ggplot with 2 y axes on each side and different scales

Question

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

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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

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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

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The following incorporates Dag Hjermann s basic data and programming  improves upon user4786271 s strategy to create a  quot transformation function quot  to optimally combine the plots and data axis  and responds to baptist s note that such a function can be created within R   Climatogram for Oslo  1961-1990  climate  lt - 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     y1 identifies the position  relative to the y1 axis    the locations of the minimum and maximum of the y2 graph   Usually this will be the min and max of y1   y1 lt - c max climate Precip   0    y1 lt - c 150  55   y1 lt - c max climate Precip   min climate Precip      y2 is the Minimum and maximum of the secondary axis data  y2 lt - c max climate Temp   min climate Temp      axis combines y1 and y2 into a dataframe used for regressions  axis lt -cbind y1 y2  axis lt -data frame axis    Regression of Temperature to Precipitation  T2P lt -lm formula   y1   y2  data   axis  T2P summary  lt - summary lm formula   y1   y2  data   axis   T2P summary      Identifies the intercept and slope of regressing Temperature to Precipitation  T2PInt lt -T2P summary coefficients 1  1   T2PSlope lt -T2P summary coefficients 2  1      Regression of Precipitation to Temperature  P2T lt -lm formula   y2   y1  data   axis  P2T summary  lt - summary lm formula   y2   y1  data   axis   P2T summary      Identifies the intercept and slope of regressing Precipitation to Temperature  P2TInt lt -P2T summary coefficients 1  1   P2TSlope lt -P2T summary coefficients 2  1      Create Plot  ggplot climate  aes Month  Precip       geom col       geom line aes y   T2PSlope Temp   T2PInt   color    quot red quot       scale y continuous  quot Precipitation quot   sec axis   sec axis    P2TSlope   P2TInt  name    quot Temperature quot        scale x continuous  quot Month quot   breaks   1 12      theme axis line y right   element line color    quot red quot             axis ticks y right   element line color    quot red quot            axis text y right   element text color    quot red quot             axis title y right   element text color    quot red quot        ggtitle  quot Climatogram for Oslo  1961-1990  quot    Most noteworthy is that a new  quot transformation function quot  works better with just two data points from the data set of each axes   usually the maximum and minimum values of each set  The resulting slopes and intercepts of the two regressions enable ggplot2 to exactly pair the plots of the minimums and maximums of each axis  As user4786271 pointed out  the two regressions transform each data set and plot to the other  One transforms the break points of the first y axis to the values of the second y axis  The second transforms the data of the secondary y axis to be  quot normalized quot  according to the first y axis  The following output shows how the axis align the minimums and maximums of each dataset   Having the maximums and minimums match may be most appropriate  however  another benefit of this method is that the plot associated with the secondary axis can be easily shifted  if desired  by altering a programming line related to the primary axis data  The output below simply changes the minimum precipitation input in the programming line of y1 to  quot 0 quot   and thus aligns the minimum Temperature level with the  quot 0 quot  Precipitation level  From  y1 lt - c max climate Precip   min climate Precip    To  y1 lt - c max climate Precip   0    Notice how the resulting new regressions and ggplot2 automatically adjusted the plot and axis to correctly align the minimum Temperature to the new  quot base quot  of the  quot 0 quot  Precipitation level  Likewise  one is easily able to elevate the Temperature plot so that it is more obvious  The following graph is created by simply changing the above-noted line to   quot y1 lt - c 150  55   quot  The above line tells the maximum of the Temperature graph to coincide with the  quot 150 quot  Precipitation level  and the minimum of the temperature line to coincide with the  quot 55 quot  Precipitation level  Again  notice how ggplot2 and the resulting new regression outputs enable the graph to maintain correct alignment with the axis   The above may not be a desirable output  however  it is an example of how the graph can be easily manipulated and still have correct relationships between the plots and the axis  The incorporation of Dag Hjermann s theme improves identification of the axis corresponding to the plot

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Here are my two cents on how to do the transformations for secondary axis  First  you want to couple the the ranges of the primary and secondary data  This is usually messy in terms of polluting your global environment with variables you don t want  To make this easier  we ll make a function factory that produces two functions  wherein scales  rescale   does all the heavy lifting  Because these are closures  they are aware of the environment in which they were created  so they  have a memory  of the to and from parameters generated before creation   One functions does the forward transformation  transforms the secondary data to the primary scale  The second function does the reverse transformation  transforms data in primary units to secondary units   library ggplot2  library scales     Function factory for secondary axis transforms train sec  lt - function primary  secondary      from  lt - range secondary    to    lt - range primary      Forward transform for the data   forward  lt - function x        rescale x  from   from  to   to          Reverse transform for the secondary axis   reverse  lt - function x        rescale x  from   to  to   from        list fwd   forward  rev   reverse     This seems all rather complicated  but making the function factory makes all the rest easier  Now  before we make a plot  we ll produce the relevant functions by showing the factory the primary and secondary data  We ll use the economics dataset which has very different ranges for the unemploy and psavert columns  sec  lt - with economics  train sec unemploy  psavert    Then we use y   sec fwd psavert  to rescale the secondary data to primary axis  and specify   sec rev    as the transformation argument to the secondary axis  This gives us a plot where the primary and secondary ranges occupy the same space on the plot  ggplot economics  aes date       geom line aes y   unemploy   colour    quot blue quot       geom line aes y   sec fwd psavert    colour    quot red quot       scale y continuous sec axis   sec axis  sec rev     name    quot psavert quot      The factory is slightly more flexible than that  because if you simply want to rescale the maximum  you can pass in data that has the lower limit at 0    Rescaling the maximum sec  lt - with economics  train sec c 0  max unemploy                                     c 0  max psavert      ggplot economics  aes date       geom line aes y   unemploy   colour    quot blue quot       geom line aes y   sec fwd psavert    colour    quot red quot       scale y continuous sec axis   sec axis  sec rev     name    quot psavert quot      Created on 2021-02-05 by the reprex package  v0 3 0  I admit the difference in this example is not that very obvious  but if you look closely you can see that the maxima are the same and the red line goes lower than the blue one

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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  lt -    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  lt -    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

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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  lt - 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

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We definitely could build a plot with dual Y-axises using base R funtion plot          pseudo dataset df  lt - 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

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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   gt         Select only the columns you need for the plot      select date  column1  column2  column3    gt         Create an id column     needed in the  gather    function      mutate id   n      gt         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    gt         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

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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 lt -c  1998   1999   2000   2001   2002   2003   2004   Persons Involved lt -c 281 248 301 276 295 231 311  Fatalities lt -c 20 17 24 16 34 18 35  rate 100 Fatalities Persons Involved df lt -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 lt -df df2 Persons Involved  lt - 100 df Persons Involved df Persons Involved 1  df2 rate  lt - 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

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I found this answer helped me the most  but found that there were some edge cases that it didn t seem to handle correctly  in particular negative cases  and also the case where my limits had 0 distance  which can happen if we are grabbing our limits from max min of data   Testing seems to indicate that this works consistently  I use the following code  Here I assume we have  x1 x2  that we want to transform to  y1 y2   The way I handled this was to transform  x1 x2  to  0 1   a simple enough transformaton   then  0 1  to  y1 y2    climate  lt - 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  lt - c 0  180      in this example  precipitation ylim sec  lt - c -4  18       in this example  temperature      b  lt - diff ylim sec  diff ylim prim    If all values are the same this messes up the transformation  so we need to modify it here if b  0     ylim sec  lt - c ylim sec 1 -1  ylim sec 2  1    b  lt - diff ylim sec  diff ylim prim    if  is na b      ylim prim  lt - c ylim prim 1 -1  ylim prim 2  1    b  lt - diff ylim sec  diff ylim prim      ggplot climate  aes Month  Precip       geom col       geom line aes y   ylim prim 1   Temp-ylim sec 1   b   color    red       scale y continuous  Precipitation   sec axis   sec axis     -ylim prim 1    b    ylim sec 1    name    Temperature    limits   ylim prim      scale x continuous  Month   breaks   1 12      ggtitle  Climatogram for Oslo  1961-1990        The key parts here are that we transform the secondary y axis with     -ylim prim 1    b    ylim sec 1   and then apply the inverse to the actual values y   ylim prim 1   Temp-ylim sec 1   b   We should also ensure that limits   ylim prim

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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    For those interested in knowing more about this method  please follow the link below  How to plot a 2- y axis chart with bars side by side without re-scaling the data

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There are common use-cases dual 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  lt - 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 following two values to values close to the limits of the data  you can play around with these to adjust the positions of the graphs  the axes will still be correct   ylim prim  lt - c 0  180      in this example  precipitation ylim sec  lt - c -4  18       in this example  temperature  The following makes the necessary calculations based on these limits  and makes the plot itself  b  lt - diff ylim prim  diff ylim sec  a  lt - ylim prim 1  - b ylim sec 1     there was a bug here  ggplot climate  aes Month  Precip       geom col       geom line aes y   a   Temp b   color    quot red quot       scale y continuous  quot Precipitation quot   sec axis   sec axis      - a  b  name    quot Temperature quot        scale x continuous  quot Month quot   breaks   1 12      ggtitle  quot Climatogram for Oslo  1961-1990  quot       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    quot red quot       scale y continuous  quot Precipitation quot   sec axis   sec axis      - a  b  name    quot Temperature quot        scale x continuous  quot Month quot   breaks   1 12      theme axis line y right   element line color    quot red quot             axis ticks y right   element line color    quot red quot            axis text y right   element text color    quot red quot             axis title y right   element text color    quot red quot                 ggtitle  quot Climatogram for Oslo  1961-1990  quot    which colors the right-hand axis

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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

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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  lt - 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    gt  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  lt - c min y1  max y1     ylim2  lt - c min y2  max y2         yf  lt - Cast2To1 y2    labelsyf  lt - pretty y2      return list      yf yf      labels labelsyf      breaks Cast2To1 labelsyf           what yields some    gt  FudgeAxis  lt - CalcFudgeAxis  df y1  df y2     gt  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    gt  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  lt - plot1   with FudgeAxis  scale y continuous  breaks breaks  labels labels       extract gtable   g1 lt -ggplot gtable ggplot build plot1     g2 lt -ggplot gtable ggplot build plot2       overlap the panel of the 2nd plot on that of the 1st plot   pp lt -c subset g1 layout  name   panel   se t r     g lt -gtable add grob g1  g2 grobs  which g2 layout name   panel      pp t  pp l  pp b pp l     ia  lt - which g2 layout name     axis-l     ga  lt - g2 grobs  ia     ax  lt - ga children  2     ax widths  lt - rev ax widths    ax grobs  lt - rev ax grobs    ax grobs  1   x  lt - ax grobs  1   x - unit 1   npc     unit 0 15   cm     g  lt - gtable add cols g  g2 widths g2 layout ia    l   length g widths  - 1    g  lt - 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  lt - CalcFudgeAxis  df y1  df y2    tmpPlot  lt - 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

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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

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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

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For me the tricky part was figuring out the transformation function between the two axis  I used myCurveFit for that    gt  dput combined 80 8192   gt   filter  time  gt  270  time  lt  280   structure list run   c 268L  268L  268L  268L  268L  268L  268L   268L  268L  268L  263L  263L  263L  263L  263L  263L  263L  263L   263L  263L  269L  269L  269L  269L  269L  269L  269L  269L  269L   269L  261L  261L  261L  261L  261L  261L  261L  261L  261L  261L   267L  267L  267L  267L  267L  267L  267L  267L  267L  267L  265L   265L  265L  265L  265L  265L  265L  265L  265L  265L  266L  266L   266L  266L  266L  266L  266L  266L  266L  266L  262L  262L  262L   262L  262L  262L  262L  262L  262L  262L  264L  264L  264L  264L   264L  264L  264L  264L  264L  264L  260L  260L  260L  260L  260L   260L  260L  260L  260L  260L   repetition   c 8L  8L  8L  8L   8L  8L  8L  8L  8L  8L  3L  3L  3L  3L  3L  3L  3L  3L  3L  3L   9L  9L  9L  9L  9L  9L  9L  9L  9L  9L  1L  1L  1L  1L  1L  1L   1L  1L  1L  1L  7L  7L  7L  7L  7L  7L  7L  7L  7L  7L  5L  5L   5L  5L  5L  5L  5L  5L  5L  5L  6L  6L  6L  6L  6L  6L  6L  6L   6L  6L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  4L  4L  4L  4L   4L  4L  4L  4L  4L  4L  0L  0L  0L  0L  0L  0L  0L  0L  0L  0L    module   structure c 1L  1L  1L  1L  1L  1L  1L  1L  1L  1L   1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L   1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L   1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L   1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L   1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L   1L  1L  1L  1L  1L  1L  1L  1L  1L  1L    Label    scenario node 0  nicVLCTail phyVLC   class    factor         configname   structure c 1L  1L  1L  1L  1L  1L  1L  1L       1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L       1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L       1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L       1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L       1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L       1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L  1L       1L  1L    Label    Road-Vlc   class    factor    packetByteLength   c 8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L       8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L  8192L        numVehicles   c 2L  2L  2L  2L  2L  2L  2L  2L  2L  2L       2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L       2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L       2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L       2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L       2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L       2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L  2L        dDistance   c 80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L  80L       80L  80L  80L  80L  80L  80L  80L  80L   time   c 270 166006903445       271 173853699836  272 175873251122  273 177524313334  274 182946177105       275 188959464989  276 189675339937  277 198250244799  278 204619457189       279 212562800009  270 164199199177  271 168527215152  272 173072994958       273 179210429715  274 184351047337  275 18980754378  276 194816792995       277 198598277809  278 202398083519  279 210634593917  270 210674322891       271 212395107473  272 218871923292  273 219060500457  274 220486359614       275 22401452372  276 229646658839  277 231060448138  278 240407241942       279 2437126347  270 283554249858  271 293168593832  272 298574288769       273 304413221348  274 306272082517  275 309023049011  276 317805897347       277 324403550028  278 332855848701  279 334046374594  270 118608539613       271 127947700074  272 133887145863  273 135726000491  274 135994529981       275 136563912708  276 140120735361  277 144298344151  278 146885137621       279 147552358659  270 206015567272  271 214618077209  272 216566814903       273 225435592582  274 234014573683  275 242949179958  276 248417809711       277 248800670023  278 249750333404  279 252926560188  270 217182684494       271 218357511397  272 224698488895  273 231112784327  274 238740508457       275 242715184122  276 249053562718  277 250325509798  278 258488063493       279 261141590137  270 282904173953  271 284689544638  272 294220723234       273 299749415592  274 30628880553  275 312075103126  276 31579134717       277 321905523606  278 326305136748  279 333056502253  270 258991527456       271 260224091407  272 270076810133  273 27052037648  274 274119348094       275 280808254502  276 286353887245  277 287064312339  278 294444793276       279 296772014594  270 333066283904  271 33877455992  272 345842319903       273 350858180493  274 353972278505  275 360454510107  276 365088896161       277 369166956941  278 372571708911  279 38017503079   distanceToTx   c 80 255266401689       80 156059067023  79 98823695539  79 826647129071  79 76678667135       79 788239825292  79 734539327997  79 74766421514  79 801243848241       79 765920888341  80 255266401689  80 15850240049  79 98823695539       79 826647129071  79 76678667135  79 788239825292  79 735078924078       79 74766421514  79 801243848241  79 764622734914  80 251248121732       80 146436869316  79 984682320466  79 82292012342  79 761908518748       79 796988776281  79 736920997657  79 745038376718  79 802638836686       79 770029970452  80 243475525691  80 127918207499  79 978303140866       79 816259117883  79 749322030693  79 809916018889  79 744456560867       79 738655068783  79 788697533211  79 784288359619  80 260412958482       80 168426829066  79 992034911214  79 830845773284  79 7756751763       79 778156038931  79 732399593756  79 752769548846  79 799967731078       79 757585110481  80 251248121732  80 146436869316  79 984682320466       79 822062073459  79 75884601899  79 801590491435  79 738335109094       79 74347007248  79 803215965043  79 771471198955  80 250257298678       80 146436869316  79 983831684476  79 822062073459  79 75884601899       79 801590491435  79 738335109094  79 74347007248  79 803849157574       79 771471198955  80 243475525691  80 130180105198  79 978303140866       79 816881283718  79 749322030693  79 80984572883  79 744456560867       79 738655068783  79 790548644175  79 784288359619  80 246349000313       80 137056554491  79 980581246037  79 818924707937  79 753176142361       79 808777040341  79 741609845588  79 740770913572  79 796316397253       79 777593733292  80 238796415443  80 119021911134  79 974810568944       79 814065350562  79 743657315504  79 810146783217  79 749945098869       79 737122584544  79 781650522348  79 791554933936   headerNoError   c 0 99999999989702       0 9999999999981  0 99999999999946  0 9999999928026  0 99999873265475       0 77080141574964  0 99007491438593  0 99994396605059  0 45588747062284       0 93484381262491  0 99999999989702  0 99999999999816  0 99999999999946       0 9999999928026  0 99999873265475  0 77080141574964  0 99008458785106       0 99994396605059  0 45588747062284  0 93480223051707  0 99999999989735       0 99999999999789  0 99999999999946  0 99999999287551  0 99999876302649       0 46903147501117  0 98835168988253  0 99994427085086  0 45235035271542       0 93496741877335  0 99999999989803  0 99999999999781  0 99999999999948       0 99999999318224  0 99994254156311  0 46891362282273  0 93382613917348       0 99994594904099  0 93002915596843  0 93569767251247  0 99999999989658       0 99999999998074  0 99999999999946  0 99999999272802  0 99999871586781       0 76935240919896  0 99002587758346  0 99999881589732  0 46179415706093       0 93417422376389  0 99999999989735  0 99999999999789  0 99999999999946       0 99999999289347  0 99999876940486  0 46930769326427  0 98837353639905       0 99994447154714  0 16313586712094  0 93500824170148  0 99999999989744       0 99999999999789  0 99999999999946  0 99999999289347  0 99999876940486       0 46930769326427  0 98837353639905  0 99994447154714  0 16330039178981       0 93500824170148  0 99999999989803  0 99999999999781  0 99999999999948       0 99999999316541  0 99994254156311  0 46794586553266  0 93382613917348       0 99994594904099  0 9303627789484  0 93569767251247  0 99999999989778       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 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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     dDistance    time    distanceToTx    headerNoError    receivedPower dbm     snr    frameId    packetOkSinr    snir    ookSnirBer    ookSnrBer         Finding the transformation function   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     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   gt   filter  time  gt  270  time  lt  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

[r] ggplot with 2 y axes on each side and different scales

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