使用ggplot调整次要y轴 [英] Adjusting the secondary y axis using ggplot

问题描述

我正在尝试使用ggplot绘制两个不同的数据集，分别是重建温度(10-16)和木炭数据(0-140)，以及两个不同的时间序列值.这可能吗?

I am trying to graph two different datasets, reconstructed temperatures (10-16) and charcoal data (0-140), with two different time series values, using ggplot. Is this possible?

我使用了此代码(请参阅下文)，但不幸的是，它产生了一个图(请参阅下文)，该图限制了温度重建的可变性.有没有一种方法可以调整y轴，以便我们可以在温度记录中看到更多的可变性?

I used this code (see below) but unfortunately it produced a plot (see below) that limits the variability of the temperature reconstruction. Is there a way to adjust the y axis so we can see more variability in the temperature record?

非常感谢您的支持.

R代码

df <- data.frame(Charfiretempdata$AGETEMPS, Charfiretempdata$FIREAGE, Charfiretempdata$Comp2TEMPS,Charfiretempdata$Char.Acc.Rate..Char...cm.2.yr.1.)

ggplot(df)  + 
  
geom_col(mapping = aes(x = Charfiretempdata$FIREAGE, 
y = Charfiretempdata$Char.Acc.Rate..Char...cm.2.yr.1. * 16/150), size = 2, color = "darkblue", 
fill = "white") +
  
geom_line(mapping = aes(x = Charfiretempdata$AGETEMPS, y = Charfiretempdata$Comp2TEMPS)) + 
  
geom_point(mapping = aes(x = Charfiretempdata$AGETEMPS, y = Charfiretempdata$Comp2TEMPS), size 
= 3, shape = 21, fill = "white")+
  scale_y_continuous(
    name = expression("Temperature ("~degree~"C)"),
    sec.axis = sec_axis(~ . * 150/16 , name = "Charcoal (mm)"))

R图

推荐答案

我创建了一个与您的数据具有相似特征的随机样本数据.

I create a random sample data that would share similar characteristics to your data.

library(dplyr)
library(ggplot2)

set.seed(282930)
df <- tibble(x_axis = c(1400, 1500, 1600, 2000, 2001, 2002, 2003, 2004, 2005, 2006,
                   2007, 2008, 2009, 2010, 2011, 2012, 2013, 2015, 2016, 2017),
             y_axis_1 = runif(20, min = 10, max = 16),
             y_axis_2 = runif(20, min = 0, max = 150))

这是df

> df
# A tibble: 20 x 3
   x_axis y_axis_1 y_axis_2
    <dbl>    <dbl>    <dbl>
 1   1400     15.7     5.28
 2   1500     11.8   141.  
 3   1600     14.5   149.  
 4   2000     11.6   121.  
 5   2001     15.6    37.3 
 6   2002     15.0    72.5 
 7   2003     10.7   130.  
 8   2004     15.4    84.7 
 9   2005     11.5   118.  
10   2006     10.4    17.4 
11   2007     11.3   124.  
12   2008     13.6    22.6 
13   2009     13.0    14.5 
14   2010     15.9   142.  
15   2011     12.3   103.  
16   2012     10.3   131.  
17   2013     12.6    93.6 
18   2015     14.6    12.4 
19   2016     11.4    27.9 
20   2017     15.3   116. 

这是与您类似的ggplot，但轴调整不同

Here is the ggplot similar to your but with the different Axis adjustment

ggplot(df, 
       # as they sharing same X-axis you can define share variable aes in the 
       # main call of ggplot
       aes(x = x_axis))  + 
  geom_col(mapping = 
             # added 10 to 2nd axis value as will scale from 10 instead of 0
             aes(y = (y_axis_2 * 10 / 150) + 10), 
           # the size here is size of the border - and due to the nature of
           # your data, the col suppose to be very thin to match with that one
           # tick on x-axis - so the inner fill is covered by dark blue border
           size = 2, color = "darkblue", 
           # The fill is not really useful as you cannot see it.
           fill = "white") +
  geom_line(mapping = aes(y = y_axis_1)) + 
  geom_point(mapping = aes(y = y_axis_1), size 
             = 3, shape = 21, fill = "white") +
  # Set the main Axis start at 10 instead of 0 so it would allow more zoom into it
  coord_cartesian(ylim = c(10, 20), expand = c(0, 0)) +
  scale_y_continuous(
    name = expression("Temperature ("~degree~"C)"),
    # The calculation of second axis lable is calculate base on 1st axis.
    # and as the 1st axis start at 10, there fore the fomular need to minus 10
    # before multiply back 15 - I keep 150 / 10 so it clear reverse of original 
    # transform of the 2nd axis value above.
    sec.axis = sec_axis(~ (. - 10) * 150 / 10 , name = "Charcoal (mm)"))

这是示例输出图

即使使用y轴，我们也几乎看不到数据末尾的温度，因为末尾有很多数据点.我认为，如果最后不需要全部数据点，那么您可能只需要每10倍就可以获取一次，因为数据在600年的范围内，因此您无需在图表末尾绘制太多细节.而且，如果您需要详细信息，只需单独绘制该时间范围即可

And even with the adjsut y-axis we can hardly see the temperature at the end of the data because there are a lot more data points at the end. I think if you don't need all of data point at the end you may just take every 10 x as the data was on the range of 600 years so you don't need to graph so much details at the end. And if you need details just graph that time frame separately

最后只过滤数据每10年一次

Filter data at the end to only take every 10 year instead

ggplot(df %>% filter(x_axis <= 2000 | x_axis %% 10 == 0),
       aes(x = x_axis)) + 
  # similar code to above but I use geom_bar instead
  geom_bar(mapping = 
             aes(y = (y_axis_2 * 10 / 150) + 10),
           stat = "identity", size = 2, color = "darkblue",
           fill = "white") +
  geom_line(mapping = aes(y = y_axis_1)) + 
  geom_point(mapping = aes(y = y_axis_1), size 
             = 3, shape = 21, fill = "white")+
  scale_y_continuous(
    name = expression("Temperature ("~degree~"C)"),
    sec.axis = sec_axis(~ (. - 10) * 150/10 , name = "Charcoal (mm)")) +
  coord_cartesian(ylim = c(10, 20), expand = c(0, 0))

(如您所见，随着数据点的减少，我们开始看到填充具有更大的空间)

(As you can see that with less data point, we started to see the fill as plot have more space)

放大数据的末尾

ggplot(df %>% filter(x_axis >= 2000),
       aes(x = x_axis)) + 
  # similar code to above but I use geom_bar instead
  geom_bar(mapping = 
             aes(y = (y_axis_2 * 10 / 150) + 10),
           stat = "identity", size = 2, color = "darkblue",
           fill = "white") +
  geom_line(mapping = aes(y = y_axis_1)) + 
  geom_point(mapping = aes(y = y_axis_1), size 
             = 3, shape = 21, fill = "white")+
  scale_y_continuous(
    name = expression("Temperature ("~degree~"C)"),
    sec.axis = sec_axis(~ (. - 10) * 150/10 , name = "Charcoal (mm)")) +
  coord_cartesian(ylim = c(10, 20), expand = c(0, 0))

(现在我们可以同时看到深蓝色边框和白色填充)

(Now we can see both the darkblue border and the white fill inside)

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