本文介绍了如何按年和月绘制平均密度的处理方法，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
我要在R中绘制以下数据.
I have the following data that I am trying to plot in R..
date year month Zone.B Zone.D
1 2008-May 2008 May 1.5469086 0.9628121
2 2008-June 2008 June 0.5436808 1.3583104
3 2008-July 2008 July 0.5343952 1.0014050
4 2008-Aug 2008 Aug 0.8457998 1.9633247
5 2008-Sept 2008 Sept 1.0564309 1.0598237
6 2008-Nov 2008 Nov 0.8400382 0.6224550
7 2008-Dec 2008 Dec 0.5000899 0.4628020
8 2009-May 2009 May 1.6857730 0.4686881
9 2009-June 2009 June 2.1144817 1.2159128
10 2009-July 2009 July 1.3032429 0.9161256
11 2009-Aug 2009 Aug 1.7283975 1.5130496
12 2009-Sept 2009 Sept 1.1234053 1.5315700
13 2009-Nov 2009 Nov 1.1072778 1.3294973
14 2009-Dec 2009 Dec 1.4293872 1.2318001
15 2010-May 2010 May 1.2573056 2.9030824
16 2010-June 2010 June 0.8183244 1.9133592
17 2010-July 2010 July 1.1637721 1.0880351
18 2010-Aug 2010 Aug 1.2357399 1.4476880
19 2010-Sept 2010 Sept 0.8154475 1.9440145
20 2010-Nov 2010 Nov 0.8625087 1.7255681
21 2010-Dec 2010 Dec 0.7454908 1.8538506
22 2011-May 2011 May 1.0643353 1.9391681
23 2011-June 2011 June 1.6620765 2.2622461
24 2011-July 2011 July 0.8392645 1.4462998
25 2011-Aug 2011 Aug 1.0730535 2.2823350
26 2011-Sept 2011 Sept 1.1551744 1.1851883
27 2011-Nov 2011 Nov 0.6946148 1.1089916
28 2011-Dec 2011 Dec 1.1289277 0.9832297
29 2012-May 2012 May 0.7801685 1.3918411
30 2012-June 2012 June 0.7026750 1.3219030
31 2012-July 2012 July 1.3585219 1.6716370
32 2012-Aug 2012 Aug 1.2826630 1.6898635
33 2012-Sept 2012 Sept 1.8615806 1.2897994
34 2012-Nov 2012 Nov 1.7114777 1.0998009
35 2012-Dec 2012 Dec 0.7149941 0.3424369
36 2013-May 2013 May 1.3469518 5.3418421
37 2013-June 2013 June 3.2474936 3.6502369
38 2013-July 2013 July 1.2859735 0.9634012
39 2013-Aug 2013 Aug 2.2181734 2.5195328
40 2013-Sept 2013 Sept 2.2866214 1.1138549
41 2013-Nov 2013 Nov 0.6300820 0.8241262
42 2013-Dec 2013 Dec 0.8444934 0.5658561
43 2014-May 2014 May 0.5130557 0.7943081
44 2014-June 2014 June 0.2296881 1.7998841
45 2014-July 2014 July 0.7425870 1.1508025
46 2014-Aug 2014 Aug 0.6200843 1.2819195
47 2014-Sept 2014 Sept 0.3960585 1.1619590
48 2014-Nov 2014 Nov 0.3980511 0.7375606
49 2014-Dec 2014 Dec 0.3009843 0.6061867
50 2015-May 2015 May 1.2674316 5.4225521
51 2015-June 2015 June 1.0184140 3.5031324
52 2015-July 2015 July 1.2698522 1.2475438
53 2015-Aug 2015 Aug 1.0985706 1.3307636
54 2015-Sept 2015 Sept 1.1795278 1.1892627
55 2015-Nov 2015 Nov 0.6699403 1.4401562
56 2015-Dec 2015 Dec 0.9199800 1.7972394
57 2016-June 2016 June 0.5443802 2.3273397
58 2016-July 2016 July 0.6212349 0.9363671
59 2016-Aug 2016 Aug 1.2685108 1.7920707
60 2016-Sept 2016 Sept 1.6758284 1.3687859
61 2016-Nov 2016 Nov 0.8589559 1.1374661
62 2016-Dec 2016 Dec 0.8298990 0.8522818
63 2017-June 2017 June 1.2096848 1.8674565
64 2017-July 2017 July 1.7883816 1.5487620
65 2017-Aug 2017 Aug 2.2732680 1.5071044
66 2017-Sept 2017 Sept 2.3455175 1.8381368
67 2017-Nov 2017 Nov 1.7463599 1.9304698
68 2017-Dec 2017 Dec 0.8478681 0.9660615
69 2018-June 2018 June 4.2659266 1.2897004
70 2018-July 2018 July 1.8813193 1.6957090
71 2018-Aug 2018 Aug 3.6125893 1.6265312
72 2018-Sept 2018 Sept 2.5180816 0.9977127
73 2018-Nov 2018 Nov 1.3147816 1.3784422
74 2018-Dec 2018 Dec 1.4117959 1.6234253
Showing 1 to 20 of 74 entries, 5 total columns
我正在尝试按月和年绘制每个区域的均值.
I am trying to plot the means for each Zone by month and year.
我已经尝试过此代码
ggplot(df, aes(x=Month, y=Zone.B)) +
geom_line(aes()) +
geom_point(aes())+
labs(title = "Mean Density", y = "Mean Density (# fish/100m2", x = "Date")+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))
但是我无法弄清楚如何绘制年份，该代码按月绘制均值，但不包含年份.
But I can't figure out how to make it plot the year, that code plots the means by month but doesn't break out the year..
当我按照下面Dave2e的建议尝试aes(x = date，y = Zone.B)时，它会执行此操作..我该如何清理它，以便您可以实际读取日期?
When I try the aes(x=date, y=Zone.B) as Dave2e suggested below it does this.. how can I clean this up so you can actually read the dates?
我正在尝试执行类似的操作.如果我可以在同一图形上绘制两个区域，那就更好了！
I'm trying to do something kind of like this.. If I could plot both zones on the same figure that would be even better!
推荐答案
在R中使用日期时，始终最好将其转换为日期对象并维护它们为日期对象.分组和格式化总是可以在需要时进行.
ggplot也喜欢它的数据是整洁"的格式.因此，"Zone.B"和"Zone.D"应为Zone变量的值.
When working with dates in R it is always best to covert them to a date object and maintain them a date object. Grouping and formatting can always happen when needed.
Also ggplot likes its data is a "tidy" format. Thus "Zone.B" and "Zone.D" should be values of a Zone variable.
下面的代码将date列转换为date对象.然后它将数据框从较宽的格式转换为较长的格式，从而创建Zone变量.然后绘制数据.
最终图形接近您要求的图像.我将其留给以后的问题以完全按照需要修改轴.
The below code with convert the date column to a date object. Then it will convert the dataframe from a wide format to a longer format, creating the Zone variable. The data is then plotted.
The final graph is close to your requested image. I leave it to a future question to modify the axis exactly as desired.
#convert date column to dates
df$date <- as.Date(paste(df$date, 01), "%Y-%b %d")
#convert from wide to long
library(tidyr)
dflong <- pivot_longer(df, cols = starts_with("Zone"), names_to = "Zone", values_to = "value")
library(ggplot2)
ggplot(dflong, aes(x=date, y=value, color=Zone, group=Zone)) +
geom_line(aes()) +
geom_point(aes())+
labs(title = "Mean Density", y = "Mean Density (# fish/100m2", x = "Date")+
scale_x_date(date_breaks = "4 month", date_labels ="%b-%y") +
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
axis.text.x = element_text(angle = 60))
开始数据:
df<-structure(list(date = structure(c(5L, 4L, 3L, 1L, 7L, 6L, 2L,
12L, 11L, 10L, 8L, 14L, 13L, 9L, 19L, 18L, 17L, 15L, 21L, 20L,
16L, 26L, 25L, 24L, 22L, 28L, 27L, 23L, 33L, 32L, 31L, 29L, 35L,
34L, 30L, 40L, 39L, 38L, 36L, 42L, 41L, 37L, 47L, 46L, 45L, 43L,
49L, 48L, 44L, 54L, 53L, 52L, 50L, 56L, 55L, 51L, 60L, 59L, 57L,
62L, 61L, 58L, 66L, 65L, 63L, 68L, 67L, 64L, 72L, 71L, 69L, 74L,
73L, 70L), .Label = c("2008-Aug", "2008-Dec", "2008-July", "2008-June",
"2008-May", "2008-Nov", "2008-Sep", "2009-Aug", "2009-Dec", "2009-July",
"2009-June", "2009-May", "2009-Nov", "2009-Sep", "2010-Aug",
"2010-Dec", "2010-July", "2010-June", "2010-May", "2010-Nov",
"2010-Sep", "2011-Aug", "2011-Dec", "2011-July", "2011-June",
"2011-May", "2011-Nov", "2011-Sep", "2012-Aug", "2012-Dec", "2012-July",
"2012-June", "2012-May", "2012-Nov", "2012-Sep", "2013-Aug",
"2013-Dec", "2013-July", "2013-June", "2013-May", "2013-Nov",
"2013-Sep", "2014-Aug", "2014-Dec", "2014-July", "2014-June",
"2014-May", "2014-Nov", "2014-Sep", "2015-Aug", "2015-Dec", "2015-July",
"2015-June", "2015-May", "2015-Nov", "2015-Sep", "2016-Aug",
"2016-Dec", "2016-July", "2016-June", "2016-Nov", "2016-Sep",
"2017-Aug", "2017-Dec", "2017-July", "2017-June", "2017-Nov",
"2017-Sep", "2018-Aug", "2018-Dec", "2018-July", "2018-June",
"2018-Nov", "2018-Sep"), class = "factor"), year = c(2008L, 2008L,
2008L, 2008L, 2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L,
2009L, 2009L, 2009L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2013L, 2013L, 2013L,
2013L, 2013L, 2013L, 2013L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2017L, 2017L, 2017L,
2017L, 2017L, 2017L, 2018L, 2018L, 2018L, 2018L, 2018L, 2018L
), month = structure(c(5L, 4L, 3L, 1L, 7L, 6L, 2L, 5L, 4L, 3L,
1L, 7L, 6L, 2L, 5L, 4L, 3L, 1L, 7L, 6L, 2L, 5L, 4L, 3L, 1L, 7L,
6L, 2L, 5L, 4L, 3L, 1L, 7L, 6L, 2L, 5L, 4L, 3L, 1L, 7L, 6L, 2L,
5L, 4L, 3L, 1L, 7L, 6L, 2L, 5L, 4L, 3L, 1L, 7L, 6L, 2L, 4L, 3L,
1L, 7L, 6L, 2L, 4L, 3L, 1L, 7L, 6L, 2L, 4L, 3L, 1L, 7L, 6L, 2L
), .Label = c("Aug", "Dec", "July", "June", "May", "Nov", "Sept"
), class = "factor"), Zone.B = c(1.5469086, 0.5436808, 0.5343952,
0.8457998, 1.0564309, 0.8400382, 0.5000899, 1.685773, 2.1144817,
1.3032429, 1.7283975, 1.1234053, 1.1072778, 1.4293872, 1.2573056,
0.8183244, 1.1637721, 1.2357399, 0.8154475, 0.8625087, 0.7454908,
1.0643353, 1.6620765, 0.8392645, 1.0730535, 1.1551744, 0.6946148,
1.1289277, 0.7801685, 0.702675, 1.3585219, 1.282663, 1.8615806,
1.7114777, 0.7149941, 1.3469518, 3.2474936, 1.2859735, 2.2181734,
2.2866214, 0.630082, 0.8444934, 0.5130557, 0.2296881, 0.742587,
0.6200843, 0.3960585, 0.3980511, 0.3009843, 1.2674316, 1.018414,
1.2698522, 1.0985706, 1.1795278, 0.6699403, 0.91998, 0.5443802,
0.6212349, 1.2685108, 1.6758284, 0.8589559, 0.829899, 1.2096848,
1.7883816, 2.273268, 2.3455175, 1.7463599, 0.8478681, 4.2659266,
1.8813193, 3.6125893, 2.5180816, 1.3147816, 1.4117959), Zone.D = c(0.9628121,
1.3583104, 1.001405, 1.9633247, 1.0598237, 0.622455, 0.462802,
0.4686881, 1.2159128, 0.9161256, 1.5130496, 1.53157, 1.3294973,
1.2318001, 2.9030824, 1.9133592, 1.0880351, 1.447688, 1.9440145,
1.7255681, 1.8538506, 1.9391681, 2.2622461, 1.4462998, 2.282335,
1.1851883, 1.1089916, 0.9832297, 1.3918411, 1.321903, 1.671637,
1.6898635, 1.2897994, 1.0998009, 0.3424369, 5.3418421, 3.6502369,
0.9634012, 2.5195328, 1.1138549, 0.8241262, 0.5658561, 0.7943081,
1.7998841, 1.1508025, 1.2819195, 1.161959, 0.7375606, 0.6061867,
5.4225521, 3.5031324, 1.2475438, 1.3307636, 1.1892627, 1.4401562,
1.7972394, 2.3273397, 0.9363671, 1.7920707, 1.3687859, 1.1374661,
0.8522818, 1.8674565, 1.548762, 1.5071044, 1.8381368, 1.9304698,
0.9660615, 1.2897004, 1.695709, 1.6265312, 0.9977127, 1.3784422,
1.6234253)), class = "data.frame", row.names = c(NA, -74L))
这篇关于如何按年和月绘制平均密度的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！