How to make a 2-axis bar plot in R-CodePudding

I'm attempting to visually compare the counts of 2 species on a single plot, for multiple years in R. I saw this plot online and am hoping to create something similar in R, but haven't found much online that helps.

My data:

> head(df2)
# A tibble: 6 x 4
# Groups:   Zone, Year [6]
   Year Zone      weighted_den weighted_den_carid
  <dbl> <fct>            <dbl>              <dbl>
1  2009 West           0.00109           0.485   
2  2009 Rankin         0                 0.0869  
3  2009 Whipray        0                 0.000176
4  2009 Crocodile      0                 0.0213  
5  2010 West           0.00245           2.26    
6  2010 Rankin         0                 0

My attempt:

ggplot(df2, aes(x=Zone))   
  geom_bar(aes(y=weighted_den_carid), col = 'red')  
  geom_bar(aes(y=weighted_den), col = 'blue')  
  scale_y_continuous("Carid", sec.axis = sec_axis(~ . *100, name = "Cneb"))  
  facet_wrap(~Year)

dput format of my data:

dput(df) structure(list(Year = c(2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2021, 2021, 2021, 2021, 2021, 2021, 2021, 2021), Zone = structure(c(3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L, 3L, 3L, 2L, 2L, 4L, 4L, 1L, 1L), .Label = c("Crocodile", "Rankin", "West", "Whipray"), class = "factor"), code = c("2009-West", "2009-West", "2009-Rankin", "2009-Rankin", "2009-Whipray", "2009-Whipray", "2009-Crocodile", "2009-Crocodile", "2010-West", "2010-West", "2010-Rankin", "2010-Rankin", "2010-Whipray", "2010-Whipray", "2010-Crocodile", "2010-Crocodile", "2011-West", "2011-West", "2011-Rankin", "2011-Rankin", "2011-Whipray", "2011-Whipray", "2011-Crocodile", "2011-Crocodile", "2012-West", "2012-West", "2012-Rankin", "2012-Rankin", "2012-Whipray", "2012-Whipray", "2012-Crocodile", "2012-Crocodile", "2013-West", "2013-West", "2013-Rankin", "2013-Rankin", "2013-Whipray", "2013-Whipray", "2013-Crocodile", "2013-Crocodile", "2014-West", "2014-West", "2014-Rankin", "2014-Rankin", "2014-Whipray", "2014-Whipray", "2014-Crocodile", "2014-Crocodile", "2015-West", "2015-West", "2015-Rankin", "2015-Rankin", "2015-Whipray", "2015-Whipray", "2015-Crocodile", "2015-Crocodile", "2016-West", "2016-West", "2016-Rankin", "2016-Rankin", "2016-Whipray", "2016-Whipray", "2016-Crocodile", "2016-Crocodile", "2017-West", "2017-West", "2017-Rankin", "2017-Rankin", "2017-Whipray", "2017-Whipray", "2017-Crocodile", "2017-Crocodile", "2018-West", "2018-West", "2018-Rankin", "2018-Rankin", "2018-Whipray", "2018-Whipray", "2018-Crocodile", "2018-Crocodile", "2019-West", "2019-West", "2019-Rankin", "2019-Rankin", "2019-Whipray", "2019-Whipray", "2019-Crocodile", "2019-Crocodile", "2020-West", "2020-West", "2020-Rankin", "2020-Rankin", "2020-Whipray", "2020-Whipray", "2020-Crocodile", "2020-Crocodile", "2021-West", "2021-West", "2021-Rankin", "2021-Rankin", "2021-Whipray", "2021-Whipray", "2021-Crocodile", "2021-Crocodile"), species = c("weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid", "weighted_den", "weighted_den_carid"), density = c(0.00109451560281117, 0.484952868327336, 0, 0.086948676470023, 0, 0.000175586458772299, 0, 0.0213370469869364, 0.00244750051380243, 2.25961874077752, 0, 0, 0, 0.00109652922201019, 0, 0, 0.00155162534766244, 1.87765191982742, 0, 0.0506693553371398, 0, 0.00888790576574783, 0, 0.000113921166552746, 0.00271835365647371, 1.43239056009749, 0, 0.000182039455286541, 0, 0, 0, 0.000561519490035231, 0.00225835591184498, 2.40719953239284, 0, 0.000388080206313915, 0, 0.00309583234733728, 0, 0.000815020765525142, 9.16873040427497e-05, 0.330946212564868, 0, 0.00121708105974097, 0, 0, 0, 0, 0.00120311020545607, 0.245785509346677, 0, 0, 0, 0, 0, 0.0170052902510473, 0.00331914172039662, 0.65236339593047, 0.0018818919255523, 0.00514144664157328, 0.00049840112185749, 0.00211954056631631, 0, 0.00202007130311886, 0.00419876411388295, 0.199634354821455, 0.000976084425739813, 0.104217100578605, 0.00017317664770464, 0.00103563821616542, 0.000231762069846282, 0.000115118681029652, 0.001688624458484, 0.112896098983775, 0.00221388120855345, 0.00851524454018168, 0.000145407848151758, 0.000434852081803272, 0, 0.000412676750124396, 0.00273638649319108, 0.267608818473059, 0.00332090562164444, 0.068723241681447, 0.000426749934946656, 0.00149987725304424, 0, 0.000192673737735322, 0.000951619776409427, 0.215338040060959, 0.000434586745104274, 0.0478934449975685, 0, 0.00150998374994783, 0, 0.00129010186017751, 0.00131030492673741, 0.300718374949211, 0.00110524705272365, 0.203148234839554, 0, 0.00205469524493215, 0, 0.00279004408491279)), row.names = c(NA, -104L), groups = structure(list( Zone = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("Crocodile", "Rankin", "West", "Whipray"), class = "factor"), Year = c(2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021), .rows = structure(list(7:8, 15:16, 23:24, 31:32, 39:40, 47:48, 55:56, 63:64, 71:72, 79:80, 87:88, 95:96, 103:104, 3:4, 11:12, 19:20, 27:28, 35:36, 43:44, 51:52, 59:60, 67:68, 75:76, 83:84, 91:92, 99:100, 1:2, 9:10, 17:18, 25:26, 33:34, 41:42, 49:50, 57:58, 65:66, 73:74, 81:82, 89:90, 97:98, 5:6, 13:14, 21:22, 29:30, 37:38, 45:46, 53:54, 61:62, 69:70, 77:78, 85:86, 93:94, 101:102), ptype = integer(0), class = c("vctrs_list_of", "vctrs_vctr", "list"))), row.names = c(NA, -52L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", "tbl_df", "tbl", "data.frame"))

CodePudding user response：

With your dput, we can create the following code, which appears to do what you asked for, though I'm not sure how effective it is aas a visualization:

tidyr::pivot_wider(df, names_from = species, values_from = density) %>%
ggplot(aes(x = as.numeric(Zone) - 0.125))   
  geom_col(aes(y = weighted_den, group = 1, fill = 'weighted_den'),
           width = 0.25)  
  geom_col(aes(x = as.numeric(Zone)   0.125,
               y = weighted_den_carid / 100,
               fill = 'weighted_den_carid'), width =0.25)   
  scale_x_continuous(breaks = 1:4, labels = levels(df$Zone))  
  scale_y_continuous(sec.axis = sec_axis(trans = ~.x * 100,
                                         name = 'weighted_den_carid'))  
  facet_wrap(~Year, scales = 'free_y')  
  theme_minimal()  
  theme(axis.text.x = element_text(angle = 90))

CodePudding user response：

First I created a reproducible dataset with random data:

df <- data.frame(Year = rep(c(2009, 2010, 2011, 2012), 5),
                  Zone = rep(c("West", "Rankin", "Whipray", "Crocodile"), 5),
                  weighted_den = runif(20, 0, 1),
                  weighted_den_carid = runif(20, 0, 3))

After that you should melt your data using the following code:

library(reshape)
library(tidyverse)
df2 <- melt(df, id = c("Zone", "Year"))

And use the following code to plot the bars with second y-axis:

df2 %>% ggplot(aes(x = Zone, y = value, fill = variable))  
  geom_bar(stat = "identity", position = "dodge")  
  scale_y_continuous("Carid", sec.axis = sec_axis(~ . *100, name = "Cneb"))

Output: