Violin-shaped line chart

Question

Maybe this is a stupid idea, or maybe it's a brain wave. I have a set of lipid classes in 4 different species. The data is proportional, and the sums are 1000. I want to visualize the differences in the proportions for each class in each species. As a rule, in this case there will be a stack plan, but there are several classes, and this becomes uninterpretable, since only the lower class has a baseline (see below).

And this is apparently the best option for a bad group, with pie and donut charts that are not a bit of ridicule. Then I was inspired by this creation of a Symmetric, violin story histogram? , which creates a kind of complex schedule of violin graphics (see below).

, - , . A D "" TAG "" STEROL. , . ?

:

structure(list(Sample = c("A", "A", "A", "B", "B", "B", "C", 
"C", "C", "D", "D"), WAX = c(83.7179798600773, 317.364310355766, 
20.0147496567679, 93.0194886619568, 78.7886829173726, 79.3445694220837, 
91.0020522660375, 88.1542855137005, 78.3313314713951, 78.4449591023115, 
236.150030864875), TAG = c(67.4640254081232, 313.243238213156, 
451.287867136276, 76.308508343969, 40.127554151831, 91.1910102221636, 
61.658394708941, 104.617259648364, 60.7502685224869, 80.8373642262043, 
485.88633863193), FFA = c(41.0963382465756, 149.264019576272, 
129.672579626868, 51.049208042632, 13.7282635713804, 30.0088572108344, 
47.8878116348504, 47.9564218319094, 30.3836532949481, 34.8474205480686, 
10.9218910757234), `DAG1,2` = c(140.35876401479, 42.4556176551009, 
0, 0, 144.993393432366, 136.722412691012, 0, 140.027443968931, 
137.579074961889, 129.935353616471, 46.6128854387559), STEROL = c(73.0144390122309, 
24.1680929257195, 41.8258704279641, 78.906816661241, 67.5678558060943, 
66.7150537517493, 82.4794113296791, 76.7443442992891, 68.9357008866253, 
64.5444668132533, 29.8342694785768), AMPL = c(251.446564854412, 
57.8713327050339, 306.155806819949, 238.853696442419, 201.783872969561, 
175.935515655693, 234.169038776536, 211.986239116884, 196.931330316831, 
222.658181144794, 73.8944654414811), PE = c(167.99718650752, 
43.3839497916674, 22.1937177530762, 150.315149187176, 153.632530721031, 
141.580725482114, 164.215442147509, 155.113323256627, 143.349000132624, 
128.504657216928, 50.6281347160092), PC = c(174.904702096271, 
52.2494387772846, 28.8494085790995, 191.038328534942, 190.183655117756, 
175.33290326259, 199.2632149392, 175.400682364295, 176.64926273487, 
163.075864395099, 66.071984352649), LPC = c(0, 0, 0, 120.508804125665, 
109.194191312608, 103.16895230176, 119.324634197247, 0, 107.09037767833, 
97.151732936871, 0)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -11L), .Names = c("Sample", "WAX", "TAG", 
"FFA", "DAG1,2", "STEROL", "AMPL", "PE", "PC", "LPC"))

Answer 1

:

library(reshape2)
DFm <- melt(DF, id.vars = "Sample")
DFm1 <- DFm
DFm1$value <- -DFm1$value
DFm <- rbind(DFm, DFm1)


ggplot(DFm, aes(x = "A", y = value / 10, fill = variable, color = variable)) +
  geom_bar(stat = "identity", position = "dodge") +
  coord_flip() +
  theme_minimal() +
  facet_wrap(~ Sample, nrow = 1, switch = "x") +
  theme(axis.text = element_blank(),
        axis.title = element_blank(),
        panel.grid = element_blank())