I'm trying to use the main y-axis for mean_section_eur, and use the second y-axis for incr_eur, currently the output is not what I want since both of the data still use the main y-axis. Here's my data:

tibble::tribble(
                                                      ~Wells_per_section, ~mean_section_eur,        ~incr_eur,
                                                                      1L,  746279.041157111, 746279.041157111,
                                                                      2L,  1431269.95778565, 684990.916628538,
                                                                      3L,  2108357.80794982,  677087.85016417,
                                                                      4L,  2772843.81583265, 664486.007882829,
                                                                      5L,  3405157.94680437, 632314.130971724,
                                                                      6L,  3649485.94300659, 244327.996202213,
                                                                      7L,  3815891.88964587, 166405.946639284,
                                                                      8L,  3954427.79923768, 138535.909591812,
                                                                      9L,  4080577.72763043, 126149.928392747,
                                                                     10L,   4191966.2500121, 111388.522381674,
                                                                     11L,  4296140.38025762,  104174.13024552,
                                                                     12L,  4400781.49373418, 104641.113476554,
                                                                     13L,   4499603.6595165,  98822.165782324,
                                                                     14L,  4594918.07191796, 95314.4124014592,
                                                                     15L,  4685908.80682599, 90990.7349080276,
                                                                     16L,  4768224.63681244, 82315.8299864484
                                                      )
sec.axis = sec_axis( trans=~.*1, name="Second Axis")

My code:

avg_dual_plot <- function(reservoir_model, simulation_case, optimal_spacing,
                          Wells_per_section, well_eur, tolerance){
  avg_section <- qualified_eur %>%
    dplyr::group_by(Wells_per_section) %>%
    dplyr::summarise(mean_section_eur = mean(section_eur)) %>%
    dplyr::mutate(incr_eur = incrementalEUR(mean_section_eur))
  avg_dual_plots <- avg_section %>%
    ggplot2::ggplot(avg_section, mapping = aes(x = Wells_per_section))   
    geom_line(mapping =aes(y = mean_section_eur))  
    geom_line(mapping =aes(y = incr_eur))  
    scale_y_continuous(
      # Features of the first axis
      name = "section eur",
      # Add a second axis and specify its features
      sec.axis = sec_axis(~rescale(., c(0, 500000)), name="incr eur"))
  #return(avg_dual_plots)
  return(avg_section)
}

My code is a function based on other functions, so avg_section is a result from another function, and I'm using the avg_section which is the data I provided to make the plot. The result I'm getting is:

The output I'm expecting:

CodePudding user response：

Remember, when you add a secondary axis to a plot, it is just an inert annotation. It in no way changes the appearance of the lines or points on your plot. If the lines look wrong without a secondary axis, they will look wrong with one too.

What you need to do is multiply (or divide, or otherwise transform) one of your data series so that it is the size you want it on the plot. The secondary axis takes the inverse transformation simply so that we can interpret the numbers of the transformed series correctly.

In your example, the incr_eur line is about one-sixth the vertical size you wanted, so we need to multiply the incr_eur data by 6 to get it the size we want. We then tell sec_axis to show y values that are 1/6 the value of those on the primary y axis:

ggplot(avg_section, mapping = aes(x = Wells_per_section))   
    geom_line(mapping =aes(y = mean_section_eur),
              color = '#ec7e34')  
    geom_point(aes(y = mean_section_eur), color = '#ec7e34')  
    geom_line(mapping = aes(y = incr_eur * 6),
              color = '#2e4a7d')  
  geom_point(aes(y = incr_eur * 6), color = '#2e4a7d')   
    scale_y_continuous(
      labels = scales::comma,
      name = "section eur",
      sec.axis = sec_axis(~.x/6, name="incr eur", labels = scales::comma))  
  lims(x = c(0, 25))  
  theme_light()