 18L, 26L, 66L, 23L, 83L, 21L, 77L, 24L, 57L, 42L, 32L,
76L, 85L, 29L, 77L, 65L, 79L, 9L, 34L, 20L, 11L, 16L, 9L, 21L,
16L, 34L, 22L, 19L, 23L, 25L, 14L, 53L, 28L, 79L, 22L, 22L, 21L,
82L, 81L, 16L, 19L, 77L, 15L, 18L, 15L, 78L, 24L, 16L, 14L, 29L,
18L, 50L, 17L, 43L, 8L, 14L, 85L, 31L, 20L, 30L, 23L, 78L, 29L,
6L, 61L, 14L, 22L, 10L, 83L, 15L, 13L, 15L, 15L, 29L, 8L, 9L,
15L, 8L, 9L, 15L, 9L, 34L, 8L, 9L, 9L, 16L, 8L, 25L, 21L, 23L,
13L, 56L, 10L, 7L, 27L, 8L, 8L, 8L, 8L, 80L, 80L, 6L, 15L, 42L,
25L, 23L, 21L, 8L, 11L, 43L, 69L, 34L, 34L, 14L, 12L, 10L, 22L,
78L, 16L, 76L, 12L, 10L, 16L, 6L, 13L, 66L, 11L, 26L, 12L, 16L,
13L, 24L, 76L, 10L, 20L, 13L, 25L, 14L, 12L, 15L, 43L, 51L, 27L,
15L, 24L, 34L, 63L, 17L, 15L, 9L, 12L, 17L, 82L, 75L, 24L, 44L,
69L, 11L, 10L, 12L, 10L, 10L, 70L, 54L, 45L, 42L, 84L, 54L, 23L,
23L, 14L, 81L, 17L, 42L, 44L, 16L, 15L, 43L, 45L, 50L, 53L, 23L,
53L, 49L, 13L, 69L, 14L, 65L, 14L, 13L, 22L, 67L, 59L, 52L, 54L,
44L, 78L, 62L, 69L, 10L, 63L, 57L, 22L, 12L, 62L, 9L, 82L, 53L,
54L, 66L, 49L, 63L, 51L, 9L, 45L, 49L, 77L, 49L, 61L, 62L, 57L,
67L, 16L, 65L, 75L, 45L, 16L, 55L, 17L, 64L, 67L, 56L, 52L, 63L,
10L, 62L, 14L, 66L, 68L, 15L, 13L, 43L, 47L, 55L, 69L, 21L, 67L,
34L, 52L, 15L, 31L, 64L, 55L, 13L, 48L, 71L, 64L, 13L, 25L, 34L,
50L, 61L, 70L, 33L, 57L, 51L, 46L, 57L, 69L, 46L, 8L, 11L, 46L,
71L, 33L, 38L, 56L, 17L, 29L, 28L, 6L, 8L), Sex = structure(c(1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 2L), .Label = c("Male", "Female"), class = "factor"),
mean_FA_scaled = c(-1.52160414281774, -1.30073487609629,
-1.39164271432334, -1.83373601712535, -2.19478262184568,
-0.47769168350816, -1.66624867866514, -0.36061779499817,
-1.10976759821506, -2.01706489349897, -1.21708170925372,
-0.68001882107227, -0.770347444019124, -1.21756680205088,
-1.04908755742334, -0.654272701867476, 0.791455877697352,
0.0263414533200063, -1.48353521852673, -1.48465744813212,
0.885781086077571, 0.937258844105155, -1.76609091258925,
-1.40930154017838, -1.42620014597815, -0.395529996012095,
-1.79188771313106, -1.6968602062236, -1.6213377738768, -1.26578647412735,
-1.3364652186935, -1.52114801078458, 0.587760344033774, -1.4860765255686,
-1.41824317606643, -1.08076339305916, -1.84290933912549,
-1.42950167307528, -0.186882171702826, 0.94192876730175,
-1.96157606965602, -0.668579319288362, -1.2972378638421,
-2.10201405453099, 0.593407693015703, -1.87521507137852,
-0.399874110613579, -2.16173114991939, -1.71213049306692,
-2.03230549555918, 0.864393561856266, 1.66450706953957, -1.76062456838238,
-1.42625806750617, -0.635317881823001, -1.05738481631217,
-0.905876579394418, 0.0731565283419971, -1.15139145628828,
-0.742407546940581, -1.69348627721645, 0.153573329806466,
-1.09929828202549, -0.982123030841461, 0.725678742439884,
-0.850887328730634, -0.99078229928042, 0.215368360012574,
-0.402661584149531, 0.0241114744912448, -0.71105027970887,
0.366463906043185, 0.957024565541906, 0.669292134912623,
1.05465854121026, 1.82844671440856, -0.181835758574102, 0.736386984932541,
-1.09078381740658, 0.0590019549321627, -1.02109697900777,
0.321350275906775, -0.0449237467173357, 0.0239956314352051,
0.117669222625202, -0.725516181331811, 0.387590783388401,
0.829691326381412, 1.37355999410519, -0.459526044282955,
-0.460235583001197, -0.311304854080326, 0.578796987572713,
0.997164184459617, 0.18257029477137, 0.291839257380694, -0.863007408468775,
-1.87780705975741, 2.29568520056216, 0.00319456268509986,
0.881190804982003, 0.930713711438919, -0.525093214001351,
2.54459572703618, 0.166620153992923, 1.20602921449896, -0.289055747129726,
1.46280982859267, -0.391909900510859, 2.11139337878521, 1.59105533181948,
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0.6654186327263, -1.62880965099135, -0.961003393687248, 0.201720599972912,
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0.113397509933743, 0.191685695243484, 1.14629763872856, 0.407899519150338,
0.473039517599588, 0.589070818605222, 1.07992680780889, 0.0233440142449823,
0.303792812725778, 0.560066613449315, -0.401387310533095,
-0.286101749200717, -0.673299923821975, 1.66157479218356,
1.44751130500445, 0.402802424684597, 1.46472123901732, -0.397311082998703,
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1.48020076738061, -0.550643848049078, 0.299513859843316,
0.739782634512702, 0.517841819522891, 0.240976915588321,
0.407841597622318, 1.04632508136641, 0.140700270204069, 0.320249766874399,
-0.0720093012575883, 0.191207842637321, 1.89043722977174,
1.44823532410469, -0.403472485541808, 1.81747058484881, 0.510261339543303,
0.874862878045841, -0.274271277102676, 1.60814942277632,
-0.625188854610541, 0.262176194843562, 0.546426093600656,
-0.0371912227266948, -0.0447861830882888, 1.43379838324576,
-0.0424331210124857, 1.86971580312266, -0.228122299652913,
0.731789463645971, 0.0910470403091081, 0.618791802670374,
0.267229848163289, 0.199251694841068, 0.246957313356364,
1.87125072361518, -1.40312565725327, -0.190900477709198,
0.257180463051856, 1.48421907338698, 0.0556569866890196,
-0.667601893503029, 0.247688572647614, 0.188977863808559,
0.91364858124609, 1.5448556730327, 0.930329981315788, 0.312119032378622,
1.15772266013046, -0.0360834735033167, 1.78212397237474,
-0.861407326257228, 0.476608931763807, 1.38366006055364,
0.803771442592559, 0.145174708243597, -1.13023561817905,
0.570130478942752, 0.862605234678655, -0.328963679935357,
0.654840713671687, 0.852222800781108, 0.304538552399032,
0.652132882236762, -0.639712677761503, 0.046078213992748,
-0.171257839519489, 0.349420496423362, 0.184018332971865,
0.149583984564103, 1.29365724620189, 0.621419992004272, -0.866656464734021,
1.09066401106555, 0.810541021179871, 1.62963106948065, 1.03406743799922,
-0.118969180099629, -0.372665472826285, 1.40028353909531,
0.381002209576151, 0.508378889882659, 0.667424165633985,
0.4092534348678, 0.813183690895774, 1.08099111588625, 0.708867018932142,
0.0693192271106869, 1.26885235182742, -0.117571823236151,
0.174801569825717, 0.584835306868775, -0.84211945742664,
1.05460061968224, 1.61507104537468, -1.62830066556388, 0.0799550676933195
), RAVLT_DELAY = c(NA, 12L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 5L, NA, NA, NA, NA, NA, NA, NA,
7L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5L, 12L,
NA, NA, NA, NA, 14L, NA, NA, NA, NA, NA, 6L, 7L, NA, NA,
NA, NA, 7L, 1L, 1L, 11L, 4L, 12L, 7L, 9L, 9L, 8L, 14L, 12L,
7L, 12L, 7L, 6L, 13L, 10L, 13L, NA, 11L, 14L, 8L, 0L, 11L,
15L, 13L, 6L, 9L, 9L, 12L, 5L, 14L, 15L, 12L, 4L, 15L, 8L,
15L, 14L, 5L, 12L, 8L, 9L, 9L, 13L, 6L, 4L, 10L, NA, 4L,
13L, 9L, 14L, 8L, 15L, 14L, 9L, 15L, 14L, 11L, 11L, 15L,
12L, 9L, 13L, 14L, 7L, 13L, 9L, 12L, 10L, 6L, 9L, 10L, 11L,
15L, 11L, 11L, NA, 9L, 12L, 10L, 9L, 11L, 2L, 12L, NA, 6L,
12L, 12L, 10L, 11L, 4L, 13L, 4L, 5L, 6L, 12L, 15L, 11L, 11L,
14L, 2L, 11L, 5L, 10L, 12L, 10L, NA, 12L, 8L, 12L, 12L, 8L,
7L, 14L, 14L, 7L, 8L, NA, 9L, 6L, 15L, 7L, 14L, 8L, 14L,
11L, 13L, 6L, 12L, 11L, 14L, 15L, 10L, 6L, 13L, 7L, 4L, 12L,
14L, 7L, 13L, 3L, 13L, 7L, 10L, 6L, 8L, 3L, 15L, 11L, 15L,
11L, 11L, 8L, 4L, 7L, 10L, 5L, 7L, 8L, 9L, 14L, 12L, 14L,
12L, NA, NA, 11L, 10L, 13L, 7L, 12L, 12L, 14L, 8L, 13L, 2L,
11L, 8L, 7L, 4L, 7L, 9L, 4L, 12L, 14L, 15L, 12L, 13L, 9L,
7L, 11L, 10L, 14L, 6L, 5L, 5L, 10L, 8L, 5L, 12L, 2L, 11L,
8L, NA, 9L, 7L, 8L, 12L, 10L, 7L, 13L, 15L, 9L, 6L, 4L, 10L,
8L, 13L, 10L, 9L, 7L, 7L, 15L, 8L, 12L, 9L, 10L, 12L, 6L,
13L, 8L, 11L, 9L, 1L, 13L, 12L, NA, 8L, 2L, 11L, 9L, 7L,
6L, 10L, 13L, 15L, 6L, 5L, 7L, 5L, 5L, 11L, 11L, 13L, 9L,
4L, 10L, 2L, NA, 12L, 10L, 15L, NA, 6L)), row.names = c(NA,
-324L), class = c("tbl_df", "tbl", "data.frame"))
I am using the following model in mgcv::gam:
m1 <- gam(mean_FA_scaled ~ s(Age, bs = 'ad', k = -1) + Sex +
te(Age, by = Sex, bs ='fs') +
te(RAVLT_DELAY, by = Sex, bs = 'fs') + s(RAVLT_DELAY),
data = DF,
method = 'REML', family = gaussian)
I would like to reproduce the gam plot:
But in ggplot. However, When I use predict_gam my plot is very jagged. This doesn't happen when I try to plot the smooth term effect on age.
# Plot
m1_p <- predict_gam(m1)
m1_p %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex))
geom_smooth_ci(Sex, size = 1, alpha = 1) +
theme_classic(base_size = 24)

Your fit object has predictions for each age and each sex along the length of RAVLY_DELAY. With your existing code, each series tries to plot all the values from these various lines as one series, hence the jaggies.
If we tell ggplot to treat each Age,Sex combination as a different series (aka group), we get:
m1_p %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex, group = interaction(Age,Sex)))
There are a lot of age groups here, which we could see separately with:
m1_p %>%
mutate(Age = round(Age, 1)) %>%
ggplot(aes(x = RAVLT_DELAY, y = fit)) +
geom_line(aes(color = Sex)) +
facet_wrap(~Age, ncol = 10)
While wrong, I liked the aesthetic qualities that arose when I grouped by Age only:

I had the same problem and I finally managed to fix it, or that's what I think. I am a beginner, not an expert, so sorry for my dummy language in this field.
This is happening because you have more variables in your model, apart from the ones you are plotting, that cause variance.
So, what you have to do is to create a new database with all the variables you are not plotting fixed somehow, the numeric you can use means, the factors, choose one, etc.
Then run the model with the function predict.gam (not "_"), that let you add a new database, that will be the one with your variables fixed.
Then predict.gam has to be turned into a database to plot it, so you bind it (the result) with your new data, and then you can use ggplot2 and geom_smooth_ci with no problem.
EXAMPLE:
model<-x~gam(s(v4, by=v3) + s(v2, by=v1)
#I want to plot the first smooth, first create the data:
new=expand.grid(v1=levels(circ$v1)[1], v2=mean(circ$v2), v3=levels(circ$v3), v4=seq(0,23, 0.1))
# see that I maintain the levels and the numbers of v3 and v4, and I fix the other ones randomly.
predict<-predict.gam(model, newdata = new, se.fit = TRUE)
mew=cbind(new,preddist2n)
mew %>%
ggplot(aes(v4, fit)) +
geom_smooth_ci(v3, ci_z = 1.96, ci_alpha = 0.05 )
If after you need to plot the second smooth, you should create another database to fix the variables out of the second smooth...
Tell me if it worked for you :)

R loess regression

I think I missed something in the use of the loess function and I can't understand what i did wrong. I have a data frame in which I store the output (count) of 3 different softwares for 26 different genes on the genomes of different patients. The 3 softwares were each used on the same genome but with different rate of downsampling.
I pooled the results of all the patients by genes. At the end I have a data frame with 4 columns: samplexxx (downsampling rate), software (name of the software I used), gene (the name of the gene) and count (count results given by the software).
My goal is to estimate the downsampling effect (samplexxx) on the count given by the software, and I want to do some regression to be able to compare them with each other.
rate <- c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,
95, 100)
my attempts:
datalist <- list()
for (i in 1:22) {
name <- genes[i]
print(name)
mod <- paste("mod_", name)
xfit <- paste("xfit_", name)
df <- paste("df_", name)
mod <- loess(data2[data2$gene == name,]$count ~
data2[data2$gene == name,]$samplexxx)
xfit <- predict(mod, newdata=data2[data2$gene == name,]$samplexxx)
df <- setNames(data.frame(matrix(ncol=4, nrow=60)),
c("down", "software", "gene", "loess"))
df$down <- data2[data2$gene == name,]$samplexxx
df$software <- data2[data2$gene == name,]$software
df$gene <- data2[data2$gene == name,]$gene
df$loess <- xfit
print(xfit)
datalist[[i]] <- df
}
data_loess <- do.call(rbind, datalist)
ggplot(data_loess, aes(x=gene, y=loess, fill=software)) +
geom_boxplot()
and:
mod <- loess(data2$count ~ data$samplexxx)
xfit <- predict(mod, newdata=data2$samplexxx)
for (i in 1:20) {
down <- rate[i]
print(name)
title <- paste("loess_downsampling", down)
out <- paste("loess_downsampling", down, ".pdf", sep="")
pdf(out, width=10)
print(ggplot(data2, aes(x=down, y=loess, fill=software))) +
geom_boxplot() + ggtitle(title))
dev.off()
}
Sample data:
> dput(data2)
structure(list(samplexxx = c(5L, 10L, 15L, 20L, 25L, 30L, 35L,
40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L,
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L,
35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L,
100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L,
30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L,
95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L,
60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L,
90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L,
55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
85L, 90L, 95L, 100L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L,
50L, 55L, 60L, 65L, 70L, 75L, 80L, 85L, 90L, 95L, 100L, 5L, 10L,
15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L,
80L, 85L, 90L, 95L, 100L), software = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("EH", "GangSTR", "Tred"), class = "factor"),
gene = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L), .Label = c("AFF2", "AR", "ATN1", "ATXN1",
"ATXN10", "ATXN2", "ATXN3", "ATXN7", "C9ORF72", "CACNA1A",
"CBL", "CNBP", "CSTB", "DIP2B", "DMPK", "FMR1", "FXN", "HTT",
"JPH3", "NOP56", "PPP2R2B", "TBP"), class = "factor"), count = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 17L, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15L, 15L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, NA, NA, NA, NA, 20L, 34L, 31L, 33L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, NA, NA, NA, NA, NA,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, NA, NA, NA, NA, NA, 22L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, NA, NA,
NA, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, NA, NA, NA, NA, 6L, 8L, 8L,
8L, 8L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, NA, NA,
NA, NA, 11L, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, NA, NA, NA, 12L, 5L, NA, 12L,
12L, 5L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, NA, NA, NA, NA, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 20L, 20L, 18L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, NA, NA, NA, NA, 27L, 24L,
21L, 14L, 27L, 14L, 21L, 27L, 27L, 14L, 27L, 27L, 27L, 27L,
27L, 27L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 68L, 73L,
78L, 54L, 79L, 76L, 87L, 72L, 62L, 63L, NA, NA, NA, NA, NA,
27L, 27L, 27L, 28L, 27L, 27L, 64L, 27L, 64L, 64L, 27L, 27L,
27L, 27L, 27L, NA, NA, NA, NA, NA, 18L, 20L, 18L, 20L, 20L,
18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, NA, NA,
NA, NA, NA, 15L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 9L, 7L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, NA, NA, NA, NA, NA, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, NA, NA, NA, NA, NA, 35L, 29L, 35L, 35L, 30L, 35L,
32L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 11L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 20L, 11L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 33L, 33L, 32L, 33L, 33L, 33L, 33L, 33L, 33L, 33L,
33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, NA, 21L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 19L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 19L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 8L, 8L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 11L, NA, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 7L, 15L, 15L, 13L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 27L, 19L, 27L, 27L, 27L,
27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, NA, 76L, 23L, 23L, 23L, 32L, 65L, 32L, 28L, 32L,
28L, 32L, 32L, 23L, 28L, 32L, 28L, 28L, 32L, 84L, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 14L, 18L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 15L,
NA, NA, 15L, NA, 15L, NA, NA, 15L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 9L, NA, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, NA, 28L, 36L, 36L, NA, 36L, 36L, 36L,
36L, NA, 36L, NA, 36L, 36L, 36L, 36L, 36L, NA, 36L, 36L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
1L, 8L, 18L, 16L, 15L, 14L, 15L, 16L, 15L, 16L, 14L, 15L,
14L, 14L, 14L, 14L, 16L, 16L, 16L, 16L, 31L, 28L, 31L, 31L,
32L, 32L, 32L, 33L, 31L, 33L, 32L, 31L, 32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 7L, 18L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
19L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 5L, 6L, 6L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 12L, 11L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 5L, 7L, 7L, 7L, 7L, 11L, 11L, 7L,
11L, 15L, 15L, 11L, 7L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
1L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 4L, 20L, 17L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 1L, 2L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 1L, 15L, 6L, 22L, 13L, 14L, 13L, 14L, 13L, 14L, 14L,
27L, 27L, 14L, 14L, 27L, 14L, 27L, 14L, 27L, NA, 15L, 20L,
20L, 20L, 20L, 40L, 20L, 40L, 20L, 40L, 40L, 40L, 40L, 20L,
40L, 40L, 40L, 40L, 32L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 15L, 14L,
17L, 17L, 17L, 19L, 17L, 13L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 5L, 3L, 1L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 3L,
1L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 12L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, NA,
2L, 3L, 2L, 29L, 33L, 33L, 35L, 33L, 35L, 35L, 33L, 35L,
35L, 33L, 35L, 35L, 35L, 35L, 35L)), class = "data.frame", row.names = c(NA,
-1320L))

I believe the loess should be done on a split on the "software".
software <- unique(data2$software)
data_loess <- do.call(rbind, lapply(software, \(x) {
X <- subset(data2, software == x)
lo <- loess(count ~ samplexxx, X)
count_pred <- predict(lo, newdata=X)
return(cbind(X, count_pred))
}))
Note: R version 4.1.2 (2021-11-01)
Gives:
head(data_loess[data_loess$samplexxx > 80, ], 10)
# samplexxx software gene count count_pred
# 17 85 EH AFF2 24 22.69004
# 18 90 EH AFF2 24 22.31879
# 19 95 EH AFF2 24 21.83428
# 20 100 EH AFF2 24 21.25618
# 37 85 EH AR 21 22.69004
# 38 90 EH AR 21 22.31879
# 39 95 EH AR 21 21.83428
# 40 100 EH AR 21 21.25618
# 57 85 EH ATN1 NA 22.69004
# 58 90 EH ATN1 NA 22.31879
And here a plot of "count" predictions on "samplexxx".
plot(count_pred ~ samplexxx, data_loess, col=as.numeric(software) + 1,
pch=20, xlab='Downsampling', ylab='Count (LOESS)')
legend('topleft', legend=software, pch=19, col=as.numeric(software) + 1,
horiz=TRUE, cex=.7, title='Software')
Looks interesting, but I'm not sure if it's absolutely right.
In my answer you see something different from for loops, which is probably new to you, however it's the r-ish way and its much shorter to code. The looping job here does lapply().
Anyway, hope this helps.

Adding points to persp 3D plot - hide or obscure points when behind surface

Background:
I'm attempting to add a 3D plot to a Shiny application. I've added a button to rotate the plot ~ 90 degrees. I'd also like to include radio buttons to plot points on the surface.
Problem:
When points are plotted they simply appear on top of the image, even when they should be behind the surface.
Question:
Is there a way to plot the surface so that it's transparent and points appear either behind or in front? Or hide the points if they land out of eyesight?
Data:
d <- list(x = c(0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6,
6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), y = c(0, 0.5, 1, 1.5, 2, 2.5,
3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10),
z = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.000147818839413345, 0.00112553487724733,
0.00210325091508131, 0.00308096695291529, 0.00405868299074927,
0.00503639902858325, 0.00601411506641723, 0.00699183110425121,
0.00796954714208519, 0.00894726317991917, 0.00992497921775315,
0.0109026952555871, 0.0118804112934211, 0.0128581273312551,
0.0138358433690891, 0.0148135594069231, 0.015791275444757,
0.016768991482591, 0.017746707520425, 0.018724423558259,
0.019702139596093, 0.00332663525507192, 0.0253299512993333,
0.0473332673435947, 0.0693365833878561, 0.0913398994321175,
0.113343215476379, 0.13534653152064, 0.157349847564902, 0.179353163609163,
0.201356479653424, 0.223359795697686, 0.245363111741947,
0.267366427786209, 0.28936974383047, 0.311373059874731, 0.333376375918993,
0.355379691963254, 0.377383008007516, 0.399386324051777,
0.421389640096038, 0.4433929561403, 0.0185048854236584, 0.140901484725856,
0.263298084028054, 0.385694683330252, 0.50809128263245, 0.630487881934648,
0.752884481236846, 0.875281080539044, 0.997677679841242,
1.12007427914344, 1.24247087844564, 1.36486747774784, 1.48726407705003,
1.60966067635223, 1.73205727565443, 1.85445387495663, 1.97685047425883,
2.09924707356102, 2.22164367286322, 2.34404027216542, 2.46643687146762,
0.0575583422570596, 0.438265663185897, 0.818972984114734,
1.19968030504357, 1.58038762597241, 1.96109494690124, 2.34180226783008,
2.72250958875892, 3.10321690968776, 3.48392423061659, 3.86463155154543,
4.24533887247427, 4.6260461934031, 5.00675351433194, 5.38746083526078,
5.76816815618962, 6.14887547711845, 6.52958279804729, 6.91029011897613,
7.29099743990496, 7.6717047608338, 0.129117933403967, 0.98314083577592,
1.83716373814787, 2.69118664051983, 3.54520954289178, 4.39923244526373,
5.25325534763568, 6.10727825000764, 6.96130115237959, 7.81532405475154,
8.6693469571235, 9.52336985949545, 10.3773927618674, 11.2314156642394,
12.0854385666113, 12.9394614689833, 13.7934843713552, 14.6475072737272,
15.5015301760991, 16.3555530784711, 17.209575980843, 0.23363441995763,
1.77895922624881, 3.32428403254, 4.86960883883118, 6.41493364512237,
7.96025845141355, 9.50558325770473, 11.0509080639959, 12.5962328702871,
14.1415576765783, 15.6868824828695, 17.2322072891607, 18.7775320954518,
20.322856901743, 21.8681817080342, 23.4135065143254, 24.9588313206166,
26.5041561269078, 28.0494809331989, 29.5948057394901, 31.1401305457813,
0.36143039040365, 2.75203425835922, 5.14263812631479, 7.53324199427035,
9.92384586222592, 12.3144497301815, 14.7050535981371, 17.0956574660926,
19.4862613340482, 21.8768652020038, 24.2674690699593, 26.6580729379149,
29.0486768058705, 31.439280673826, 33.8298845417816, 36.2204884097372,
38.6110922776927, 41.0016961456483, 43.3923000136039, 45.7829038815594,
48.173507749515, 0.494048345421132, 3.76182525870662, 7.02960217199211,
10.2973790852776, 13.5651559985631, 16.8329329118486, 20.1007098251341,
23.3684867384196, 26.636263651705, 29.9040405649905, 33.171817478276,
36.4395943915615, 39.707371304847, 42.9751482181325, 46.242925131418,
49.5107020447035, 52.778478957989, 56.0462558712744, 59.3140327845599,
62.5818096978454, 65.8495866111309, 0.608277972936286, 4.63160227964344,
8.65492658635059, 12.6782508930577, 16.7015751997649, 20.724899506472,
24.7482238131792, 28.7715481198863, 32.7948724265935, 36.8181967333006,
40.8415210400078, 44.8648453467149, 48.8881696534221, 52.9114939601292,
56.9348182668364, 60.9581425735435, 64.9814668802507, 69.0047911869578,
73.028115493665, 77.0514398003722, 81.0747641070793, 0.68169864474794,
5.19064825215217, 9.6995978595564, 14.2085474669606, 18.7174970743649,
23.2264466817691, 27.7353962891733, 32.2443458965776, 36.7532955039818,
41.262245111386, 45.7711947187903, 50.2801443261945, 54.7890939335987,
59.298043541003, 63.8069931484072, 68.3159427558114, 72.8248923632157,
77.3338419706199, 81.8427915780241, 86.3517411854284, 90.8606907928326,
0.698331143785818, 5.31729285196915, 9.93625456015249, 14.5552162683358,
19.1741779765192, 23.7931396847025, 28.4121013928858, 33.0310631010692,
37.6500248092525, 42.2689865174358, 46.8879482256192, 51.5069099338025,
56.1258716419859, 60.7448333501692, 65.3637950583525, 69.9827567665359,
74.6017184747192, 79.2206801829025, 83.8396418910859, 88.4586035992692,
93.0775653074525, 0.653010606586468, 4.9722093330084, 9.29140805943032,
13.6106067858523, 17.9298055122742, 22.2490042386961, 26.568202965118,
30.88740169154, 35.2066004179619, 39.5257991443838, 43.8449978708057,
48.1641965972277, 52.4833953236496, 56.8025940500715, 61.1217927764935,
65.4409915029154, 69.7601902293373, 74.0793889557592, 78.3985876821812,
82.7177864086031, 87.036985135025, 0.553337675961259, 4.21327116124787,
7.87320464653448, 11.5331381318211, 15.1930716171077, 18.8530051023943,
22.5129385876809, 26.1728720729675, 29.8328055582542, 33.4927390435408,
37.1526725288274, 40.812606014114, 44.4725394994006, 48.1324729846872,
51.7924064699738, 55.4523399552604, 59.112273440547, 62.7722069258337,
66.4321404111203, 70.0920738964069, 73.7520073816935, 0.418509049668882,
3.18664747819306, 5.95478590671724, 8.72292433524142, 11.4910627637656,
14.2592011922898, 17.027339620814, 19.7954780493381, 22.5636164778623,
25.3317549063865, 28.0998933349107, 30.8680317634349, 33.636170191959,
36.4043086204832, 39.1724470490074, 41.9405854775316, 44.7087239060558,
47.4768623345799, 50.2450007631041, 53.0131391916283, 55.7812776201525,
0.274945103406177, 2.09351057307846, 3.91207604275075, 5.73064151242304,
7.54920698209532, 9.36777245176761, 11.1863379214399, 13.0049033911122,
14.8234688607845, 16.6420343304568, 18.460599800129, 20.2791652698013,
22.0977307394736, 23.9162962091459, 25.7348616788182, 27.5534271484905,
29.3719926181628, 31.1905580878351, 33.0091235575073, 34.8276890271796,
36.6462544968519, 0.14939138421548, 1.1375086826693, 2.12562598112311,
3.11374327957693, 4.10186057803075, 5.08997787648456, 6.07809517493838,
7.06621247339219, 8.05432977184601, 9.04244707029983, 10.0305643687536,
11.0186816672075, 12.0067989656613, 12.9949162641151, 13.9830335625689,
14.9711508610227, 15.9592681594765, 16.9473854579304, 17.9355027563842,
18.923620054838, 19.9117373532918, 0.0610345623904979, 0.464734596487648,
0.868434630584799, 1.27213466468195, 1.6758346987791, 2.07953473287625,
2.4832347669734, 2.88693480107055, 3.2906348351677, 3.69433486926485,
4.098034903362, 4.50173493745915, 4.9054349715563, 5.30913500565345,
5.7128350397506, 6.11653507384775, 6.52023510794491, 6.92393514204206,
7.32763517613921, 7.73133521023636, 8.13503524433351, 0.0150842607904164,
0.114855871447028, 0.214627482103639, 0.31439909276025, 0.414170703416861,
0.513942314073472, 0.613713924730083, 0.713485535386694,
0.813257146043305, 0.913028756699917, 1.01280036735653, 1.11257197801314,
1.21234358866975, 1.31211519932636, 1.41188680998297, 1.51165842063958,
1.61143003129619, 1.71120164195281, 1.81097325260942, 1.91074486326603,
2.01051647392264, 0.00112075907879118, 0.00853377984279572,
0.0159468006068003, 0.0233598213708048, 0.0307728421348093,
0.0381858628988139, 0.0455988836628184, 0.0530119044268229,
0.0604249251908275, 0.067837945954832, 0.0752509667188366,
0.0826639874828411, 0.0900770082468456, 0.0974900290108502,
0.104903049774855, 0.112316070538859, 0.119729091302864,
0.127142112066868, 0.134555132830873, 0.141968153594877,
0.149381174358882, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(21L, 21L)), facetcol = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 6L, 6L, 1L, 2L, 2L, 3L, 4L, 4L, 5L, 6L, 6L, 7L, 8L,
9L, 9L, 10L, 11L, 11L, 12L, 13L, 13L, 14L, 1L, 3L, 4L, 5L,
7L, 8L, 9L, 11L, 12L, 13L, 15L, 16L, 17L, 19L, 20L, 21L,
23L, 24L, 25L, 27L, 2L, 4L, 6L, 9L, 11L, 13L, 15L, 17L, 19L,
22L, 24L, 26L, 28L, 30L, 33L, 35L, 37L, 39L, 41L, 44L, 3L,
6L, 9L, 12L, 15L, 18L, 21L, 25L, 28L, 31L, 34L, 37L, 40L,
44L, 47L, 50L, 53L, 56L, 59L, 62L, 3L, 7L, 11L, 15L, 19L,
23L, 28L, 32L, 36L, 40L, 44L, 48L, 52L, 56L, 60L, 64L, 68L,
72L, 76L, 80L, 4L, 8L, 13L, 18L, 23L, 27L, 32L, 37L, 42L,
46L, 51L, 56L, 61L, 65L, 70L, 75L, 80L, 84L, 89L, 94L, 4L,
9L, 14L, 19L, 24L, 29L, 34L, 39L, 45L, 50L, 55L, 60L, 65L,
70L, 75L, 80L, 85L, 90L, 95L, 100L, 4L, 9L, 14L, 19L, 24L,
29L, 34L, 39L, 44L, 49L, 54L, 59L, 64L, 69L, 74L, 78L, 83L,
88L, 93L, 98L, 3L, 8L, 12L, 17L, 21L, 26L, 30L, 35L, 39L,
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46L, 48L, 51L, 2L, 3L, 5L, 6L, 8L, 9L, 11L, 12L, 14L, 16L,
17L, 19L, 20L, 22L, 23L, 25L, 27L, 28L, 30L, 31L, 1L, 2L,
3L, 3L, 4L, 5L, 6L, 6L, 7L, 8L, 9L, 10L, 10L, 11L, 12L, 13L,
13L, 14L, 15L, 16L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("(-0.357,3.59]", "(3.59,7.18]",
"(7.18,10.8]", "(10.8,14.4]", "(14.4,17.9]", "(17.9,21.5]",
"(21.5,25.1]", "(25.1,28.7]", "(28.7,32.3]", "(32.3,35.9]",
"(35.9,39.5]", "(39.5,43.1]", "(43.1,46.6]", "(46.6,50.2]",
"(50.2,53.8]", "(53.8,57.4]", "(57.4,61]", "(61,64.6]", "(64.6,68.2]",
"(68.2,71.8]", "(71.8,75.3]", "(75.3,78.9]", "(78.9,82.5]",
"(82.5,86.1]", "(86.1,89.7]", "(89.7,93.3]", "(93.3,96.9]",
"(96.9,100]", "(100,104]", "(104,108]", "(108,111]", "(111,115]",
"(115,118]", "(118,122]", "(122,126]", "(126,129]", "(129,133]",
"(133,136]", "(136,140]", "(140,144]", "(144,147]", "(147,151]",
"(151,154]", "(154,158]", "(158,161]", "(161,165]", "(165,169]",
"(169,172]", "(172,176]", "(176,179]", "(179,183]", "(183,187]",
"(187,190]", "(190,194]", "(194,197]", "(197,201]", "(201,204]",
"(204,208]", "(208,212]", "(212,215]", "(215,219]", "(219,222]",
"(222,226]", "(226,230]", "(230,233]", "(233,237]", "(237,240]",
"(240,244]", "(244,248]", "(248,251]", "(251,255]", "(255,258]",
"(258,262]", "(262,265]", "(265,269]", "(269,273]", "(273,276]",
"(276,280]", "(280,283]", "(283,287]", "(287,291]", "(291,294]",
"(294,298]", "(298,301]", "(301,305]", "(305,309]", "(309,312]",
"(312,316]", "(316,319]", "(319,323]", "(323,326]", "(326,330]",
"(330,334]", "(334,337]", "(337,341]", "(341,344]", "(344,348]",
"(348,352]", "(352,355]", "(355,359]"), class = "factor"))
Code
flip <- 1 # 1 or 2
theta = c(-300,120)[flip]
pmat <- persp(d$x, d$y, d$z, asp = 1,col = color[d$facetcol], phi = 30, theta = theta, border = "grey10"
,d = .8,r = 2.8,expand = .6,shade = .2,axes = F,box = T,cex = .1)
xx <- c(7.76245335753423, 6.73123147037805)
yy <- c(4.88402435072353, 4.20867046100364)
zz <- c(68.727, 48.558)
mypoints <- trans3d(xx,yy,zz,pmat = pmat)
points(mypoints,pch = 16,col = 2)
The image below is correct, but when the plot is rotated (set flip to 2) the points do not jive. In other words, when the plot is rotated the points should be hidden from view, or seen through a semi-transparent surface. Help is appreciated!

In case this is helpful to anyone. I ended up using the persp3D() function from the plot3D package. All my custom axes labels and tick marks transferred seamlessly from the base persp() with the added bonus of a transparency argument (alpha =) and proper point plotting (points3D).

Ranking according to value across two variables - r

I have this dataframe:
df<-data.frame(
var1 = c(rep(c(rep(1,2), rep(2,3), rep(3,2), rep(4,1)),2), 1),
var2 = c(rep(1,8), rep(2,8),3)
)
df
var1 var2
#1 1 1
#2 1 1
#3 2 1
#4 2 1
#5 2 1
#6 3 1
#7 3 1
#8 4 1
#9 1 2
#10 1 2
#11 2 2
#12 2 2
#13 2 2
#14 3 2
#15 3 2
#16 4 2
#17 1 3
I would like to make a third variable that is a rank. Rows get the highest rank if 1) they have the lowest numbers in var2 - and then according to how low the numbers are in var1. e.g. Rows 1 and 2 with var2=1 and var1=1 should be ranked 1. Whereas, rows 9 and 10 with var2=2 and var1=1 would be ranked 5.
If my data are arranged in ascending order of var2 and then var1, I did the following using my favorite R function rle to achieve the ranking I'm after:
rle(df$var1)
N <- length(rle(df$var1)$lengths)
df$ranks <- rep(1:N, rle(df$var1)$lengths)
df
var1 var2 ranks
#1 1 1 1
#2 1 1 1
#3 2 1 2
#4 2 1 2
#5 2 1 2
#6 3 1 3
#7 3 1 3
#8 4 1 4
#9 1 2 5
#10 1 2 5
#11 2 2 6
#12 2 2 6
#13 2 2 6
#14 3 2 7
#15 3 2 7
#16 4 2 8
#17 1 3 9
This works, but it requires my df to be pre-sorted. I'd like a solution that does not need this. I feel like this should be a simple one-liner using rank and that I'm having a blind-spot. Any help appreciated - thanks.
EDIT 1:
- adding a larger example for testing of suggested answer
dput(df1)
df1 <- structure(list(var1 = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 7L,
7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 6L, 9L, 10L, 10L, 10L, 11L,
12L, 12L, 12L, 13L, 14L, 14L, 14L, 14L, 15L, 16L, 16L, 16L, 16L,
16L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 19L, 19L,
20L, 20L, 21L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L,
24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 1L, 2L, 2L,
2L, 2L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L,
8L, 9L, 10L, 10L, 10L, 10L, 3L, 11L, 11L, 11L, 11L, 12L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L,
12L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L,
18L, 18L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 21L, 22L,
22L, 22L, 23L, 25L, 24L, 24L, 24L, 24L, 24L, 26L, 26L, 26L, 26L,
26L, 27L, 27L, 27L, 27L, 27L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L,
11L, 12L, 12L, 13L, 14L, 15L, 16L, 17L, 17L, 18L, 18L, 19L, 19L,
19L, 19L, 20L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L,
23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L,
25L, 25L, 26L, 26L, 26L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 5L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L), var2 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), ranks = c(1L, 1L,
1L, 1L, 1L, 12L, 12L, 12L, 12L, 12L, 19L, 19L, 19L, 19L, 19L,
20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L, 23L, 23L, 23L,
23L, 23L, 24L, 24L, 24L, 24L, 24L, 22L, 25L, 2L, 2L, 2L, 3L,
4L, 4L, 4L, 5L, 6L, 6L, 6L, 6L, 7L, 8L, 8L, 8L, 8L, 8L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 13L, 13L, 14L,
15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L,
17L, 17L, 18L, 18L, 18L, 18L, 18L, 26L, 37L, 37L, 37L, 37L, 47L,
48L, 48L, 48L, 48L, 49L, 49L, 49L, 49L, 49L, 50L, 50L, 50L, 50L,
51L, 52L, 27L, 27L, 27L, 27L, 46L, 28L, 28L, 28L, 28L, 29L, 30L,
30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L,
29L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 35L, 35L, 35L,
35L, 35L, 36L, 36L, 36L, 36L, 36L, 38L, 38L, 38L, 38L, 39L, 40L,
40L, 40L, 41L, 43L, 42L, 42L, 42L, 42L, 42L, 44L, 44L, 44L, 44L,
44L, 45L, 45L, 45L, 45L, 45L, 53L, 64L, 64L, 64L, 64L, 74L, 74L,
74L, 74L, 74L, 75L, 75L, 75L, 75L, 75L, 76L, 76L, 76L, 76L, 76L,
77L, 77L, 77L, 77L, 77L, 78L, 78L, 78L, 78L, 78L, 79L, 80L, 80L,
80L, 80L, 54L, 54L, 54L, 54L, 55L, 56L, 56L, 57L, 58L, 59L, 60L,
61L, 61L, 62L, 62L, 63L, 63L, 63L, 63L, 65L, 66L, 66L, 66L, 66L,
66L, 67L, 67L, 67L, 67L, 67L, 68L, 68L, 68L, 68L, 68L, 69L, 69L,
69L, 69L, 69L, 70L, 70L, 70L, 70L, 70L, 71L, 71L, 71L, 72L, 72L,
73L, 73L, 73L, 73L, 73L, 81L, 81L, 81L, 81L, 81L, 82L, 82L, 82L,
82L, 82L, 83L, 83L, 83L, 83L, 83L, 84L, 84L, 84L, 84L, 85L, 86L,
87L, 87L, 87L, 87L, 88L, 88L, 88L, 88L, 88L)), .Names = c("var1",
"var2", "ranks"), row.names = c(NA, -300L), class = "data.frame")
The ranks variable was got from this suggested answer:
df1$ranks1 <- dense_rank(paste(df1$var2, df1$var1))

Solutions involving paste[0] will only work if values within each vector are integer with a fixed number of digits. This is because paste converts to character and:
character (lexicographic) ordering differs from numeric: rank(c(1 , 2, 11)); rank(as.character(c(1 , 2, 11)))
concatenation introduces ambiguities: paste0(2,12); paste0(21,2)
Peter Dalgaard made a relevant post in 2011 http://r.789695.n4.nabble.com/Function-rank-for-data-frames-or-multiple-vectors-td3765685.html
For now ignore identical rows. Note that rank(x) == order(order(x)) and order accepts multiple ordering columns so if you don't mind how identical rows are split order(order(df$var2, df$var1)) does the job.
This splits identical rows according to their original ordering. There are a number of ways of ranking identical rows http://en.wikipedia.org/wiki/Ranking#Strategies_for_assigning_rankings.
In 2011 Peter Dalgaard suggested ave(order(order(df$var2, df$var1)), df$var2, df$var1) which gives what Wikipedia calls 'Fractional ranking' and in base::rank is the default ties.method="average".
Your example is what Wikipedia calls 'Dense ranking' which isn't available in base::rank but - as commented by David Arenburg - is provided by dplyr::dense_rank, so you can library(dyplr) and use:
dense_rank(ave(order(order(df$var2, df$var1)), df$var2, df$var1))
Looking at the code for dense_rank it is just
function (x)
{
r <- rank(x)
match(r, sort(unique(r)))
}
suggesting that if you don't want to load dplyr and are happy with a 2-statement solution creating another variable - e.g. r - you could use
r <- ave(order(order(df$var2, df$var1)), df$var2, df$var1); match(r, sort(unique(r)))
Edited to add ...
You can make things a little neater by realising that a data frame is really a list so to rank by columns in order of occurrence:
dense_rank(ave(order(do.call(order, df)), df))
You are ranking by columns in reverse order of occurrence so
dense_rank(ave(order(do.call(order, rev(df))), df))
or explicitly specifying columns and their order
dense_rank(ave(order(do.call(order, df[,2:1])), df[,2:1]))

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