ggplot2, by Hadley Wickham, is an excellent and flexible package for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a ggplot, the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills.
The ‘ggpubr’ package provides some easy-to-use functions for creating and customizing ‘ggplot2’- based publication ready plots.
Find out more at https://rpkgs.datanovia.com/ggpubr.
Installation and loading
- Install from CRAN as follow:
install.packages("ggpubr")
- Or, install the latest version from GitHub as follow:
# Install if(!require(devtools)) install.packages("devtools") devtools::install_github("kassambara/ggpubr")
Distribution
library(ggpubr) #> Loading required package: ggplot2 #> Loading required package: magrittr # Create some data format # ::::::::::::::::::::::::::::::::::::::::::::::::::: set.seed(1234) wdata = data.frame( sex = factor(rep(c("F", "M"), each=200)), weight = c(rnorm(200, 55), rnorm(200, 58))) head(wdata, 4) #> sex weight #> 1 F 53.79293 #> 2 F 55.27743 #> 3 F 56.08444 #> 4 F 52.65430 # Density plot with mean lines and marginal rug # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change outline and fill colors by groups ("sex") # Use custom palette ggdensity(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800"))
# Histogram plot with mean lines and marginal rug # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change outline and fill colors by groups ("sex") # Use custom color palette gghistogram(wdata, x = "weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800"))
Box plots and violin plots
# Load data data("ToothGrowth") df <- ToothGrowth head(df, 4) #> len supp dose #> 1 4.2 VC 0.5 #> 2 11.5 VC 0.5 #> 3 7.3 VC 0.5 #> 4 5.8 VC 0.5 # Box plots with jittered points # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change outline colors by groups: dose # Use custom color palette # Add jitter points and change the shape by groups p <- ggboxplot(df, x = "dose", y = "len", color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"), add = "jitter", shape = "dose") p
# Add p-values comparing groups # Specify the comparisons you want my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") ) p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value stat_compare_means(label.y = 50) # Add global p-value
# Violin plots with box plots inside # ::::::::::::::::::::::::::::::::::::::::::::::::::: # Change fill color by groups: dose # add boxplot with white fill color ggviolin(df, x = "dose", y = "len", fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add = "boxplot", add.params = list(fill = "white"))+ stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels stat_compare_means(label.y = 50) # Add global the p-value
Bar plots
Demo data set
Load and prepare data:
# Load data data("mtcars") dfm <- mtcars # Convert the cyl variable to a factor dfm$cyl <- as.factor(dfm$cyl) # Add the name colums dfm$name <- rownames(dfm) # Inspect the data head(dfm[, c("name", "wt", "mpg", "cyl")]) #> name wt mpg cyl #> Mazda RX4 Mazda RX4 2.620 21.0 6 #> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6 #> Datsun 710 Datsun 710 2.320 22.8 4 #> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6 #> Hornet Sportabout Hornet Sportabout 3.440 18.7 8 #> Valiant Valiant 3.460 18.1 6
Ordered bar plots
Change the fill color by the grouping variable “cyl”. Sorting will be done globally, but not by groups.
ggbarplot(dfm, x = "name", y = "mpg", fill = "cyl", # change fill color by cyl color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "desc", # Sort the value in dscending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90 # Rotate vertically x axis texts )
Sort bars inside each group. Use the argument sort.by.groups = TRUE.
ggbarplot(dfm, x = "name", y = "mpg", fill = "cyl", # change fill color by cyl color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "asc", # Sort the value in dscending order sort.by.groups = TRUE, # Sort inside each group x.text.angle = 90 # Rotate vertically x axis texts )
Deviation graphs
The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.
Calculate the z-score of the mpg data:
# Calculate the z-score of the mpg data dfm$mpg_z <- (dfm$mpg -mean(dfm$mpg))/sd(dfm$mpg) dfm$mpg_grp <- factor(ifelse(dfm$mpg_z < 0, "low", "high"), levels = c("low", "high")) # Inspect the data head(dfm[, c("name", "wt", "mpg", "mpg_z", "mpg_grp", "cyl")]) #> name wt mpg mpg_z mpg_grp cyl #> Mazda RX4 Mazda RX4 2.620 21.0 0.1508848 high 6 #> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 0.1508848 high 6 #> Datsun 710 Datsun 710 2.320 22.8 0.4495434 high 4 #> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 0.2172534 high 6 #> Hornet Sportabout Hornet Sportabout 3.440 18.7 -0.2307345 low 8 #> Valiant Valiant 3.460 18.1 -0.3302874 low 6
Create an ordered barplot, colored according to the level of mpg:
ggbarplot(dfm, x = "name", y = "mpg_z", fill = "mpg_grp", # change fill color by mpg_level color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "asc", # Sort the value in ascending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90, # Rotate vertically x axis texts ylab = "MPG z-score", xlab = FALSE, legend.title = "MPG Group" )
Rotate the plot: use rotate = TRUE and sort.val = “desc”
ggbarplot(dfm, x = "name", y = "mpg_z", fill = "mpg_grp", # change fill color by mpg_level color = "white", # Set bar border colors to white palette = "jco", # jco journal color palett. see ?ggpar sort.val = "desc", # Sort the value in descending order sort.by.groups = FALSE, # Don't sort inside each group x.text.angle = 90, # Rotate vertically x axis texts ylab = "MPG z-score", legend.title = "MPG Group", rotate = TRUE, ggtheme = theme_minimal() )
Dot charts
Lollipop chart
Lollipop chart is an alternative to bar plots, when you have a large set of values to visualize.
Lollipop chart colored by the grouping variable “cyl”:
ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "ascending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots ggtheme = theme_pubr() # ggplot2 theme )
- Sort in decending order. sorting = “descending”.
- Rotate the plot vertically, using rotate = TRUE.
- Sort the mpg value inside each group by using group = “cyl”.
- Set dot.size to 6.
- Add mpg values as label. label = “mpg” or label = round(dfm$mpg).
ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots rotate = TRUE, # Rotate vertically group = "cyl", # Order by groups dot.size = 6, # Large dot size label = round(dfm$mpg), # Add mpg values as dot labels font.label = list(color = "white", size = 9, vjust = 0.5), # Adjust label parameters ggtheme = theme_pubr() # ggplot2 theme )
Deviation graph:
- Use y = “mpg_z”
- Change segment color and size: add.params = list(color = “lightgray”, size = 2)
ggdotchart(dfm, x = "name", y = "mpg_z", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots add.params = list(color = "lightgray", size = 2), # Change segment color and size group = "cyl", # Order by groups dot.size = 6, # Large dot size label = round(dfm$mpg_z,1), # Add mpg values as dot labels font.label = list(color = "white", size = 9, vjust = 0.5), # Adjust label parameters ggtheme = theme_pubr() # ggplot2 theme )+ geom_hline(yintercept = 0, linetype = 2, color = "lightgray")
Cleveland’s dot plot
Color y text by groups. Use y.text.col = TRUE.
ggdotchart(dfm, x = "name", y = "mpg", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order rotate = TRUE, # Rotate vertically dot.size = 2, # Large dot size y.text.col = TRUE, # Color y text by groups ggtheme = theme_pubr() # ggplot2 theme )+ theme_cleveland() # Add dashed grids
More
Find out more at https://rpkgs.datanovia.com/ggpubr.
Blog posts
- A. Kassambara. ggpubr R Package: ggplot2-Based Publication Ready Plots
