ggplot2, by Hadley Wickham, is an excellent and flexible package for elegant data visualization in R. However the default generated plots require 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/.
New publication-ready plot types and figure builders:
ggcompare()— one-call group-comparison figures (box/violin/strip plus points, means, adjusted-p brackets and an omnibus test label), including two-way designs with simple main effects.ggrocplot()— ROC curves with the AUC and its confidence interval, an optional optimal cut-point marker, and multi-marker overlays.ggestimates()— forest and estimation plots (odds/hazard/risk ratios, regression coefficients, meta-analysis).ggraincloud()— raincloud plots (half-violin plus box plus jittered points).ggvolcano()— volcano plots for differential-expression results.- Statistical annotation helpers:
stat_cld()(compact letter display),add_test_label()(omnibus test subtitle) and automatic bracket packing withgeom_pwc(pack = "auto").
See the package vignettes (browseVignettes("ggpubr")) for worked
examples.
Other updates:
- Modern compatibility updates for current
ggplot2,dplyr, andtidyr. - New p-value formatting helpers:
format_p_value(),get_p_format_style(),list_p_format_styles(). - Extended p-value formatting parameters across statistical layers:
p.format.style,p.digits,p.leading.zero,p.min.threshold,p.decimal.mark. - Sparse grouped subsets are handled robustly in
stat_compare_means()andgeom_pwc(): non-comparable subsets are skipped while valid comparisons are kept (#663).
- Install from CRAN as follows:
install.packages("ggpubr")- Or, install the latest version from GitHub as follows:
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")Read more: Histogram and Density Plots in R (ggpubr).
library(ggpubr)
#> Loading required package: ggplot2
# 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"))
Read more: Box Plots and Violin Plots in R (ggpubr).
# 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
#> `stat_compare_means()` with `comparisons` displays *unadjusted* p-values (no correction for multiple comparisons).
#> ℹ For p-values adjusted for multiple comparisons, use `geom_pwc()`, or `stat_pvalue_manual()` together with `compare_means(..., p.adjust.method = )`.
#> This message is displayed once per session.
# 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
Read more: Bar Plots in R (ggpubr): Ordered, Lollipop & Dot Charts.
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$name <- rownames(dfm)
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "cyl")])
#> name wt mpg cyl
#> Mazda RX4 Mazda RX4 2.620 21.0 6
#> Mazda RX4 Wag Mazda RX4 Wag 2.875 21.0 6
#> Datsun 710 Datsun 710 2.320 22.8 4
#> Hornet 4 Drive Hornet 4 Drive 3.215 21.4 6
#> Hornet Sportabout Hornet Sportabout 3.440 18.7 8
#> Valiant Valiant 3.460 18.1 6Change the fill color by the grouping variable “cyl”. Sorting will be done globally, but not by groups.
ggbarplot(dfm, x = "name", y = "mpg",
fill = "cyl", # change fill color by cyl
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palette. see ?ggpar
sort.val = "desc", # Sort the value in descending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 90 # Rotate vertically x axis texts
)
Sort bars inside each group. Use the argument sort.by.groups = TRUE.
ggbarplot(dfm, x = "name", y = "mpg",
fill = "cyl", # change fill color by cyl
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palette. see ?ggpar
sort.val = "asc", # Sort the value in ascending order
sort.by.groups = TRUE, # Sort inside each group
x.text.angle = 90 # Rotate vertically x axis texts
)
The deviation graph shows the deviation of quantitative values to a reference value. In the R code below, we’ll plot the mpg z-score from the mtcars dataset.
Calculate the z-score of the mpg data:
# Calculate the z-score of the mpg data
dfm$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 6Create 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 palette. 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 palette. 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()
)
Read more: Bar Plots in R (ggpubr): Ordered, Lollipop & Dot Charts.
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 descending 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")
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/.
Step-by-step lessons for the main plot types, on Datanovia:
- Box Plots and Violin Plots in R (ggpubr)
- Bar Plots in R (ggpubr): Ordered, Lollipop & Dot Charts
- Plot Means and Error Bars in R (ggpubr)
- Histogram and Density Plots in R (ggpubr)
- Scatter Plots with Correlation in R (ggpubr)
- ggpubr Pie Chart in R: Publication-Ready Pie & Donut in One Call
- Add P-values to ggplots in R (ggpubr)
- Auto P-values in ggplot with geom_pwc (ggpubr)
- Arrange and Combine Multiple ggplots in R with ggpubr
- Customize ggpubr Plots in R with ggpar()
- The full ggpubr lesson series