ggpubr v0.2.3

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'ggplot2' Based Publication Ready Plots

The 'ggplot2' package is excellent and flexible 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. 'ggpubr' provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.

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ggpubr: 'ggplot2' Based Publication Ready Plots

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

Functions in ggpubr

Name Description
ggarrange Arrange Multiple ggplots
font Change the Appearance of Titles and Axis Labels
facet Facet a ggplot into Multiple Panels
ggballoonplot Ballon plot
ggdotchart Cleveland's Dot Plots
ggdotplot Dot plot
ggbarplot Bar plot
ggboxplot Box plot
gghistogram Histogram plot
ggexport Export ggplots
gene_citation Gene Citation Index
ggmaplot MA-plot from means and log fold changes
stat_bracket Add Brackets with Labels to a GGPlot
ggline Line plot
ggpubr_args ggpubr General Arguments Description
ggparagraph Draw a Paragraph of Text
axis_scale Change Axis Scale: log2, log10 and more
get_palette Generate Color Palettes
ggpie Pie chart
geom_exec Execute ggplot2 functions
ggstripchart Stripcharts
ggdensity Density plot
ggadd Add Summary Statistics or a Geom onto a ggplot
show_point_shapes Point shapes available in R
ggtext Text
stat_stars Add Stars to a Scatter Plot
ggdonutchart Donut chart
ggscatterhist Scatter Plot with Marginal Histograms
ggscatter Scatter plot
reexports Objects exported from other packages
ggqqplot QQ Plots
stat_chull Plot convex hull of a set of points
rotate Rotate a ggplot Horizontally
stat_compare_means Add Mean Comparison P-values to a ggplot
text_grob Create a Text Graphical object
rremove Remove a ggplot Component
set_palette Set Color Palette
rotate_axis_text Rotate Axes Text
stat_central_tendency Add Central Tendency Measures to a GGPLot
show_line_types Line types available in R
stat_pvalue_manual Add Manually P-values to a ggplot
stat_regline_equation Add Regression Line Equation and R-Square to a GGPLOT.
ggecdf Empirical cumulative density function
get_legend Extract Legends from a ggplot object
gradient_color Set Gradient Color
ggerrorplot Visualizing Error
ggpaired Plot Paired Data
ggpar Graphical parameters
stat_overlay_normal_density Overlay Normal Density Plot
theme_pubr Publication ready theme
ggviolin Violin plot
ggtexttable Draw a Textual Table
stat_mean Draw group mean points
grids Add Grids to a ggplot
stat_conf_ellipse Plot confidence ellipses.
stat_cor Add Correlation Coefficients with P-values to a Scatter Plot
theme_transparent Create a ggplot with Transparent Background
background_image Add Background Image to ggplot2
compare_means Comparison of Means
border Set ggplot Panel Border Line
add_summary Add Summary Statistics onto a ggplot.
diff_express Differential gene expression analysis results
bgcolor Change ggplot Panel Background Color
as_ggplot Storing grid.arrange() arrangeGrob() and plots
annotate_figure Annotate Arranged Figure
desc_statby Descriptive statistics by groups
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Details

Type Package
Date 2019-09-03
License GPL-2
LazyData TRUE
Encoding UTF-8
URL https://rpkgs.datanovia.com/ggpubr/
BugReports https://github.com/kassambara/ggpubr/issues
RoxygenNote 6.1.1
Collate 'utilities_color.R' 'utilities_base.R' 'desc_statby.R' 'utilities.R' 'add_summary.R' 'annotate_figure.R' 'as_ggplot.R' 'axis_scale.R' 'background_image.R' 'bgcolor.R' 'border.R' 'compare_means.R' 'diff_express.R' 'facet.R' 'font.R' 'gene_citation.R' 'geom_bracket.R' 'geom_exec.R' 'get_legend.R' 'get_palette.R' 'ggadd.R' 'ggarrange.R' 'ggballoonplot.R' 'ggpar.R' 'ggbarplot.R' 'ggboxplot.R' 'ggdensity.R' 'ggpie.R' 'ggdonutchart.R' 'stat_conf_ellipse.R' 'stat_chull.R' 'ggdotchart.R' 'ggdotplot.R' 'ggecdf.R' 'ggerrorplot.R' 'ggexport.R' 'gghistogram.R' 'ggline.R' 'ggmaplot.R' 'ggpaired.R' 'ggparagraph.R' 'ggpubr_args.R' 'ggqqplot.R' 'utilities_label.R' 'stat_cor.R' 'stat_stars.R' 'ggscatter.R' 'ggscatterhist.R' 'ggstripchart.R' 'ggtext.R' 'ggtexttable.R' 'ggviolin.R' 'gradient_color.R' 'grids.R' 'reexports.R' 'rotate.R' 'rotate_axis_text.R' 'rremove.R' 'set_palette.R' 'show_line_types.R' 'show_point_shapes.R' 'stat_central_tendency.R' 'stat_compare_means.R' 'stat_mean.R' 'stat_overlay_normal_density.R' 'stat_pvalue_manual.R' 'stat_regline_equation.R' 'text_grob.R' 'theme_pubr.R' 'theme_transparent.R'
NeedsCompilation no
Packaged 2019-09-03 21:16:16 UTC; kassambara
Repository CRAN
Date/Publication 2019-09-03 22:00:02 UTC

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