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对先前提出的问题 ( Plotting depth range in 'time-series' using ggplot ) 进行跟进,但完全相同。
使用 ggplot 我想要一个图来显示特定个体(例如 7303,请参见下面的代码示例)深度范围如何使用 geom_ribbon()
在第 3m 个周期内围绕平均值(最小值和最大值)变化。
数据集中的变量;
names(df)
[1] "ID" "MONTH" "DAY" "MIN" "MEAN" "MAX" "date"
df<-Dybde_dag_gjsn[which(Dybde_dag_gjsn$ID=="7303"),]
ggplot(data=df)+
geom_line(aes(x=date,y=-MEAN,group=ID,colour=as.factor(ID))) +
geom_ribbon(aes(x=date,ymin=-MIN,ymax=-MAX,group=ID),fill="grey") +
theme(legend.position="none")
grouping factor
有关(引用:书“ggplot2:用于数据分析的优雅图形(使用 R!)”底部的第 50 页)。我使用 ID 作为差异图的分组因子,其中我在同一个图中有很多个人,但是当然这在这里不起作用,因为我现在只有
一个 ID。
> dput(df)
structure(list(ID = c(7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L, 7303L,
7303L, 7303L, 7303L, 7303L, 7303L, 7303L), MONTH = c(6L, 7L,
8L, 9L, 10L, 11L, 12L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L,
6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 5L, 7L, 8L, 10L), DAY = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 20L,
20L, 20L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L,
24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L,
26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L,
28L, 28L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 29L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L), MIN = c(11.802917,
8.604167, 14.318936, 14.836458, 10.16625, 8.58093, 12.620833,
11.216667, 9.742083, 16.554167, 14.662708, 11.58375, 8.401957,
17.662553, 11.33413, 8.3275, 16.664167, 13.108, 12.163542, 8.024783,
16.394255, 12.450625, 7.730208, 17.397083, 14.680833, 12.08875,
8.810435, 15.019792, 10.742292, 11.911489, 17.621111, 14.555319,
11.092292, 10.339583, 15.487083, 9.9375, 12.747708, 16.411458,
13.903542, 10.94875, 10.164375, 16.915625, 7.065417, 12.83375,
18.342979, 13.549167, 11.229792, 10.56875, 18.430444, 6.761042,
12.088723, 18.041489, 12.976458, 10.995625, 9.83, 17.677083,
9.044737, 8.485208, 8.480833, 18.887609, 13.97125, 10.979583,
9.73, 12.843913, 12.495833, 9.311042, 11.773958, 17.697447, 12.004583,
9.507083, 8.385417, 16.856596, 13.131458, 8.656458, 10.16625,
17.634255, 14.123542, 9.138958, 9.999583, 18.634043, 12.649583,
8.672917, 13.166458, 17.596222, 13.121875, 8.397708, 10.421667,
20.087805, 14.496042, 10.430833, 10.553333, 17.742979, 12.378125,
8.736458, 8.713125, 19.968421, 13.571875, 11.819792, 8.847021,
18.748085, 14.197917, 8.918125, 9.3475, 11.85625, 11.089583,
13.461458, 18.761277, 11.001702, 8.621458, 9.194375, 11.738333,
12.283404, 15.249583, 17.522609, 11.655625, 8.725957, 9.024792,
12.361875, 12.311702, 11.959167, 16.179333, 11.733542, 11.0825,
11.635208, 12.139792, 10.24875, 11.147917, 15.690851, 11.7175,
8.537708, 8.896458, 11.342917, 9.42875, 12.289167, 13.904894,
13.622766, 9.262292, 10.925417, 12.507917, 9.640417, 11.619167,
13.693333, 12.81375, 8.737021, 8.725217, 12.851042, 9.978958,
11.987917, 12.68875, 13.178125, 8.613125, 8.759574, 13.125833,
12.803191, 10.768085, 14.104583, 12.426042, 8.590208, 10.004792,
11.335417, 10.126458, 11.02, 13.675625, 12.153333, 8.786667,
8.901522, 10.190208, 9.675417, 12.787708, 12.376875, 12.686596,
9.266042, 9.486875, 11.187083, 11.984583, 14.908125, 12.341042,
8.603617, 8.950417, 9.289574, 12.193125, 11.034583, 14.760417,
14.270417, 10.900426, 9.456667, 9.817333, 11.300625, 9.979362,
18.275532, 14.270833, 10.328333, 8.991667, 9.73234, 10.31625,
12.294167, 17.291875, 14.166889, 10.655625, 9.542917, 9.423542,
13.408542, 11.412708, 15.77875, 13.872174, 10.414583, 8.771458,
10.196667, 13.245957, 9.7525, 14.025625, 14.0225, 10.283333,
10.124375, 11.077708, 11.284375, 12.960625, 13.672917, 8.689535
), MEAN = c(12.770743, 10.404807, 15.731308, 15.718022, 11.039786,
9.592478, 13.566293, 11.997254, 11.060343, 17.321561, 15.434626,
12.521116, 9.173196, 20.231528, 12.183485, 9.494385, 17.661506,
13.833904, 13.130883, 9.006861, 17.73971, 13.204396, 8.896995,
18.11753, 15.600875, 13.121007, 10.101822, 16.769785, 11.697461,
12.764369, 18.282592, 15.310545, 12.133705, 11.40675, 16.982279,
11.110351, 13.55874, 17.101128, 14.624049, 11.851105, 11.142173,
18.371564, 8.477442, 13.410731, 18.981083, 14.22986, 12.254827,
11.94385, 19.470508, 8.074123, 12.675037, 18.886745, 13.915082,
12.136526, 11.31768, 18.839881, 10.007904, 9.772924, 9.81347,
19.389361, 14.811928, 12.119235, 10.951059, 14.149285, 13.293671,
10.514356, 12.565706, 18.040379, 12.948702, 10.605055, 9.573924,
18.062445, 14.017553, 9.602413, 11.364352, 18.259258, 15.170781,
9.967036, 11.390614, 19.553701, 13.97901, 9.462041, 13.8245,
18.099865, 14.177431, 9.98118, 11.685662, 20.804762, 15.019522,
11.354073, 11.534092, 18.394013, 13.326405, 9.616245, 9.949249,
20.426667, 14.46714, 12.737376, 9.717531, 19.227388, 15.078364,
9.844263, 10.303065, 12.92404, 12.265582, 14.105216, 19.288396,
12.052717, 9.491347, 10.529289, 13.096898, 13.369252, 16.28977,
18.160727, 12.549057, 9.604447, 10.280789, 13.554763, 13.393719,
12.50955, 16.820558, 12.850228, 12.094866, 12.630164, 13.385122,
11.273304, 11.961472, 16.330921, 12.611663, 9.617874, 10.167019,
12.655299, 10.373309, 13.374857, 14.925013, 14.537989, 10.447603,
12.508138, 13.349818, 10.680128, 12.326957, 15.049259, 13.660166,
9.696145, 9.914241, 13.980217, 10.761571, 12.556846, 13.99646,
14.085468, 9.438403, 9.728357, 14.105513, 13.989112, 11.698711,
14.95036, 13.363272, 9.401711, 10.865912, 12.682327, 11.131757,
11.846839, 14.42893, 13.07079, 9.819241, 9.933585, 11.365231,
10.918715, 14.062566, 13.084255, 13.71815, 10.182433, 10.690687,
12.208993, 13.50174, 15.4533, 13.201188, 9.811705, 9.881868,
10.245386, 12.971602, 11.964325, 15.679961, 15.016042, 11.940247,
10.361256, 10.998961, 12.15253, 11.044938, 19.022599, 14.954921,
11.48031, 10.01924, 10.652463, 11.569661, 13.298019, 17.929547,
15.186122, 11.539514, 10.618905, 10.354405, 14.389687, 12.624194,
16.338045, 14.791213, 11.211347, 9.772962, 12.016292, 14.260883,
10.820083, 14.909629, 14.889066, 11.19011, 11.194178, 12.819223,
12.179042, 14.309247, 14.722921, 9.69601), MAX = c(13.891875,
12.046875, 16.884468, 16.663333, 12.111875, 10.590465, 14.557708,
12.835833, 12.286458, 18.15125, 16.4475, 13.609167, 9.929783,
22.586596, 13.212826, 10.684792, 18.319792, 14.831333, 14.148958,
10.042826, 19.206383, 14.106667, 9.974375, 18.710625, 16.462917,
14.298958, 11.42913, 18.739583, 12.672708, 13.598723, 18.842222,
16.099149, 13.169167, 12.736458, 18.593125, 12.367708, 14.195833,
17.82625, 15.53375, 12.754375, 12.278125, 20.19, 9.94375, 13.917292,
19.738511, 14.916458, 13.339375, 13.453542, 20.28, 9.341042,
13.397234, 19.54, 14.982083, 13.249792, 12.602083, 20.058333,
10.945789, 11.00875, 10.901875, 19.990217, 15.745, 13.379583,
12.361042, 15.389565, 14.012917, 11.743958, 13.204167, 18.383404,
13.976667, 11.698958, 10.67125, 19.185106, 14.927083, 10.43,
12.549167, 18.929149, 16.060833, 10.792708, 12.95, 20.521277,
15.096667, 10.184792, 14.455208, 18.589778, 15.085208, 11.300208,
13.256875, 21.602439, 15.5625, 12.430625, 12.52375, 18.934043,
14.175417, 10.624792, 11.299583, 20.831579, 15.508333, 13.814792,
10.412766, 19.688511, 15.974583, 10.839583, 11.752083, 14.33625,
13.332708, 14.671667, 19.667447, 12.983404, 10.354583, 11.980833,
14.325833, 14.310426, 17.290833, 18.684783, 13.448958, 10.530851,
11.582708, 14.854375, 14.357234, 13.0875, 17.491111, 14.056458,
12.958125, 13.423333, 14.718542, 12.291458, 12.82125, 16.907872,
13.553125, 11.034375, 11.5625, 13.86375, 11.493542, 14.3025,
15.835957, 15.34383, 11.731667, 14.293125, 14.225625, 11.83875,
12.958125, 16.238333, 14.615625, 10.958085, 11.225435, 14.90375,
11.537083, 13.103958, 15.006458, 14.866458, 10.4675, 10.701702,
15.096875, 15.155319, 12.481702, 15.745208, 14.3425, 10.37875,
11.7625, 13.89875, 12.302708, 12.761875, 15.10625, 14.171875,
10.985625, 10.838696, 12.592708, 12.229167, 15.030833, 13.759792,
14.696809, 11.215, 12.2125, 13.277292, 14.844375, 15.933542,
13.974583, 11.086809, 10.918542, 11.214894, 13.733542, 12.78375,
16.320625, 15.781667, 12.809362, 11.3175, 12.139778, 13.163958,
11.853617, 19.77383, 15.644375, 12.645625, 11.055417, 11.401702,
12.80125, 14.210625, 18.491667, 15.958444, 12.377083, 11.693333,
11.346667, 15.291458, 13.693542, 16.73125, 15.604565, 12.090833,
10.700417, 13.640208, 15.333404, 11.697292, 15.565, 15.66375,
12.119583, 12.334167, 14.415833, 13.394792, 15.494583, 15.681042,
10.907907), date = structure(c(15522, 15553, 15492, 15522, 15553,
15492, 15522, 15554, 15493, 15523, 15554, 15493, 15523, 15554,
15494, 15524, 15555, 15494, 15524, 15555, 15494, 15525, 15556,
15495, 15525, 15556, 15495, 15525, 15557, 15496, 15526, 15557,
15496, 15526, 15557, 15497, 15527, 15558, 15497, 15527, 15558,
15497, 15528, 15559, 15498, 15528, 15559, 15498, 15528, 15560,
15499, 15529, 15560, NA, 15499, 15499, 15500, 15500, 15500, 15530,
15500, 15500, 15500, 15561, 15501, 15531, 15501, 15501, 15531,
15501, 15531, 15562, 15532, 15502, 15532, 15502, 15532, 15532,
15502, 15532, 15533, 15564, 15533, 15564, 15564, 15564, 15564,
15564, 15565, 15565, 15565, 15565, 15534, 15534, 15534, 15504,
15505, 15505, 15535, 15505, 15535, 15505, 15505, 15536, 15506,
15506, 15536, 15506, 15506, 15506, 15537, 15507, 15568, 15568,
15568, 15568, 15568, 15569, 15569, 15569, 15569, 15569, 15569,
15569, 15570, 15570, 15570, 15570, 15570, 15539, 15570, 15571,
15540, 15571, 15540, 15540, 15571, 15540, 15572, 15541, 15511,
15541, 15511, 15541, 15541, 15542, 15512, 15512, 15512, 15573,
15512, 15512, 15513, 15513, 15513, 15574, 15513, 15543, 15574,
15514, 15544, 15514, 15514, 15514, 15514, 15575, 15545, 15576,
15515, 15545, 15515, 15576, 15576, 15546, 15577, 15546, 15516,
15546, 15577, 15546, 15578, 15517, 15517, 15547, 15578, 15547,
15547, 15579, 15518, 15579, 15518, 15579, 15548, 15548, 15580,
15580, 15519, 15549, 15519, 15549, 15519, 15520, 15520, 15581,
15550, 15581, 15550, 15520, 15521, 15551, 15582, 15582, 15582,
15521, 15551, 15583, 15552, 15583, 15583), class = "Date")), .Names = c("ID",
"MONTH", "DAY", "MIN", "MEAN", "MAX", "date"), row.names = 1128:1346, class = "data.frame")
最佳答案
一个问题是日期不是唯一的,这会导致这些垂直线。自月date
列与 MONTH
不相同列,数据似乎有问题。此外,您需要切换geom_line
的顺序和 geom_ribbon
, 将线条绘制在顶部。
您可以按如下方式重新创建日期:
df$date2 <- as.Date(paste("2012",df$MONTH,df$DAY,sep="-"),"%Y-%m-%d")
ggplot(data=df)+
geom_ribbon(aes(x=date2,ymin=-MIN,ymax=-MAX),fill="grey") +
geom_line(aes(x=date2,y=-MEAN)) +
theme(legend.position="none")
关于r - ggplot2 中日期列和 geom_ribbon 的问题,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/15719772/
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我想使用相同的 ggplot 代码以我的数据框中的数字为条件生成 8 个不同的数字。通常我会使用 facet_grid,但在这种情况下,我希望最终得到每个单独数字的 pdf。例如,我想要这里的每一行一
当我在 ggplot 上使用 geom_text 时,与 ggplot 的“填充”选项发生冲突。 这是问题的一个明显例子: library(ggplot2) a=ChickWeight str(a)
是否可以结合使用 ggplot ly 和拼凑而成的ggplot? 例子 这将并排显示两个图 library(ggplot2) library(plotly) library(patchwork) a
我想绘制一个图表,其中 y 轴以百分比表示: p = ggplot(test, aes(x=creation_date, y=value, color=type)) + geom_line(aes
如何去除ggsave中的白边距? 我的问题和Remove white space (i.e., margins) ggplot2 in R一模一样。然而,那里的答案对我来说并不理想。我不想对固定但未知
我有一个带有一些文本层的条形图,在 ggplot 库中一切正常,但现在我想添加一些与 ggplotly 的交互性,但它无法显示文本层 我更新了所有软件包但问题仍然存在 df = read.table(
当我尝试在 ggplot 中为我的箱线图设置自定义宽度时,它工作正常: p=ggplot(iris, aes(x = Species,y=Sepal.Length )) + geom_boxplot(
我正在尝试为 ggplot 密度创建一个图例,将一个组与所有组进行比较。使用此示例 - R: Custom Legend for Multiple Layer ggplot - 我可以使用下面的代码成
所以我试图在一个多面的 ggplot 上编辑 y 值,因为我在编织时在情节上有几个不准确之处。我对 R 和 R Markdown 很陌生,所以我不太明白为什么,例如,美国的 GDP PPP 在美元金额
我需要在 python 条形图的 x 轴 ggplot 上格式化日期。 我该怎么做? 最佳答案 使用 scale_x_date() 格式化 x 轴上的日期。 p = ggplot(aes(x='dat
我想使用 ggplotly因为它的副作用相同ggplot甚至graphics做。我的意思是当我 knitr::knit或 rmarkdown::render我期望的 Rmd 文档 print(obj)
我在下面有一个简单的应用程序,它显示了一个 ggplot。 ggplot 在控制台中生成警告(见底部图片)。我想捕获警告,并将其显示在应用程序的情节下方。 这是我的代码: library(shiny)
如果显示的基本数据集很大(下面的示例工作代码),则在 Shiny 的应用程序中向/从 ggplot 添加/删除图层可能需要一段时间。 问题是: 有没有办法缓存 ggplot(基本图)并添加/删除/修改
我正在组合 ggplot 的多个绘图,使用网格视口(viewport),这是必要的(我相信),因为我想旋转绘图,这在标准 ggplot 中是不可能的,甚至可能是 gridExtra 包。 我想在两个图
我可以使用 lattice 在 R 中绘制相对频率直方图包裹: a <- runif(100) library(lattice) histogram(a) 我想在 ggplot 中获得相同的图形.我试
我需要重新安装 R,但我现在遇到了 ggplot 的一个小问题。我确信有一个简单的解决方案,我感谢所有提示! 我经常使用堆叠面积图,通常我通过定义因子水平并以相反的顺序绘制来获得所需的堆叠和图例顺序。
新的并且坚持使用ggplot: 我有以下数据: tribe rho preference_watermass 1 Luna2 -1.000 hypolimnic 2 OP10I-A1
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