Question

我有大量数据，请耐心等待。

df <- structure(list(W = c(5216400.4123, 5399804.7349, 5595563.3087, 
5792353.9932, 5993467.7466, 6189404.9279, 6380940.454, 6566630.3544, 
6747453.6816, 6917820.9796, 7086201.8275, 7248213.5225, 5402700.4252, 
5592654.9057, 5795404.8549, 5999223.7818, 6207520.1695, 6410455.1037, 
6608831.1825, 6801152.8706, 6988434.1695, 7164886.0132, 7339280.46, 
7507078.2886, 5589000.4397, 5785505.0748, 5995246.3993, 6206093.5662, 
6421572.589, 6631505.2817, 6836721.9131, 7035675.3828, 7229414.6578, 
7411951.0459, 7592359.0997, 7765943.0584, 5775300.4552, 5978355.2455, 
6195087.9484, 6412963.3504, 6635625.0103, 6852555.4544, 7064612.6447, 
7270197.897, 7470395.1463, 7659016.0859, 7845437.7356, 8024807.8293, 
5961600.4694, 6171205.4145, 6394929.4952, 6619833.1358, 6849677.4283, 
7073605.633, 7292503.372, 7504720.4062, 7711375.6354, 7906081.1161, 
8098516.3724, 8283672.5987, 6147900.4816, 6364055.583, 6594771.0412, 
6826702.9212, 7063729.8478, 7294655.8114, 7520394.1023, 7739242.9195, 
7952356.1253, 8153146.1541, 8351595.0092, 8542537.3639, 6334200.5005, 
6556905.7553, 6794612.5881, 7033572.7089, 7277782.2661, 7515705.9882, 
7748284.8337, 7973765.4322, 8193336.6123, 8400211.1873, 8604673.6477, 
8801402.1321, 6520500.5136, 6749755.9226, 6994454.1327, 7240442.494, 
7491834.6868, 7736756.1617, 7976175.5621, 8208287.9459, 8434317.1037, 
8647276.2228, 8857752.2827, 9060266.9029, 6706800.528, 6942606.0899, 
7194295.6809, 7447312.2777, 7705887.1079, 7957806.3409, 8204066.2961, 
8442810.4584, 8675297.5886, 8894341.2573, 9110830.9206, 9319131.6718, 
6893100.5418, 7135456.2602, 7394137.2288, 7654182.0653, 7919939.5232, 
8178856.5136, 8431957.0234, 8677332.969, 8916278.0804, 9141406.2918, 
9363909.5543, 9577996.4404, 7079400.5588, 7328306.4296, 7593978.7729, 
7861051.8503, 8133991.9462, 8399906.6912, 8659847.7579, 8911855.4821, 
9157258.5684, 9388471.3277, 9616988.1903, 9836861.209, 7265700.5699, 
7521156.5994, 7793820.3185, 8067921.639, 8348044.3652, 8620956.868, 
8887738.4844, 9146377.9962, 9398239.0552, 9635536.362, 9870066.8286, 
10095725.9764, 7377480.5806, 7636866.7018, 7913725.2471, 8192043.5082, 
8476475.8166, 8753586.9712, 9024472.9255, 9287091.5011, 9542827.3494, 
9783775.3823, 10021914.0099, 10251044.8357, 7452000.5846, 7714006.7682, 
7993661.8669, 8274791.4235, 8562096.7846, 8842007.041, 9115629.2162, 
9380900.5049, 9639219.5459, 9882601.398, 10123145.4642, 10354590.7474, 
7526520.5952, 7791146.8356, 8073598.4841, 8357539.3348, 8647717.7534, 
8930427.113, 9206785.5068, 9474709.5143, 9735611.7438, 9981427.4094, 
10224376.9194, 10458136.6527, 7638300.6025, 7906856.9365, 8193503.4143, 
8481661.2074, 8776149.2063, 9063057.2172, 9343519.9463, 9615423.0209, 
9880200.0383, 10129666.4312, 10376224.1032, 10613455.5131, 7824600.6172, 
8099707.1074, 8393344.9601, 8688530.993, 8990201.627, 9284107.395, 
9571410.6772, 9849945.5329, 10121180.5245, 10376731.4677, 10629302.7393, 
10872320.2844, 8010900.6306, 8292557.2766, 8593186.5077, 8895400.7788, 
9204254.0428, 9505157.5709, 9799301.4089, 10084468.0455, 10362161.0109, 
10623796.504, 10882381.3729, 11131185.0534, 8197200.6444, 8485407.4453, 
8793028.0567, 9102270.5614, 9418306.465, 9726207.7477, 10027192.1371, 
10318990.5604, 10603141.4991, 10870861.5368, 11135460.012, 11390049.8203, 
8383500.6593, 8678257.6128, 8992869.6005, 9309140.3471, 9632358.8856, 
9947257.9225, 10255082.8657, 10553513.0731, 10844121.988, 11117926.5692, 
11388538.6484, 11648914.5899, 8569800.6735, 8871107.7799, 9192711.1487, 
9516010.1349, 9846411.3046, 10168308.0987, 10482973.5998, 10788035.5824, 
11085102.4738, 11364991.6051, 11641617.2821, 11907779.3576, 8756100.6891, 
9063957.9534, 9392552.6936, 9722879.9204, 10060463.7195, 10389358.2746, 
10710864.3279, 11022558.0956, 11326082.9668, 11612056.6477, 11894695.9209, 
12166644.1258, 8942400.705, 9256808.1204, 9592394.2396, 9929749.7037, 
10274516.1413, 10610408.4494, 10938755.0613, 11257080.6099, 11567063.455, 
11859121.6771, 12147774.5594, 12425508.8956, 9128700.7203, 9449658.2901, 
9792235.786, 10136619.4908, 10488568.5635, 10831458.6249, 11166645.792, 
11491603.1215, 11808043.9427, 12106186.7106, 12400853.1962, 12684373.664, 
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11052508.7998, 11394536.5185, 11726125.6341, 12049024.431, 12353251.7462, 
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10550359.0623, 10916673.4022, 11273558.9758, 11622427.2509, 11960648.1479, 
12290004.92, 12600316.7835, 12907010.4688, 13202103.2002, 9687600.7612, 
10028208.7979, 10391760.4269, 10757228.8467, 11130725.8198, 11494609.1532, 
11850317.9787, 12195170.6632, 12530985.4111, 12847381.8161, 13160089.1051, 
13460967.9711), t = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
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9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L), .Label = c("t_m75_tc", "t_m5_tc", "t_m25_tc", "t_p0_tc", 
"t_p25_tc", "t_p5_tc", "t_p75_tc", "t_p10_tc", "t_p125_tc", "t_p15_tc", 
"t_p175_tc", "t_p20_tc"), class = "factor"), p = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 
13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 
14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 
15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 
17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 
21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 
23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 
24L, 24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 
25L, 25L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 
26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 27L, 
27L, 27L, 27L, 27L), .Label = c("h_30", "h_27.5", "h_25", "h_22.5", 
"h_20", "h_17.5", "h_15", "h_12.5", "h_10", "h_7.5", "h_5", "h_2.5", 
"h_1", "h0", "h1", "h2.5", "h5", "h7.5", "h10", "h12.5", "h15", 
"h17.5", "h20", "h22.5", "h25", "h27.5", "h30"), class = "factor"), 
    tt = c(-7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 
    20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 
    -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, -7.5, 
    -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, -7.5, -5, 
    -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, -7.5, -5, -2.5, 
    0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 
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    5, 7.5, 10, 12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 
    7.5, 10, 12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 
    10, 12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 
    12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 
    15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 
    17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 
    20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, 
    -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, -7.5, 
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    0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 
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    10, 12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 
    12.5, 15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 
    15, 17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 
    17.5, 20, -7.5, -5, -2.5, 0, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 
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    -2.5, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
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    47.3773783417225, 51.4356648980012, 55.2592827852321, 59.0383180864958, 
    62.6744081170616)), class = "data.frame", .Names = c("W", 
"t", "p", "tt", "hh", "pChange"), row.names = c(NA, -324L))

我正在尝试绘制带有tt, hh and W列的彩色等高线图，W是z轴。

这是我正在使用的代码::

ggplot(df, aes(x = tt, y = hh, z = W)) +
  stat_contour(geom = "polygon", aes(fill = ..level..) ) +
  geom_tile(aes(fill = W)) +
  stat_contour(bins = 10) +
  xlab("% change in temperature") +
  ylab("% change in ppt") +
  guides(fill = guide_colorbar(title = "W"))

这是我得到的结果：

我想要的是连续的颜色，如here我正在关注this example工作。

这里出了什么问题？

Answer 1

网格间距不均匀。制作均匀间隔网格的一种方法是在均匀间隔的网格上使用loess进行插值：

model <- loess(W ~ tt + hh, data = df)

使用expand.grid：

创建均匀间隔的网格

new.data <- expand.grid(tt = seq(from = min(df$tt), to = max(df$tt), length.out = 500),
                        hh = seq(from = min(df$hh), to = max(df$hh), length.out = 500))

使用该模型预测新数据：

gg <- predict(model, newdata =  new.data)

结合预测和新数据：

new.data = data.frame(W = as.vector(gg),
                      new.data)

现在情节如下：

  ggplot(new.data, aes(x = tt, y = hh, z = W)) +
    stat_contour(geom = "polygon", aes(fill = ..level..) ) +
    geom_tile(aes(fill = W)) +
    stat_contour(bins = 10) +
    xlab("% change in temperature") +
    ylab("% change in ppt") +
    guides(fill = guide_colorbar(title = "W"))

您可能还想检查loess

的某些拟合优度指标

caret::RMSE(model$fitted, df$W)
#output
7498.393

使用较窄的跨度可以提供更好的拟合，尤其是在数据不平滑的情况下：

model2 <- loess(W ~ tt + hh, data = df, span = 0.1) 
caret::RMSE(model2$fitted, df$W)
#output
964.7582

ggplot(new.data2, aes(x = tt, y = hh, z = W)) +
  stat_contour(geom = "polygon", aes(fill = ..level..) ) +
  geom_tile(aes(fill = W)) +
  stat_contour(bins = 10) +
  xlab("% change in temperature") +
  ylab("% change in ppt") +
  guides(fill = guide_colorbar(title = "W"))

差异太小了

ggplot(new.data, aes(x = tt, y = hh, z = W)) +
  geom_tile(aes(fill = W)) +
  geom_contour(aes(x = tt, y = hh, z = W),
               color = "red")+ 
  geom_contour(data = new.data2,
               aes(x = tt, y = hh, z = W),
               color = "white", inherit.aes = FALSE)

编辑：还要查看@Henrik的精彩帖子，评论中链接了他。特别是?akima::interp函数。

EDIT2：回答评论中的问题：

要指定不同的填充，可以使用

scale_fill_gradient
scale_fill_gradient2
scale_fill_gradientn

以下是基于scale_fill_gradientn使用quantiles 5种颜色的示例：

v <- ggplot(new.data2, aes(x = tt, y = hh, z = floor(W))) +
  geom_tile(aes(fill = W), show.legend = FALSE) +
  stat_contour(bins = 10, aes(colour = ..level..)) +
  xlab("% change in temperature") +
  ylab("% change in ppt") +
  guides(fill = guide_colorbar(title = "W")) +
  scale_fill_gradientn(values = scales::rescale(quantile(new.data2$W)),
                       colors = rainbow(5))

我删除了polygon内容，因为它位于geom_tile图层下方并且不可见。

添加直接标签：

library(directlabels)

direct.label(v, list("far.from.others.borders", "calc.boxes", "enlarge.box", 
                     box.color = NA, fill = "transparent", "draw.rects"))

ggplot2中的2d轮廓颜色图

1 个答案: