knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "tools/README-", warning = FALSE )
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/.
install.packages("ggpubr")
# Install if(!require(devtools)) install.packages("devtools") devtools::install_github("kassambara/ggpubr")
library(ggpubr) # 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) # 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"))
# Load data data("ToothGrowth") df <- ToothGrowth head(df, 4) # 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
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")])
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 )
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")])
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() )
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 )
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$mpg), # Add mpg values as dot labels font.label = list(color = "white", size = 9, vjust = 0.5), # Adjust label parameters ggtheme = theme_pubr() # ggplot2 theme )
Deviation graph:
ggdotchart(dfm, x = "name", y = "mpg_z", color = "cyl", # Color by groups palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette sorting = "descending", # Sort value in descending order add = "segments", # Add segments from y = 0 to dots add.params = list(color = "lightgray", size = 2), # Change segment color and size group = "cyl", # Order by groups dot.size = 6, # Large dot size label = round(dfm$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")
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
Find out more at https://rpkgs.datanovia.com/ggpubr/.
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