2d类别的密度图

本文介绍了2d类别的密度图的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我正在尝试制作一个2d密度图，其中显示每个类别的密度。例如，在下面的图片中，我们每天都有一个密度图，并且所有的每日密度都合并到彩色图中。这些类型的地块在关于大气科学和气溶胶污染研究的科学文献中很常见。

到目前为止，我已经得到了

ggplot（dat，aes（y =`dN / dlogDp`，x = date））+ stat_density2d（geom =tile，aes（fill = ..density ..），contour = FALSE）+ scale_fill_gradient（low =blue，high =red）+ geom_point（alpha = 0.1） + theme_bw（）

但是我希望能够在白天面对它，并且我不确定从哪里开始。

这里是示例数据：

structure（list（date = structure（c（1359244800,1359245400,1359246000， 1359246600， 1359247200,1359247800,1359248400,1359249000,1359249600， 1359250200,13592508 1359251400,135252000，1359252600,1359253200,1359252600,1359253200,1356253800,1359254400,1359255000,1359255600,1359256200,1359256800， 1359257400,1359258000,1359258600,1359259200,1359259800,1359260400， 1359261000,1359261600,1359251600， 1359262200，1359262800,1359263400,1359264000， 1359264600,1359265200,1359265800,1359266400,1359267000,1359267600， 1359268200,1359268800,1359269400,1359270000,1359270600,1359271200， 1359271800,1359272400,1359273000， 1359273600，1359274200,1359274800， 1359275400,1359276000,1359276600,1359277200,1359277800,1359278400， 1359279000,1359279600,1359280200,1359280800,1359281400,1359282000,1359282000， 1359282600,1359283200,1359283800,1359284400,1359284400， 1359285000，1359285600， 1359286200,1359286800,1359287400,1359288000,1359288600,1359289200， 1359289800,1359290400,1359291000,1359291600,1359292200,1359292800， 1359293400,1359294000,1359294600,1359295200,1359295800,1359295003,1359295003， 1359296400， 135929 （POSIXct，POSIXt），tzone = 1359297600，1359298200,1359298800,1359299400,1359300000,1359301200,1359301800,1359302400,1359303000,1359303600,1359303000,1359303600， 1359304200），class = UTC），`dN / dlogDp` = c（49.8,49.275,47.4,47.975,48.625,51.725， 50.7,47.55,45.975,45.35,45.4,47.75,49.625,48.225， 47.65,47.3,48.75,50.075,34.725,42.025,48.825,52.25 54.05,49.15,34.6,34.375,42.85,30.325,43.15,36.875 b $ b 32.85,36.85,35.725,39.8 ，38.65,40.1,42.675,38.5,37.2， 34.425,25.2,14.725,22.675,14.875,37.45,46.025,49.275， 35.425,30,38.9,28.6,41.675,46.05,48.6,62.425 ，62.65， 61.7,49.5,70.05,71.875,59.4,38.525,36.85,25.625,14.675， 14.7,14.6,14.725,15.6,15,14.6,14.75,15.05,14.975， 15.425,15.1,15.95,14.95,15,14.6,14.725,14.85,15.175， 28.95,14.975,14.725,16.6,18.925,53.225,60.2,56.425， 54.55,41.4 ，19.025,19.825,31.875,14.85,16.375,16.65， 34.325），直径= c（14.6,15.1,15.7,16.3,16.8,17.5， 18.1， 21.7,22.5,23.3,24.1,25， 25.9,26.9,27.9,28.9,30,31.1,32.2,33.4,34.6,35.9， 37.2,38.5,40,41.4,42.9,44.5， 46.1,47.8,49.6,51.4， 53.3,55.2,57.3,59.4,61.5,63.8,66.1,68.5,71,73.7， 76.4,79.1,82,85.1,88.2,91.4,94.7， 98.2,101.8,105.5， 109.4,113.4,117.6,121.9,126.3,131,135.8,140.7,145.9， 151.2,156.8,162.5,168.5,174.7,181.1,187.7,19​​4.6,201.7， 209.1,216.7,224.7,232.9,241.4,250.3,259.5,269,278.8， 289,299.6,310.6,322,333.8,346,358.7,371.8,385.4， 399.5， （日期，dN / dlogDp，直径），row.names = c（NA，100L） ，class = c（tbl_df，tbl，data.frame））

更新这个问题n被误导了，我现在认为使用类别与重新创建该情节无关。这些其他问题与重新创建此情节的任务密切相关：

• 然后我们得到每个小时一个单独的密度图，像问题中的示例图一样紧紧地挤在一起。

  I'm trying to make a 2d density plot where the density is displayed for each category. For example, in the image below, we have a density plot for each day, and all the daily densities are combined into the coloured plots. These types of plots are common in the scientific literature on atmospheric sciences and aerosol pollution studies.

  So far I've got this

  ggplot(dat, aes(y = `dN/dlogDp`, x = date)) + stat_density2d(geom="tile", aes(fill = ..density..), contour = FALSE) + scale_fill_gradient(low="blue", high="red") + geom_point(alpha = 0.1) + theme_bw()

  But I want to facet it by day, and I'm not sure where to start.

  Here are the example data:

  structure(list(date = structure(c(1359244800, 1359245400, 1359246000, 1359246600, 1359247200, 1359247800, 1359248400, 1359249000, 1359249600, 1359250200, 1359250800, 1359251400, 1359252000, 1359252600, 1359253200, 1359253800, 1359254400, 1359255000, 1359255600, 1359256200, 1359256800, 1359257400, 1359258000, 1359258600, 1359259200, 1359259800, 1359260400, 1359261000, 1359261600, 1359262200, 1359262800, 1359263400, 1359264000, 1359264600, 1359265200, 1359265800, 1359266400, 1359267000, 1359267600, 1359268200, 1359268800, 1359269400, 1359270000, 1359270600, 1359271200, 1359271800, 1359272400, 1359273000, 1359273600, 1359274200, 1359274800, 1359275400, 1359276000, 1359276600, 1359277200, 1359277800, 1359278400, 1359279000, 1359279600, 1359280200, 1359280800, 1359281400, 1359282000, 1359282600, 1359283200, 1359283800, 1359284400, 1359285000, 1359285600, 1359286200, 1359286800, 1359287400, 1359288000, 1359288600, 1359289200, 1359289800, 1359290400, 1359291000, 1359291600, 1359292200, 1359292800, 1359293400, 1359294000, 1359294600, 1359295200, 1359295800, 1359296400, 1359297000, 1359297600, 1359298200, 1359298800, 1359299400, 1359300000, 1359300600, 1359301200, 1359301800, 1359302400, 1359303000, 1359303600, 1359304200), class = c("POSIXct", "POSIXt"), tzone = "UTC"), `dN/dlogDp` = c(49.8, 49.275, 47.4, 47.975, 48.625, 51.725, 50.7, 47.55, 45.975, 45.35, 45.4, 47.75, 49.625, 48.225, 47.65, 47.3, 48.75, 50.075, 34.725, 42.025, 48.825, 52.25, 54.05, 49.15, 34.6, 34.375, 42.85, 30.325, 43.15, 36.875, 32.85, 36.85, 35.725, 39.8, 38.65, 40.1, 42.675, 38.5, 37.2, 34.425, 25.2, 14.725, 22.675, 14.875, 37.45, 46.025, 49.275, 35.425, 30, 38.9, 28.6, 41.675, 46.05, 48.6, 62.425, 62.65, 61.7, 49.5, 70.05, 71.875, 59.4, 38.525, 36.85, 25.625, 14.675, 14.7, 14.6, 14.725, 15.6, 15, 14.6, 14.75, 15.05, 14.975, 15.425, 15.1, 15.95, 14.95, 15, 14.6, 14.725, 14.85, 15.175, 28.95, 14.975, 14.725, 16.6, 18.925, 53.225, 60.2, 56.425, 54.55, 41.4, 19.025, 19.825, 31.875, 14.85, 16.375, 16.65, 34.325), Diameter = c(14.6, 15.1, 15.7, 16.3, 16.8, 17.5, 18.1, 18.8, 19.5, 20.2, 20.9, 21.7, 22.5, 23.3, 24.1, 25, 25.9, 26.9, 27.9, 28.9, 30, 31.1, 32.2, 33.4, 34.6, 35.9, 37.2, 38.5, 40, 41.4, 42.9, 44.5, 46.1, 47.8, 49.6, 51.4, 53.3, 55.2, 57.3, 59.4, 61.5, 63.8, 66.1, 68.5, 71, 73.7, 76.4, 79.1, 82, 85.1, 88.2, 91.4, 94.7, 98.2, 101.8, 105.5, 109.4, 113.4, 117.6, 121.9, 126.3, 131, 135.8, 140.7, 145.9, 151.2, 156.8, 162.5, 168.5, 174.7, 181.1, 187.7, 194.6, 201.7, 209.1, 216.7, 224.7, 232.9, 241.4, 250.3, 259.5, 269, 278.8, 289, 299.6, 310.6, 322, 333.8, 346, 358.7, 371.8, 385.4, 399.5, 414.2, 429.4, 445.1, 461.4, 478.3, 495.8, 514)), .Names = c("date", "dN/dlogDp", "Diameter"), row.names = c(NA, 100L), class = c("tbl_df", "tbl", "data.frame"))

  UPDATE This question is misguided and I now think that using categories isn't relevant to recreating this plot. These other questions are more closely related to the task of recreating this plot:

  • geom_raster interpolation with log scale
  • Use R to recreate contour plot made in Igor

  And after I asked this question I have been keeping an updated gist of R code that combines details from the answers to these questions, and successfully replicates these plots (example output included in the gist). That gist is here: gist.github/benmarwick/9a54cbd325149a8ff405

  解决方案

  The key steps are to strip away much of the decoration in the panels, and use scale_*_continuous(expand = c(0,0)) to make the density plot fill the entire panel. Here's an example of how to put it together:

  # get the day and hour to use as facet panels dat$day <- as.Date(dat$date) dat$hour <- as.numeric(format(dat$date, "%H")) library(ggplot2) library(viridis) # theme to suppress many details squeeze_grid_theme <- theme_bw() + theme(axis.title = element_blank(), axis.ticks = element_blank(), axis.text = element_blank(), strip.text = element_blank(), strip.background = element_blank(), panel.margin.y = unit(0, "lines"), panel.margin.x = unit(-1,"lines"), panel.border = element_blank(), panel.grid = element_blank(), axis.text.x = element_text(margin=margin(0,0,0,0,"pt")), axis.text.y = element_text(margin=margin(0,0,0,0,"pt"))) p <- ggplot(dat, aes(z = Diameter, y = `dN/dlogDp`, x = date)) + stat_density2d(geom="tile", aes(fill = ..density..), contour = FALSE) + scale_fill_viridis() + geom_point(alpha = 0.1) + facet_grid(~hour) + scale_y_continuous(expand = c(0,0)) + scale_x_datetime(expand = c(0,0)) + squeeze_grid_theme p

  Then we get a separate density plot for each hour, tightly squeezed together like the example plot in the question.