I'm trying to produce a line plot with 2 y axes. I would like to include groups for each Y axis (i.e., Male, Female, All). There should be 6 lines on the plot in total. EASR on AXis 1 and ASR on Axis 2 with Year as the x axis.
plt_EASR_ASR = ggplot(EASR_ASR, aes(x = Year, y = c_rate_AllAges))
geom_line(aes(color = "Type"))
geom_line(aes(y = EASR, color = "EASR"))
scale_x_continuous(breaks = seq(1996,2020,by=1))
labs(x = "Year", y = "Age Standardised Rate Per 100,000 Population", color = "")
scale_color_manual(values = c("orange2", "gray30"))
This is the code I've tried but I can't figure out how to get groups within each Y axis
Any help would be greatly appreciated.
"Year","Sex","CancerSite","EASR","c_rate_AllAges" 1996,"All","Leukaemias",16.4644014652654,13.6876275237177 1996,"Female","Leukaemias",12.9788937078698,12.0597163887387 1996,"Male","Leukaemias",21.3221816440295,15.4473604629304 1997,"All","Leukaemias",16.1304181900654,13.6130969008565 1997,"Female","Leukaemias",13.6990871096256,12.7602310927074 1997,"Male","Leukaemias",19.6538259658305,14.5353479189067 1998,"All","Leukaemias",15.7099429183033,13.235980595107 1998,"Female","Leukaemias",12.6770034224731,11.8267875980969 1998,"Male","Leukaemias",19.944293692904,14.7602020671663 1999,"All","Leukaemias",16.5343082389775,13.8605467325191 1999,"Female","Leukaemias",12.1832360670675,11.4592957845349 1999,"Male","Leukaemias",22.725789023191,16.4577976511727 2000,"All","Leukaemias",15.9486981922539,13.6481965024274 2000,"Female","Leukaemias",12.4588080610428,11.8585457926108 2000,"Male","Leukaemias",21.5410513177349,15.5843557739833 2001,"All","Leukaemias",17.3899183283119,14.8493345444493 2001,"Female","Leukaemias",12.9272492377534,12.2031563216722 2001,"Male","Leukaemias",23.6576342297075,17.7094200555279 2002,"All","Leukaemias",16.342652248546,14.251875246743 2002,"Female","Leukaemias",12.2920630166641,11.7854366219137 2002,"Male","Leukaemias",22.4486690429546,16.9155066337937 2003,"All","Leukaemias",18.8384318858525,16.3559238433462 2003,"Female","Leukaemias",14.1376004765121,13.4959846594374 2003,"Male","Leukaemias",25.3493810717121,19.4414639668462 2004,"All","Leukaemias",16.8847449806357,14.928308715064 2004,"Female","Leukaemias",12.8283318157582,12.3971782960666 2004,"Male","Leukaemias",22.887108143603,17.6571352197312 2005,"All","Leukaemias",17.3223262286823,15.2831591718524 2005,"Female","Leukaemias",12.953461245947,12.4201280065959 2005,"Male","Leukaemias",23.4074022185601,18.3644730330715 2006,"All","Leukaemias",16.2159145501269,14.3580751996883 2006,"Female","Leukaemias",11.4258263330529,11.1740839508551 2006,"Male","Leukaemias",23.1230246136623,17.7773180814718 2007,"All","Leukaemias",15.5548363062569,14.0038684719536 2007,"Female","Leukaemias",11.6685220505893,11.4085180858676 2007,"Male","Leukaemias",20.7487826782262,16.7831002591968 2008,"All","Leukaemias",15.9445080762995,14.3958177170424 2008,"Female","Leukaemias",11.9466644838843,11.6832775090955 2008,"Male","Leukaemias",21.0028913088726,17.2941734935582 2009,"All","Leukaemias",16.6510295938606,15.0232229209274 2009,"Female","Leukaemias",12.5247418708032,12.2595725594469 2009,"Male","Leukaemias",22.4208474149683,17.9702042165603 2010,"All","Leukaemias",15.1844362788918,13.7965109649956 2010,"Female","Leukaemias",11.0402986296808,10.943329764162 2010,"Male","Leukaemias",20.8759969211269,16.8352943115542 2011,"All","Leukaemias",16.5825212501415,15.1323609879432 2011,"Female","Leukaemias",11.6265977143274,11.5035169988277 2011,"Male","Leukaemias",23.2356253141588,18.9861105707505 2012,"All","Leukaemias",14.8398166311995,13.8136103583258 2012,"Female","Leukaemias",10.917884698992,10.8174877846443 2012,"Male","Leukaemias",19.678158008849,16.9945950979517 2013,"All","Leukaemias",15.3664342290484,14.433995908178 2013,"Female","Leukaemias",11.5092416333904,11.5650378326316 2013,"Male","Leukaemias",20.2009806078599,17.474136731254 2014,"All","Leukaemias",15.0768981887139,14.2306829231805 2014,"Female","Leukaemias",11.0073849526538,11.1956438767461 2014,"Male","Leukaemias",19.9480316140044,17.4463611050132 2015,"All","Leukaemias",15.3885098810921,14.7217569328122 2015,"Female","Leukaemias",11.1836648830556,11.4025869754946 2015,"Male","Leukaemias",20.7368901755758,18.2342713129327 2016,"All","Leukaemias",15.304270816888,14.7279219938202 2016,"Female","Leukaemias",11.3364493262975,11.5584166337498 2016,"Male","Leukaemias",20.4067238009368,18.0780002915315 2017,"All","Leukaemias",13.6796869098636,13.3645480017697 2017,"Female","Leukaemias",10.5377741734033,10.8457532770695 2017,"Male","Leukaemias",17.6552137663892,16.020906715146 2018,"All","Leukaemias",13.8134223485237,13.5341387616999 2018,"Female","Leukaemias",10.4671825211221,10.8268990362984 2018,"Male","Leukaemias",17.9647211234894,16.3850811193653 2019,"All","Leukaemias",12.4496132202679,12.3185620412571 2019,"Female","Leukaemias",9.74267026583603,10.0703603939154 2019,"Male","Leukaemias",15.7610080489667,14.6826721562086 2020,"All","Leukaemias",11.4067274612956,11.3794365166484 2020,"Female","Leukaemias",9.07667268404083,9.56873565582258 2020,"Male","Leukaemias",14.3256054709995,13.2822454649011
CodePudding user response：
Perhaps you want to map Sex to an aesthetic to separate the series?
ggplot(EASR_ASR, aes(x = Year, y = c_rate_AllAges))
geom_line(aes(color = "Type", linetype = Sex))
geom_line(aes(y = EASR, color = "EASR", linetype = Sex))
scale_x_continuous(breaks = seq(1996,2020,by=1))
labs(x = "Year", y = "Age Standardised Rate Per 100,000 Population", color = "")
scale_color_manual(values = c("orange2", "gray30"))
Example data
EASR_ASR <- data.frame(
stringsAsFactors = FALSE,
Year = c(1996L,
1996L,1996L,1997L,1997L,1997L,1998L,
1998L,1998L,1999L,1999L,1999L,2000L,2000L,
2000L,2001L,2001L,2001L,2002L,2002L,
2002L,2003L,2003L,2003L,2004L,2004L,2004L,
2005L,2005L,2005L,2006L,2006L,2006L,
2007L,2007L,2007L,2008L,2008L,2008L,
2009L,2009L,2009L,2010L,2010L,2010L,2011L,
2011L,2011L,2012L,2012L,2012L,2013L,
2013L,2013L,2014L,2014L,2014L,2015L,2015L,
2015L,2016L,2016L,2016L,2017L,2017L,
2017L,2018L,2018L,2018L,2019L,2019L,2019L,
2020L,2020L,2020L),
Sex = c("All",
"Female","Male","All","Female","Male",
"All","Female","Male","All","Female",
"Male","All","Female","Male","All","Female",
"Male","All","Female","Male","All",
"Female","Male","All","Female","Male","All",
"Female","Male","All","Female","Male",
"All","Female","Male","All","Female",
"Male","All","Female","Male","All","Female",
"Male","All","Female","Male","All",
"Female","Male","All","Female","Male","All",
"Female","Male","All","Female","Male",
"All","Female","Male","All","Female",
"Male","All","Female","Male","All","Female",
"Male","All","Female","Male"),
CancerSite = c("Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias","Leukaemias","Leukaemias",
"Leukaemias"),
EASR = c(16.46440147,12.97889371,21.32218164,16.13041819,
13.69908711,19.65382597,15.70994292,
12.67700342,19.94429369,16.53430824,12.18323607,
22.72578902,15.94869819,12.45880806,
21.54105132,17.38991833,12.92724924,23.65763423,
16.34265225,12.29206302,22.44866904,
18.83843189,14.13760048,25.34938107,
16.88474498,12.82833182,22.88710814,17.32232623,
12.95346125,23.40740222,16.21591455,
11.42582633,23.12302461,15.55483631,11.66852205,
20.74878268,15.94450808,11.94666448,
21.00289131,16.65102959,12.52474187,22.42084741,
15.18443628,11.04029863,20.87599692,
16.58252125,11.62659771,23.23562531,14.83981663,
10.9178847,19.67815801,15.36643423,
11.50924163,20.20098061,15.07689819,11.00738495,
19.94803161,15.38850988,11.18366488,
20.73689018,15.30427082,11.33644933,20.4067238,
13.67968691,10.53777417,17.65521377,
13.81342235,10.46718252,17.96472112,12.44961322,
9.742670266,15.76100805,11.40672746,
9.076672684,14.32560547),
c_rate_AllAges = c(13.68762752,12.05971639,15.44736046,13.6130969,
12.76023109,14.53534792,13.2359806,
11.8267876,14.76020207,13.86054673,11.45929578,
16.45779765,13.6481965,11.85854579,
15.58435577,14.84933454,12.20315632,17.70942006,
14.25187525,11.78543662,16.91550663,
16.35592384,13.49598466,19.44146397,14.92830872,
12.3971783,17.65713522,15.28315917,
12.42012801,18.36447303,14.3580752,11.17408395,
17.77731808,14.00386847,11.40851809,
16.78310026,14.39581772,11.68327751,17.29417349,
15.02322292,12.25957256,17.97020422,
13.79651096,10.94332976,16.83529431,15.13236099,
11.503517,18.98611057,13.81361036,
10.81748778,16.9945951,14.43399591,11.56503783,
17.47413673,14.23068292,11.19564388,
17.44636111,14.72175693,11.40258698,18.23427131,
14.72792199,11.55841663,18.07800029,
13.364548,10.84575328,16.02090672,13.53413876,
10.82689904,16.38508112,12.31856204,
10.07036039,14.68267216,11.37943652,9.568735656,
13.28224546)
)
CodePudding user response：
Mapping Sex to an aesthetic is probably what you want, but as an exercise, here's how to get closer to recreating the plot above.
library(dplyr)
library(tidyr)
library(ggplot2)
EASR_ASR <- tibble::tribble(
~"Year",~"Sex",~"CancerSite",~"EASR",~"c_rate_AllAges",
1996,"All","Leukaemias",16.4644014652654,13.6876275237177,
1996,"Female","Leukaemias",12.9788937078698,12.0597163887387,
1996,"Male","Leukaemias",21.3221816440295,15.4473604629304,
1997,"All","Leukaemias",16.1304181900654,13.6130969008565,
1997,"Female","Leukaemias",13.6990871096256,12.7602310927074,
1997,"Male","Leukaemias",19.6538259658305,14.5353479189067,
1998,"All","Leukaemias",15.7099429183033,13.235980595107,
1998,"Female","Leukaemias",12.6770034224731,11.8267875980969,
1998,"Male","Leukaemias",19.944293692904,14.7602020671663,
1999,"All","Leukaemias",16.5343082389775,13.8605467325191,
1999,"Female","Leukaemias",12.1832360670675,11.4592957845349,
1999,"Male","Leukaemias",22.725789023191,16.4577976511727,
2000,"All","Leukaemias",15.9486981922539,13.6481965024274,
2000,"Female","Leukaemias",12.4588080610428,11.8585457926108,
2000,"Male","Leukaemias",21.5410513177349,15.5843557739833,
2001,"All","Leukaemias",17.3899183283119,14.8493345444493,
2001,"Female","Leukaemias",12.9272492377534,12.2031563216722,
2001,"Male","Leukaemias",23.6576342297075,17.7094200555279,
2002,"All","Leukaemias",16.342652248546,14.251875246743,
2002,"Female","Leukaemias",12.2920630166641,11.7854366219137,
2002,"Male","Leukaemias",22.4486690429546,16.9155066337937,
2003,"All","Leukaemias",18.8384318858525,16.3559238433462,
2003,"Female","Leukaemias",14.1376004765121,13.4959846594374,
2003,"Male","Leukaemias",25.3493810717121,19.4414639668462,
2004,"All","Leukaemias",16.8847449806357,14.928308715064,
2004,"Female","Leukaemias",12.8283318157582,12.3971782960666,
2004,"Male","Leukaemias",22.887108143603,17.6571352197312,
2005,"All","Leukaemias",17.3223262286823,15.2831591718524,
2005,"Female","Leukaemias",12.953461245947,12.4201280065959,
2005,"Male","Leukaemias",23.4074022185601,18.3644730330715,
2006,"All","Leukaemias",16.2159145501269,14.3580751996883,
2006,"Female","Leukaemias",11.4258263330529,11.1740839508551,
2006,"Male","Leukaemias",23.1230246136623,17.7773180814718,
2007,"All","Leukaemias",15.5548363062569,14.0038684719536,
2007,"Female","Leukaemias",11.6685220505893,11.4085180858676,
2007,"Male","Leukaemias",20.7487826782262,16.7831002591968,
2008,"All","Leukaemias",15.9445080762995,14.3958177170424,
2008,"Female","Leukaemias",11.9466644838843,11.6832775090955,
2008,"Male","Leukaemias",21.0028913088726,17.2941734935582,
2009,"All","Leukaemias",16.6510295938606,15.0232229209274,
2009,"Female","Leukaemias",12.5247418708032,12.2595725594469,
2009,"Male","Leukaemias",22.4208474149683,17.9702042165603,
2010,"All","Leukaemias",15.1844362788918,13.7965109649956,
2010,"Female","Leukaemias",11.0402986296808,10.943329764162,
2010,"Male","Leukaemias",20.8759969211269,16.8352943115542,
2011,"All","Leukaemias",16.5825212501415,15.1323609879432,
2011,"Female","Leukaemias",11.6265977143274,11.5035169988277,
2011,"Male","Leukaemias",23.2356253141588,18.9861105707505,
2012,"All","Leukaemias",14.8398166311995,13.8136103583258,
2012,"Female","Leukaemias",10.917884698992,10.8174877846443,
2012,"Male","Leukaemias",19.678158008849,16.9945950979517,
2013,"All","Leukaemias",15.3664342290484,14.433995908178,
2013,"Female","Leukaemias",11.5092416333904,11.5650378326316,
2013,"Male","Leukaemias",20.2009806078599,17.474136731254,
2014,"All","Leukaemias",15.0768981887139,14.2306829231805,
2014,"Female","Leukaemias",11.0073849526538,11.1956438767461,
2014,"Male","Leukaemias",19.9480316140044,17.4463611050132,
2015,"All","Leukaemias",15.3885098810921,14.7217569328122,
2015,"Female","Leukaemias",11.1836648830556,11.4025869754946,
2015,"Male","Leukaemias",20.7368901755758,18.2342713129327,
2016,"All","Leukaemias",15.304270816888,14.7279219938202,
2016,"Female","Leukaemias",11.3364493262975,11.5584166337498,
2016,"Male","Leukaemias",20.4067238009368,18.0780002915315,
2017,"All","Leukaemias",13.6796869098636,13.3645480017697,
2017,"Female","Leukaemias",10.5377741734033,10.8457532770695,
2017,"Male","Leukaemias",17.6552137663892,16.020906715146,
2018,"All","Leukaemias",13.8134223485237,13.5341387616999,
2018,"Female","Leukaemias",10.4671825211221,10.8268990362984,
2018,"Male","Leukaemias",17.9647211234894,16.3850811193653,
2019,"All","Leukaemias",12.4496132202679,12.3185620412571,
2019,"Female","Leukaemias",9.74267026583603,10.0703603939154,
2019,"Male","Leukaemias",15.7610080489667,14.6826721562086,
2020,"All","Leukaemias",11.4067274612956,11.3794365166484,
2020,"Female","Leukaemias",9.07667268404083,9.56873565582258,
2020,"Male","Leukaemias",14.3256054709995,13.2822454649011)
mxdat <- EASR_ASR %>% filter(Sex!= "All") %>% select(4:5) %>% summarise(across(everything(), max))
rs_fac <- mxdat$c_rate_AllAges/mxdat$EASR
inv_rs_fac <- mxdat$EASR/mxdat$c_rate_AllAges
tmp <- EASR_ASR %>%
filter(Sex %in% c("Male", "Female")) %>%
mutate(EASR = EASR*rs_fac) %>%
pivot_longer(4:5, names_to="vbl", values_to="vals") %>%
rowwise() %>%
mutate(group = paste(Sex, vbl, sep=": ")) %>%
ungroup()
ggplot(tmp, aes(x = Year, y = vals, colour=group, linetype=group))
geom_line()
scale_colour_manual(values=c("orange2", "orange2", "gray30", "gray30"))
scale_linetype_manual(values=c(1,3,1,3))
scale_x_continuous(breaks = seq(1996,2020,by=1))
scale_y_continuous(limits = c(0,20),
sec.axis = sec_axis(trans=~.x*inv_rs_fac,
breaks = round(c(0,5,10,15,20)*inv_rs_fac, 1), name = "EASR"))
labs(x = "Year", y = "Age Standardised Rate Per 100,000 Population",
color = "", linetype="")
theme_bw()
theme(axis.text.x=element_text(angle=45, hjust=1),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
legend.position = c(.5, .15),
legend.justification = "center",
legend.background = element_blank())
guides(col=guide_legend(ncol=2))
Created on 2023-02-03 by the reprex package (v2.0.1)
My answer to this question explains more about double y-axes in ggplot2.