Adjusting the secondary y axis using ggplot - r

Question

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?
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?
Thank you very much for your support.
R code
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 plot

Answer 1

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))
Here is the 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.
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)"))
Here is the sample output plot
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
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))
(As you can see that with less data point, we started to see the fill as plot have more space)
Zoom in at the end of the data
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))
(Now we can see both the darkblue border and the white fill inside)