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I have looked at similar questions and am trying to follow an example from a climate graph with temperature and precipitation.

I am on the correct track, as both of the axes seem to have the correct limits. For some reason the bar graph of the proportion of the population isn't showing up.

Im not getting any errors, I guess I'm just missing something. Any help appreciated thanks.

Here's my code:

  ## plot per capita spending and old people increase on same plot
  ##calculate a and b for scaling second axis
  ylim.prim=range(TA1_summ$Total.Health.Expenditure.per.Capita.in.Dollars)
  ylim.sec=range(Prop75pls_df$Prop_75pls)
  ## formula to scale axes for double axes
  b <- diff(ylim.prim)/diff(ylim.sec)
  a <- ylim.prim[1] - b*ylim.sec[1]
  ##


    ggplot()+
   geom_point(data=TA1_summ,aes(x=Year,y=Total.Health.Expenditure.per.Capita.in.Dollars))+
    geom_col(data=Prop75pls_df,aes(x=Year,y=Prop_75pls))+
    #
    scale_y_continuous("per capita health care spending (CDN)", sec.axis = sec_axis(~ (. - a)/b, name = "Proportion of population 75+")) +
    theme_bw()

Here's the graph, notice the points for the percapita spending are there, but the bars for the proportion 75+ are not.

enter image description here

Below are the 2 dataframes. Thanks

 > dput(TA1_summ)
structure(list(Year = 1975:2021, Total.Health.Expenditure.in.Millions.of.Dollars = c(12199.4, 
14049.8, 15450, 17106.8, 19169.7, 22298.4, 26276.7, 30759.1, 
34038.6, 36743.1, 39842.4, 43338.1, 46789.2, 50960.1, 56096.2, 
61092.9, 66437.4, 69853.4, 71519.1, 73159.6, 74237.4, 75082.3, 
78741.4, 84066.9, 90467.9, 98609.9, 107201.8, 115055.9, 123591.3, 
131570.3, 140489.5, 151037.6, 162992.3, 171964.7, 182026, 192956, 
199383.5, 207501.6, 212366.5, 218591.2, 228095.8, 237351.6, 246090.5, 
255913.1, 267215.6, 301454.8, 308043.3), Total.Health.Expenditure.per.Capita.in.Dollars = c(527.1, 
599.1, 651.2, 713.9, 792.1, 909.6, 1058.7, 1224.6, 1341.9, 1434.9, 
1541.8, 1660.4, 1769.2, 1902.1, 2056.6, 2206.2, 2369.6, 2462.1, 
2493.3, 2522.7, 2533.5, 2535.7, 2633, 2787.8, 2975.8, 3213.5, 
3455.8, 3668.9, 3905.7, 4119.2, 4357.1, 4637.2, 4955.8, 5172.3, 
5412.8, 5674.4, 5806.3, 5977.4, 6053.3, 6168.4, 6388.7, 6573.1, 
6733.8, 6904.4, 7108, 7931.9, 8018.5), Total.Health.Expenditure.in.Constant.1997.Millions..of.Dollars = structure(c(22L, 
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L), .Label = c("", "100,842.1", "105,685.2", 
"110,371.9", "114,160.9", "119,135.3", "125,204.7", "128,118.1", 
"131,863.3", "137,580.0", "138,330.6", "141,924.8", "142,284.2", 
"144,006.1", "148,241.5", "154,367.0", "158,689.7", "162,505.5", 
"165,289.9", "177,972.0", "180,029.3", "39,684.0", "40,766.5", 
"41,609.5", "42,940.6", "44,203.5", "46,676.2", "51,123.1", "53,484.5", 
"55,455.0", "57,311.8", "59,558.8", "62,192.5", "63,808.3", "66,399.6", 
"69,198.5", "71,242.7", "73,922.6", "75,123.8", "75,381.6", "75,804.7", 
"76,063.2", "76,161.2", "78,741.4", "82,555.7", "86,940.8", "91,035.0", 
"96,707.7"), class = "factor"), Total.Health.Expenditure..per.Capita.in.Constant..1997.Dollars = structure(c(2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
18L, 23L, 24L, 21L, 20L, 19L, 17L, 22L, 25L, 26L, 27L, 28L, 29L, 
30L, 31L, 32L, 33L, 34L, 35L, 36L, 38L, 37L, 41L, 39L, 40L, 42L, 
43L, 44L, 45L, 46L, 47L, 48L), .Label = c("", "1,714.7", "1,738.5", 
"1,753.8", "1,791.9", "1,826.5", "1,903.9", "2,059.8", "2,129.4", 
"2,186.2", "2,238.1", "2,304.7", "2,382.8", "2,412.7", "2,478.4", 
"2,536.9", "2,572.1", "2,572.8", "2,595.8", "2,613.9", "2,627.9", 
"2,633.0", "2,636.6", "2,647.9", "2,737.7", "2,859.8", "2,966.7", 
"3,117.5", "3,215.6", "3,339.8", "3,455.5", "3,540.6", "3,657.7", 
"3,806.9", "3,853.5", "3,921.1", "4,028.3", "4,045.9", "4,055.7", 
"4,063.7", "4,088.4", "4,152.1", "4,275.0", "4,342.3", "4,384.3", 
"4,396.8", "4,682.8", "4,686.3"), class = "factor"), Total.Health.Expenditure.as.a.Percentage.of.GDP = c(7, 
7, 7, 7, 6.8, 7.1, 7.1, 7.9, 8.1, 8, 8, 8.2, 8.1, 8.1, 8.4, 8.8, 
9.5, 9.7, 9.6, 9.2, 8.9, 8.7, 8.7, 8.9, 9, 8.9, 9.4, 9.6, 9.8, 
9.9, 9.9, 10.1, 10.3, 10.4, 11.6, 11.6, 11.2, 11.4, 11.2, 11, 
11.5, 11.7, 11.5, 11.5, 11.6, 13.7, 12.7)), row.names = c(NA, 
47L), na.action = structure(48:105, .Names = c("48", "49", "50", 
"51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", 
"62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", 
"73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", 
"84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", 
"95", "96", "97", "98", "99", "100", "101", "102", "103", "104", 
"105"), class = "omit"), class = "data.frame")


    > dput( Prop75pls_df)
structure(list(Year = c(1975, 1976, 1977, 1978, 1979, 1980, 1981, 
1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 
1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 
2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2021), Prop_75pls = c(2.95766554965851, 
3.00127503220918, 3.05129670837176, 3.11304063523594, 3.18934390585209, 
3.25769147676869, 3.33837995428998, 3.41631016897725, 3.50054745341369, 
3.59622982947609, 3.6940577447635, 3.79014384913296, 3.89859027580637, 
4.00409456787977, 4.10658248874934, 4.2127362212327, 4.28299210465241, 
4.34725032514741, 4.39672019981841, 4.44576578342527, 4.55353117703572, 
4.67241226584368, 4.79907907522439, 4.91693361767564, 5.03853805953953, 
5.15658755870346, 5.67448038744973, 5.78812317405195, 5.91193068088551, 
6.01461053319038, 6.12839020321239, 6.25496029096157, 6.34749433891701, 
6.4232424596923, 6.47606470566458, 6.54317971748121, 6.61162617975518, 
6.67251883104279, 6.73754838318347, 6.81701708941406, 6.89445520796233, 
7.00259186733946, 7.12988965237494, 7.2674973568116, 7.42862666992543, 
7.59432265315821, 7.82893516903733)), class = "data.frame", row.names = c("1975", 
"1976", "1977", "1978", "1979", "1980", "1981", "1982", "1983", 
"1984", "1985", "1986", "1987", "1988", "1989", "1990", "1991", 
"1992", "1993", "1994", "1995", "1996", "1997", "1998", "1999", 
"2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", 
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", 
"2016", "2017", "2018", "2019", "2020", "2021"))
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  • The bars are there. Have a closer look at the zero line. But to make them visible you also have to scale the data by the inverse transformation applied for the second y scale. Commented Nov 26, 2021 at 18:53

1 Answer 1

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You've got to use the same transformation on the data that you did on the axes:

ggplot()+
  geom_col(data=Prop75pls_df,aes(x=Year,y=(a+Prop_75pls*b)), alpha=.25)+
  geom_point(data=TA1_summ,aes(x=Year,y=Total.Health.Expenditure.per.Capita.in.Dollars))+
  #
  scale_y_continuous("per capita health care spending (CDN)", sec.axis = sec_axis(~ (. - a)/b, name = "Proportion of population 75+")) +
  theme_bw()

enter image description here

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1 Comment

OH thanks. I see. This is why people don't think its not that statistically sound using the two axes, it seems a bit dodgy applying the transformation.

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