geom_col renders an invisible plot - r

While trying to generate an error plot, I found the following undesirable behavior:
# sample data (please excuse the length, you'll see it's important!)
a <- structure(list(valor = c(22.35, 23.9, 32, 36.2, 35.2, 24.3, 42,
36.4, 16.65, 40.95, 21, 33.2, 32, 33, 28.9, 28, 40.9, 28.4, 32.5,
24.9, 28.35, 36.4, 31.05, 28.4, 37.9, 35.9, 24, 27.6, 28.6, 37.4,
31.6, 31.9, 28.6, 33.9, 31.2, 27, 25.6, 31.2, 32.5, 26.4, 40,
32.9, 32.9, 31.5, 24.9, 21.9, 33.4, 31.8, 31.1, 29.6, 31.5, 29.8,
32.9, 26.6, 24.6, 35.9), error = c(-18.7573531872138, 1.31066637545209,
NA, 0.277829536700779, -2.64925385673394, -11.8996081065239,
-2.60692704590275, -1.33715023334453, NA, -7.61175343400322,
2.55982080155896, 4.4863429357563, 4.16085789426681, -3.90087313834282,
-1.8721045665811, 0.369086865146173, 12.2927002385953, -0.848796857979458,
4.13045179906004, 4.28348313246773, 3.05347592474616, -5.33715023334453,
-1.68395695575215, 5.15120314202054, -3.45360182568537, 0.700053120316895,
4.50817359293553, 1.58628172614129, 7.54200618644399, 7.58601073994592,
-6.61548902751109, -1.03317248113754, 3.54200618644399, 1.84047336001635,
3.60755820405548, 1.41075911687027, 0.661540377840424, 6.60755820405548,
-15.86954820094, 4.2336254711588, -15.4283737200925, 1.90546464068269,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), row.names = c(NA,
-56L), class = "data.frame")
This plot works:
p32 <- ggplot(a[1:32,], aes(x = valor, y = error))+geom_col(position = "jitter")
p32
This doesn't work:
p33 <- ggplot(a[1:33,], aes(x = valor, y = error))+geom_col(position = "jitter")
p33
My reasearch
I understand now that this is caused by a zero-width of the bars (see this closed issue), as can be seen here:
# Notice xmin == xmax:
head(ggplot_build(p33)$data[[1]], 3)
x y PANEL group ymin ymax xmin xmax colour
1 22.35 -18.752212 1 -1 -1.875920e+01 0.002793391 22.35 22.35 NA
2 23.90 1.315615 1 -1 -3.452852e-05 1.304455085 23.90 23.90 NA
3 32.00 NA 1 -1 NA NA 32.00 32.00 NA
fill size linetype alpha
1 grey35 0.5 1 NA
2 grey35 0.5 1 NA
3 grey35 0.5 1 NA
What I tried:
I know I can set the width manually:
ggplot(a[1:33,], aes(x = valor, y = error))+geom_col(position = "jitter", width = 0.1)
but the problem is I'm using the ggplot call within a function that takes the data.frame (a in my example) as argument. Obviously, it can have any length and the data may be essentially different, so fixing a width manually creates some plots with very thin bars and other with very thick ones:
ggplot(a, aes(x = valor, y = error))+geom_col(position = "jitter", width = .1)
ggplot(b, aes(x = valor, y = error))+geom_col(position = "jitter", width = .1)
# with b=
b <- structure(list(valor = c(1.03, 0.43, 1.25, 1.2, 0.74, 2.33, 1.49,
1.5, 0.3, 0.96, 0.81, 1.13, 0.83, 0.68, 2.22, 0.68, 0.9, 1.03,
0.39, 0.84, 1.4, 0.85, 0.7, 1.55, 1.08, 0.37, 0.66, 0.67, 1.36,
0.97, 1.03, 0.64, 1, 0.78, 0.62, 0.5, 0.94, 0.56, 2.09, 1.01),
error = c(2.23998224289866, 0.224579421022632, -0.637159523178084,
-2.74850423807165, -2.69675570480791, 4.59843342442166, 2.34260767883423,
-12.4611961378406, 1.02484359455743, 2.46750883802447, 0.376157081501411,
-1.354215218894, 0.947671489701406, 0.426378012316505, 19.9389705823977,
-1.5736219876835, -22.1173385165668, 5.23998224289866, -0.540189922794198,
7.23019854807831, -3.46146029781903, -2.48937236945532, 5.06312219297025,
-1.49229963183367, -3.53967661036512, 0.534698732147042,
1.77779803536164, 6.10360322576836, 6.71339758402689, -5.27443362843563,
1.23998224289866, 1.11679330753741, -0.510113509024535, 0.502074779997471,
1.44272604499763, -0.91952618750328, -17.0537006712522, 3.33491106257746,
-8.09000221353266, 1.7414648468139)), row.names = c(NA, -40L
), class = "data.frame")
Per this post I tried adding +scale_x_continuous(oob = scales::rescale_none) AND / OR +scale_y_continuous(oob = scales::rescale_none), but none of them worked (neither to thicken the thin bars nor to thin the thick ones).
How can I address this and have a call that can handle a and b and produce an output that looks similar? (regarding the appearance of the bars)


A barplot conceptually can't work on a continuous x scale. However, you can use a different geom:
+ geom_segment(aes(xend = valor, yend = 0))

Related

Combine data into one long column using melt

Sorry if this is a replicate question but I couldn't find the answer to my question.
I have a data frame which is currently:
Depth Year Counts Depth.1 Year.1 Counts.1 Depth.2 Year.2 Counts.2
etc
I want to create a data frame that is instead just Depth Year and Count in 3 separate columns.
I thought that the following would work, but it doesn't. Does anyone know where I am going wrong?
library(data.table)
A <- melt(df, id.vars="Depth","Year","Counts")
structure(list(Depth = c(0.5, 0.5, 0.5, 0.5, 0.5, 0.5), Year = c(2001.539328,
2001.57432, 2001.609313, 2001.644306, 2001.679298, 2001.714291
), Counts = c(2.87e-06, 3.56e-06, 4.38e-06, 5.36e-06, 6.52e-06,
7.94e-06), Depth.1 = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5), Year.1 = c(1984.293532,
1984.364339, 1984.435146, 1984.505954, 1984.576761, 1984.647568
), Counts.1 = c(1.46e-06, 1.81e-06, 2.22e-06, 2.73e-06, 3.34e-06,
4.07e-06), Depth.2 = c(2.5, 2.5, 2.5, 2.5, 2.5, 2.5), Year.2 = c(1981.470822,
1981.544366, 1981.61791, 1981.691454, 1981.764998, 1981.838542
), Counts.2 = c(1.59e-06, 1.98e-06, 2.45e-06, 3.01e-06, 3.7e-06,
4.53e-06), Depth.3 = c(3.5, 3.5, 3.5, 3.5, 3.5, 3.5), Year.3 = c(1977.871611,
1977.945227, 1978.018842, 1978.092458, 1978.166074, 1978.239689
), Counts.3 = c(1.52e-06, 1.9e-06, 2.34e-06, 2.89e-06, 3.56e-06,
4.35e-06), Depth.4 = c(4.5, 4.5, 4.5, 4.5, 4.5, 4.5), Year.4 = c(1973.91751,
1973.996106, 1974.074703, 1974.1533, 1974.231896, 1974.310493
), Counts.4 = c(2.68e-06, 3.35e-06, 4.17e-06, 5.2e-06, 6.44e-06,
7.93e-06), Depth.5 = c(5.5, 5.5, 5.5, 5.5, 5.5, 5.5), Year.5 = c(1968.953544,
1969.036898, 1969.120252, 1969.203607, 1969.286961, 1969.370316
), Counts.5 = c(1.17e-06, 1.47e-06, 1.84e-06, 2.28e-06, 2.81e-06,
3.47e-06), Depth.6 = c(6.5, 6.5, 6.5, 6.5, 6.5, 6.5), Year.6 = c(1962.314375,
1962.408079, 1962.501784, 1962.595488, 1962.689193, 1962.782897
), Counts.6 = c(1.5e-06, 1.95e-06, 2.5e-06, 3.18e-06, 4.02e-06,
5.09e-06), Depth.7 = c(7.5, 7.5, 7.5, 7.5, 7.5, 7.5), Year.7 = c(1958.713624,
1958.805065, 1958.896505, 1958.987945, 1959.079385, 1959.170826
), Counts.7 = c(1.12e-06, 1.43e-06, 1.8e-06, 2.25e-06, 2.8e-06,
3.49e-06), Depth.8 = c(8.5, 8.5, 8.5, 8.5, 8.5, 8.5), Year.8 = c(1951.664415,
1951.763029, 1951.861644, 1951.960258, 1952.058872, 1952.157487
), Counts.8 = c(1.03e-06, 1.31e-06, 1.66e-06, 2.09e-06, 2.61e-06,
3.23e-06), Depth.9 = c(9.5, 9.5, 9.5, 9.5, 9.5, 9.5), Year.9 = c(1945.090332,
1945.195825, 1945.301319, 1945.406813, 1945.512306, 1945.6178
), Counts.9 = c(9.86e-07, 1.26e-06, 1.6e-06, 2.01e-06, 2.51e-06,
3.13e-06), Depth.10 = c(10.5, 10.5, 10.5, 10.5, 10.5, 10.5),
Year.10 = c(1935.501068, 1935.619252, 1935.737435, 1935.855618,
1935.973801, 1936.091984), Counts.10 = c(1.65e-06, 2.1e-06,
2.65e-06, 3.32e-06, 4.15e-06, 5.17e-06), Depth.11 = c(11.5,
11.5, 11.5, 11.5, 11.5, 11.5), Year.11 = c(1925.293378, 1925.407495,
1925.521611, 1925.635728, 1925.749844, 1925.863961), Counts.11 = c(9.04e-07,
1.13e-06, 1.4e-06, 1.72e-06, 2.1e-06, 2.58e-06), Depth.12 = c(12.5,
12.5, 12.5, 12.5, 12.5, 12.5), Year.12 = c(1915.470281, 1915.590233,
1915.710185, 1915.830138, 1915.95009, 1916.070042), Counts.12 = c(1.18e-06,
1.45e-06, 1.78e-06, 2.17e-06, 2.65e-06, 3.23e-06), Depth.13 = c(13.5,
13.5, 13.5, 13.5, 13.5, 13.5), Year.13 = c(1907.029774, 1907.144991,
1907.260209, 1907.375426, 1907.490644, 1907.605861), Counts.13 = c(1.33e-06,
1.68e-06, 2.11e-06, 2.62e-06, 3.24e-06, 4.02e-06), Depth.14 = c(14.5,
14.5, 14.5, 14.5, 14.5, 14.5), Year.14 = c(1896.291234, 1896.410534,
1896.529835, 1896.649135, 1896.768436, 1896.887736), Counts.14 = c(8.64e-07,
1.1e-06, 1.39e-06, 1.75e-06, 2.2e-06, 2.74e-06), Depth.15 = c(15.5,
15.5, 15.5, 15.5, 15.5, 15.5), Year.15 = c(1889.864627, 1889.969479,
1890.074332, 1890.179184, 1890.284037, 1890.388889), Counts.15 = c(1.05e-06,
1.36e-06, 1.75e-06, 2.22e-06, 2.81e-06, 3.55e-06), Depth.16 = c(16.5,
16.5, 16.5, 16.5, 16.5, 16.5), Year.16 = c(1886.325239, 1886.425704,
1886.526169, 1886.626634, 1886.727099, 1886.827564), Counts.16 = c(1.27e-06,
1.68e-06, 2.21e-06, 2.89e-06, 3.73e-06, 4.77e-06), Depth.17 = c(17.5,
17.5, 17.5, 17.5, 17.5, 17.5), Year.17 = c(1882.108412, 1882.108412,
1882.108412, 1882.108412, 1882.108412, 1882.108412), Counts.17 = c(1.38e-06,
1.86e-06, 2.47e-06, 3.24e-06, 4.22e-06, 5.45e-06), Depth.18 = c(18.5,
18.5, 18.5, 18.5, 18.5, 18.5), Year.18 = c(1864.178957, 1864.300768,
1864.422579, 1864.544389, 1864.6662, 1864.788011), Counts.18 = c(1.1e-06,
1.52e-06, 2.08e-06, 2.81e-06, 3.73e-06, 4.9e-06), Depth.19 = c(19.5,
19.5, 19.5, 19.5, 19.5, 19.5), Year.19 = c(1844.224683, 1844.373854,
1844.523025, 1844.672196, 1844.821367, 1844.970538), Counts.19 = c(1.05e-06,
1.52e-06, 2.18e-06, 3.07e-06, 4.25e-06, 5.78e-06), Depth.20 = c(20.5,
20.5, 20.5, 20.5, 20.5, 20.5), Year.20 = c(1826.063901, 1826.239023,
1826.414145, 1826.589268, 1826.76439, 1826.939512), Counts.20 = c(7.96e-07,
1.1e-06, 1.5e-06, 2.02e-06, 2.68e-06, 3.53e-06), Depth.21 = c(21.5,
21.5, 21.5, 21.5, 21.5, 21.5), Year.21 = c(1794.869238, 1795.097941,
1795.326644, 1795.555348, 1795.784051, 1796.012754), Counts.21 = c(5.72e-07,
7.74e-07, 1.03e-06, 1.37e-06, 1.81e-06, 2.36e-06), Depth.22 = c(22.5,
22.5, 22.5, 22.5, 22.5, 22.5), Year.22 = c(1776.381101, 1776.645157,
1776.909213, 1777.173268, 1777.437324, 1777.70138), Counts.22 = c(4.57e-07,
6.08e-07, 7.98e-07, 1.04e-06, 1.34e-06, 1.72e-06), Depth.23 = c(23.5,
23.5, 23.5, 23.5, 23.5, 23.5), Year.23 = c(1766.787508, 1767.066229,
1767.344949, 1767.62367, 1767.90239, 1768.181111), Counts.23 = c(4.28e-07,
5.65e-07, 7.39e-07, 9.57e-07, 1.23e-06, 1.57e-06), Depth.24 = c(24.5,
24.5, 24.5, 24.5, 24.5, 24.5), Year.24 = c(1724.904818, 1725.249971,
1725.595125, 1725.940278, 1726.285432, 1726.630586), Counts.24 = c(3.85e-07,
5.28e-07, 7.13e-07, 9.51e-07, 1.25e-06, 1.63e-06), Depth.25 = c(25.5,
25.5, 25.5, 25.5, 25.5, 25.5), Year.25 = c(1666.304304, 1666.747587,
1667.19087, 1667.634153, 1668.077436, 1668.520719), Counts.25 = c(3.14e-07,
4.35e-07, 5.93e-07, 7.99e-07, 1.07e-06, 1.42e-06), Depth.26 = c(26.5,
26.5, 26.5, 26.5, 26.5, 26.5), Year.26 = c(1646.315863, 1646.752634,
1647.189405, 1647.626176, 1648.062946, 1648.499717), Counts.26 = c(2.97e-07,
4.01e-07, 5.43e-07, 7.26e-07, 9.58e-07, 1.25e-06), Depth.27 = c(27.5,
27.5, 27.5, 27.5, 27.5, 27.5), Year.27 = c(1631.425358, 1631.862129,
1632.298899, 1632.73567, 1633.172441, 1633.609212), Counts.27 = c(3.01e-07,
4.02e-07, 5.39e-07, 7.13e-07, 9.33e-07, 1.21e-06), Depth.28 = c(28.5,
28.5, 28.5, 28.5, 28.5, 28.5), Year.28 = c(1623.821174, 1624.214018,
1624.606862, 1624.999706, 1625.39255, 1625.785393), Counts.28 = c(3.08e-07,
4.09e-07, 5.38e-07, 7.01e-07, 9.11e-07, 1.17e-06), Depth.29 = c(29.5,
29.5, 29.5, 29.5, 29.5, 29.5), Year.29 = c(1612.475829, 1612.864893,
1613.253957, 1613.643021, 1614.032085, 1614.421149), Counts.29 = c(3.4e-07,
4.66e-07, 6.3e-07, 8.4e-07, 1.11e-06, 1.44e-06), Depth.30 = c(30.5,
30.5, 30.5, 30.5, 30.5, 30.5), Year.30 = c(1600.26273, 1600.609876,
1600.957023, 1601.304169, 1601.651316, 1601.998462), Counts.30 = c(4.18e-07,
5.85e-07, 8.07e-07, 1.1e-06, 1.49e-06, 1.99e-06), Depth.31 = c(31.5,
31.5, 31.5, 31.5, 31.5, 31.5), Year.31 = c(1549.137398, 1549.553381,
1549.969364, 1550.385346, 1550.801329, 1551.217311), Counts.31 = c(3.27e-07,
4.48e-07, 6.06e-07, 8.1e-07, 1.07e-06, 1.41e-06), Depth.32 = c(32.5,
32.5, 32.5, 32.5, 32.5, 32.5), Year.32 = c(1379.9456, 1380.656236,
1381.366871, 1382.077507, 1382.788142, 1383.498778), Counts.32 = c(3.71e-07,
5.02e-07, 6.71e-07, 8.88e-07, 1.16e-06, 1.5e-06), Depth.33 = c(33.5,
33.5, 33.5, 33.5, 33.5, 33.5), Year.33 = c(1176.400716, 1177.495517,
1178.590318, 1179.685119, 1180.77992, 1181.874721), Counts.33 = c(1.21e-07,
1.66e-07, 2.24e-07, 2.99e-07, 3.94e-07, 5.13e-07), Depth.34 = c(34.5,
34.5, 34.5, 34.5, 34.5, 34.5), Year.34 = c(984.8733315, 986.2808571,
987.6883826, 989.0959082, 990.5034338, 991.9109593), Counts.34 = c(9.87e-08,
1.37e-07, 1.86e-07, 2.51e-07, 3.34e-07, 4.43e-07), Depth.35 = c(35.5,
35.5, 35.5, 35.5, 35.5, 35.5), Year.35 = c(931.6673674, 933.0776679,
934.4879684, 935.8982688, 937.3085693, 938.7188698), Counts.35 = c(8.67e-08,
1.15e-07, 1.53e-07, 2.01e-07, 2.61e-07, 3.34e-07), Depth.36 = c(36.5,
36.5, 36.5, 36.5, 36.5, 36.5), Year.36 = c(894.7139257, 896.1463371,
897.5787485, 899.01116, 900.4435714, 901.8759828), Counts.36 = c(7.95e-08,
1.04e-07, 1.34e-07, 1.71e-07, 2.19e-07, 2.78e-07), Depth.37 = c(37.5,
37.5, 37.5, 37.5, 37.5, 37.5), Year.37 = c(867.2347826, 868.6591119,
870.0834411, 871.5077704, 872.9320996, 874.3564289), Counts.37 = c(7.45e-08,
9.58e-08, 1.22e-07, 1.54e-07, 1.95e-07, 2.44e-07), Depth.38 = c(38.5,
38.5, 38.5, 38.5, 38.5, 38.5), Year.38 = c(822.8193907, 824.2840456,
825.7487006, 827.2133555, 828.6780105, 830.1426654), Counts.38 = c(7.25e-08,
9.32e-08, 1.19e-07, 1.5e-07, 1.89e-07, 2.37e-07), Depth.39 = c(39.5,
39.5, 39.5, 39.5, 39.5, 39.5), Year.39 = c(780.7261404, 782.1666312,
783.6071219, 785.0476127, 786.4881034, 787.9285942), Counts.39 = c(7.24e-08,
9.24e-08, 1.17e-07, 1.48e-07, 1.86e-07, 2.31e-07), Depth.40 = c(40.5,
40.5, 40.5, 40.5, 40.5, 40.5), Year.40 = c(743.4256597, 744.8411919,
746.2567241, 747.6722563, 749.0877885, 750.5033208), Counts.40 = c(1.09e-07,
1.41e-07, 1.8e-07, 2.29e-07, 2.89e-07, 3.64e-07), Depth.41 = c(41.5,
41.5, 41.5, 41.5, 41.5, 41.5), Year.41 = c(673.4489487, 674.8279538,
676.2069588, 677.5859639, 678.964969, 680.3439741), Counts.41 = c(1.06e-07,
1.36e-07, 1.73e-07, 2.19e-07, 2.77e-07, 3.48e-07), Depth.42 = c(42.5,
42.5, 42.5, 42.5, 42.5, 42.5), Year.42 = c(624.182451, 625.532222,
626.881993, 628.231764, 629.581535, 630.931306), Counts.42 = c(8e-08,
1.03e-07, 1.32e-07, 1.67e-07, 2.1e-07, 2.63e-07), Depth.43 = c(43.5,
43.5, 43.5, 43.5, 43.5, 43.5), Year.43 = c(566.5185196, 567.8721804,
569.2258412, 570.579502, 571.9331628, 573.2868236), Counts.43 = c(8.43e-08,
1.1e-07, 1.42e-07, 1.83e-07, 2.34e-07, 2.97e-07), Depth.44 = c(44.5,
44.5, 44.5, 44.5, 44.5, 44.5), Year.44 = c(518.6933347, 520.014935,
521.3365354, 522.6581358, 523.9797362, 525.3013366), Counts.44 = c(9.23e-08,
1.24e-07, 1.65e-07, 2.16e-07, 2.8e-07, 3.6e-07), Depth.45 = c(45.5,
45.5, 45.5, 45.5, 45.5, 45.5), Year.45 = c(443.0346844, 444.2413453,
445.4480063, 446.6546672, 447.8613282, 449.0679891), Counts.45 = c(1.1e-07,
1.51e-07, 2.04e-07, 2.72e-07, 3.59e-07, 4.67e-07), Depth.46 = c(46.5,
46.5, 46.5, 46.5, 46.5, 46.5), Year.46 = c(368.5762277, 369.8150017,
371.0537756, 372.2925496, 373.5313236, 374.7700976), Counts.46 = c(1.54e-07,
2.24e-07, 3.19e-07, 4.49e-07, 6.21e-07, 8.47e-07)), class = c("data.table",
"data.frame"), row.names = c(NA, -6L))

You can try the base R code (don't need data.table) below, using stack + unstack
res <- unstack(
transform(
stack(df),
ind = gsub("\\..*", "", ind)
)
)
which gives
> head(res, 20)
Counts Depth Year
1 2.87e-06 0.5 2001.539
2 3.56e-06 0.5 2001.574
3 4.38e-06 0.5 2001.609
4 5.36e-06 0.5 2001.644
5 6.52e-06 0.5 2001.679
6 7.94e-06 0.5 2001.714
7 1.46e-06 1.5 1984.294
8 1.81e-06 1.5 1984.364
9 2.22e-06 1.5 1984.435
10 2.73e-06 1.5 1984.506
11 3.34e-06 1.5 1984.577
12 4.07e-06 1.5 1984.648
13 1.59e-06 2.5 1981.471
14 1.98e-06 2.5 1981.544
15 2.45e-06 2.5 1981.618
16 3.01e-06 2.5 1981.691
17 3.70e-06 2.5 1981.765
18 4.53e-06 2.5 1981.839
19 1.52e-06 3.5 1977.872
20 1.90e-06 3.5 1977.945```

This could be done by:
setDT(df)
melt(df,, patterns("Counts", "Depth", "Year"))
If you want to be more sophisticated, just do:
nms <- c('Count', 'Depth', 'Year')
melt(df, measure.vars = patterns(nms), value.name = nms)
variable Count Depth Year
1: 1 2.87e-06 0.5 2001.539
2: 1 3.56e-06 0.5 2001.574
3: 1 4.38e-06 0.5 2001.609
4: 1 5.36e-06 0.5 2001.644
5: 1 6.52e-06 0.5 2001.679
---
206: 35 NA 34.5 NA
207: 35 NA 34.0 NA
208: 35 NA 34.5 NA
209: 35 NA 34.5 NA
210: 35 NA 34.0 NA

Using pivot_longer from tidyr :
tidyr::pivot_longer(df, cols = everything(),
names_to = '.value',
names_pattern = '(\\w+)')
# A tibble: 210 x 3
# Depth Year Counts
# <dbl> <dbl> <dbl>
# 1 0.5 2002. 0.00000287
# 2 1.5 1984. 0.00000146
# 3 2.5 1981. 0.00000159
# 4 3.5 1978. 0.00000152
# 5 4.5 1974. 0.00000268
# 6 5.5 1969. 0.00000117
# 7 6.5 1962. 0.0000015
# 8 7.5 1959. 0.00000112
# 9 8.5 1952. 0.00000103
#10 9.5 1945. 0.000000986
# … with 200 more rows

How to make Hierarchical Cluster Heatmap in R?

I have this data set, I want to make Hierarchical Cluster Heatmap in R. Please help me
structure(list(Location = c("Karnaphuli River", "Sangu River", "Kutubdia Channel", "Moheshkhali Channel", "Bakkhali River", "Naf River", "St. Martin's Island", "Mean "), Cr = c(114.92, 2.75, 18.88, 27.6, 39.5, 12.8, 17.45, 33.41), Pb = c(31.29, 26.42, 52.3, 59.45, 34.65, 12.8, 9.5, 32.34), Cu = c(9.48, 54.39, 52.4, 73.28, 76.26, 19.48, 8.94, 42.03), Zn = c(66.2, 71.17, 98.7, 95.3, 127.84, 27.76, 21.78, 72.67), As = c(89.67, 9.85, 8.82, 18.54, 15.38, 7.55, 16.45, 23.75), Cd = c(1.06, 0, 0.96, 2.78, 3.12, 0.79, 0.45, 1.53)), class = "data.frame", row.names = c(NA, -8L))

library(tidyverse)
library(gplots)
#>
#> Attaching package: 'gplots'
#> The following object is masked from 'package:stats':
#>
#> lowess
dat <- structure(list(Location = c("Karnaphuli River", "Sangu River", "Kutubdia Channel", "Moheshkhali Channel", "Bakkhali River", "Naf River", "St. Martin's Island", "Mean "), Cr = c(114.92, 2.75, 18.88, 27.6, 39.5, 12.8, 17.45, 33.41), Pb = c(31.29, 26.42, 52.3, 59.45, 34.65, 12.8, 9.5, 32.34), Cu = c(9.48, 54.39, 52.4, 73.28, 76.26, 19.48, 8.94, 42.03), Zn = c(66.2, 71.17, 98.7, 95.3, 127.84, 27.76, 21.78, 72.67), As = c(89.67, 9.85, 8.82, 18.54, 15.38, 7.55, 16.45, 23.75), Cd = c(1.06, 0, 0.96, 2.78, 3.12, 0.79, 0.45, 1.53)), class = "data.frame", row.names = c(NA, -8L))
dat %>%
column_to_rownames(var = "Location") %>%
as.matrix() %>%
heatmap.2(., # source data
scale = "none", # set scaling by column, row or none
Rowv = T, # toggles clustering of rows
Colv = T, # toggles clustering of columns
trace = "none", # turn off trace in each column of heatmap
margin = c(3, 10), # set margins around plot
col = colorRampPalette(c("white", "red"))(11), # set color scheme
symkey = F, # set color scale to be asymetric
symbreaks = F, # set color scale to be asymetric
main = "Heatmap Title", # set main plot title
cexRow = 1, # set font size for rows
cexCol = 1, # set font size for columns
tracecol = "black", # set color of histogram on key
key.xlab = "Value", # set title for legend
lhei = c(1,3), # set key height as proportion to total plot height
lwid = c(1,3), # set key width as proportion to total plot width
keysize = 2 # set overall key size
)
Created on 2021-04-21 by the reprex package (v1.0.0)
There are many options to customize the details of the heatmap - check the documentation for more. I've shown a few of the ones I commonly use here.

Removing NAs from ggplot x-axis in ggplot2

I would like to get rid off the whole NA block (highlighted here ).
I tried na.ommit and na.rm = TRUE unsuccesfully.
Here is the code I used :
library(readxl)
data <- read_excel("Documents/TFB/xlsx_geochimie/solfatara_maj.xlsx")
View(data)
data <- gather(data,FeO:`Fe2O3(T)`,key = "Element",value="Pourcentage")
library(ggplot2)
level_order <- factor(data$Element,levels = c("SiO2","TiO2","Al2O3","Fe2O3","FeO","MgO","CaO","Na2O","K2O"))
ggplot(data=data,mapping=aes(x=level_order,y=data$Pourcentage,colour=data$Ech)+geom_point()+geom_line(group=data$Ech) +scale_y_log10()
And here is my original file
https://drive.google.com/file/d/1bZi7fPWebbpodD1LFScoEcWt5Bs-cqhb/view?usp=sharing

If I run your code and look at data that goes into ggplot:
table(data$Element)
Al2O3 CaO Fe2O3 Fe2O3(T) FeO K2O LOI LOI2 MgO MnO
12 12 12 12 12 12 12 12 12 12
Na2O P2O5 SiO2 SO4 TiO2 Total Total 2 Total N Total S
12 12 12 12 12 12 12 12 12
You have included Total into the melted data frame.. which is not intended I guess. Hence when you do factor on these, and these "Total.." are not included in the levels, they become NA.
So we can do it from scratch:
data <- read_excel("solfatara_maj.xlsx")
The data:
structure(list(Ech = c("AGN 1A", "AGN 2A", "AGN 3B", "SOL 4B",
"SOL 8Ag", "SOL 8Ab", "SOL 16A", "SOL 16B", "SOL 16C", "SOL 22 A",
"SOL 22D", "SOL 25B"), FeO = c(0.2, 0.8, 1.7, 0.3, 1.7, NA, 0.2,
NA, 0.1, 0.7, 1.3, 2), `Total S` = c(5.96, 45.3, 0.22, 17.3,
NA, NA, NA, NA, NA, NA, 2.37, 0.36), SO4 = c(NA, 6.72, NA, 4.08,
0.06, 0.16, 42.2, 35.2, 37.8, 0.32, 6.57, NA), `Total N` = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, 15.2, NA, NA), SiO2 = c(50.2,
31.05, 56.47, 62.14, 61.36, 75.66, 8.41, 21.74, 17.44, 13.52,
19.62, 56.35), Al2O3 = c(15.53, 7.7, 17.56, 4.44, 17.75, 10.92,
31.92, 26.38, 27.66, 0.64, 3.85, 17.28), Fe2O3 = c(0.49, 0.63,
2.06, NA, 1.76, 0.11, 0.64, 0.88, 1.71, NA, 1.32, 2.67), MnO = c(0.01,
0.01, 0.13, 0.01, 0.09, 0.01, 0.01, 0.01, 0.01, 0.005, 0.04,
0.12), MgO = c(0.06, 0.07, 0.88, 0.03, 0.97, 0.05, 0.04, 0.07,
0.03, 0.02, 1.85, 1.63), CaO = c(0.2, 0.09, 3.34, 0.09, 2.58,
0.57, 0.2, 0.26, 0.15, 0.06, 35.66, 4.79), Na2O = c(0.15, 0.14,
3.23, 0.13, 3.18, 2.04, 0.68, 0.68, 0.55, 0.05, 0.45, 3.11),
K2O = c(4.39, 1.98, 8, 1.26, 8.59, 5.94, 8.2, 6.97, 8.04,
0.2, 0.89, 7.65), TiO2 = c(0.42, 0.27, 0.46, 0.79, 0.55,
0.16, 0.09, 0.22, 0.16, 0.222, 0.34, 0.53), P2O5 = c(0.11,
0.09, 0.18, 0.08, 0.07, 0.07, 0.85, 0.68, 0.62, NA, 0.14,
0.28), LOI = c(27.77, 57.06, 6.13, 29.03, 1.38, 4.92, 42.58,
37.58, 38.76, NA, 26.99, 3.92), LOI2 = c(27.79, 57.15, 6.32,
29.06, 1.57, 4.93, 42.6, 37.59, 38.77, 0.08, 27.13, 4.15),
Total = c(99.52, 99.88, 100.2, 98.25, 99.99, 100.5, 93.81,
95.57, 95.23, 15.25, 92.45, 100.3), `Total 2` = c(99.54,
99.96, 100.3, 98.28, 100.2, 100.6, 93.83, 95.58, 95.24, 15.33,
92.59, 100.6), `Fe2O3(T)` = c(0.71, 1.52, 3.95, 0.27, 3.65,
0.22, 0.87, 0.99, 1.82, 0.61, 2.76, 4.9)), row.names = c(NA,
-12L), class = c("tbl_df", "tbl", "data.frame"))
First we set the plotting level like you did:
plotlvls = c("SiO2","TiO2","Al2O3","Fe2O3","FeO","MgO","CaO","Na2O","K2O")
Then we select only these columns, and also Ech, note I use pivot_longer() because gather() will supposedly be deprecated, and then we do the factoring too:
plotdf = data %>% select(c(plotlvls,"Ech")) %>%
pivot_longer(-Ech,names_to = "Element",values_to = "Pourcentage") %>%
mutate(Element=factor(Element,levels=toplot))
Finally we plot, and there are no NAs:
ggplot(data=plotdf,mapping=aes(x=Element,y=Pourcentage,colour=Ech))+
geom_point()+geom_line(aes(group=Ech)) +scale_y_log10()

1.Create reproducible minimal data
data <- data.frame(Element = c("SiO2","TiO2","Al2O3","Fe2O3","FeO","MgO","CaO","Na2O","K2O",NA),
Pourcentage = 1:10,
Ech = c("AGN 1A", "SOL 16"))
2.Set factor levels for variable 'Element'
data$Element <- factor(data$Element,levels = c("SiO2","TiO2","Al2O3","Fe2O3","FeO","MgO","CaO","Na2O","K2O"))
3.Remove rows containing NA in the variable 'Element'
data <- data[!is.na(data$Element), ]
4.Plot data using ggplot2 (ggplot2 syntax uses NSE (non standard evaluation), which means you dont't have to pass the variable names as strings or using the $ notation):
ggplot(data=data,aes(x=Element,y=Pourcentage,colour=Ech)) +
geom_point() +
geom_line(aes(group=Ech)) +
scale_y_log10()

Data labels for mean and percentiles in a distribution chart

I'm creating a custom chart to visualize a variable's distribution using geom_density. I added 3 vertical lines for a custom value, the 5th percentile and the 95th percentile.
How do I add labels for those lines?
I tried using geom_text but i don't know how to parameter the x and y variables
library(ggplot2)
ggplot(dataset, aes(x = dataset$`Estimated percent body fat`)) +
geom_density() +
geom_vline(aes(xintercept = dataset$`Estimated percent body fat`[12]),
color = "red", size = 1) +
geom_vline(aes(xintercept = quantile(dataset$`Estimated percent body fat`,
0.05, na.rm = TRUE)),
color = "grey", size = 0.5) +
geom_vline(aes(xintercept = quantile(dataset$`Estimated percent body fat`,
0.95, na.rm = TRUE)),
color="grey", size=0.5) +
geom_text(aes(x = dataset$`Estimated percent body fat`[12],
label = "Custom", y = 0),
colour = "red", angle = 0)
I'd like to obtain the following:
for the custom value, I'd like to add the label at the top of the chart, just to the right of the line
for the percentiles label, I'd like to add them in the middle of the chart; at the left of the line for the 5th percentile and right of the line for 95th percentile
Here is what I was able to obtain https://i.imgur.com/thSQwyg.png
And these are the first 50 lines of my dataset:
structure(list(`Respondent sequence number` = c(21029L, 21034L,
21043L, 21056L, 21067L, 21085L, 21087L, 21105L, 21107L, 21109L,
21110L, 21125L, 21129L, 21138L, 21141L, 21154L, 21193L, 21195L,
21206L, 21215L, 21219L, 21221L, 21232L, 21239L, 21242L, 21247L,
21256L, 21258L, 21287L, 21310L, 21325L, 21367L, 21380L, 21385L,
21413L, 21418L, 21420L, 21423L, 21427L, 21432L, 21437L, 21441L,
21444L, 21453L, 21466L, 21467L, 21477L, 21491L, 21494L, 21495L
), `Estimated percent body fat` = c(NA, 7.2, NA, NA, 24.1, 25.1,
30.2, 23.6, 24.3, 31.4, NA, 14.1, 20.5, NA, 23.1, 30.6, 21, 20.9,
NA, 24, 26.7, 16.6, NA, 26.9, 16.9, 21.3, 15.9, 27.4, 13.9, NA,
20, NA, 12.8, NA, 33.8, 18.1, NA, NA, 28.4, 10.9, 38.1, 33, 39.3,
15.9, 32.7, NA, 20.4, 16.8, NA, 29)), row.names = c(NA, 50L), class =
"data.frame")

First I recommend clean column names.
dat <- dataset
names(dat) <- tolower(gsub("\\s", "\\.", names(dat)))
Whith base R plots you could do the following. The clou is, that you can store the quantiles and custom positions to use them as coordinates later which gives you a dynamic positioning. I'm not sure if/how this is possible with ggplot.
plot(density(dat$estimated.percent.body.fat, na.rm=TRUE), ylim=c(0, .05),
main="Density curve")
abline(v=c1 <- dat$estimated.percent.body.fat[12], col="red")
abline(v=q1 <- quantile(dat$estimated.percent.body.fat, .05, na.rm=TRUE), col="grey")
abline(v=q2 <- quantile(dat$estimated.percent.body.fat, .95, na.rm=TRUE), col="grey")
text(c1 + 4, .05, c(expression("" %<-% "custom")), cex=.8)
text(q1 - 5.5, .025, c(expression("5% percentile" %->% "")), cex=.8)
text(q2 + 5.5, .025, c(expression("" %<-% "95% percentile")), cex=.8)
Note: Case you don't like the arrows just do e.g. "5% percentile" instead of c(expression("5% percentile" %->% "")).
Or in ggplot you could use annotate.
library(ggplot2)
ggplot(dataset, aes(x = dataset$`Estimated percent body fat`)) +
geom_density() +
geom_vline(aes(xintercept = dataset$`Estimated percent body fat`[12]),
color = "red", size = 1) +
geom_vline(aes(xintercept = quantile(dataset$`Estimated percent body fat`,
0.05, na.rm = TRUE)),
color = "grey", size = 0.5) +
geom_vline(aes(xintercept = quantile(dataset$`Estimated percent body fat`,
0.95, na.rm = TRUE)),
color="grey", size=0.5) +
annotate("text", x=16, y=.05, label="custom") +
annotate("text", x=9.5, y=.025, label="5% percentile") +
annotate("text", x=38, y=.025, label="95% percentile")
Note, that in either solution the result (i.e. exact label positions) depends on your export size. To learn how to control this, take e.g. a look into How to save a plot as image on the disk?.
Data
dataset <- structure(list(`Respondent sequence number` = c(21029L, 21034L,
21043L, 21056L, 21067L, 21085L, 21087L, 21105L, 21107L, 21109L,
21110L, 21125L, 21129L, 21138L, 21141L, 21154L, 21193L, 21195L,
21206L, 21215L, 21219L, 21221L, 21232L, 21239L, 21242L, 21247L,
21256L, 21258L, 21287L, 21310L, 21325L, 21367L, 21380L, 21385L,
21413L, 21418L, 21420L, 21423L, 21427L, 21432L, 21437L, 21441L,
21444L, 21453L, 21466L, 21467L, 21477L, 21491L, 21494L, 21495L
), `Estimated percent body fat` = c(NA, 7.2, NA, NA, 24.1, 25.1,
30.2, 23.6, 24.3, 31.4, NA, 14.1, 20.5, NA, 23.1, 30.6, 21, 20.9,
NA, 24, 26.7, 16.6, NA, 26.9, 16.9, 21.3, 15.9, 27.4, 13.9, NA,
20, NA, 12.8, NA, 33.8, 18.1, NA, NA, 28.4, 10.9, 38.1, 33, 39.3,
15.9, 32.7, NA, 20.4, 16.8, NA, 29)), row.names = c(NA, 50L), class =
"data.frame")

Vertical gradient color with geom_area [duplicate]

This question already has answers here:
How to make gradient color filled timeseries plot in R
(4 answers)
Closed 5 years ago.
I have hard time finding a solution for creating gradient color.
This is how it should look like(dont mind the blue bars)
Something similar to How to make gradient color filled timeseries plot in R, but a bit to advanced for me to reuse this example. I dont have any negative values and max is 80.I have tried the answer offered by nograpes, my PC was frozen for some 6-7 min and then I got message:
Error in rowSums(na) :
'Calloc' could not allocate memory (172440001 of 16 bytes)
This is only a subset of data with 841 rows (some containing NAs), and solution in previous answer could hardly work for me.
df <- structure(list(date = structure(c(1497178800, 1497182400, 1497186000,
1497189600, 1497193200, 1497196800, 1497200400, 1497204000, 1497207600,
1497211200, 1497214800, 1497218400, 1497222000, 1497225600, 1497229200,
1497232800, 1497236400, 1497240000, 1497243600, 1497247200, 1497250800,
1497254400, 1497258000, 1497261600, 1497265200, 1497268800, 1497272400,
1497276000, 1497279600, 1497283200, 1497286800, 1497290400, 1497294000,
1497297600, 1497301200, 1497304800, 1497308400, 1497312000, 1497315600,
1497319200, 1497322800, 1497326400, 1497330000, 1497333600, 1497337200,
1497340800, 1497344400, 1497348000, 1497351600, 1497355200), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), dk_infpressure = c(22, 21.6, 21.2,
20.9, 20.5, 20.1, 19.8, 19.4, 19, 18.6, 18.2, 17.9, 17.5, 17.1,
16.8, 16.4, 16, 15.6, 15.2, 14.9, 14.5, 14.1, 13.8, 13.4, 13,
12.5, 11.9, 11.4, 10.8, 10.3, 9.8, 9.2, 8.7, 8.1, 7.6, 7, 6.5,
6, 5.4, 4.9, 4.3, 3.8, 3.2, 2.7, 2.2, 1.6, 1.1, 0.5, 0, 0)), .Names = c("date",
"dk_infpressure"), row.names = c(NA, -50L), class = c("tbl_df",
"tbl", "data.frame"))
Code to get basic plot:
ggplot()+
geom_area(data=df, aes(x = date, y= dk_infpressure ) )+
scale_y_continuous(limits = c(0, 80))

Because geom_area can't take a gradient fill, it's a somewhat hard problem.
Here's a decidedly hacky but possibly sufficient option that makes a raster (but using geom_tile since x and y sizes differ) and covering the ragged edges with cropping and ggforce::geom_link (a version of geom_segment that can plot a gradient):
library(tidyverse)
df %>%
mutate(dk_infpressure = map(dk_infpressure, ~seq(0, .x, .05))) %>% # make grid of points
unnest() %>%
ggplot(aes(date, dk_infpressure, fill = dk_infpressure)) +
geom_tile(width = 3600, height = 0.05) +
# hide square tops
ggforce::geom_link(aes(color = dk_infpressure, xend = lag(date), yend = lag(dk_infpressure)),
data = df, size = 2.5, show.legend = FALSE) +
scale_x_datetime(expand = c(0, 0)) + # hide overplotting of line
scale_y_continuous(expand = c(0, 0))

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