Ridgeline plot in R (Geology, Geochemistry, Petrology)
I am learning data visualization and statistics using R.Today I am learning Ridgeline plot/Joy plot. This plot shows the distribution of a numeric value for several groups. In geology, this would be very helpful to see the compositional distribution of samples (type of rocks, locality, mineral group etc.)
.png)
Library
library(ggplot2) library(tidyverse) library(ggridges) library(viridis) library(RColorBrewer) Load the test dataset (Iris data)
data <- irisview first six rows of iris dataset
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaVisualization_1
ggplot(data, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges() + theme_ridges().png)
Visualization_2
Adding color [scale_fill_viridis(name = "X", option = "A")]
Changing the "height" of the peak and including 0 [geom_density_ridges_gradient(scale = 3, rel_min_height = 0)]
p <- ggplot(data, aes(x = Sepal.Length, y = Species, fill = ..x..)) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0)
p <- p + scale_fill_viridis(name = "X", option = "A")
plot(p)
Visualization_3
Changing color [scale_fill_viridis(name = "Sepal.Length", option = "D")]
Changing the "height" of the peak and excluding 0 [geom_density_ridges_gradient(scale = 1, rel_min_height = 0)]
p <- ggplot(data, aes(x = Sepal.Length, y = Species, fill = ..y..)) +
geom_density_ridges_gradient(scale = 1, rel_min_height = 0.01)
p <- p + scale_fill_viridis(name = "Species", option = "D")
plot(p)
Visualization_4
p <- ggplot(data, aes(x = Sepal.Length, y = Species, fill = ..x..)) +
geom_density_ridges_gradient(scale = 1, rel_min_height = 0.01)
p <- p + scale_fill_distiller(name = "Sepal.Length", palette = "Spectral")
plot(p)
Visualization_5
p <- ggplot(data, aes(x = Sepal.Length, y = Species, fill = ..y..)) +
geom_density_ridges_gradient(scale = 1, rel_min_height = 0.01)
p <- p + scale_fill_distiller(name = "Species", palette = "RdYlBu")
plot(p)
Next, I use geological data. Dataset is mantle xenolith from PetDB.
Next, I use geological data. Dataset is mantle xenolith from PetDB (data-to-go).
This time, I used the 'data' sheet in excel. I named the file petdb_mantle_xenolith and saved as csv file.
Load the dataset
data <- read.csv("petdb_mantle_xenolith.csv")
Check the dataset
head(data)
## SAMPLE_ID SAMPLE_NAME IGSN SAMPLE_TYPE LATITUDE LONGITUDE ELEVATION_MIN
## 1 11-01 11-01 Mineral 23.3994 58.1436 NA
## 2 11-10 11-10 Mineral 23.0494 58.6818 NA
## 3 11-33C 11-33C Mineral 23.8822 56.6274 NA
## 4 13-113 13-113 Mineral 25.4489 56.1223 NA
## 5 13-121 13-121 Mineral 25.4489 56.0957 NA
## 6 13-33 13-33 Whole Rock 23.6489 56.9362 NA
## ELEVATION_MAX TECTONIC_SETTING ROCK.NAME REFERENCE
## 1 NA OPHIOLITE LHERZOLITE PRIGENT, 2018[3698]
## 2 NA OPHIOLITE LHERZOLITE PRIGENT, 2018[3698]
## 3 NA OPHIOLITE LHERZOLITE PRIGENT, 2018[3698]
## 4 NA OPHIOLITE LHERZOLITE PRIGENT, 2018[3698]
## 5 NA OPHIOLITE LHERZOLITE PRIGENT, 2018[3698]
## 6 NA OPHIOLITE HARZBURGITE PRIGENT, 2018[3698]
## METHOD EXPEDITION.ID SiO2 TiO2 Al2O3 Cr2O3 Fe2O3 Fe2O3T FeO FeOT
## 1 EMP[82443] 47.91 0.38 10.61 0.64 NA NA 3.20 NA
## 2 LA-ICPMS[82447] 56.81 0.04 49.72 0.69 NA NA 3.17 NA
## 3 EMP[82445] 52.14 0.08 49.54 16.19 NA NA 13.25 NA
## 4 CALC[82444] 54.31 0.07 5.19 0.61 NA NA 6.56 NA
## 5 LA-ICPMS[82447] 39.93 0.03 50.18 0.31 NA NA 1.93 NA
## 6 CALC[82438] NA NA NA NA NA NA NA NA
## NiO MnO MgO CaO SrO Na2O K2O P2O5 BaO LOI H2O H2OM H2OP SO2 SO3 V2O3
## 1 0.07 0.05 19.43 12.72 NA 1.83 0.01 NA NA NA NA NA NA NA NA 0.15
## 2 0.29 0.05 19.98 12.75 NA 1.08 0.01 NA NA 6.43 NA NA NA NA NA 0.15
## 3 0.30 0.12 18.14 23.72 NA 0.17 0.01 NA NA 13.95 NA NA NA NA NA 0.15
## 4 0.25 0.09 32.47 0.33 NA 0.06 0.02 NA NA NA NA NA NA NA NA 0.09
## 5 0.40 0.06 17.22 23.86 NA 0.16 0.01 NA NA 6.16 NA NA NA NA NA 0.13
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## .......Using a ridge line plot, I try to see the relationship between lithology (rock type) and compositions. I use SiO2 as the x-axis.
p <- ggplot(data, aes(x = SiO2, y = ROCK.NAME, fill = ..y..)) +
geom_density_ridges_gradient(scale = 2, rel_min_height = 0.)
p <- p + scale_fill_distiller(palette = "RdYlBu")
plot(p)
Because low SiO2 contents are not realistic to peridotites (probably they are chromitites?), I screen the data. Here, I use the data which has SiO2 > 30 wt.%.
Data screening
data <- data %>% filter(SiO2 >= 30)Reorder
I remove the legend and also rearranged the sample order (based on SiO2 contents).
theme(legend.position = "none")
I order the ridgeline plotbased on its mean/median using forcats’ fct_reoder()
function. y=fct_reorder(ROCK.NAME,SiO2)
p <- ggplot(data, aes(x = SiO2, y=fct_reorder(ROCK.NAME,SiO2), fill = ..y..)) +
geom_density_ridges_gradient(scale = 2, rel_min_height = 0.)
p <- p + scale_fill_distiller(palette = "RdYlBu")
p <- p + theme(legend.position = "none") #removing legend
plot(p)
Rename y-axis and change the theme
library(ggdark)
p <- ggplot(data, aes(x = SiO2, y=fct_reorder(ROCK.NAME,SiO2), fill = ..y..)) +
geom_density_ridges_gradient(scale = 2, rel_min_height = 0.)
p <- p + ylab("Lithology")
p <- p + scale_fill_distiller(palette = "RdYlBu")
p <- p + dark_theme_gray()
p <- p + theme(legend.position = "none") #removing legend
plot(p).png)
This ridgeline plot shows that metaperidotite resulted after deserpentinization has low SiO2 contents among ultramafic rocks.
*Metaperidotite and eclogite are in mantle xenolith????
Keywords: geology in R, geochemistry in R, petrology in R, Ridgeline plot
References
https://r-graph-gallery.com/294-basic-ridgeline-plot.htmlhttps://www.analyticsvidhya.com/blog/2021/06/ridgeline-plots-visualize-data-with-a-joy/https://ggplot2.tidyverse.org/reference/scale_brewer.html
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