specific stripchart with ggplot2 - r

I've got this dataframe
df <- structure(list(rang = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25.5, 25.5, 27.5,
27.5, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42.5,
42.5, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54.5, 54.5, 56,
57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88
), dr = c(164, 176, 260, 297, 308, 313, 327, 333, 339, 365, 396,
403, 404, 410, 413, 414, 422, 424, 440, 442, 443, 451, 477, 496,
530, 530, 546, 546, 548, 565, 567, 574, 576, 587, 590, 603, 619,
626, 629, 630, 642, 653, 653, 660, 667, 670, 677, 682, 689, 711,
716, 737, 763, 772, 772, 776, 778, 792, 794, 820, 835, 838, 842,
855, 861, 888, 890, 899, 906, 908, 969, 1011, 1046, 1058, 1069,
1072, 1074, 1100, 1153, 1348, 1368, 1432, 1468, 1516, 1612, 1712,
1714, 1731), signe = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L), .Label = c("negatif", "positif"), class = "factor")), .Names = c("rang",
"dr", "signe"), row.names = c(NA, -88L), class = "data.frame")
and this chart when I use the stripchart function in base R
stripchart(df[,1]~df[,3],
method="stack", vertical=FALSE, ylim=c(0.5,2.5),
group.names=levels(df[,3]),
xlab="Rang des différences dr", pch=18, cex=1.2)
Can I have the same plot with the library ggplot2?
I used geom_dotplot but I didn't the same plot. This an example
ggplot(data = df, aes(y=df[,1], x=factor(df[,3]))) +
geom_dotplot(binaxis = "y", dotsize = 0.5) +
coord_cartesian(ylim=c(0, 88)) +
scale_y_continuous(breaks=seq(0, 88, 1))
Help me, please!


You have to flip coordinates, and set binwidth = 1 to get the same plot:
ggplot(data = df, aes(y=rang, x=factor(signe))) +
geom_dotplot(binaxis = "y", dotsize = 0.8, binwidth=1) +
coord_cartesian(ylim=c(0, 88)) +
scale_y_continuous(name='Rang des différences dr') +
scale_x_discrete(name='') +
coord_flip() +
theme_bw(base_size = 20)


Is this along the lines of what you were looking for:
ggplot(df) + geom_point(aes(df[,1],df[,3])) + theme_bw()

Related

Using gratia::draw() in R to display partial effect plots within an HGAM that are not relative to the global smooth

I have a data set that looks like this:
df <- data.frame(
Lake = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L), .Label = c("Fork", "TBend"), class = "factor"),
Depth = c(1.63, 3, 10, 3, 10, 4, 13, 17, 14, 2.81, 20, 3, 28, 24, 6,
1.81999999999999, 7, 25, 2.20999999999998, 10, 15, 7.25999999999999,
4, 4, 6.64999999999998, 8.83999999999997, 6, 2.20999999999998,
22.96, 5.63, 11, 30, 32.31, 25, 1, 3, 4, 7.41000000000003, 2,
6, 17, 7, 5, 4.20999999999998, 3, 22, 5, 4.74000000000001, 7,
10, 3, 11, 14, 2, 24, 1, 7, 15, 16, 2.68000000000001, 12, 11,
5, 10, 10, 6, 12, 4, 4, 4.64999999999998, 18, 7.5, 13, 3, 15,
10, 22, 19, 4, 12, 2, 3, 5.41000000000003, 6, 19, 6, 3, 3, 34,
3.63, 11, 6, 7, 25, 4, 2.81, 4.70999999999998, 3, 12.31, 5, 17,
28, 3.63, 8, 9, 3, 30, 20, 11, 4, 12, 3, 4, 16, 5, 10, 2, 14,
58, 10, 2.06, 15, 2.74000000000001, 7, 10.74, 2.81, 11, 6, 5,
7.25999999999999, 10, 2.68000000000001, 9, 2.83999999999997,
5.5, 15, 7, 6.56, 14, 6, 3.25999999999999, 2.31, 1, 7, 3, 4,
2, 3, 9, 28, 18.84, 5, 5, 2.75999999999999, 7.63, 8.20999999999998,
18, 3, 11, 1, 24, 4, 22, 2, 3, 4.20999999999998, 14.65, 16, 9,
5, 3, 7, 1, 2, 4.5, 2, 20, 1, 10, 17, 4, 2, 1, 23, 5, 11, 12,
17, 10, 3, 18, 6, 7, 5, 3, 32, 16, 5, 7, 9, 29, 2, 12, 4, 23,
14, 4, 5, 11, 11.82, 6.20999999999998, 7, 12, 3, 6, 4, 17, 4,
24, 6, 12, 11.63, 4, 2, 25, 2, 54, 7, 8, 9.25999999999999, 14,
15, 11, 6, 21, 1, 3, 8, 1, 2.83999999999997, 19, 6, 19, 2.06,
3, 3, 4, 8, 6, 9.41000000000003, 4, 8.64999999999998, 3, 3, 2.5,
30, 12, 14, 15, 16, 10.56, 24, 12, 16.71, 25, 1, 10, 17, 1, 1.25999999999999,
12, 4, 24, 15, 8.68000000000001, 8, 3, 15.82, 17, 5, 3, 6.70999999999998,
5.63, 10, 10.68, 8, 3, 8.81, 5.25999999999999, 22, 12, 5.81999999999999,
6, 6, 3.5, 1.52999999999997, 4, 22, 15, 4, 23, 12, 25, 4, 22,
5.41000000000003, 9, 19, 8, 4, 8.56, 20, 10.21, 24, 1, 6, 3,
10, 3, 28, 12, 6, 17, 1, 3.41000000000003, 6.16000000000003,
4, 20.68, 4, 2.74000000000001, 5, 12, 1, 45, 4.74000000000001,
18, 15, 1, 8, 20, 21, 3, 16, 1, 3, 30, 10, 6.06, 4, 10.84, 25,
26, 12, 2.56, 2, 6, 10.56, 10.31, 16, 29.26, 5, 6, 3.81999999999999,
15, 1, 8, 3, 2, 22, 5, 2.95999999999998, 4.5, 1, 18, 2.66000000000003,
19, 12, 4, 14, 3, 7, 28, 4, 23, 6, 5, 3, 22, 1, 4, 12, 7, 1.63,
12.21, 15, 4, 3, 9, 20.65, 4.74000000000001, 22, 8.81, 5.81999999999999,
4.16000000000003, 7, 10, 24, 4.95999999999998, 30, 2, 10, 5,
9, 5, 12, 29.82, 2, 6.5, 6.20999999999998, 1, 1, 22, 22, 6.64999999999998,
32, 11, 15, 1, 18, 1.81999999999999, 4, 8, 20, 15, 4, 7, 22,
2, 2, 1, 1, 15, 20, 3, 5, 1.63, 4.66000000000003, 22, 6, 2, 31,
20, 5, 9.5, 30, 18, 13, 12, 12, 4.20999999999998, 12, 10.06,
2.68000000000001, 2, 1, 5, 2, 9, 2, 4, 1, 6, 1, 1, 2.16000000000003,
7, 8.95999999999998, 2.74000000000001, 5, 4, 5, 15, 20, 5.41000000000003,
29.41, 7, 32, 4, 14, 2.74000000000001, 4, 15, 8, 21, 32, 13.41,
3, 14, 4, 3, 18, 2.31, 25, 3.5, 4.74000000000001, 19, 21, 5.25999999999999,
10.21, 12.84, 2.95999999999998, 2, 4.31, 7, 7, 2.31, 17, 10.71,
23.41, 3, 3.41000000000003, 4.68000000000001, 22, 3, 13, 15,
8.74000000000001, 14.81, 5, 1, 4, 16, 1.41000000000003, 13, 3,
2, 6.06, 7, 3, 22, 4.83999999999997, 7, 2.81, 21, 3, 19, 6, 14,
2, 1, 10, 7.5, 8.70999999999998, 30, 14, 20, 1, 18, 30, 28, 1.41000000000003,
20, 5, 1.41000000000003, 3.5, 4.64999999999998, 5, 9.5, 3, 1.63,
11, 21, 2.66000000000003, 20.74, 15, 15, 14, 5, 14, 4.5, 4, 6.06,
4.20999999999998, 12, 18, 10.16, 7.81999999999999, 1, 2.95999999999998,
15, 2.5, 2.70999999999998, 11, 13.63, 18, 6, 18, 11, 6, 12, 7.5,
4.56, 1.38, 2.95999999999998, 17, 4, 1, 15, 4.74000000000001,
5.5, 11, 4, 1, 3, 25, 3, 9, 15, 11, 29, 8.56, 23, 14.65, 1, 7,
8, 14.06, 2, 3, 26, 2.56, 2.5, 25, 2.74000000000001, 1, 3, 8.56,
9.38, 2, 18, 3, 30, 16.96, 4, 22, 11, 6, 4, 3, 8.83999999999997,
22, 18, 7, 2.68000000000001, 6, 14.76, 7, 5, 1, 21, 3.81999999999999,
10, 3, 5, 7, 6, 20, 6.81, 7, 19, 24, 5, 1, 21.41, 3, 1.81999999999999,
10, 11.41, 6, 30, 3, 4, 4, 4, 1.5, 10.5, 18, 10, 2, 25, 14, 4,
5.63, 4.20999999999998, 2, 10.84, 10, 7, 30, 1, 17, 3, 3, 22,
2.74000000000001, 1, 8, 7, 32.65, 4, 3, 5, 4, 5, 1, 5, 10.76,
4, 2, 3.41000000000003, 4, 17),
OrdDay = c(254, 271, 286, 88, 181, 209, 246, 259, 218, 324, 230, 181,
271, 351, 364, 224, 268, 232, 210, 215, 260, 281, 286, 351, 195,
167, 248, 54, 308, 254, 322, 125, 33, 248, 336, 319, 322, 238,
181, 304, 195, 181, 273, 210, 153, 230, 28, 349, 195, 78, 286,
41, 355, 109, 78, 187, 31, 286, 41, 336, 187, 146, 305, 70, 290,
129, 290, 160, 83, 195, 147, 7, 159, 195, 146, 195, 181, 11,
349, 230, 140, 146, 268, 305, 181, 244, 299, 124, 155, 254, 232,
218, 12, 78, 286, 324, 177, 131, 33, 304, 56, 211, 254, 218,
60, 167, 147, 167, 322, 181, 299, 167, 215, 351, 230, 334, 25,
63, 11, 246, 5, 281, 349, 209, 91, 324, 246, 63, 203, 281, 167,
336, 63, 167, 88, 248, 153, 184, 237, 28, 281, 33, 195, 167,
109, 260, 56, 268, 248, 259, 187, 11, 124, 75, 254, 54, 218,
319, 322, 91, 12, 204, 195, 211, 125, 54, 195, 271, 364, 83,
335, 75, 75, 109, 75, 299, 160, 124, 334, 7, 146, 153, 184, 129,
146, 181, 131, 364, 31, 124, 11, 304, 290, 181, 204, 195, 322,
290, 305, 28, 336, 101, 174, 335, 109, 322, 273, 304, 364, 224,
210, 246, 25, 305, 349, 319, 83, 160, 28, 224, 187, 254, 124,
7, 167, 195, 12, 12, 187, 281, 101, 336, 304, 195, 244, 75, 232,
322, 246, 167, 237, 167, 336, 5, 125, 232, 187, 204, 286, 268,
131, 195, 322, 155, 104, 325, 28, 215, 195, 224, 184, 224, 174,
177, 167, 21, 363, 244, 268, 281, 286, 286, 335, 286, 336, 286,
109, 224, 181, 322, 299, 177, 254, 124, 336, 268, 218, 324, 281,
12, 119, 224, 248, 187, 215, 234, 159, 7, 204, 167, 78, 167,
325, 244, 290, 238, 305, 322, 246, 334, 184, 195, 210, 335, 160,
248, 218, 299, 78, 322, 167, 41, 211, 184, 238, 21, 281, 336,
322, 349, 268, 363, 273, 334, 349, 83, 78, 75, 204, 25, 237,
104, 232, 195, 319, 363, 355, 5, 335, 167, 237, 349, 286, 184,
75, 91, 184, 33, 215, 281, 28, 78, 224, 215, 116, 268, 124, 248,
7, 70, 308, 160, 336, 237, 105, 195, 273, 305, 273, 155, 248,
281, 160, 209, 259, 63, 101, 143, 67, 187, 203, 11, 254, 210,
31, 167, 363, 70, 195, 91, 41, 324, 224, 21, 351, 146, 268, 308,
28, 334, 259, 56, 12, 232, 174, 224, 101, 335, 54, 195, 143,
25, 171, 195, 167, 336, 281, 203, 25, 224, 75, 218, 248, 160,
181, 237, 195, 133, 172, 146, 75, 143, 260, 215, 56, 254, 105,
271, 319, 88, 364, 12, 230, 271, 125, 203, 248, 211, 286, 54,
63, 5, 336, 259, 105, 28, 299, 224, 172, 125, 75, 299, 177, 105,
21, 28, 308, 91, 88, 63, 281, 167, 349, 238, 238, 204, 12, 237,
349, 91, 364, 174, 237, 63, 363, 268, 167, 28, 181, 155, 160,
33, 304, 244, 349, 248, 28, 281, 54, 167, 308, 116, 33, 224,
181, 33, 364, 177, 268, 268, 238, 336, 281, 181, 299, 246, 349,
324, 56, 75, 273, 271, 268, 195, 246, 181, 5, 248, 146, 322,
167, 140, 324, 286, 286, 174, 322, 60, 187, 260, 335, 104, 177,
167, 203, 304, 177, 232, 336, 209, 238, 125, 260, 268, 203, 195,
363, 88, 232, 254, 203, 246, 105, 349, 268, 160, 336, 336, 260,
88, 56, 5, 54, 363, 31, 21, 224, 260, 308, 355, 25, 177, 167,
254, 224, 70, 349, 281, 119, 7, 75, 184, 124, 308, 273, 146,
202, 167, 349, 88, 218, 70, 210, 160, 147, 155, 181, 244, 195,
56, 184, 41, 195, 160, 260, 101, 5, 116, 230, 351, 184, 25, 224,
349, 91, 67, 184, 124, 355, 237, 167, 209, 308, 167, 268, 31,
218, 101, 155, 167, 12, 125, 143, 336, 286, 75, 167, 187, 260,
304, 224, 203, 290, 125, 195, 290, 355, 324, 153, 187, 349, 355,
324, 238, 260, 224, 281, 238, 140, 290, 273, 119, 181, 153, 129,
271, 75, 230, 116, 41, 91, 167, 254, 54, 290, 167, 11, 237, 336,
105, 181, 11, 286, 244, 349, 91, 230, 336, 195, 119, 230, 349,
349, 203, 238, 63, 75, 335, 91, 268, 322, 83),
stringsAsFactors = FALSE)
I am running an HGAM (GI model from Pederson et al. 2019 https://peerj.com/articles/6876/) that looks like this:
library(mgcv)
hgam_gi<-gam(Depth~Lake+s(OrdDay,bs="cc")+s(OrdDay,by=Lake,bs='cc')+s(Lake,bs="re"),data=df,family=nb)
My partial effect plots look like this:
library(gratia)
draw(hgam_gi)
My understanding of these partial effect plots is that the individual smooth LakeTBend doesn't differ much from the global smooth s(OrdDay) thus leadings to no effect in the plot and LakeFork has a stronger effect somewhere around OrdDay 200-250. I am wondering if there's a way to use an HGAM with a global smooth, and extract the individual smooths from that model similar to what you get with an HGAM without a global smooth.
With a model without a global smooth (I model according to Pederson et al. 2019), I can get a better understanding of the individual trends.
hgam_i<-gam(Depth~Lake+s(OrdDay,by=Lake,bs='cc'),data=df,family=nb)
draw(hgam_i)
My question is if there is a call I can make in gratia::draw() with an HGAM that can pull the partial effect plots independent of the global smooth? I know I can use predict.gam() to get the the individual predictions for each lake, which is great, but I really like the interpretability of partial effect plots and would love to find a way to run a single parsimonious model that displays global trends and individual trends.
Thanks!

Your models are somewhat mis-specified - you have the group (lake) means in the model twice:
the parametric Lake effect, and
the random effect s(Lake, bs = "re")
you shouldn't include this effect twice; use one or the other.
Also, your first model really should have something for the by = Lake smooths to help the model identify those lake-specific differences from the global smooth. Typically we would add m = 1 to those by = Lake smooths when the model already contains a "global" smooth. You should also set knots when fitting a bs = "cc" smooth of day (assuming OrdDay is day of year?):
library("mgcv")
library("dplyr")
knots <- list(OrdDay = c(0.5, 366.5))
m <- gam(Depth ~ Lake +
s(OrdDay, bs = "cc") +
s(OrdDay, by = Lake, bs = "cc", m = 1),
data = df, family = nb, knots = knots)
another way of specifying this model is using an ordered factor instead of a normal factor. We have to set the contrasts differently so that the parametric term is easier to understand
df <- df |>
mutate(oLake = ordered(Lake))
contrasts(df$oLake) <- contr.treatment
Now when you fit the model we use oLake in place of Lake and the first smooth of OrdDay represents the smooth for the reference level of oLake (here the reference is: Fork). The the by = oLake smooth (there is only one here as you only have two levels/lakes) represents the smooth difference between the two lakes, such that the smooth for TBend is the linear combination of the two smooths in the model
m2 <- gam(Depth ~ oLake +
s(OrdDay, bs = "cc") +
s(OrdDay, by = oLake, bs = "cc"),
data = df, family = nb, knots = knots)
With either model, to get estimated smooths for your two lakes, you need to add combinations of smooths. With m (the global smooth plus m = 1 smooths for each lake) we need to form the following combinations:
For Fork we want s(OrdDay) + s(OrdDay):LakeFork
For TBend we want s(OrdDay) + s(OrdDay):LakeTBend
For m2 where we used ordered factors, we want:
For Fork we want s(OrdDay)
For TBend we want s(OrdDay) + s(OrdDay):oLakeTBend
You can choose to include the group effects or not. Including them would mean you are actually generating fitted values on the response scale which is not what draw() shows (it shows partial effects).
To get fitted values we just predict over the range of OrdDay for both lakes. Using the development version of {gratia} on GitHub we create a data slice and then use fitted_values() on the data slice to yield predicted/fitted values:
ds <- data_slice(m, OrdDay = evenly(OrdDay, n = 100), Lake = evenly(Lake))
fv <- fitted_values(m, data = ds, scale = "response")
fv
library("ggplot2")
fv |>
ggplot(aes(x = OrdDay, y = fitted, group = Lake)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = Lake), alpha = 0.2) +
geom_line(aes(colour = Lake))
while for m2 we use
ds2 <- data_slice(m2, OrdDay = evenly(OrdDay, n = 100), oLake = evenly(oLake))
fv2 <- fitted_values(m2, data = ds2, scale = "response")
fv2
library("ggplot2")
fv2 |>
ggplot(aes(x = OrdDay, y = fitted, group = oLake)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = oLake), alpha = 0.2) +
geom_line(aes(colour = oLake)) +
labs(fill = "Lake", colour = "Lake") +
scale_colour_discrete() +
scale_fill_discrete()
If you don't want the group means, so you are seeing something similar to that draw() produces (partial effects), you'll need to currently switch back to predict.gam(), using type = "terms", and then sum the rows over the relevant columns:
p <- predict(m2, newdata = ds2, type = "terms")
p contains
r$> head(p)
oLake s(OrdDay) s(OrdDay):oLakeTBend
1 0.0000000 0.2274295 0.00000000
2 0.3846254 0.2274295 0.07137302
3 0.0000000 0.2286423 0.00000000
4 0.3846254 0.2286423 0.07287456
5 0.0000000 0.2263583 0.00000000
6 0.3846254 0.2263583 0.07405621
so we only want to sum the rows over columns 2 and 3:
fv3 <- rowSums(p[, -1])
Getting the standard errors is a bit more difficult in this case, so if including the group means is OK with you, I'll omit the code to get the correct standard errors for the type = "terms" idea. Comment if you need that.

how to add the names on the y axis when graphing multiple variables through a function

I have this function that allows me to create multiple graphs on various variables of the dataset.
However in the output on the y-axis it always put the name of the list "varlist" instead of the name of each variable in the list, i.e. insuline, glucose, hdl and ldl.
How could I do that? thank you
# Multiple box plot per group per time
library(ggplot2)
names(dflinear) <- c("id", "group", "sex", "time", "insuline", "glucose", "hdl", "ldl")
# Create a list wherein the function will be applied to
varlist<-c(list(dflinear$insuline, dflinear$glucose, dflinear$hdl, dflinear$ldl))
names(varlist)<-c("insuline", "glucose", "hdl", "ldl")
# Create the function boxplot
A <- function (varlist) {
dflinear %>% group_by('group')%>%
ggplot(mapping = aes_string(x='time', y='varlist', fill='group')) +
geom_boxplot()
}
# Apply it to the whole list and graph the plots
plots<-lapply(varlist, FUN = A)
plots
Reproducible dataset
dflinear<- structure(list(id = structure(c("SA01", "SA02", "SA03", "SA04",
"SA05", "SA06", "SA07", "SA08", "SA09", "SA10", "SA11", "SA12",
"SA13", "SA14", "SA15", "SA16", "SA17", "SA18", "SA19", "SA20",
"SA21", "SA22", "SA23", "SA24", "SA25", "SA26", "SA27", "SA28",
"SA29", "SA30", "SA31", "SA32", "SA33", "SA34", "SA35", "SA36",
"SA37", "SA38", "SA39", "SA40", "SA41", "SA42", "SA43", "SA44",
"SA45", "SA46", "SA47", "SA48", "SA49", "SA50", "SA51", "SA52",
"SA53", "SA54", "SA56", "SA57", "SA58", "SA59", "SA60", "SA61",
"SA62", "SA63", "SA64", "SA65", "SA66", "SA67", "SA68", "SA69",
"SA72", "SA73", "SA74", "SA75", "SA76", "SA77", "SA78", "SA79",
"SA80", "SA81", "SA82", "SA83", "SA84", "SA85", "SA86", "SA87",
"SA88", "SA89", "SA90", "SA92", "SA93", "SA94", "SA95", "SA96",
"SA97", "SA99", "SA100", "SA101", "SA102", "SA103", "SA104",
"SA105", "SA107", "SA108", "SA109", "SA110", "SA111", "SA112",
"SA113", "SA114", "SA115", "SA116", "SA118", "SC01", "SC02",
"SC03", "SC04", "SC05", "SC06", "SC07", "SC08", "SC09", "SC10",
"SC11", "SC12", "SC13", "SC14", "SC15", "SC16", "SC17", "SC18",
"SC19", "SC20", "SC21", "SC22", "SC23", "SC24", "SC25", "SC26",
"SC27", "SC28", "SC29", "SC30", "SC31", "SC32", "SC33", "SC34",
"SC35", "SC36", "SC37", "SC38", "M01", "M02", "M03", "M04", "M05",
"M06", "M07", "M08", "M09", "M10", "M11", "M12", "M13", "M14",
"M15", "M16", "M17", "M18", "M19", "M20", "M21", "M22", "M23",
"M24", "M25", "M26", "M27", "M28", "M29", "M30", "M31", "M32",
"M33", "M34", "M35", "M36", "M37", "M38", "M39", "M40", "M41",
"M42", "M43", "M44", "M45", "M46", "M47", "M48", "M49", "M50",
"M51", "M52", "M53", "SA01", "SA02", "SA03", "SA04", "SA05",
"SA06", "SA07", "SA08", "SA09", "SA10", "SA11", "SA12", "SA13",
"SA14", "SA15", "SA16", "SA17", "SA18", "SA19", "SA20", "SA21",
"SA22", "SA23", "SA24", "SA25", "SA26", "SA27", "SA28", "SA29",
"SA30", "SA31", "SA32", "SA33", "SA34", "SA35", "SA36", "SA37",
"SA38", "SA39", "SA40", "SA41", "SA42", "SA43", "SA44", "SA45",
"SA46", "SA47", "SA48", "SA49", "SA50", "SA51", "SA52", "SA53",
"SA54", "SA56", "SA57", "SA58", "SA59", "SA60", "SA61", "SA62",
"SA63", "SA64", "SA65", "SA66", "SA67", "SA68", "SA69", "SA72",
"SA73", "SA74", "SA75", "SA76", "SA77", "SA78", "SA79", "SA80",
"SA81", "SA82", "SA83", "SA84", "SA85", "SA86", "SA87", "SA88",
"SA89", "SA90", "SA92", "SA93", "SA94", "SA95", "SA96", "SA97",
"SA99", "SA100", "SA101", "SA102", "SA103", "SA104", "SA105",
"SA107", "SA108", "SA109", "SA110", "SA111", "SA112", "SA113",
"SA114", "SA115", "SA116", "SA118", "SC01", "SC02", "SC03", "SC04",
"SC05", "SC06", "SC07", "SC08", "SC09", "SC10", "SC11", "SC12",
"SC13", "SC14", "SC15", "SC16", "SC17", "SC18", "SC19", "SC20",
"SC21", "SC22", "SC23", "SC24", "SC25", "SC26", "SC27", "SC28",
"SC29", "SC30", "SC31", "SC32", "SC33", "SC34", "SC35", "SC36",
"SC37", "SC38", "M01", "M02", "M03", "M04", "M05", "M06", "M07",
"M08", "M09", "M10", "M11", "M12", "M13", "M14", "M15", "M16",
"M17", "M18", "M19", "M20", "M21", "M22", "M23", "M24", "M25",
"M26", "M27", "M28", "M29", "M30", "M31", "M32", "M33", "M34",
"M35", "M36", "M37", "M38", "M39", "M40", "M41", "M42", "M43",
"M44", "M45", "M46", "M47", "M48", "M49", "M50", "M51", "M52",
"M53"), label = "Code of PrevenGo", format.spss = "A5", display_width = 12L),
group = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 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, 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, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = c("Metab", "SA", "SC"), class = "factor"),
sex = structure(c(2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L), .Label = c("F", "M"), class = "factor"),
time = 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, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L), insuline = structure(c(9, 4.1, 3.3, 9.4, 22.9, 16.2,
8.7, 16.7, 21.2, 21, 12.8, 7.3, 38.4, 20.2, 19.6, 6.4, 18.9,
12.1, 8.2, 17, 15.6, 12.5, 19.1, 13.7, 8, 20.1, 19.8, 6.8,
15.4, 14.7, 11.9, 8.8, 7.9, 51.2, 10.8, 8.1, 28.6, 8.6, 27.9,
13.3, 9, 16.3, 13.3, 5.8, 27.3, 4.2, 8.2, 9.9, 20.1, 11.7,
8.7, 18.1, 10.9, 27.4, 14.6, 29.1, 10.2, 20.2, 9.7, 12.3,
18.2, 1.9, 11.6, 14.6, 7.9, 11.2, 13.8, 21.2, 23.8, 18, 23.5,
21.4, 11.4, 12, 6.6, 13.5, 10.4, 25.3, 56.8, 10.7, 21.5,
8.5, 30.2, 5.3, 7.5, 15.9, 11.6, 22.4, 25.2, 6.1, 15.1, 9.3,
24.3, 30.8, 8.9, 9.8, 34.1, 13.4, 23.1, 21.1, 4.8, 20.1,
38.5, 16.1, 34.1, 16.1, 17.7, 41.4, 20.4, 21.5, 36.3, 15.9,
8.8, 6.1, 29, 4, 23.1, 36.8, 16.4, 15.5, 28.8, 15.9, NA,
7.1, 6.1, 10, 9.1, 25.2, 19.1, 6.9, 14.7, 23.1, 19.3, 12.3,
7.3, 5.9, 8, 0.5, 9, 4, 10.4, 21.4, 14.6, 8.8, 24.5, 5.3,
9.8, 17.6, 10.2, 10.7, 23, 14.5, 4.6, 33.3, 23.3, 7.2, 3.7,
13.1, 6.7, 20, 7.5, 9.2, 4.5, 2.1, 7.7, 11.7, 7.6, 22.5,
8.8, 5.1, 14.8, 15.1, 18.8, 24.3, 14, 17.2, 16.2, 23.6, 17.4,
16.5, 12.1, 15.3, 11.4, 8.7, 22.6, 10.5, 7.4, 15.1, 13.1,
24.6, 19.3, 19.7, 14.1, 5.9, 19.7, 14.9, 5.9, 17.2, 16.9,
6.2, 11.2, 4.1, 10, 3.7, 3.6, 11.6, 16.9, NA, 8, 17.3, NA,
18.3, 4, 3.1, 26.4, 12.9, 17.9, 10.3, 22.5, NA, NA, 23.4,
15.1, NA, 11.9, 27, 6.2, NA, 21.5, 11.6, 15.8, 8.6, 15.2,
10.1, 20.6, 21.7, 45.3, 8.3, 19.5, 29.2, 21.5, 11.4, 9.5,
31.8, 35.3, 11.2, 15.4, NA, 8.5, 22.6, 14.3, NA, 11.8, 11.4,
4.2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 35.8, NA, NA,
NA, NA, NA, 19.7, 42.8, 30.6, 12.2, 5.2, 4.9, 20.4, NA, 23.5,
NA, 13.6, 19.4, 6.9, 16.7, 7.2, 14.7, 59.2, 22, 41.4, 18.1,
10.5, 19.8, 17.4, NA, 25.9, NA, 8.3, 25.9, 5.7, 17.1, 25.2,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.3, 9.1, 14.8,
13.7, 6.2, 17.9, 13.9, 14.6, 70.4, 23.6, 13.8, 15.2, 9.9,
14, 27.6, 14.3, 23.7, 11, 12.1, 13.5, 21, NA, 7.2, 12.3,
4.4, 6.2, 3.9, 15, 9.6, NA, 9, 10.3, NA, 13.3, 6, 11.3, 17.6,
8.5, 10, NA, 11.8, 10.4, 26.2, NA, 10, 5.7, 16.3, 4.7, 20.3,
7.7, 14.6, 9.4, 6.3, 10, 11.1, 6.7, 42.5, NA, NA, NA, 7.7,
18.6, NA, 16.7, 25.4, 21.8, 26.8, 10.2, 13.8, 11.6, 19.1,
8.3, 3.8, 31.1, NA, 7.1, 11.1, 8.7, 19, 16, 31.8, 11.7, 3.4,
17.6, 12.3, 5.1, 17.5, 6.7, 3.8, 16.6, 6.1), format.spss = "F4.2", display_width = 11L),
glucose = structure(c(90, 95, 79, 85, 95, 97, 86, 74, 88,
95, 94, 88, 86, 94, 86, 95, 97, 88, 88, 88, 83, 103, 79,
67, 88, 79, 90, 79, 97, 94, 85, 83, 88, 97, 81, 95, 92, 94,
99, 79, 83, 92, 81, 92, 79, 94, 83, 79, 81, 92, 86, 95, 92,
95, 92, 85, 94, 81, 86, 85, 99, 92, 85, 72, 86, 81, 79, 86,
97, 88, 92, 97, 83, 103, 97, 95, 85, 77, 77, 83, 99, 90,
77, 77, 83, 92, 88, 83, 88, 86, 88, 97, 101, 99, 88, 101,
94, 86, 85, 83, 86, 88, 92, 94, 94, 90, 160, 94, 83, 95,
97, 88, 88, 95, 90, 92, 113, 104, 85, 101, 91.8, 99, 94,
85, 85, 83, 86, 88, 95, 79, 101, 92, 83, 90, 85, 95, 88,
79, 90, 79, 94, 99, 83, 85, 85, 77, 99, 81, 92, 86.4, 95.4,
82.8, 73.8, 81, 90, 82.8, 79.2, 90, 82.8, 91.8, 90, 84.6,
84.6, 84.6, 77.4, 77.4, 75.6, 88.2, 79.2, 92, 90, 113, 81,
81, 81, 84.6, 88.2, 73.8, 81, 81, 82.8, 79.2, 70.2, 91.8,
97.2, 82.8, 70.2, 91.8, 93.6, 86.4, 93.6, 73.8, 95.4, 81,
97.2, 77.4, 90, 82.8, 86.4, 88.2, 88.2, 73.8, 90, 92, 83,
86, 99, NA, 86, 81, NA, 99, 83, 86, 76, 90, 85, 90, 92, NA,
NA, 79, 79, NA, 86, 81, 88, NA, 90, 86, 92, 85, 92, 83, 92,
90, 92, 95, 94, 88, 90, 86, 88, 101, 95, 92, 81, NA, 92,
90, 81, NA, 90, 81, 88, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 85, NA, NA, NA, NA, NA, 85, 88, 86, 88, 106, 101, 88,
NA, 79, NA, 85, 99, 92, 79, 88, 88, 95, 81, 86, 77, 81, 92,
97, NA, 86, NA, 88, 94, 81, 86, 85, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 85, 88, 95, 83, 92, 112, 94, 95, 108,
97, 90, 88, 86, 97, 95, 88, 90, 88, 77, 94, 81, NA, 79, 83,
95, 88, 81, 92, 92, NA, 88, 86, NA, 85, 85, 97, 81, 88, 90,
NA, 77.4, 94, 83, NA, 95, 85, 92, 83, 95, 88, 94, 94, 88,
77, 90, 86, 92, NA, NA, NA, 95, 92, NA, 90, 103, 90, 85,
92, 83, 81, 94, 81, 79, 94, NA, 92, 99, 95, 84, 95, 72, 90,
79, 97.5, 85, 88, 79, 81, 72, 85, 88), format.spss = "F4.2", display_width = 11L),
hdl = structure(c(54, 55, 48, 38, 46, 50, 45, 38, 50, 43,
39, 32, 35, 34, 40, 48, 53, 33, 42, 34, 41, 48, 51, 38, 53,
38, 37, 44, 37, 33, 54, 47, 51, 39, 44, 54, 32, 53, 39, 36,
58, 41, 34, 43, 40, 49, 49, 50, 37, 36, 54, 47, 35, 40, 50,
44, 40, 43, 45, 41, 34, 50, 46, 46, 50, 53, 53, 45, 37, 70,
51, 55, 51, 58, 58, 49, 44, 37, 32, 64, 41, 63, 46, 55, 46,
65, 43, 55, 42, 56, 39, 50, 38, 46, 45, 53, 53, 39, 45, 47,
48, 32, 45, 45, 36, 60, 30, 43, 43, 57, 36, 56, 45, 40, 40,
61, 50, 29, 55, 38, 35, 47, 42, 50, 46, 26, 60, 33, 36, 34,
44, 59, 45, 44, 55, 45, 53, 38, 50, 40, 57, 46, 48, 45, 43,
49, 53, 39, 46, 39, 36, 39, 36, 42, 40, 50, 63, 46, 45, 39,
43, 30, 57, 46, 40, 39, 39, 53, 40, 54, 56, 40, 37, 48, 43,
29, 46, 45, 82, 31, 34, 37, 41, 63, 34, 50, 37, 51, 36, 42,
41, 34, 55, 40, 42, 60, 36, 38, 52, 57, 48, 48, 46, 47, 50,
41, 48, NA, 40, 45, NA, 43, 58, 42, 48, 44, 46, 47, 55, NA,
NA, 38, 52, NA, 53, 31, 51, NA, 32, 51, 41, 38, 57, 36, 50,
41, 60, 65, 39, 52, 36, 36, 49, 43, 34, 44, 41, NA, 50, 52,
37, NA, 58, 45, 34, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
46, NA, NA, NA, NA, NA, 59, 55, 50, 46, 58, 58, 42, NA, 31,
NA, 48, 43, 66, 55, 51, 41, 50, 38, 46, 41, 43, 38, 48, NA,
46, NA, 56, 44, 46, 48, 49, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 63, 41, 39, 46, 58, 53, 33, 53, 48, 33, 44, 46,
49, 48, 44, 55, 44, 39, 32, 46, 50, NA, 47, 53, 39, 51, 61,
48, 32, NA, 42, 46, NA, 49, 48, 52, 39, 40, 38, NA, 31, 46,
48, NA, 51, 58, 43, 49, 43, 65, 41, 61, 49, 35, 37, 36, 58,
NA, NA, NA, 38, 45, NA, 58, 31, 49, 52, 65, 32, 45, 39, 37,
41, 34, NA, 42, 51, 39, 48, 36, 35, 55, 38, 48, 53, 41, 39,
49, 63, 41, 47), label = "HDL-Cholesterol", format.spss = "F3.2", display_width = 11L),
ldl = structure(c(100, 104, 171, 153, 107, 152, 87, 101,
70, 137, 96, 95, 98, 94, 92, 102, 63, 104, 62, 75, 125, 117,
114, 132, 112, 146, 121, 91, 113, 120, 96, 96, 95, 87, 96,
134, 98, 92, 88, 101, 133, 113, 77, 128, 97, 169, 136, 96,
74, 59, 121, 66, 109, 103, 116, 86, 87, 124, 88, 94, 77,
98, 90, 133, 79, 78, 98, 129, 62, 62, 96, 72, 85, 98, 101,
132, 69, 196, 76, 125, 105, 108, 89, 108, 123, 51, 92, 50,
121, 105, 80, 103, 59, 96, 89, 65, 77, 90, 92, 65, 123, 96,
80, 128, 92, 124, 96, 83, 120, 145, 114, 134, 116, 65, 91,
103, 84, 123, 99, 96, 61, 82, 85, 116, 116, 113, 121, 69,
82, 100, 108, 99, 144, 152, 158, 128, 112, 89, 119, 61, 99,
147, 109, 121, 92, 115, 95, 62, 72, 130, 96, 76, 117, 96,
108, 131, 120, 67, 99, 105, 63, 63, 103, 128, 92, 120, 146,
106, 103, 94, 85, 122, 111, 102, 143, 74, 87, 80, 67, 140,
85, 87, 101, 94, 122, 124, 82, 150, 92, 84, 119, 98, 89,
97, 117, 122, 111, 86, 90, 110, 107, 150, 103, 94, 149, 159,
91, NA, 109, 126, NA, 167, 77, 90, 103, 80, 68, 75, 55, NA,
NA, 74, 113, NA, 102, 116, 84, NA, 66, 85, 114, 111, 101,
95, 92, 86, 96, 90, 92, 77, 91, 108, 86, 118, 85, 127, 99,
NA, 160, 80, 63, NA, 123, 86, 94, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 106, NA, NA, NA, NA, NA, 70, 85, 70, 96,
102, 117, 101, NA, 146, NA, 94, 122, 122, 94, 110, 121, 39,
72, 48, 109, 110, 60, 95, NA, 83, NA, 79, 87, 113, 103, 55,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 157, 103, 56,
92, 114, 78, 97, 106, 117, 61, 72, 83, 91, 122, 106, 103,
89, 51, 89, 153, 90, NA, 132, 132, 110, 84, 84, 96, 72, NA,
104, 122, NA, 80, 113, 106, 62, 72, 121, NA, 102, 125, 130,
NA, 111, 119, 66, 109, 119, 91, 92, 120, 160, 93, 117, 126,
88, NA, NA, NA, 115, 100, NA, 200, 79, 95, 99, 89, 123, 108,
82, 108, 81, 103, NA, 103, 149, 116, 115, 122, 95, 106, 89,
128, 118, 123, 51, 90, 130, 119, 120), label = "LDL-Cholesterol", format.spss = "F4.2", display_width = 11L)), row.names = c(NA,
-404L), class = c("tbl_df", "tbl", "data.frame"), reshapeLong = list(
varying = list(c("age_1", "age_2"), c("whz_1", "whz_2"),
c("haz_1", "haz_2"), c("waz_1", "waz_2"), c("zbmi_1",
"zbmi_2"), c("wc_1", "wc_2"), c("abc_1", "abc_2"), c("PA_1",
"PA_2"), c("PAextra_1", "PAextra_2"), c("TVweekdays_1",
"TVweekdays_2"), c("TVweekend_1", "TVweekend_2"), c("kidmed_1",
"kidmed_2"), c("totalcholesterol_1", "totalcholesterol_2"
), c("ldl_1", "ldl_2"), c("hdl_1", "hdl_2"), c("triglycerides_1",
"triglycerides_2"), c("glucose_1", "glucose_2"), c("insuline_1",
"insuline_2"), c("hba1c_1", "hba1c_2"), c("homair_1",
"homair_2"), c("fatmass_1", "fatmass_2"), c("energykcal_1",
"energykcal_2"), c("protein_1", "protein_2"), c("proteinpc_1",
"proteinpc_2"), c("carbohydrates_1", "carbohydrates_2"
), c("carbohydratespc_1", "carbohydratespc_2"), c("sugar_1",
"sugar_2"), c("sugarpc_1", "sugarpc_2"), c("starch_1",
"starch_2"), c("fruitportions_1", "fruitportions_2"),
c("vegetablesportions_1", "vegetablesportions_2"), c("vegetalfiber_1",
"vegetalfiber_2"), c("solublefiber_1", "solublefiber_2"
), c("insolublefiber_1", "insolublefiber_2"), c("lipids_1",
"lipids_2"), c("lipidspc_1", "lipidspc_2"), c("sfa_1",
"sfa_2"), c("sfapc_1", "sfapc_2"), c("mufa_1", "mufa_2"
), c("mufapc_1", "mufapc_2"), c("pufa_1", "pufa_2"),
c("pufapc_1", "pufapc_2"), c("cholesterolintake_1", "cholesterolintake_2"
)), v.names = c("age", "whz", "haz", "waz", "zbmi", "wc",
"abc", "PA", "PAextra", "TVweekdays", "TVweekend", "kidmed",
"totalcholesterol", "ldl", "hdl", "triglycerides", "glucose",
"insuline", "hba1c", "homair", "fatmass", "energykcal", "protein",
"proteinpc", "carbohydrates", "carbohydratespc", "sugar",
"sugarpc", "starch", "fruitportions", "vegetablesportions",
"vegetalfiber", "solublefiber", "insolublefiber", "lipids",
" lipidspc", "sfa", "sfapc", "mufa", "mufapc", "pufa", "pufapc",
"cholesterolintake"), idvar = c("id", "group"), timevar = "time"))

Instead of making your varlist a list of vectors you could simply pass a vector with names of the colums you want to plot. Then use aes_string(..., y = varlist) inside your function and you will automatically get the name of the variable as the y axis title:
# Multiple box plot per group per time
library(ggplot2)
library(dplyr)
# Create a list wherein the function will be applied to
varlist <- c("insuline", "glucose", "hdl", "ldl")
names(varlist) <- varlist
# Create the function boxplot
A <- function(varlist) {
dflinear %>%
group_by("group") %>%
ggplot(mapping = aes_string(x = "time", y = varlist, fill = "group")) +
geom_boxplot()
}
# Apply it to the whole list and graph the plots
plots <- lapply(varlist, FUN = A)
plots[[1]]

Randomly sample by group based on numeric variables

Is it possible to randomly sample patients by group so that they have similar distributions based on other variables? To me, this sounds like a matching problem, but there's no "treatment" here, so I'm not sure if the concept applies.
Sample data:
structure(list(id = c(8350L, 22543L, 24144L, 9392L, 27648L, 2943L,
34686L, 27153L, 11143L, 15209L, 11952L, 22669L, 8211L, 27765L,
28671L, 9693L, 30274L, 25807L, 14839L, 22400L, 24494L, 6540L,
6861L, 31825L, 34190L, 19606L, 21077L, 5037L, 25943L, 20530L,
23730L, 34774L, 7210L, 2051L, 28410L, 18318L, 34848L, 26596L,
8973L, 24885L, 9652L, 8387L, 16168L, 36893L, 24048L, 17769L,
1273L, 22734L, 36796L, 25497L, 28300L, 166L, 21172L, 20026L,
16265L, 1699L, 33140L, 23997L, 10216L, 27408L, 6813L, 10196L,
15015L, 2748L, 34979L, 21763L, 27438L, 6255L, 17047L, 30593L,
30723L, 7914L, 218L, 20134L, 29952L, 27126L, 3795L, 1367L, 33585L,
5940L, 26250L, 22519L, 35611L, 26168L, 26848L, 21276L, 8971L,
22554L, 16655L, 5315L, 18121L, 32526L, 21513L, 9262L, 36882L,
7408L, 18873L, 17238L, 15216L, 23667L, 30138L, 2978L, 25451L,
2492L, 30983L, 7677L, 22880L, 29674L, 7093L, 24910L, 20839L,
18176L, 23031L, 17197L, 4613L, 35801L, 30822L, 3889L, 11752L,
11314L, 22317L, 12825L, 17433L, 4407L, 3986L, 10173L, 32409L,
2697L, 3410L, 26834L, 3203L, 5474L, 34678L, 35336L, 19462L, 15835L,
7888L, 27897L, 9245L, 16524L, 13316L, 21604L, 30458L, 9191L,
1220L, 1779L, 1724L, 26382L, 11566L, 21310L, 12600L, 25063L,
30912L, 31189L, 9480L, 16804L, 2372L, 26238L, 20113L, 33753L,
32711L, 11543L, 10578L, 4475L, 13187L, 23395L, 35342L, 6903L,
26905L, 12026L, 5697L, 15352L, 33985L, 1132L, 15806L, 13611L,
29930L, 15896L, 6057L, 10849L, 12944L, 25561L, 3328L, 27481L,
28790L, 3260L, 24986L, 22177L, 26580L, 11639L, 2256L, 4839L,
22805L, 616L, 6702L, 18360L, 4439L, 1300L, 33779L, 24940L, 10043L,
21268L, 35127L, 36621L, 17618L, 6688L, 15937L, 31057L, 2144L,
30866L, 12500L, 29753L, 36497L, 21247L, 9481L, 36465L, 20665L,
15017L, 21234L, 34258L, 576L, 31187L, 4528L, 15314L, 3657L, 24489L,
33871L, 106L, 24916L, 2524L, 17469L, 2799L, 13311L, 26585L, 7131L,
21401L, 6191L, 22338L, 11647L, 11681L, 22744L, 14000L, 5356L,
2892L, 24481L, 24116L, 21461L, 13992L, 22751L, 11129L, 8802L,
29963L, 4660L, 29020L, 20843L, 21796L, 3607L, 10692L, 29168L,
25034L, 3307L, 35010L, 20280L, 31894L, 7276L, 24259L, 34059L,
35867L, 11165L, 16010L, 34082L, 26586L, 30958L, 25030L, 34851L,
29185L, 25721L, 8968L, 29427L, 20213L, 34667L, 28721L, 21472L,
17132L, 35247L, 9798L, 36826L, 21226L, 28335L, 16077L, 2654L,
20466L, 21324L, 36969L, 22553L, 5895L, 16514L, 10644L, 4376L,
13592L, 11206L, 32440L, 13413L, 31416L, 22540L, 15986L, 11506L,
16928L, 18652L, 17858L, 13522L, 8566L, 10665L, 29442L, 28219L,
22549L, 2209L, 8017L, 6066L, 21718L, 21930L, 11540L, 4100L, 35236L,
240L, 24900L, 425L, 26880L, 21409L, 18885L, 5803L, 33335L, 25597L,
12547L, 8930L, 4328L, 17360L, 4696L, 25198L, 26469L, 14679L,
1691L, 32989L, 6099L, 14427L, 31797L, 23408L, 29296L, 23928L,
31889L, 31737L, 6420L, 11304L, 34798L, 20785L, 9806L, 35018L,
35008L, 1450L, 3246L, 15123L, 19603L, 8519L, 32012L, 3397L, 11682L,
27102L, 18022L, 20408L, 15836L, 18284L, 12897L, 29580L, 14510L,
23925L, 28821L, 35825L, 14922L, 36643L, 10948L, 4220L, 23791L,
65L, 35772L, 1423L, 29386L, 755L, 23627L, 27201L, 12353L, 3578L,
1914L, 35373L, 16702L, 13057L, 3021L, 27531L, 1990L, 205L, 21559L,
29081L, 26301L, 18894L, 3088L, 9782L, 10522L, 12570L, 8948L,
36240L, 33943L, 33022L, 2750L, 32649L, 30134L, 13920L, 11498L,
8314L, 16849L, 15559L, 22529L, 31406L, 5680L, 17908L, 14931L,
2122L, 2581L, 33546L, 12143L, 17220L, 16713L, 7454L, 13659L,
15973L, 20116L, 27689L, 35285L, 36106L, 21834L, 29850L, 29030L,
7957L, 31698L, 12307L, 23642L, 5615L, 12016L, 1161L, 15291L,
32738L, 1089L, 32988L, 33382L, 3642L, 18661L, 35584L, 8009L,
24000L, 30587L, 25870L, 19944L, 34970L, 29983L, 24774L, 28702L,
21199L, 17292L, 29831L, 476L, 18881L, 29923L, 31476L, 4570L,
31081L, 10544L, 3373L, 13435L, 22651L, 17861L, 3818L, 35387L,
11459L, 35637L, 308L, 35697L, 12696L, 15175L, 7990L, 16691L,
19494L, 9008L, 30695L, 28889L, 446L, 22178L, 13000L, 26166L,
15431L, 19332L, 35991L, 2840L), race_f = structure(c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 4L, 2L, 3L, 4L, 1L, 1L, 3L, 3L, 3L, 3L, 1L,
3L, 1L, 3L, 3L, 1L, 1L, 3L, 2L, 2L, 1L, 4L, 5L, 1L, 4L, 1L, 1L,
5L, 1L, 1L, 3L, 2L, 3L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 2L, 1L, 1L,
2L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 3L, 3L, 1L, 1L, 3L, 3L, 3L,
1L, 1L, 1L, 3L, 3L, 2L, 1L, 1L, 3L, 4L, 4L, 1L, 1L, 3L, 1L, 2L,
3L, 4L, 1L, 1L, 1L, 3L, 1L, 1L, 5L, 3L, 1L, 1L, 3L, 2L, 1L, 1L,
3L, 1L, 4L, 1L, 1L, 3L, 1L, 4L, 3L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
3L, 3L, 4L, 4L, 1L, 2L, 1L, 4L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L,
1L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 4L, 5L, 1L, 4L, 3L, 3L, 3L,
1L, 2L, 1L, 2L, 2L, 4L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 4L, 1L, 5L,
2L, 1L, 2L, 3L, 1L, 5L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 3L,
1L, 4L, 4L, 3L, 2L, 4L, 2L, 1L, 3L, 3L, 1L, 4L, 3L, 3L, 3L, 1L,
1L, 4L, 1L, 4L, 2L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 1L,
3L, 4L, 1L, 3L, 1L, 1L, 4L, 3L, 4L, 1L, 3L, 1L, 2L, 4L, 3L, 3L,
1L, 1L, 3L, 1L, 5L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 2L, 1L, 4L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 1L, 1L, 1L, 4L, 1L, 4L, 3L, 1L, 3L, 2L, 1L, 1L, 2L, 3L, 1L,
4L, 2L, 3L, 1L, 3L, 4L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 5L, 4L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 4L, 1L, 1L, 3L,
1L, 3L, 3L, 1L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 2L, 1L, 3L, 1L, 4L,
1L, 4L, 3L, 3L, 2L, 3L, 3L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 1L, 1L,
4L, 1L, 1L, 3L, 3L, 1L, 4L, 3L, 3L, 4L, 1L, 3L, 1L, 5L, 3L, 4L,
1L, 4L, 4L, 1L, 3L, 4L, 1L, 4L, 1L, 1L, 1L, 3L, 2L, 1L, 2L, 4L,
1L, 1L, 5L, 4L, 1L, 1L, 4L, 3L, 3L, 1L, 3L, 1L, 1L, 1L, 1L, 2L,
1L, 3L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L,
1L, 3L, 4L, 1L, 1L, 2L, 5L, 3L, 3L, 1L, 1L, 4L, 1L, 4L, 1L, 4L,
2L, 3L, 3L, 1L, 1L, 1L, 4L, 1L, 4L, 3L, 1L, 1L, 1L, 1L, 3L, 1L,
3L, 1L, 1L, 1L, 1L, 4L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 1L,
3L, 5L, 2L, 5L, 1L, 1L, 4L, 3L, 1L, 3L, 2L, 1L, 1L, 5L, 1L, 3L,
3L, 4L, 1L, 1L, 1L, 2L, 5L, 1L, 1L, 4L, 3L, 1L, 1L, 1L, 2L, 1L,
1L, 3L, 3L, 1L, 1L, 4L, 3L, 2L, 4L, 4L, 1L, 1L, 2L, 3L, 1L, 3L,
3L, 1L), .Label = c("White", "Black", "Hispanic", "Asian", "Other"
), class = "factor"), cops2_avg_12mo = c(82.9166666666667, 66,
23.3333333333333, 28, 9.33333333333333, 69.9166666666667, 6,
33.3333333333333, 0, 12, 102, NA, 66, 6, 45, 58.5, 10, 55.9166666666667,
19.5, 6, 10, 234.666666666667, 28, 23, 51.5833333333333, 10,
38, 123.5, 0, 24, 10, 0, 73, 10, 25, 6, 20, 13.4166666666667,
13.8333333333333, 8, 14.8333333333333, 53.5, 42, NA, 57.1666666666667,
0, 24.6666666666667, 10, NA, 54.6666666666667, 38.75, 41, 22,
0.833333333333333, 13, 113.083333333333, 27.3333333333333, 9,
33.1666666666667, 18.75, 57.75, 30, 60.3333333333333, 23.1666666666667,
37, 16.5, 0, 145.5, 45, 31.3333333333333, 0, 10, 187.5, 27.4166666666667,
10, 54.9166666666667, 78.8333333333333, 103.75, 6.66666666666667,
30.4166666666667, 10, 10, 24.6666666666667, 10, 118.333333333333,
61.25, 17, 10, 28, 51, 6, 32.0833333333333, 80.75, 8.83333333333333,
NA, 10, 74.25, 42.25, 47, 60, 41.6666666666667, 19.0833333333333,
98.5, 73.5, 10, 6.66666666666667, 49.8333333333333, 10, 79.8333333333333,
10, 42, 95.8333333333333, 130.583333333333, 5.41666666666667,
47.25, 6, 8, 17.8333333333333, 10, 73.9166666666667, 10, 8, 27.8333333333333,
125.916666666667, 134.166666666667, 88, 10, 58, 62.5, 10.3333333333333,
28.8333333333333, 100.083333333333, 35.5, 0, 0, 10, 105, 7.33333333333333,
35, 9.66666666666667, 10, 4.16666666666667, 10, 8.33333333333333,
70.6666666666667, 28.4166666666667, 38.1666666666667, 8, 101.5,
26.75, 61.1666666666667, 14, 95.5833333333333, 35, 65, 0, 51.75,
57.5, 10, 13.6666666666667, 10, 67.5, 10, 62.3333333333333, 72.6666666666667,
10, 45.5, 20.8333333333333, 31, 84.5, 10, 98.1666666666667, 47.5,
56, 126, 14, 10, 10, 8, 36, 111.5, 54.5, 45.5, 8, 37.5, 84.8333333333333,
39.1666666666667, 56.25, 37.9166666666667, 37.75, 27, 55.6666666666667,
10, 34, 5.83333333333333, 37, 80.0833333333333, 57, 102.166666666667,
12.6666666666667, 10, 19.3333333333333, 10, NA, 51, 25.9166666666667,
14, 36.9090909090909, 38.6666666666667, 0, 6.33333333333333,
NA, 31, 43, 26.5, 10, 34.4166666666667, 77.1666666666667, 10,
10, 89.9166666666667, 59, 37, 77.3333333333333, 64, 52, 19.6666666666667,
66.5, 24, 106.083333333333, 29.6666666666667, 38.1666666666667,
6.66666666666667, 10, 16.75, NA, 86.75, 1, 14, 20.3333333333333,
8, 21, 38.9166666666667, 50.8333333333333, 57.5, 29, 0, 26.5,
51.9166666666667, 71.25, 42.6666666666667, 82, 58.0833333333333,
11.3333333333333, 82, 9.5, 78.6666666666667, 102.5, 71, 10, 70.6666666666667,
NA, 33.8333333333333, 61.25, 87, 36.5, 10, 40.4166666666667,
51.8333333333333, 23, 9.66666666666667, 44.5, 8, 10, 4.16666666666667,
0, 48.8333333333333, 49.25, 15, 70, 10, 6, 10, 34.8333333333333,
108.75, 36, NA, 31, 51, 69.5, 122.5, 48, 43.5833333333333, NA,
10, 20, 80.75, 54.75, 106.916666666667, 53.5, 90.6666666666667,
8.33333333333333, 85.5, 40.5833333333333, 5.5, 10, 61.3333333333333,
69.8333333333333, 10, 51, 0, 49.0833333333333, 13.6666666666667,
13.3333333333333, 5.83333333333333, 33.8333333333333, 14.4166666666667,
11.25, 14, 6, 14.5833333333333, 36, 21, 10, 29.5833333333333,
13, 34, 10, 2.5, 10, 211.916666666667, 19.75, 7.33333333333333,
6, 59.6666666666667, 30.25, 34.25, 16.1666666666667, 10, NA,
NA, 97, 75, 26.5, 8, 32.25, 0, 39, 37, 165.333333333333, 45,
33.1666666666667, 21, 10, 57, 70.3333333333333, 10, 10, 62, 79.1666666666667,
38, 26.1666666666667, 13, 8, 69.6666666666667, 40.5, 100, 0.833333333333333,
8, 82.5, 10, 19.8333333333333, 20.0833333333333, 8, 25.8333333333333,
16.75, 10, 36, NA, 12.8333333333333, 31.4166666666667, 10, 61.4166666666667,
14, 67.5, 3, 83.1666666666667, 48, 43.75, 35.4166666666667, 73,
44.1666666666667, 8, 29.75, 10, 10, 62.6666666666667, 26.9166666666667,
29.6666666666667, 10, NA, 15, 19.4166666666667, 112, 29, 3, 33.5,
62.5, 10, 84.6666666666667, 8, 84.4166666666667, 81.5, 56.1666666666667,
10, 101.416666666667, 16, 10, 19.6666666666667, 60, 73.6666666666667,
74.9166666666667, 21, 5, 15.0833333333333, 17.0833333333333,
17.5, 46, 61.8333333333333, 115.333333333333, 92, 30, 0, 22.75,
16.6666666666667, 15, 15, 10, NA, 56.25, 54, 10, 40, 9.83333333333333,
10.9166666666667, 22.25, 84.75, 80, 1.66666666666667, 99.8333333333333,
10, 38.6666666666667, 169.75, 35.0833333333333, 8, 78.5, 6.33333333333333,
21, 10, 42, 105.166666666667, 162.416666666667, 14, 69.25, 35.8333333333333,
13, 5.83333333333333, 34, 51, 12.75, 44.3333333333333, 39.5,
10, 23, 46.8333333333333, 89.9166666666667, 15, 28, 128.416666666667,
10, 91.6666666666667, 3.5, 54, 23, NA, 29.75, 37.1666666666667,
12.6666666666667, 31.9166666666667, 23, 0, 11, 67.9166666666667,
3.16666666666667, 8.33333333333333, 51, NA, 10, 0, 58.8333333333333
), AGE = c(86, 82, 83, 92, 45, 81, 52, 64, 71, 96, 79, 64, 76,
37, 81, 79, 72, 79, 74, 46, 45, 71, 89, 76, 53, 48, 52, 77, 63,
52, 57, 62, 84, 88, 55, 69, 67, 63, 67, 51, 86, 53, 65, 59, 71,
60, 70, 20, 78, 62, 58, 73, 68, 71, 66, 72, 71, 65, 95, 67, 79,
70, 86, 77, 81, 54, 44, 66, 80, 71, 30, 77, 67, 75, 48, 65, 83,
85, 70, 70, 74, 58, 81, 28, 78, 66, 79, 47, 74, 41, 74, 58, 73,
55, 53, 56, 84, 74, 62, 85, 68, 47, 78, 72, 57, 56, 64, 55, 86,
76, 77, 58, 74, 55, 71, 61, 74, 62, 65, 75, 81, 68, 39, 58, 65,
76, 27, 79, 86, 61, 87, 52, 72, 58, 53, 69, 78, 65, 81, 69, 66,
68, 61, 72, 74, 80, 88, 46, 53, 77, 89, 83, 41, 67, 83, 62, 90,
70, 60, 62, 33, 78, 80, 62, 81, 37, 55, 90, 81, 73, 67, 97, 32,
71, 70, 69, 46, 57, 60, 79, 79, 56, 75, 60, 52, 78, 61, 51, 70,
67, 71, 36, 53, 70, 53, 74, 89, 78, 70, 56, 58, 83, 50, 77, 70,
50, 75, 53, 86, 65, 45, 63, 62, 78, 65, 69, 75, 79, 71, 56, 88,
63, 72, 85, 68, 72, 45, 81, 46, 70, 84, 71, 82, 63, 57, 77, 70,
42, 87, 84, 61, 64, 79, 53, 65, 64, 69, 68, 71, 89, 49, 70, 82,
63, 79, 65, 64, 54, 73, 36, 80, 38, 68, 62, 84, 80, 65, 73, 91,
59, 35, 80, 67, 68, 65, 47, 60, 67, 72, 81, 22, 35, 58, 57, 68,
94, 38, 77, 75, 73, 78, 71, 78, 53, 58, 61, 77, 44, 95, 53, 72,
68, 72, 73, 78, 41, 75, 80, 60, 53, 68, 79, 80, 74, 25, 79, 55,
68, 85, 64, 72, 78, 78, 71, 73, 82, 73, 73, 58, 69, 58, 72, 78,
56, 74, 67, 66, 72, 38, 58, 62, 77, 81, 37, 46, 88, 55, 76, 50,
57, 72, 39, 56, 29, 76, 77, 36, 31, 70, 70, 70, 54, 74, 47, 81,
46, 81, 55, 53, 70, 28, 71, 79, 68, 78, 81, 30, 83, 43, 70, 79,
47, 94, 60, 64, 82, 81, 92, 57, 90, 86, 58, 61, 69, 50, 64, 79,
56, 76, 52, 55, 53, 85, 89, 64, 86, 58, 82, 64, 74, 45, 64, 71,
75, 61, 79, 82, 63, 81, 60, 70, 79, 63, 59, 80, 53, 80, 41, 83,
67, 90, 60, 82, 74, 75, 52, 62, 35, 53, 49, 71, 69, 73, 67, 44,
77, 81, 96, 52, 75, 30, 83, 74, 56, 62, 78, 63, 63, 62, 71, 62,
89, 83, 77, 66, 64, 24, 96, 63, 51, 65, 71, 50, 68, 83, 82, 90,
91, 84, 90, 76, 62, 79, 20, 75, 79, 80, 62, 62, 71, 51, 81, 84,
65, 65, 55, 65, 51, 26, 70)), row.names = c(NA, -500L), class = c("tbl_df",
"tbl", "data.frame"))
I'm hoping to sample by race_f so that the different race groups are similar in AGE and cops2_avg_12mo. Is this at all possible? Thank you!

The answer depends on if you want to ensure that their ages/cops2_avg_12mo will always be within a specific range - in which case you would simply create a subset of your data frame with only the patients whose age and cops2_avg_12mo are within some range. I do think that this is the safer thing to do in terms of quality control. You can view a plot of the two columns of your data (AGE and cops2_avg_12mo) to get an idea of what ranges of values most of the patients fall into:
plot(x[,c("AGE", "cops2_avg_12mo")])
Pick ranges for these values that contain enough patients to sample from. (I don't know how many samples you need). Basically, draw a box in the dot plot which contains enough patients to sample from.
So once you determine the ranges/boundaries of the box, just create indexes like so:
idx = (x[,"AGE"] > 50) & (x[,"AGE"] < 75) & (x[,"cops2_avg_12mo"] > 0) & (x[,"cops2_avg_12mo"] < 75) & !is.na(x[,"cops2_avg_12mo"])
then get the subset of your data:
subsetX = x[idx,]
After you create that subset, you can randomly sample using R's sample() function. If you want to do sampling from each race equally, then call sample() with the subsetX data, with each race selected at a time, to get n samples at a time:
sample(subsetX[subsetX[,"race_f"]=="Asian",], n, replace=FALSE)
Alternatively, if you are ok with sampling patients that have outlier values (but I feel like this will produce more variation in your results), then you can create a histogram of each of the columns - for example, AGE - then get the histogram bin counts, divide them by the total number of patients to get a probability distribution, then create a vector the same length as the number of patients where each value is the probability we calculated for the bin it belongs to (found by getting bin indexes when calculating the histogram), then pass that vector into the sample() function as the prob input argument so that values are sampled with their specified probability.

Superimposing two plots in R with same axis and limits

I have two plots from two different data frames
The DPUT from data frame 1 is as follows
ppv_npv2 <- structure(list(pred.prob = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
44, 45, 46, 47, 48, 49, 50, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
44, 45, 46, 47, 48, 49, 50, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
44, 45, 46, 47, 48, 49, 50), variable = 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, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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), .Label = c("ppv_2.5", "ppv_50", "ppv_97.5"), class = "factor"),
value = c(4.8, 9.3, 13.4, 17.2, 20.8, 24.2, 27.3, 30.3, 33.1,
35.7, 38.2, 40.5, 42.8, 44.9, 46.9, 48.8, 50.6, 52.3, 54,
55.6, 57.1, 58.5, 59.9, 61.2, 62.5, 63.7, 64.9, 66, 67.1,
68.2, 69.2, 70.2, 71.1, 72, 72.9, 73.8, 74.6, 75.4, 76.2,
76.9, 77.7, 78.4, 79, 79.7, 80.4, 81, 81.6, 82.2, 82.8, 83.3,
7.2, 13.6, 19.3, 24.4, 28.9, 33, 36.8, 40.2, 43.3, 46.2,
48.9, 51.3, 53.6, 55.7, 57.7, 59.6, 61.3, 62.9, 64.5, 65.9,
67.3, 68.6, 69.8, 70.9, 72, 73.1, 74.1, 75, 75.9, 76.8, 77.6,
78.4, 79.2, 79.9, 80.6, 81.3, 82, 82.6, 83.2, 83.8, 84.3,
84.8, 85.4, 85.9, 86.3, 86.8, 87.3, 87.7, 88.1, 88.5, 11.7,
21.1, 28.8, 35.3, 40.8, 45.5, 49.7, 53.3, 56.4, 59.3, 61.8,
64.1, 66.2, 68.1, 69.8, 71.4, 72.9, 74.2, 75.5, 76.6, 77.7,
78.7, 79.7, 80.5, 81.4, 82.2, 82.9, 83.6, 84.3, 84.9, 85.5,
86, 86.6, 87.1, 87.6, 88.1, 88.5, 88.9, 89.3, 89.7, 90.1,
90.5, 90.8, 91.1, 91.5, 91.8, 92.1, 92.4, 92.6, 92.9)),
.Names =c("pred.prob","variable", "value"), row.names = c(NA, -150L),
class = "data.frame")
The plot that i have created is from the following code
p1 <- ggplot(ppv_npv2,aes(x=pred.prob,y=value))+
geom_line(data=ppv_npv2[ppv_npv2$variable=="ppv_50",],
colour="red",linetype=2)+
geom_line(data=ppv_npv2[ ppv_npv2$variable=="ppv_2.5", ],
colour="blue",linetype=4)+
geom_line(data=ppv_npv2[ ppv_npv2$variable=="ppv_97.5", ],
colour="blue",linetype=4)+
theme_classic()+
ylab("Predicted positive predictive value (%) \n")+
xlab("\n Prevalence (%)")+
scale_x_continuous(limits=c(0,50),breaks=seq(0,50,2))+
scale_y_continuous(limits=c(0,100),breaks=seq(0,100,10), expand=c(0,0))+
theme(axis.text.x = element_text(size=12,hjust=.5,vjust=.8,face="plain"),
axis.text.y = element_text(size=12,hjust=.5,vjust=.8,face="plain"))+
theme(axis.title.x = element_text(size=14,face="bold"),
axis.title.y = element_text(size=14,face="bold"))
p1
The dput for the second data frame is
dat <- structure(list(PPV = c(57, 89, 19, 52, 52, 62, 63, 46, 31, 52,
54, 13, 17, 47, 48, 52, 96, 88, 64, 33, 62, 77, 75, 72), Prevalence = c(19,
35, 12, 16, 24, 6, 28, 13, 8, 19, 30, 6, 8, 20, 11, 25, 29, 55,
46, 13, 16, 22, 23, 20), total = c(939L, 323L, 306L, 703L, 137L,
833L, 360L, 317L, 440L, 2072L, 209L, 386L, 142L, 358L, 167L,
503L, 180L, 233L, 342L, 478L, 4870L, 1104L, 1813L, 1567L),
Author = structure(c(1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 9L, 10L, 11L, 12L,
15L,18L, 19L, 8L, 14L, 16L, 17L, 21L, 20L, 20L, 13L, 10L),
.Label = c("Aldous",
"Bahrmann", "Body", "Christ ", "Collinson", "Eggers", "Freund",
"Giannitis", "Hammerer-Lercher", "Hoeller", "Inoue", "Invernizi",
"Keller", "Khan", "Lotze", "Melki ", "Normann", "Santalol", "Sebbane",
"Shah", "Thelin "), class = "factor"), Study.assay = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("TnI", "TnT"), class = "factor")),
.Names = c("PPV", "Prevalence", "total", "Author", "Study.assay"),
class ="data.frame", row.names = c(NA, -24L))
And the plot from dataframe 2 is as follows
p2 <- ggplot(dat, aes(x=dat$Prevalence, y=dat$PPV, size=dat$total,
label=dat$Author),guide=F)+
geom_point(colour="white", fill="red", shape=21)+
scale_size_area(max_size = 10)+
scale_x_continuous(name="\n Prevalence", limits=c(0,100))+
scale_y_continuous(name="Predicted positive predictive value (%) \n",
limits=c(0,100))+
geom_text(size=2.5)+
theme_classic()+
ylab("Predicted positive predictive value (%) \n")+
xlab("\n Prevalence (%)")+
scale_x_continuous(limits=c(0,50),breaks=seq(0,50,2))+
scale_y_continuous(limits=c(0,100),breaks=seq(0,100,10), expand=c(0,0))+
theme(axis.text.x = element_text(size=12,hjust=.5,vjust=.8,face="plain"),
axis.text.y = element_text(size=12,hjust=.5,vjust=.8,face="plain"))+
theme(axis.title.x = element_text(size=14,face="bold"),
axis.title.y = element_text(size=14,face="bold"))+
theme(legend.position='none')
p2
As you can see both plots have the same axis and limits. I have two questions:
a) Can i overlay plot 2 onto plot 1?
b) Can i make the bubbles on plot 2 more transparent and choose colours by the factor dat$Study.assay (green and purple)?
Many thanks in advance - have spent a day researching this but no solution yet.

Here's a start using your data,
(plot2 <- ggplot() +
geom_line(data = ppv_npv2,aes(pred.prob, value,
group= variable, colour = variable)) +
geom_point(data = dat, aes(Prevalence, PPV, label=Author, size = total,
colour = Study.assay), alpha = I(0.4)) +
geom_text(data = dat, aes(Prevalence, PPV, label=Author,
size = total), size=3, hjust=-1, vjust=0)
)
It's not the orthodox ggplot2 way, but it's a start.

plotting melted data.frame with ggplot, does aes(x) need to be a factor?

I don't understand why ggplot can't plot this data.frame :
sample <- dput(melted)
structure(list(alleles = c(98, 100, 102, 106, 124, 126, 128,
132, 134, 142, 144, 145, 146, 147, 148, 149, 151, 152, 153, 156,
158, 159, 165, 167, 169, 171, 173, 175, 177, 181, 184, 187, 193,
194, 196, 197, 200, 228, 233, 234, 238, 240, 241, 242, 243, 244,
245, 246, 247, 249, 251, 253, 363, 364, 365, 367, 371, 377, 380,
384, 391, 98, 100, 102, 106, 124, 126, 128, 132, 134, 142, 144,
145, 146, 147, 148, 149, 151, 152, 153, 156, 158, 159, 165, 167,
169, 171, 173, 175, 177, 181, 184, 187, 193, 194, 196, 197, 200,
228, 233, 234, 238, 240, 241, 242, 243, 244, 245, 246, 247, 249,
251, 253, 363, 364, 365, 367, 371, 377, 380, 384, 391), variable = 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, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 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("Parents", "Nymphes"
), class = "factor"), value = c(11, 17, 13, 1, 1, 18, 5, 8, 10,
6, 9, 1, 25, 17, 2, 1, 1, 2, 0, 2, 0, 2, 1, 1, 8, 9, 6, 10, 35,
1, 3, 4, 20, 3, 2, 39, 2, 0, 0, 1, 8, 25, 2, 27, 6, 8, 0, 3,
1, 8, 1, 2, 14, 17, 2, 5, 1, 2, 1, 2, 0, 12, 49, 33, 0, 4, 35,
6, 7, 14, 7, 25, 0, 44, 26, 3, 0, 6, 0, 9, 3, 1, 0, 1, 4, 15,
22, 8, 24, 69, 0, 2, 5, 35, 2, 18, 92, 0, 2, 6, 0, 22, 44, 6,
56, 13, 12, 1, 6, 2, 21, 0, 3, 12, 9, 5, 3, 0, 1, 1, 0, 1)), .Names = c("alleles",
"variable", "value"), row.names = c(NA, -122L), class = "data.frame")
ggplot command :
ggplot(sample,aes(x=alleles,y=value)) + geom_bar(aes(fill=variable),position="dodge") + theme(axis.text.x=element_text(angle = -75, hjust = 0))
error :
stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
Error in pmin(y, 0) : object 'y' not found
If first column of data.frame (alleles) is a factor, then the plot comes out, but then I have problems with the ordering of data. I need column alleles to be in order, as in the above data.frame. There is obviously something that escapes me...

I think you're using geom_bar wrongly. If you already have the frequency (or length of the bar) calculated, you should use the weight = aesthetic as follows:
sample$alleles <- factor(sample$alleles)
ggplot(sample, aes(x = alleles)) +
geom_bar(aes(weight = value, fill = variable), position = "dodge") +
theme(axis.text.x=element_text(angle = -75, hjust = 0))
If by factoring it doesn't order the plot in the order you desire, then you can order your factor in this manner (if you want the numerical order preserved):
sample$alleles <- factor(sample$alleles,
levels=sample$alleles[!duplicated(sample$alleles)], ordered = T)
Now, plotting should result in the numerical order. If you want to change the order to something else, set the levels = appropriately.
After reordering and plotting, this is what I get:

If this is what you are looking for:
you need to set stat = "identity" in geom_bar
ggplot(sample,aes(x=alleles + (as.numeric (variable)/2 - .75), y=value)) +
geom_bar(aes(fill=variable),position="identity", stat = "identity") +
theme(axis.text.x=element_text(angle = -75, hjust = 0))
geom_bar defaults to counting (i.e. makeing a histogram), but if I understood you correctly, the counts (or values) are already calculated.

Resources