Custom heatmap theme in R?

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英文:

Custom heatmap theme in R?

问题

I understand that you're looking for a translation of the content you provided without the code. Here's the translated text:

以下是要翻译的内容：

下面显示的图表中使用了什么主题，或者可以使用什么类似的主题来创建一个吸引人的图表，就像提供的照片中的那个一样？我在互联网上看到了这张照片，但没有提供代码。我已经找到了代码，但目前的主题难以解释。

如上所示，每个“周”和“年龄”组合都有一个单独的方框。非常容易看出某个年龄组在特定周的COVID数字是多少。有人可以告诉我以下主题是什么，或者建议一个类似的主题来改进图表的外观，以适应科学观众吗？

这是我的数据的可重现示例：

在我的图表中，对于“mean_temp” [17.17, 17.6] 和“total_count” [0, 14.5] 的组合，应该有一个单独的方框，其中“total_count”为53.5。具有值的单元格已合并，因此难以解释。我不希望个别方框重叠。每个单元格中有多个观察结果。假设对于“mean_temp”的一个特定范围，与“total_count”的一个范围相结合，我有值5、15、100，所以我必须使用平均数。感谢任何帮助。

英文:

What is the theme used in the graph shown below, or what similar theme can be used to create an appealing graph like the one in the provided photo? I saw the photo on the internet but no code was given. I have already sourced the code, but it's hard to interpret with the current theme.

Custom heatmap theme in R?

As shown above, There is a separate box for each week and age combination. It is very easy to tell what were covid number in a particular week for a certain age group. Can someone tell what's the following theme, or suggest a similar theme to improve the graph's appearance for scientific audience?

Here is the reproducible example of my data

  1. df <- structure(
  2.   list(
  3.     total_count = c(
  4.       10L,
  5.       0L,
  6.       15L,
  7.       0L,
  8.       20L,
  9.       0L,
  10.       0L,
  11.       50L,
  12.       0L,
  13.       6L,
  14.       1L,
  15.       10L,
  16.       7L,
  17.       0L,
  18.       0L,
  19.       29L,
  20.       0L,
  21.       2L,
  22.       11L,
  23.       3L,
  24.       0L,
  25.       12L,
  26.       0L,
  27.       30L,
  28.       0L,
  29.       0L,
  30.       29L,
  31.       44L,
  32.       10L,
  33.       5L,
  34.       2L,
  35.       145L,
  36.       0L,
  37.       70L
  38.     ),
  39.     mean_temp = c(
  40.       18.87,
  41.       18.87,
  42.       18.87,
  43.       18.87,
  44.       18.87,
  45.       18.87,
  46.       18.87,
  47.       18.87,
  48.       18.87,
  49.       21.85,
  50.       21.85,
  51.       21.85,
  52.       21.85,
  53.       21.85,
  54.       21.85,
  55.       21.85,
  56.       21.85,
  57.       21.85,
  58.       17.11,
  59.       17.11,
  60.       17.11,
  61.       17.11,
  62.       17.11,
  63.       17.11,
  64.       17.11,
  65.       17.11,
  66.       18.82,
  67.       18.82,
  68.       18.82,
  69.       18.82,
  70.       18.82,
  71.       18.82,
  72.       18.82,
  73.       18.82
  74.     ),
  75.     lwd_duration = c(
  76.       64.32,
  77.       64.32,
  78.       64.32,
  79.       64.32,
  80.       64.32,
  81.       64.32,
  82.       64.32,
  83.       64.32,
  84.       64.32,
  85.       104.2,
  86.       104.2,
  87.       104.2,
  88.       104.2,
  89.       104.2,
  90.       104.2,
  91.       104.2,
  92.       104.2,
  93.       104.2,
  94.       53.53,
  95.       53.53,
  96.       53.53,
  97.       53.53,
  98.       53.53,
  99.       53.53,
  100.       53.53,
  101.       53.53,
  102.       60.43,
  103.       60.43,
  104.       60.43,
  105.       60.43,
  106.       60.43,
  107.       60.43,
  108.       60.43,
  109.       60.43
  110.     )
  111.   ),
  112.   row.names = c(NA,-34L),
  113.   class = c("tbl_df", "tbl", "data.frame"),
  114.   na.action = structure(
  115.     c(
  116.       `4` = 4L,
  117.       `5` = 5L,
  118.       `6` = 6L,
  119.       `7` = 7L,
  120.       `8` = 8L,
  121.       `9` = 9L,
  122.       `78` = 78L,
  123.       `87` = 87L,
  124.       `96` = 96L,
  125.       `105` = 105L,
  126.       `114` = 114L,
  127.       `123` = 123L,
  128.       `132` = 132L,
  129.       `141` = 141L,
  130.       `150` = 150L,
  131.       `159` = 159L,
  132.       `168` = 168L,
  133.       `177` = 177L,
  134.       `186` = 186L,
  135.       `849` = 849L,
  136.       `850` = 850L,
  137.       `851` = 851L,
  138.       `852` = 852L,
  139.       `891` = 891L,
  140.       `892` = 892L,
  141.       `893` = 893L,
  142.       `894` = 894L,
  143.       `921` = 921L,
  144.       `922` = 922L,
  145.       `923` = 923L,
  146.       `924` = 924L,
  147.       `937` = 937L,
  148.       `938` = 938L,
  149.       `939` = 939L,
  150.       `940` = 940L,
  151.       `969` = 969L,
  152.       `970` = 970L,
  153.       `971` = 971L,
  154.       `972` = 972L,
  155.       `985` = 985L,
  156.       `986` = 986L,
  157.       `987` = 987L,
  158.       `988` = 988L,
  159.       `1017` = 1017L,
  160.       `1018` = 1018L,
  161.       `1019` = 1019L,
  162.       `1020` = 1020L,
  163.       `1033` = 1033L,
  164.       `1034` = 1034L,
  165.       `1035` = 1035L,
  166.       `1036` = 1036L
  167.     ),
  168.     class = "omit"
  169.   )
  170. )

Code used to produce the figure:

  1. dat2 <-
  2.   df %>%
  3.   as_tibble() %>%
  4.   mutate(
  5.     mean_temp = cut_interval(mean_temp, n = 10),
  6.     total_count = cut_interval(total_count, n = 10),
  7.   ) %>%
  8.   group_by(mean_temp, total_count) %>%
  9.   summarize(lwd_duration = mean(lwd_duration))
  10. #> `summarise()` has grouped output by 'mean_temp'. You can override using the
  11. #> `.groups` argument.
  13. ggplot(dat2, aes(mean_temp, total_count)) +
  14.   geom_tile(aes(fill = lwd_duration)) +
  15.   geom_text(aes(label = round(lwd_duration, 1))) +
  16.   scale_fill_gradient(low = "white", high = "red")

Custom heatmap theme in R?

In my graph, I should have a separate box for the mean_temp [17.17, 17.6] and total_count [0, 14.5] combination with total_count 53.5 inside the box. Cells with values have merged, so it's hard to interpret. I don't want individual boxes overlapping. I have multiple observations in each cell. Suppose for one particular range of mean_temp combined with one range of total_count, I have values 5, 15, 100, so I had to use mean numbers. Thanks for any assistance.

答案1

得分: 0

以下是您要翻译的代码部分：

  1. library(ggplot2)
  2. library(dplyr)
  4. df %>%
  5.   mutate(
  6.     mean_temp = cut_interval(mean_temp, n = 10),
  7.     total_count = cut_interval(total_count, n = 10),
  8.   ) %>%
  9.   group_by(mean_temp, total_count) %>%
  10.   summarize(lwd_duration = mean(lwd_duration)) %>%
  11.   ggplot(aes(mean_temp, total_count, fill = lwd_duration)) +
  12.   geom_tile(color = 'white', linewidth = 1.5) +
  13.   geom_text(aes(label = round(lwd_duration, 1))) +
  14.   scale_fill_gradient(low = "white", high = "red") +
  15.   theme(panel.grid.major = element_blank(),
  16.         panel.background = element_rect(fill = 'white'))

请注意，这是R编程语言中的一段代码，用于创建可视化图表。

英文: 
  1. library(ggplot2)
  2. library(dplyr)
  5. df %>%
  6.   mutate(
  7.     mean_temp = cut_interval(mean_temp, n = 10),
  8.     total_count = cut_interval(total_count, n = 10),
  9.   ) %>%
  10.   group_by(mean_temp, total_count) %>%
  11.   summarize(lwd_duration = mean(lwd_duration)) %>%
  12.   ggplot(aes(mean_temp, total_count, fill = lwd_duration)) +
  13.   geom_tile(color = 'white', linewidth = 1.5) +
  14.   geom_text(aes(label = round(lwd_duration, 1))) +
  15.   scale_fill_gradient(low = "white", high = "red") +
  16.   theme(panel.grid.major = element_blank(),
  17.         panel.background = element_rect(fill = 'white'))
  18. #> `summarise()` has grouped output by 'mean_temp'. You can override using the
  19. #> `.groups` argument.

Custom heatmap theme in R?<!-- -->

<sup>Created on 2023-04-19 with reprex v2.0.2</sup>

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  • 本文由 发表于 2023年4月20日 00:45:23
  • 转载请务必保留本文链接：https://go.coder-hub.com/76056985.html
  • dplyr
  • ggplot2
  • r
  • visualization
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