#### [SOLVED] Plot with 2 y axes, one y axis on the left, and another y axis on the right

By lokheart

I need to plot a bar chart showing counts and a line chart showing rate all in one chart, I can do both of them separately, but when I put them together, I scale of the first layer (i.e. the `geom_bar`) is overlapped by the second layer (i.e. the `geom_line`).

Can I move the axis of the `geom_line` to the right?

#### @ambrish dhaka 2019-01-29 17:35:03

It seemingly appears to be a simple question but it boggles around 2 fundamental questions. A) How to deal with a multi-scalar data while presenting in a comparative chart, and secondly, B) whether this can be done without some thumb rule practices of R programming such as i) melting data, ii) faceting, iii) adding another layer to existing one. The solution given below satisfies both the above conditions as it deals data without having to rescale it and secondly, the techniques mentioned are not used.

Here is the result,

#### @Dag Hjermann 2018-12-10 09:54:08

There are common use-cases duel y axes, e.g., the climatograph showing monthly temperature and precipitation. Here is a simple solution, generalized from Megatron's solution by allowing you to set the lower limit of the variables to something else than zero:

Example data:

``````climate <- tibble(
Month = 1:12,
Temp = c(-4,-4,0,5,11,15,16,15,11,6,1,-3),
Precip = c(49,36,47,41,53,65,81,89,90,84,73,55)
)
``````

Set the limits of each axis manually:

``````ylim.prim <- c(0, 180)   # in this example, precipitation
ylim.sec <- c(-4, 18)    # in this example, temperature
``````

The following makes the necessary calculations based on these limits, and makes the plot itself:

``````b <- diff(ylim.prim)/diff(ylim.sec)
a <- b*(ylim.prim[1] - ylim.sec[1])

ggplot(climate, aes(Month, Precip)) +
geom_col() +
geom_line(aes(y = a + Temp*b), color = "red") +
scale_y_continuous("Precipitation", sec.axis = sec_axis(~ (. - a)/b, name = "Temperature")) +
scale_x_continuous("Month", breaks = 1:12) +
ggtitle("Climatogram for Oslo (1961-1990)")
``````

If you want to make sure that the red line corresponds to the right-hand y axis, you can add a `theme` sentence to the code:

``````ggplot(climate, aes(Month, Precip)) +
geom_col() +
geom_line(aes(y = a + Temp*b), color = "red") +
scale_y_continuous("Precipitation", sec.axis = sec_axis(~ (. - a)/b, name = "Temperature")) +
scale_x_continuous("Month", breaks = 1:12) +
theme(axis.line.y.right = element_line(color = "red"),
axis.ticks.y.right = element_line(color = "red"),
axis.text.y.right = element_text(color = "red"),
axis.title.y.right = element_text(color = "red")
) +
ggtitle("Climatogram for Oslo (1961-1990)")
``````

which colors the right-hand axis:

#### @Megatron 2018-08-14 14:42:04

You can create a scaling factor which is applied to the second geom and right y-axis. This is derived from Sebastian's solution.

``````library(ggplot2)

scaleFactor <- max(mtcars\$cyl) / max(mtcars\$hp)

ggplot(mtcars, aes(x=disp)) +
geom_smooth(aes(y=cyl), method="loess", col="blue") +
geom_smooth(aes(y=hp * scaleFactor), method="loess", col="red") +
scale_y_continuous(name="cyl", sec.axis=sec_axis(~./scaleFactor, name="hp")) +
theme(
axis.title.y.left=element_text(color="blue"),
axis.text.y.left=element_text(color="blue"),
axis.title.y.right=element_text(color="red"),
axis.text.y.right=element_text(color="red")
)
``````

Note: using `ggplot2` v3.0.0

#### @Sebastian Rothbucher 2018-03-26 19:05:28

Taking above answers and some fine-tuning (and for whatever it's worth), here is a way of achieving two scales via `sec_axis`:

Assume a simple (and purely fictional) data set `dt`: for five days, it tracks the number of interruptions VS productivity:

``````        when numinter prod
1 2018-03-20        1 0.95
2 2018-03-21        5 0.50
3 2018-03-23        4 0.70
4 2018-03-24        3 0.75
5 2018-03-25        4 0.60
``````

(the ranges of both columns differ by about factor 5).

The following code will draw both series that they use up the whole y axis:

``````ggplot() +
geom_bar(mapping = aes(x = dt\$when, y = dt\$numinter), stat = "identity", fill = "grey") +
geom_line(mapping = aes(x = dt\$when, y = dt\$prod*5), size = 2, color = "blue") +
scale_x_date(name = "Day", labels = NULL) +
scale_y_continuous(name = "Interruptions/day",
sec.axis = sec_axis(~./5, name = "Productivity % of best",
labels = function(b) { paste0(round(b * 100, 0), "%")})) +
theme(
axis.title.y = element_text(color = "grey"),
axis.title.y.right = element_text(color = "blue"))
``````

Here's the result (above code + some color tweaking):

The point (aside from using `sec_axis` when specifying the y_scale is to multiply each value the 2nd data series with 5 when specifying the series. In order to get the labels right in the sec_axis definition, it then needs dividing by 5 (and formatting). So a crucial part in above code is really `*5` in the geom_line and `~./5` in sec_axis (a formula dividing the current value `.` by 5).

In comparison (I don't want to judge the approaches here), this is how two charts on top of one another look like:

You can judge for yourself which one better transports the message (“Don’t disrupt people at work!”). Guess that's a fair way to decide.

The full code for both images (it's not really more than what's above, just complete and ready to run) is here: https://gist.github.com/sebastianrothbucher/de847063f32fdff02c83b75f59c36a7d a more detailed explanation here: https://sebastianrothbucher.github.io/datascience/r/visualization/ggplot/2018/03/24/two-scales-ggplot-r.html

#### @tstenner 2016-10-01 11:09:39

Starting with ggplot2 2.2.0 you can add a secondary axis like this (taken from the ggplot2 2.2.0 announcement):

``````ggplot(mpg, aes(displ, hwy)) +
geom_point() +
scale_y_continuous(
"mpg (US)",
sec.axis = sec_axis(~ . * 1.20, name = "mpg (UK)")
)``````

#### @discipulus 2016-10-27 05:41:12

The downside is, it only can use some formula transformation of current axes not a new variable, for example.

#### @Andreas 2010-06-25 10:40:51

Sometimes a client wants two y scales. Giving them the "flawed" speech is often pointless. But I do like the ggplot2 insistence on doing things the right way. I am sure that ggplot is in fact educating the average user about proper visualization techniques.

Maybe you can use faceting and scale free to compare the two data series? - e.g. look here: https://github.com/hadley/ggplot2/wiki/Align-two-plots-on-a-page

#### @Ken Williams 2012-05-31 22:14:41

I concur with Andreas - sometimes (such as now, for me) a client wants two sets of data on the same plot, and does not want to hear me talk about Plotting Theory. I either have to convince them to not want that anymore (not always a battle I want to wage), or tell them "the plotting package I'm using doesn't support that." So I'm switching away from ggplot today for this particular project. =(

#### @cboettig 2012-08-31 19:10:07

This answer (from the wiki link) is an excellent suggestion, except that both plots are simply labeled "y". One could just hide the y-axis label and use the facet names for the y-axis, but that seems like a sub-par solution. Any hints on how to get independent y-axes names for the top and bottom plots?

#### @colin 2014-11-19 18:15:22

why does a plotting package need to insert its own personal opinions into how it operates? No thank you.

#### @Stingery 2016-02-11 15:33:49

Cannot agree with this comment (re rant). It is very (!) common to condense information as much as possible, e.g. given the strict restrictions imposed by scientific journals etc., in order to bring across the message quickly. Hence, adding a second y axis is being done anyway, and ggplot should, in my opinion, help in doing so.

#### @Thomas Browne 2017-08-25 10:24:32

Amazing how unquestioningly words like "flawed" and "right way" are thrown about as if they weren't based on a theory that is itself actually quite opinionated and dogmatic, but is unthinkingly accepted by far too many people, as can be seen by the fact that this completely unhelpful answer (which throws a link-bone) has 72 upvotes at time of writing. Whe comparing time series, for example, it can be invaluable to have both on the same chart, because correlation of differences is much easier to spot. Just ask the thousands of highly educated finance pros who do this all day every day.

#### @bonna 2017-09-28 13:35:26

I acknowledge and agree with hadley (and others), that separate y-scales are "fundamentally flawed". Having said that – I often wish `ggplot2` had the feature – particularly, when the data is in wide-format and I quickly want to visualise or check the data (i.e. for personal use only).

While the `tidyverse` library makes it fairly easy to convert the data to long-format (such that `facet_grid()` will work), the process is still not trivial, as seen below:

``````library(tidyverse)
df.wide %>%
# Select only the columns you need for the plot.
select(date, column1, column2, column3) %>%
# Create an id column – needed in the `gather()` function.
mutate(id = n()) %>%
# The `gather()` function converts to long-format.
# In which the `type` column will contain three factors (column1, column2, column3),
# and the `value` column will contain the respective values.
# All the while we retain the `id` and `date` columns.
gather(type, value, -id, -date) %>%
# Create the plot according to your specifications
ggplot(aes(x = date, y = value)) +
geom_line() +
# Create a panel for each `type` (ie. column1, column2, column3).
# If the types have different scales, you can use the `scales="free"` option.
facet_grid(type~., scales = "free")
``````

At the time of writing ggplot2 already supported this via `sec_axis`.

#### @Demo 2017-04-05 20:37:05

We definitely could build a plot with dual Y-axises using base R funtion `plot`.

``````# pseudo dataset
df <- data.frame(x = seq(1, 1000, 1), y1 = sample.int(100, 1000, replace=T), y2 = sample(50, 1000, replace = T))

# plot first plot
with(df, plot(y1 ~ x, col = "red"))

# set new plot
par(new = T)

# plot second plot, but without axis
with(df, plot(y2 ~ x, type = "l", xaxt = "n", yaxt = "n", xlab = "", ylab = ""))

# define y-axis and put y-labs
axis(4)
with(df, mtext("y2", side = 4))
``````

#### @user4786271 2017-04-01 20:31:15

For me the tricky part was figuring out the transformation function between the two axis. I used myCurveFit for that.

``````> dput(combined_80_8192 %>% filter (time > 270, time < 280))
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0.9999999999978, 0.99999999999948, 0.99999999311433, 0.99999878195152,
0.47101897739483, 0.93368891853679, 0.99994556595217, 0.7571113417265,
0.93553999975802, 0.99999999998191, 0.99999999999784, 0.99999999999971,
0.99999891129658, 0.99994309267792, 0.46510628979591, 0.93442584181035,
0.99894450514543, 0.99890078483692, 0.76933812306423), receivedPower_dbm = c(-93.023492290586,
-92.388378035287, -92.205716340607, -93.816400586752, -95.023489422885,
-100.86308557253, -98.464763536915, -96.175707680373, -102.06189538385,
-99.716653422746, -93.023492290586, -92.384760627397, -92.205716340607,
-93.816400586752, -95.023489422885, -100.86308557253, -98.464201120719,
-96.175707680373, -102.06189538385, -99.717150021506, -93.022927803442,
-92.404017215549, -92.204561341714, -93.814319484729, -95.016990717792,
-102.01669022332, -98.558088145955, -96.173817001483, -102.07406915124,
-99.71517574876, -93.021813165972, -92.409586309743, -92.20229160243,
-93.805335867418, -96.184419849593, -102.01709540787, -99.728735187547,
-96.163233028048, -99.772547164798, -99.706399753853, -93.024204617071,
-92.745813384859, -92.206884754512, -93.818508150122, -95.027018807793,
-100.87000577258, -98.467607232407, -95.005311380324, -102.04157607608,
-99.724619517, -93.022927803442, -92.404017215549, -92.204561341714,
-93.813803344588, -95.015606885523, -102.0157405687, -98.556982278361,
-96.172566862738, -103.21871579865, -99.714687230796, -93.022787428238,
-92.404017215549, -92.204274688493, -93.813803344588, -95.015606885523,
-102.0157405687, -98.556982278361, -96.172566862738, -103.21784988098,
-99.714687230796, -93.021813165972, -92.409950613665, -92.20229160243,
-93.805838770576, -96.184419849593, -102.02042267497, -99.728735187547,
-96.163233028048, -99.768774335378, -99.706399753853, -93.022228914406,
-92.411048503835, -92.203136463155, -93.807357409082, -95.012865008237,
-102.00985717796, -99.730352912911, -96.165675535906, -100.92744056572,
-99.708301333236, -92.735781110993, -92.408137395049, -92.119533319039,
-94.982938427575, -96.181073124017, -102.03018610927, -99.721633629806,
-97.32940323644, -97.347613268692, -100.87007386786), snr = c(49.848348091678,
57.698190927109, 60.17669971462, 41.529809724535, 31.452202106925,
8.1976890851341, 14.240447804094, 24.122884195464, 6.2202875499406,
10.674183333671, 49.848348091678, 57.746270018264, 60.17669971462,
41.529809724535, 31.452202106925, 8.1976890851341, 14.242292077376,
24.122884195464, 6.2202875499406, 10.672962852322, 49.854827699773,
57.49079026127, 60.192705735317, 41.549715223147, 31.499301851462,
6.2853718719014, 13.937702343688, 24.133388256416, 6.2028757927148,
10.677815810561, 49.867624820879, 57.417115267867, 60.224172277442,
41.635752021705, 24.074540962859, 6.2847854917092, 10.644529778044,
24.19227425387, 10.537686730745, 10.699414795917, 49.84017267426,
53.139646558768, 60.160512118809, 41.509660845114, 31.42665220053,
8.1846370024428, 14.231126423354, 31.584125885363, 6.2494585568733,
10.654622041348, 49.854827699773, 57.49079026127, 60.192705735317,
41.55465351989, 31.509340361646, 6.2867464196657, 13.941251828322,
24.140336174865, 4.765718874642, 10.679016976694, 49.856439162736,
57.49079026127, 60.196678846453, 41.55465351989, 31.509340361646,
6.2867464196657, 13.941251828322, 24.140336174865, 4.7666691818074,
10.679016976694, 49.867624820879, 57.412299088098, 60.224172277442,
41.630930975211, 24.074540962859, 6.279972363168, 10.644529778044,
24.19227425387, 10.546845071479, 10.699414795917, 49.862851240855,
57.397787176282, 60.212457625018, 41.61637603957, 31.529239767749,
6.2952688513108, 10.640565481982, 24.178672145334, 8.0771089950663,
10.694731030907, 53.262541905639, 57.43627424514, 61.382796189332,
31.747253311549, 24.093100244121, 6.2658701281075, 10.661949889074,
18.495227442305, 18.417839037171, 8.1845086722809), frameId = c(15051,
15106, 15165, 15220, 15279, 15330, 15385, 15452, 15511, 15566,
15019, 15074, 15129, 15184, 15239, 15298, 15353, 15412, 15471,
15526, 14947, 14994, 15057, 15112, 15171, 15226, 15281, 15332,
15391, 15442, 14971, 15030, 15085, 15144, 15203, 15262, 15321,
15380, 15435, 15490, 14915, 14978, 15033, 15092, 15147, 15198,
15257, 15312, 15371, 15430, 14975, 15034, 15089, 15140, 15195,
15254, 15313, 15368, 15427, 15478, 14987, 15046, 15105, 15160,
15215, 15274, 15329, 15384, 15447, 15506, 14943, 15002, 15061,
15116, 15171, 15230, 15285, 15344, 15399, 15454, 14971, 15026,
15081, 15136, 15195, 15258, 15313, 15368, 15423, 15478, 15039,
15094, 15149, 15204, 15263, 15314, 15369, 15428, 15487, 15546
), packetOkSinr = c(0.99999999314881, 0.9999999998736, 0.99999999996428,
0.99999952114066, 0.99991568416005, 3.00628034688444e-08,
0.51497487795954, 0.99627877136019, 0, 0.011303253101957,
0.99999999314881, 0.99999999987726, 0.99999999996428, 0.99999952114066,
0.99991568416005, 3.00628034688444e-08, 0.51530974419663,
0.99627877136019, 0, 0.011269851265775, 0.9999999931708,
0.99999999985986, 0.99999999996428, 0.99999952599145, 0.99991770469509,
0, 0.45861812482641, 0.99629897628155, 0, 0.011403119534097,
0.99999999321568, 0.99999999985437, 0.99999999996519, 0.99999954639936,
0.99618434878558, 0, 0.010513119213425, 0.99641022914441,
0.00801687746446111, 0.012011103529927, 0.9999999931195,
0.99999999871861, 0.99999999996428, 0.99999951617905, 0.99991456738049,
2.6525298291169e-08, 0.51328066587104, 0.9999212220316, 0,
0.010777054258914, 0.9999999931708, 0.99999999985986, 0.99999999996428,
0.99999952718674, 0.99991812902805, 0, 0.45929307038653,
0.99631228046814, 0, 0.011436292559188, 0.99999999317629,
0.99999999985986, 0.99999999996428, 0.99999952718674, 0.99991812902805,
0, 0.45929307038653, 0.99631228046814, 0, 0.011436292559188,
0.99999999321568, 0.99999999985437, 0.99999999996519, 0.99999954527918,
0.99618434878558, 0, 0.010513119213425, 0.99641022914441,
0.00821047996950475, 0.012011103529927, 0.99999999319919,
0.99999999985345, 0.99999999996519, 0.99999954188106, 0.99991896371849,
0, 0.010410830482692, 0.996384831822, 9.12484388049251e-09,
0.011877185067536, 0.99999999879646, 0.9999999998562, 0.99999999998077,
0.99992756868677, 0.9962208785486, 0, 0.010971897073662,
0.93214999078663, 0.92943956665979, 2.64925478221656e-08),
snir = c(49.848348091678, 57.698190927109, 60.17669971462,
41.529809724535, 31.452202106925, 8.1976890851341, 14.240447804094,
24.122884195464, 6.2202875499406, 10.674183333671, 49.848348091678,
57.746270018264, 60.17669971462, 41.529809724535, 31.452202106925,
8.1976890851341, 14.242292077376, 24.122884195464, 6.2202875499406,
10.672962852322, 49.854827699773, 57.49079026127, 60.192705735317,
41.549715223147, 31.499301851462, 6.2853718719014, 13.937702343688,
24.133388256416, 6.2028757927148, 10.677815810561, 49.867624820879,
57.417115267867, 60.224172277442, 41.635752021705, 24.074540962859,
6.2847854917092, 10.644529778044, 24.19227425387, 10.537686730745,
10.699414795917, 49.84017267426, 53.139646558768, 60.160512118809,
41.509660845114, 31.42665220053, 8.1846370024428, 14.231126423354,
31.584125885363, 6.2494585568733, 10.654622041348, 49.854827699773,
57.49079026127, 60.192705735317, 41.55465351989, 31.509340361646,
6.2867464196657, 13.941251828322, 24.140336174865, 4.765718874642,
10.679016976694, 49.856439162736, 57.49079026127, 60.196678846453,
41.55465351989, 31.509340361646, 6.2867464196657, 13.941251828322,
24.140336174865, 4.7666691818074, 10.679016976694, 49.867624820879,
57.412299088098, 60.224172277442, 41.630930975211, 24.074540962859,
6.279972363168, 10.644529778044, 24.19227425387, 10.546845071479,
10.699414795917, 49.862851240855, 57.397787176282, 60.212457625018,
41.61637603957, 31.529239767749, 6.2952688513108, 10.640565481982,
24.178672145334, 8.0771089950663, 10.694731030907, 53.262541905639,
57.43627424514, 61.382796189332, 31.747253311549, 24.093100244121,
6.2658701281075, 10.661949889074, 18.495227442305, 18.417839037171,
8.1845086722809), ookSnirBer = c(8.8808636558081e-24, 3.2219795637026e-27,
2.6468895519653e-28, 3.9807779074715e-20, 1.0849324265615e-15,
2.5705217057696e-05, 4.7313805615763e-08, 1.8800438086075e-12,
0.00021005320203921, 1.9147343768384e-06, 8.8808636558081e-24,
3.0694773489537e-27, 2.6468895519653e-28, 3.9807779074715e-20,
1.0849324265615e-15, 2.5705217057696e-05, 4.7223753038869e-08,
1.8800438086075e-12, 0.00021005320203921, 1.9171738578051e-06,
8.8229427230445e-24, 3.9715925056443e-27, 2.6045198111088e-28,
3.9014083702734e-20, 1.0342658440386e-15, 0.00019591630514278,
6.4692014108683e-08, 1.8600094209271e-12, 0.0002140067535655,
1.9074922485477e-06, 8.7096574467175e-24, 4.2779443633862e-27,
2.5231916788231e-28, 3.5761615214425e-20, 1.9750692814982e-12,
0.0001960392878411, 1.9748966344895e-06, 1.7515881895994e-12,
2.2078334799411e-06, 1.8649940680806e-06, 8.954486301678e-24,
3.2021085732779e-25, 2.690441113724e-28, 4.0627628846548e-20,
1.1134484878561e-15, 2.6061691733331e-05, 4.777159157954e-08,
9.4891388749738e-16, 0.00020359398491544, 1.9542110660398e-06,
8.8229427230445e-24, 3.9715925056443e-27, 2.6045198111088e-28,
3.8819641115984e-20, 1.0237769828158e-15, 0.00019562832342849,
6.4455095380046e-08, 1.8468752030971e-12, 0.0010099091367628,
1.9051035165106e-06, 8.8085966897635e-24, 3.9715925056443e-27,
2.594108048185e-28, 3.8819641115984e-20, 1.0237769828158e-15,
0.00019562832342849, 6.4455095380046e-08, 1.8468752030971e-12,
0.0010088638355194, 1.9051035165106e-06, 8.7096574467175e-24,
4.2987746909572e-27, 2.5231916788231e-28, 3.593647329558e-20,
1.9750692814982e-12, 0.00019705170257492, 1.9748966344895e-06,
1.7515881895994e-12, 2.1868296425817e-06, 1.8649940680806e-06,
8.7517439682173e-24, 4.3621551072316e-27, 2.553168170837e-28,
3.6469582463164e-20, 1.0032983660212e-15, 0.00019385229409318,
1.9830820164805e-06, 1.7760568361323e-12, 2.919419915209e-05,
1.8741284335866e-06, 2.8285944348148e-25, 4.1960751547207e-27,
7.8468215407139e-29, 8.0407329049747e-16, 1.9380328071065e-12,
0.00020004849911333, 1.9393279417733e-06, 5.9354475879597e-10,
6.4258355913627e-10, 2.6065221215415e-05), ookSnrBer = c(8.8808636558081e-24,
3.2219795637026e-27, 2.6468895519653e-28, 3.9807779074715e-20,
1.0849324265615e-15, 2.5705217057696e-05, 4.7313805615763e-08,
1.8800438086075e-12, 0.00021005320203921, 1.9147343768384e-06,
8.8808636558081e-24, 3.0694773489537e-27, 2.6468895519653e-28,
3.9807779074715e-20, 1.0849324265615e-15, 2.5705217057696e-05,
4.7223753038869e-08, 1.8800438086075e-12, 0.00021005320203921,
1.9171738578051e-06, 8.8229427230445e-24, 3.9715925056443e-27,
2.6045198111088e-28, 3.9014083702734e-20, 1.0342658440386e-15,
0.00019591630514278, 6.4692014108683e-08, 1.8600094209271e-12,
0.0002140067535655, 1.9074922485477e-06, 8.7096574467175e-24,
4.2779443633862e-27, 2.5231916788231e-28, 3.5761615214425e-20,
1.9750692814982e-12, 0.0001960392878411, 1.9748966344895e-06,
1.7515881895994e-12, 2.2078334799411e-06, 1.8649940680806e-06,
8.954486301678e-24, 3.2021085732779e-25, 2.690441113724e-28,
4.0627628846548e-20, 1.1134484878561e-15, 2.6061691733331e-05,
4.777159157954e-08, 9.4891388749738e-16, 0.00020359398491544,
1.9542110660398e-06, 8.8229427230445e-24, 3.9715925056443e-27,
2.6045198111088e-28, 3.8819641115984e-20, 1.0237769828158e-15,
0.00019562832342849, 6.4455095380046e-08, 1.8468752030971e-12,
0.0010099091367628, 1.9051035165106e-06, 8.8085966897635e-24,
3.9715925056443e-27, 2.594108048185e-28, 3.8819641115984e-20,
1.0237769828158e-15, 0.00019562832342849, 6.4455095380046e-08,
1.8468752030971e-12, 0.0010088638355194, 1.9051035165106e-06,
8.7096574467175e-24, 4.2987746909572e-27, 2.5231916788231e-28,
3.593647329558e-20, 1.9750692814982e-12, 0.00019705170257492,
1.9748966344895e-06, 1.7515881895994e-12, 2.1868296425817e-06,
1.8649940680806e-06, 8.7517439682173e-24, 4.3621551072316e-27,
2.553168170837e-28, 3.6469582463164e-20, 1.0032983660212e-15,
0.00019385229409318, 1.9830820164805e-06, 1.7760568361323e-12,
2.919419915209e-05, 1.8741284335866e-06, 2.8285944348148e-25,
4.1960751547207e-27, 7.8468215407139e-29, 8.0407329049747e-16,
1.9380328071065e-12, 0.00020004849911333, 1.9393279417733e-06,
5.9354475879597e-10, 6.4258355913627e-10, 2.6065221215415e-05
)), class = "data.frame", row.names = c(NA, -100L), .Names = c("run",
"repetition", "module", "configname", "packetByteLength", "numVehicles",
"snr", "frameId", "packetOkSinr", "snir", "ookSnirBer", "ookSnrBer"
))
``````

Finding the transformation function

1. y1 --> y2 This function is used to transform the data of the secondary y axis to be "normalized" according to the first y axis

transformation function: `f(y1) = 0.025*x + 2.75`

1. y2 --> y1 This function is used to transform the break points of the first y axis to the values of the second y axis. Note that the axis are swapped now.

transformation function: `f(y1) = 40*x - 110`

Plotting

Note how the transformation functions are used in the `ggplot` call to transform the data "on-the-fly"

``````ggplot(data=combined_80_8192 %>% filter (time > 270, time < 280), aes(x=time) ) +
stat_summary(aes(y=receivedPower_dbm ), fun.y=mean, geom="line", colour="black") +
stat_summary(aes(y=packetOkSinr*40 - 110 ), fun.y=mean, geom="line", colour="black", position = position_dodge(width=10)) +
scale_x_continuous() +
scale_y_continuous(breaks = seq(-0,-110,-10), "y_first", sec.axis=sec_axis(~.*0.025+2.75, name="y_second") )
``````

The first `stat_summary` call is the one that sets the base for the first y axis. The second `stat_summary` call is called to transform the data. Remember that all of the data will take as base the first y axis. So that data needs to be normalized for the first y axis. To do that I use the transformation function on the data: `y=packetOkSinr*40 - 110`

Now to transform the second axis I use the opposite function within the `scale_y_continuous` call: `sec.axis=sec_axis(~.*0.025+2.75, name="y_second")`.

#### @baptiste 2017-04-01 21:36:53

R can do this sort of thing, `coef(lm(c(-70, -110) ~ c(1,0)))` and `coef(lm(c(1,0) ~ c(-70, -110)))`. You could define a helper function such as `equationise <- function(range = c(-70, -110), target = c(1,0)){ c = coef(lm(target ~ range)) as.formula(substitute(~ a*. + b, list(a=c[[2]], b=c[[1]]))) }`

#### @user4786271 2017-04-02 15:25:40

yeap, I know... just thought the site would be more intuitive

#### @Alessandro Jacopson 2017-02-27 22:15:47

The answer by Hadley gives an interesting reference to Stephen Few's report Dual-Scaled Axes in Graphs Are They Ever the Best Solution?.

I do not know what the OP means with "counts" and "rate" but a quick search gives me Counts and Rates, so I get some data about Accidents in North American Mountaineering1:

``````Years<-c("1998","1999","2000","2001","2002","2003","2004")
Persons.Involved<-c(281,248,301,276,295,231,311)
Fatalities<-c(20,17,24,16,34,18,35)
rate=100*Fatalities/Persons.Involved
df<-data.frame(Years=Years,Persons.Involved=Persons.Involved,Fatalities=Fatalities,rate=rate)
print(df,row.names = FALSE)

Years Persons.Involved Fatalities      rate
1998              281         20  7.117438
1999              248         17  6.854839
2000              301         24  7.973422
2001              276         16  5.797101
2002              295         34 11.525424
2003              231         18  7.792208
2004              311         35 11.254019
``````

And then I tried to do the graph as Few suggested at page 7 of the aforementioned report (and following the request of OP to graph the counts as a bar chart and the rates as a line chart) :

The other less obvious solution, which works only for time series, is to convert all sets of values to a common quantitative scale by displaying percentage differences between each value and a reference (or index) value. For instance, select a particular point in time, such as the first interval that appears in the graph, and express each subsequent value as the percentage difference between it and the initial value. This is done by dividing the value at each point in time by the value for the initial point in time and then multiplying it by 100 to convert the rate to a percentage, as illustrated below.

``````df2<-df
df2\$Persons.Involved <- 100*df\$Persons.Involved/df\$Persons.Involved[1]
df2\$rate <- 100*df\$rate/df\$rate[1]
plot(ggplot(df2)+
geom_bar(aes(x=Years,weight=Persons.Involved))+
geom_line(aes(x=Years,y=rate,group=1))+
theme(text = element_text(size=30))
)
``````

And this is the result:

But I do not like it a lot and I am not able to easily put a legend on it...

1 WILLIAMSON, Jed, et al. Accidents in North American Mountaineering 2005. The Mountaineers Books, 2005.

#### @C.K 2016-03-20 12:26:20

The technical backbone to the solution of this challenge has been provided by Kohske some 3 years ago [KOHSKE]. The topic and the technicalities around its solution have been discussed on several instances here on Stackoverflow [IDs: 18989001, 29235405, 21026598]. So i shall only provide a specific variation and some explanatory walkthrough, using above solutions.

Let us assume we do have some data y1 in group G1 to which some data y2 in group G2 is related in some way, e.g. range/scale transformed or with some noise added. So one wants to plot the data together on one plot with the scale of y1 on the left and y2 on the right.

``````  df <- data.frame(item=LETTERS[1:n],  y1=c(-0.8684, 4.2242, -0.3181, 0.5797, -0.4875), y2=c(-5.719, 205.184, 4.781, 41.952, 9.911 )) # made up!

> df
item      y1         y2
1    A -0.8684 -19.154567
2    B  4.2242 219.092499
3    C -0.3181  18.849686
4    D  0.5797  46.945161
5    E -0.4875  -4.721973
``````

If we now plot our data together with something like

``````ggplot(data=df, aes(label=item)) +
theme_bw() +
geom_segment(aes(x='G1', xend='G2', y=y1, yend=y2), color='grey')+
geom_text(aes(x='G1', y=y1), color='blue') +
geom_text(aes(x='G2', y=y2), color='red') +
theme(legend.position='none', panel.grid=element_blank())
``````

it doesnt align nicely as the smaller scale y1 obviosuly gets collapsed by larger scale y2.

The trick here to meet the challenge is to techncially plot both data sets against the first scale y1 but report the second against a secondary axis with labels showing the original scale y2.

So we build a first helper function CalcFudgeAxis which calculates and collects features of the new axis to be shown. The function can be amended to ayones liking (this one just maps y2 onto the range of y1).

``````CalcFudgeAxis = function( y1, y2=y1) {
Cast2To1 = function(x) ((ylim1[2]-ylim1[1])/(ylim2[2]-ylim2[1])*x) # x gets mapped to range of ylim2
ylim1 <- c(min(y1),max(y1))
ylim2 <- c(min(y2),max(y2))
yf <- Cast2To1(y2)
labelsyf <- pretty(y2)
return(list(
yf=yf,
labels=labelsyf,
breaks=Cast2To1(labelsyf)
))
}
``````

what yields some:

``````> FudgeAxis <- CalcFudgeAxis( df\$y1, df\$y2 )

> FudgeAxis
\$yf
[1] -0.4094344  4.6831656  0.4029175  1.0034664 -0.1009335

\$labels
[1] -50   0  50 100 150 200 250

\$breaks
[1] -1.068764  0.000000  1.068764  2.137529  3.206293  4.275058  5.343822

> cbind(df, FudgeAxis\$yf)
item      y1         y2 FudgeAxis\$yf
1    A -0.8684 -19.154567   -0.4094344
2    B  4.2242 219.092499    4.6831656
3    C -0.3181  18.849686    0.4029175
4    D  0.5797  46.945161    1.0034664
5    E -0.4875  -4.721973   -0.1009335
``````

Now I wraped Kohske's solution in the second helper function PlotWithFudgeAxis (into which we throw the ggplot object and helper object of the new axis):

``````library(gtable)
library(grid)

PlotWithFudgeAxis = function( plot1, FudgeAxis) {
# based on: https://rpubs.com/kohske/dual_axis_in_ggplot2
plot2 <- plot1 + with(FudgeAxis, scale_y_continuous( breaks=breaks, labels=labels))

#extract gtable
g1<-ggplot_gtable(ggplot_build(plot1))
g2<-ggplot_gtable(ggplot_build(plot2))

#overlap the panel of the 2nd plot on that of the 1st plot
pp<-c(subset(g1\$layout, name=="panel", se=t:r))

ia <- which(g2\$layout\$name == "axis-l")
ga <- g2\$grobs[[ia]]
ax <- ga\$children[[2]]
ax\$widths <- rev(ax\$widths)
ax\$grobs <- rev(ax\$grobs)
ax\$grobs[[1]]\$x <- ax\$grobs[[1]]\$x - unit(1, "npc") + unit(0.15, "cm")
g <- gtable_add_cols(g, g2\$widths[g2\$layout[ia, ]\$l], length(g\$widths) - 1)
g <- gtable_add_grob(g, ax, pp\$t, length(g\$widths) - 1, pp\$b)

grid.draw(g)
}
``````

Now all can be put together: Below code shows, how the proposed solution could be used in a day-to-day environment. The plot call now doesnt plot the original data y2 anymore but a cloned version yf (held inside the pre-calculated helper object FudgeAxis), which runs of the scale of y1. The original ggplot objet is then manipulated with Kohske's helper function PlotWithFudgeAxis to add a second axis preserving the scales of y2. It plots as well the manipulated plot.

``````FudgeAxis <- CalcFudgeAxis( df\$y1, df\$y2 )

tmpPlot <- ggplot(data=df, aes(label=item)) +
theme_bw() +
geom_segment(aes(x='G1', xend='G2', y=y1, yend=FudgeAxis\$yf), color='grey')+
geom_text(aes(x='G1', y=y1), color='blue') +
geom_text(aes(x='G2', y=FudgeAxis\$yf), color='red') +
theme(legend.position='none', panel.grid=element_blank())

PlotWithFudgeAxis(tmpPlot, FudgeAxis)
``````

This now plots as desired with two axis, y1 on the left and y2 on the right

Above solution is, to put it straight, a limited shaky hack. As it plays with the ggplot kernel it will throw some warnings that we exchange post-the-fact scales, etc. It has to be handled with care and may produce some undesired behaviour in another setting. As well one may need to fiddle around with the helper functions to get the layout as desired. The placement of the legend is such an issue (it would be placed between the panel and the new axis; this is why I droped it). The scaling / alignment of the 2 axis is as well a bit challenging: The code above works nicely when both scales contain the "0", else one axis gets shifted. So definetly with some opportunities to improve...

In case on wants to save the pic one has to wrap the call into device open / close:

``````png(...)
PlotWithFudgeAxis(tmpPlot, FudgeAxis)
dev.off()
``````

#### @jchaykow 2015-09-27 04:51:43

You can use `facet_wrap(~ variable, ncol= )` on a variable to create a new comparison. It's not on the same axis, but it is similar.

#### @Stas Prihod'co 2015-05-11 04:00:32

The following article helped me to combine two plots generated by ggplot2 on a single row:

Multiple graphs on one page (ggplot2) by Cookbook for R

And here is what the code may look like in this case:

``````p1 <-
ggplot() + aes(mns)+ geom_histogram(aes(y=..density..), binwidth=0.01, colour="black", fill="white") + geom_vline(aes(xintercept=mean(mns, na.rm=T)), color="red", linetype="dashed", size=1) +  geom_density(alpha=.2)

p2 <-
ggplot() + aes(mns)+ geom_histogram( binwidth=0.01, colour="black", fill="white") + geom_vline(aes(xintercept=mean(mns, na.rm=T)), color="red", linetype="dashed", size=1)

multiplot(p1,p2,cols=2)
``````

#### @Danka 2017-07-04 09:00:10

What happened to the multiplot function? I get an error that the function could not be found, despite of the fact that i have ggplot2 library installled and loaded.

#### @Dribbel 2017-07-21 08:01:29

@Danka The multiplot function is a custom function (at the bottom of the linked page).

#### @Tung 2018-07-21 16:32:59

Recently, there are many packages that has more options/features than `multiplot` stackoverflow.com/a/51220506

It's not possible in ggplot2 because I believe plots with separate y scales (not y-scales that are transformations of each other) are fundamentally flawed. Some problems:

• The are not invertible: given a point on the plot space, you can not uniquely map it back to a point in the data space.

• They are relatively hard to read correctly compared to other options. See A Study on Dual-Scale Data Charts by Petra Isenberg, Anastasia Bezerianos, Pierre Dragicevic, and Jean-Daniel Fekete for details.

• They are easily manipulated to mislead: there is no unique way to specify the relative scales of the axes, leaving them open to manipulation. Two examples from the Junkcharts blog: one, two

• They are arbitrary: why have only 2 scales, not 3, 4 or ten?

You also might want to read Stephen Few's lengthy discussion on the topic Dual-Scaled Axes in Graphs Are They Ever the Best Solution?.

#### @KarlP 2010-08-12 20:37:18

Would you mind elaborate Your opinion? Not beeing enlightened , I think its a rather compact way of plotting two independent variables. It is also a feature that seems to be asked for, and it's beein used widely.

#### @Richie Cotton 2010-08-25 13:08:21

@hadley: Mostly I agree, but there is a genuine use for multiple y scales - the use of 2 different units for the same data, e.g., Celsius and Fahrenheit scales on temperature time series.

#### @Nicholas Hamilton 2013-02-13 21:37:10

@Hadley In your opinion. Not in mine, nor many other scientists. Surely this can be achieved by putting a second plot (with a fully transparent background) directly over the first, so they appear as one. I just don't know how to ensure the corners of the bounding boxex are aligned / registered with each other.

@ADP You are welcome to implement it yourself, but given that I don't believe they're useful, I don't have any plans to. (Especially given that it's already trivial by rescaling each series independently before plotting)

#### @sebschub 2014-03-25 07:51:48

@hadley For example, in Walther-Lieth Climate Diagrams, two y axes are commonly used. Since there is a fixed prescription how to do that the possible confusion is minimal...

#### @sebschub 2014-03-25 14:11:32

@hadley I am sorry, I do not see what is problematic with the given climate diagram. Putting temperature and precipitation in one diagram (with the fixed prescription), one gets a quick first guess whether it is humid or arid climate. Or the way around: what would be a better way to visualize temperature, precipitation and their "relation"? Anyway, thanks a lot for your work in ggplot2!

#### @John 2018-07-12 21:39:37

Sometimes you may only be interested in comparing patterns in which the absolute magnitudes are not important. In those cases, scaling both variables (`scale(x)`) will allow you to use standard ggplot syntax.

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