Easy Guides

Bar Plots and Modern Alternatives

Wed, 28 Jun 2017 15:00:41 +0200

This article describes how to create easily basic and ordered bar plots using ggplot2 based helper functions available in the ggpubr R package. We’ll also present some modern alternatives to bar plots, including lollipop charts and cleveland’s dot plots.

Note that, the approach to build a bar plot, using ggplot2 standard verbs, has been described in our previous article available at: ggplot2 barplots : Quick start guide.

You might be also interested by the following articles:

Bar plots and modern alternatives

Contents:

Prerequisites
Basic bar plots
Multiple grouping variables
Ordered bar plots
Deviation graphs
Alternatives to bar plots
Infos

Prerequisites

Required R package

You need to install the R package ggpubr (version >= 0.1.3), to easily create ggplot2-based publication ready plots.

Install from CRAN:

install.packages("ggpubr")

Or, install the latest developmental version from GitHub as follow:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")

Load ggpubr:

library(ggpubr)

Basic bar plots

Create a demo data set:

df <- data.frame(dose=c("D0.5", "D1", "D2"),
                 len=c(4.2, 10, 29.5))
print(df)

  dose  len
1 D0.5  4.2
2   D1 10.0
3   D2 29.5

Basic bar plots:

# Basic bar plots with label
p <- ggbarplot(df, x = "dose", y = "len",
          color = "black", fill = "lightgray")
p

# Rotate to create horizontal bar plots
p + rotate()

Bar plots and modern alternatives

Change fill and outline colors by groups:

ggbarplot(df, x = "dose", y = "len",
   fill = "dose", color = "dose", palette = "jco")

Bar plots and modern alternatives

Multiple grouping variables

Create a demo data set:

df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                  dose=rep(c("D0.5", "D1", "D2"),2),
                  len=c(6.8, 15, 33, 4.2, 10, 29.5))
print(df2)

  supp dose  len
1   VC D0.5  6.8
2   VC   D1 15.0
3   VC   D2 33.0
4   OJ D0.5  4.2
5   OJ   D1 10.0
6   OJ   D2 29.5

Plot y = “len” by x = “dose” and change color by a second group: “supp”

# Stacked bar plots, add labels inside bars
ggbarplot(df2, x = "dose", y = "len",
  fill = "supp", color = "supp", 
  palette = c("gray", "black"),
  label = TRUE, lab.col = "white", lab.pos = "in")

# Change position: Interleaved (dodged) bar plot
ggbarplot(df2, x = "dose", y = "len",
          fill = "supp", color = "supp", 
          palette = c("gray", "black"),
          position = position_dodge(0.9))

Bar plots and modern alternatives

Ordered bar plots

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

Create 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
          )

Bar plots and modern alternatives

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
          )

Bar plots and modern alternatives

Deviation graphs

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 data set.

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 bar plot, 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"
          )

Bar plots and modern alternatives

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()
          )

Bar plots and modern alternatives

Alternatives to bar plots

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
           )

Bar plots and modern alternatives

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
           )

Bar plots and modern alternatives

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")

Bar plots and modern alternatives

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

Bar plots and modern alternatives

Infos

This analysis has been performed using R software (ver. 3.3.2) and ggpubr (ver. 0.1.4).

Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data

Mon, 12 Jun 2017 16:30:13 +0200

In genomic fields, it’s very common to explore the gene expression profile of one or a list of genes involved in a pathway of interest. Here, we present some helper functions in the ggpubr R package to facilitate exploratory data analysis (EDA) for life scientists.

Exploratory Data visualization: Gene Expression Data

Standard graphical techniques used in EDA, include:

Box plot
Violin plot
Stripchart
Dot plot
Histogram and density plots
ECDF plot
Q-Q plot

All these plots can be created using the ggplot2 R package, which is highly flexible.

However, to customize a ggplot, the syntax might appear opaque for a beginner and this raises the level of difficulty for researchers with no advanced R programming skills. If you’re not familiar with ggplot2 system, you can start by reading our Guide to Create Beautiful Graphics in R.

Previously, we described how to Add P-values and Significance Levels to ggplots. In this article, we present the ggpubr package, a wrapper around ggplot2, which provides some easy-to-use functions for creating ‘ggplot2’- based publication ready plots. We’ll use the ggpubr functions to visualize gene expression profile from TCGA genomic data sets.

Contents:

Prerequisites
Gene expression data
Box plots
Violin plots
Stripcharts and dot plots
Density plots
Histogram plots
Empirical cumulative density function
Quantile - Quantile plot
Infos

Prerequisites

ggpubr package

Required R package: ggpubr (version >= 0.1.3).

Install from CRAN as follow:

install.packages("ggpubr")

Or, install the latest developmental version from GitHub as follow:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")

Load ggpubr:

library(ggpubr)

TCGA data

The Cancer Genome Atlas (TCGA) data is a publicly available data containing clinical and genomic data across 33 cancer types. These data include gene expression, CNV profiling, SNP genotyping, DNA methylation, miRNA profiling, exome sequencing, and other types of data.

The RTCGA R package, by Marcin Marcin Kosinski et al., provides a convenient solution to access to clinical and genomic data available in TCGA. Each of the data packages is a separate package, and must be installed (once) individually.

The following R code installs the core RTCGA package as well as the clinical and mRNA gene expression data packages.

# Load the bioconductor installer. 
source("https://bioconductor.org/biocLite.R")

# Install the main RTCGA package
biocLite("RTCGA")

# Install the clinical and mRNA gene expression data packages
biocLite("RTCGA.clinical")
biocLite("RTCGA.mRNA")

To see the type of data available for each cancer type, use this:

library(RTCGA)
infoTCGA()

# A tibble: 38 x 13
     Cohort    BCR Clinical     CN   LowP Methylation   mRNA mRNASeq    miR miRSeq   RPPA    MAF rawMAF
 *                       
 1      ACC     92       92     90      0          80      0      79      0     80     46     90      0
 2     BLCA    412      412    410    112         412      0     408      0    409    344    130    395
 3     BRCA   1098     1097   1089     19        1097    526    1093      0   1078    887    977      0
 4     CESC    307      307    295     50         307      0     304      0    307    173    194      0
 5     CHOL     51       45     36      0          36      0      36      0     36     30     35      0
 6     COAD    460      458    451     69         457    153     457      0    406    360    154    367
 7 COADREAD    631      629    616    104         622    222     623      0    549    491    223    489
 8     DLBC     58       48     48      0          48      0      48      0     47     33     48      0
 9     ESCA    185      185    184     51         185      0     184      0    184    126    185      0
10     FPPP     38       38      0      0           0      0       0      0     23      0      0      0
# ... with 28 more rows

More information about the disease names can be found at: http://gdac.broadinstitute.org/

Gene expression data

The R function expressionsTCGA() [in RTCGA package] can be used to easily extract the expression values of genes of interest in one or multiple cancer types.

In the following R code, we start by extracting the mRNA expression for five genes of interest - GATA3, PTEN, XBP1, ESR1 and MUC1 - from 3 different data sets:

Breast invasive carcinoma (BRCA),
Ovarian serous cystadenocarcinoma (OV) and
Lung squamous cell carcinoma (LUSC)

library(RTCGA)
library(RTCGA.mRNA)
expr <- expressionsTCGA(BRCA.mRNA, OV.mRNA, LUSC.mRNA,
                        extract.cols = c("GATA3", "PTEN", "XBP1","ESR1", "MUC1"))
expr

# A tibble: 1,305 x 7
            bcr_patient_barcode   dataset     GATA3       PTEN      XBP1       ESR1      MUC1
                                                          
 1 TCGA-A1-A0SD-01A-11R-A115-07 BRCA.mRNA  2.870500  1.3613571  2.983333  3.0842500  1.652125
 2 TCGA-A1-A0SE-01A-11R-A084-07 BRCA.mRNA  2.166250  0.4283571  2.550833  2.3860000  3.080250
 3 TCGA-A1-A0SH-01A-11R-A084-07 BRCA.mRNA  1.323500  1.3056429  3.020417  0.7912500  2.985250
 4 TCGA-A1-A0SJ-01A-11R-A084-07 BRCA.mRNA  1.841625  0.8096429  3.131333  2.4954167 -1.918500
 5 TCGA-A1-A0SK-01A-12R-A084-07 BRCA.mRNA -6.025250  0.2508571 -1.451750 -4.8606667 -1.171500
 6 TCGA-A1-A0SM-01A-11R-A084-07 BRCA.mRNA  1.804500  1.3107857  4.041083  2.7970000  3.529750
 7 TCGA-A1-A0SO-01A-22R-A084-07 BRCA.mRNA -4.879250 -0.2369286 -0.724750 -4.4860833 -1.455750
 8 TCGA-A1-A0SP-01A-11R-A084-07 BRCA.mRNA -3.143250 -1.2432143 -1.193083 -1.6274167 -0.986750
 9 TCGA-A2-A04N-01A-11R-A115-07 BRCA.mRNA  2.034000  1.2074286  2.278833  4.1155833  0.668000
10 TCGA-A2-A04P-01A-31R-A034-07 BRCA.mRNA -0.293125  0.2883571 -1.605083  0.4731667  0.011500
# ... with 1,295 more rows

To display the number of sample in each data set, type this:

nb_samples <- table(expr$dataset)
nb_samples


BRCA.mRNA LUSC.mRNA   OV.mRNA 
      590       154       561

We can simplify data set names by removing the “mRNA” tag. This can be done using the R base function gsub().

expr$dataset <- gsub(pattern = ".mRNA", replacement = "",  expr$dataset)

Let’s simplify also the patients’ barcode column. The following R code will change the barcodes into BRCA1, BRCA2, …, OV1, OV2, …., etc

expr$bcr_patient_barcode <- paste0(expr$dataset, c(1:590, 1:561, 1:154))
expr

# A tibble: 1,305 x 7
   bcr_patient_barcode dataset     GATA3       PTEN      XBP1       ESR1      MUC1
                                               
 1               BRCA1    BRCA  2.870500  1.3613571  2.983333  3.0842500  1.652125
 2               BRCA2    BRCA  2.166250  0.4283571  2.550833  2.3860000  3.080250
 3               BRCA3    BRCA  1.323500  1.3056429  3.020417  0.7912500  2.985250
 4               BRCA4    BRCA  1.841625  0.8096429  3.131333  2.4954167 -1.918500
 5               BRCA5    BRCA -6.025250  0.2508571 -1.451750 -4.8606667 -1.171500
 6               BRCA6    BRCA  1.804500  1.3107857  4.041083  2.7970000  3.529750
 7               BRCA7    BRCA -4.879250 -0.2369286 -0.724750 -4.4860833 -1.455750
 8               BRCA8    BRCA -3.143250 -1.2432143 -1.193083 -1.6274167 -0.986750
 9               BRCA9    BRCA  2.034000  1.2074286  2.278833  4.1155833  0.668000
10              BRCA10    BRCA -0.293125  0.2883571 -1.605083  0.4731667  0.011500
# ... with 1,295 more rows

The above (expr) dataset has been saved at https://raw.githubusercontent.com/kassambara/data/master/expr_tcga.txt. This data is required to practice the R code provided in this tutotial.

If you experience some issues in installing the RTCGA packages, You can simply load the data as follow:

expr <- read.delim("https://raw.githubusercontent.com/kassambara/data/master/expr_tcga.txt",
                   stringsAsFactors = FALSE)

Box plots

(ggplot2 way of creating box plot)

Create a box plot of a gene expression profile, colored by groups (here data set/cancer type):

library(ggpubr)
# GATA3
ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco")

# PTEN
ggboxplot(expr, x = "dataset", y = "PTEN",
          title = "PTEN", ylab = "Expression",
          color = "dataset", palette = "jco")

Exploratory Data visualization: Gene Expression Data

Note that, the argument palette is used to change color palettes. Allowed values include:

“grey” for grey color palettes;
brewer palettes e.g. “RdBu”, “Blues”, …;. To view all, type this in R: RColorBrewer::display.brewer.all() or click here to see all brewer palettes;
or custom color palettes e.g. c(“blue”, “red”) or c(“#00AFBB”, “#E7B800”);
and scientific journal palettes from the ggsci R package, e.g.: “npg”, “aaas”, “lancet”, “jco”, “ucscgb”, “uchicago”, “simpsons” and “rickandmorty”.

Instead of repeating the same R code for each gene, you can create a list of plots at once, as follow:

# Create a  list of plots
p <- ggboxplot(expr, x = "dataset", 
               y = c("GATA3", "PTEN", "XBP1"),
               title = c("GATA3", "PTEN", "XBP1"),
               ylab = "Expression", 
               color = "dataset", palette = "jco")

# View GATA3
p$GATA3

# View PTEN
p$PTEN

# View XBP1
p$XBP1

Note that, when the argument y contains multiple variables (here multiple gene names), then the arguments title, xlab and ylab can be also a character vector of same length as y.

To add p-values and significance levels to the boxplots, read our previous article: Add P-values and Significance Levels to ggplots. Briefly, you can do this:

my_comparisons <- list(c("BRCA", "OV"), c("OV", "LUSC"))
ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco")+
  stat_compare_means(comparisons = my_comparisons)

Exploratory Data visualization: Gene Expression Data

For each of the genes, you can compare the different groups as follow:

compare_means(c(GATA3, PTEN, XBP1) ~ dataset, data = expr)

# A tibble: 9 x 8
     .y. group1 group2             p         p.adj p.format p.signif   method
                                    
1  GATA3   BRCA     OV 1.111768e-177 3.335304e-177  < 2e-16     **** Wilcoxon
2  GATA3   BRCA   LUSC  6.684016e-73  1.336803e-72  < 2e-16     **** Wilcoxon
3  GATA3     OV   LUSC  2.965702e-08  2.965702e-08  3.0e-08     **** Wilcoxon
4   PTEN   BRCA     OV  6.791940e-05  6.791940e-05  6.8e-05     **** Wilcoxon
5   PTEN   BRCA   LUSC  1.042830e-16  3.128489e-16  < 2e-16     **** Wilcoxon
6   PTEN     OV   LUSC  1.280576e-07  2.561153e-07  1.3e-07     **** Wilcoxon
7   XBP1   BRCA     OV 2.551228e-123 7.653685e-123  < 2e-16     **** Wilcoxon
8   XBP1   BRCA   LUSC  1.950162e-42  3.900324e-42  < 2e-16     **** Wilcoxon
9   XBP1     OV   LUSC  4.239570e-11  4.239570e-11  4.2e-11     **** Wilcoxon

If you want to select items (here cancer types) to display or to remove a particular item from the plot, use the argument select or remove, as follow:

# Select BRCA and OV cancer types
ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco",
          select = c("BRCA", "OV"))

# or remove BRCA
ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco",
          remove = "BRCA")

Exploratory Data visualization: Gene Expression Data

To change the order of the data sets on x axis, use the argument order. For example order = c(“LUSC”, “OV”, “BRCA”):

# Order data sets
ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco",
          order = c("LUSC", "OV", "BRCA"))

Exploratory Data visualization: Gene Expression Data

To create horizontal plots, use the argument rotate = TRUE:

ggboxplot(expr, x = "dataset", y = "GATA3",
          title = "GATA3", ylab = "Expression",
          color = "dataset", palette = "jco",
          rotate = TRUE)

Exploratory Data visualization: Gene Expression Data

To combine the three gene expression plots into a multi-panel plot, use the argument combine = TRUE:

ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE,
          ylab = "Expression",
          color = "dataset", palette = "jco")

Exploratory Data visualization: Gene Expression Data

You can also merge the 3 plots using the argument merge = TRUE or merge = “asis”:

ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          merge = TRUE,
          ylab = "Expression", 
          palette = "jco")

Exploratory Data visualization: Gene Expression Data

In the plot above, It’s easy to visually compare the expression level of the different genes in each cancer type.

But you might want to put genes (y variables) on x axis, in order to compare the expression level in the different cell subpopulations.

In this situation, the y variables (i.e.: genes) become x tick labels and the x variable (i.e.: dataset) becomes the grouping variable. To do this, use the argument merge = “flip”.

ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          merge = "flip",
          ylab = "Expression", 
          palette = "jco")

Exploratory Data visualization: Gene Expression Data

You might want to add jittered points on the boxplot. Each point correspond to individual observations. To add jittered points, use the argument add = “jitter” as follow. To customize the added elements, specify the argument add.params.

ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE,
          color = "dataset", palette = "jco",
          ylab = "Expression", 
          add = "jitter",                              # Add jittered points
          add.params = list(size = 0.1, jitter = 0.2)  # Point size and the amount of jittering
          )

Exploratory Data visualization: Gene Expression Data

Note that, when using ggboxplot() sensible values for the argument add are one of c(“jitter”, “dotplot”). If you decide to use add = “dotplot”, you can adjust dotsize and binwidth wen you have a strong dense dotplot. Read more about binwidth.

You can add and adjust a dotplot as follow:

ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE,
          color = "dataset", palette = "jco",
          ylab = "Expression", 
          add = "dotplot",                              # Add dotplot
          add.params = list(binwidth = 0.1, dotsize = 0.3)
          )

Exploratory Data visualization: Gene Expression Data

You might want to label the boxplot by showing the names of samples with the top n highest or lowest values. In this case, you can use the following arguments:

label: the name of the column containing point labels.
label.select: can be of two formats:
- a character vector specifying some labels to show.
- a list containing one or the combination of the following components:
  - top.up and top.down: to display the labels of the top up/down points. For example, label.select = list(top.up = 10, top.down = 4).
  - criteria: to filter, for example, by x and y variables values, use this: label.select = list(criteria = “`y` > 3.9 & `y` < 5 & `x` %in% c(‘BRCA’, ‘OV’)”).

For example:

ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE,
          color = "dataset", palette = "jco",
          ylab = "Expression", 
          add = "jitter",                               # Add jittered points
          add.params = list(size = 0.1, jitter = 0.2),  # Point size and the amount of jittering
          label = "bcr_patient_barcode",                # column containing point labels
          label.select = list(top.up = 2, top.down = 2),# Select some labels to display
          font.label = list(size = 9, face = "italic"), # label font
          repel = TRUE                                  # Avoid label text overplotting
          )

Exploratory Data visualization: Gene Expression Data

A complex criteria for labeling can be specified as follow:

label.select.criteria <- list(criteria = "`y` > 3.9 & `x` %in% c('BRCA', 'OV')")
ggboxplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE,
          color = "dataset", palette = "jco",
          ylab = "Expression", 
          label = "bcr_patient_barcode",              # column containing point labels
          label.select = label.select.criteria,       # Select some labels to display
          font.label = list(size = 9, face = "italic"), # label font
          repel = TRUE                                # Avoid label text overplotting
          )

Other types of plots, with the same arguments as the function ggboxplot(), are available, such as stripchart and violin plots.

Violin plots

(ggplot2 way of creating violin plot)

The following R code draws violin plots with box plots inside:

ggviolin(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE, 
          color = "dataset", palette = "jco",
          ylab = "Expression", 
          add = "boxplot")

Exploratory Data visualization: Gene Expression Data

Instead of adding a box plot inside the violin plot, you can add the median + interquantile range as follow:

ggviolin(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE, 
          color = "dataset", palette = "jco",
          ylab = "Expression", 
          add = "median_iqr")

Exploratory Data visualization: Gene Expression Data

When using the function ggviolin(), sensible values for the argument add include: “mean”, “mean_se”, “mean_sd”, “mean_ci”, “mean_range”, “median”, “median_iqr”, “median_mad”, “median_range”.

You can also add “jitter” points and “dotplot” inside the violin plot as described previously in the box plot section.

Stripcharts and dot plots

To draw a stripchart, type this:

ggstripchart(expr, x = "dataset",
             y = c("GATA3", "PTEN", "XBP1"),
             combine = TRUE, 
             color = "dataset", palette = "jco",
             size = 0.1, jitter = 0.2,
             ylab = "Expression", 
             add = "median_iqr",
             add.params = list(color = "gray"))

Exploratory Data visualization: Gene Expression Data

(ggplot2 way of creating stripcharts)

For a dot plot, use this:

ggdotplot(expr, x = "dataset",
          y = c("GATA3", "PTEN", "XBP1"),
          combine = TRUE, 
          color = "dataset", palette = "jco",
          fill = "white",
          binwidth = 0.1,
          ylab = "Expression", 
          add = "median_iqr",
          add.params = list(size = 0.9))

Exploratory Data visualization: Gene Expression Data

(ggplot2 way of creating dot plots)

Density plots

(ggplot2 way of creating density plots)

To visualize the distribution as a density plot, use the function ggdensity() as follow:

# Basic density plot
ggdensity(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..density..",
       combine = TRUE,                  # Combine the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE                       # Add marginal rug
)

Exploratory Data visualization: Gene Expression Data

# Change color and fill by dataset
ggdensity(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..density..",
       combine = TRUE,                  # Combine the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE,                      # Add marginal rug
       color = "dataset", 
       fill = "dataset",
       palette = "jco"
)

Exploratory Data visualization: Gene Expression Data

# Merge the 3 plots
# and use y = "..count.." instead of "..density.."
ggdensity(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..count..",
       merge = TRUE,                    # Merge the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE ,                     # Add marginal rug
       palette = "jco"                  # Change color palette
)

Exploratory Data visualization: Gene Expression Data

# color and fill by x variables
ggdensity(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..count..",
       color = ".x.", fill = ".x.",     # color and fill by x variables
       merge = TRUE,                    # Merge the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE ,                     # Add marginal rug
       palette = "jco"                  # Change color palette
)

Exploratory Data visualization: Gene Expression Data

# Facet by "dataset"
ggdensity(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..count..",
       color = ".x.", fill = ".x.", 
       facet.by = "dataset",            # Split by "dataset" into multi-panel
       merge = TRUE,                    # Merge the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE ,                     # Add marginal rug
       palette = "jco"                  # Change color palette
)

Exploratory Data visualization: Gene Expression Data

Histogram plots

(ggplot2 way of creating histogram plots)

To visualize the distribution as a histogram plot, use the function gghistogram() as follow:

# Basic histogram plot 
gghistogram(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..density..",
       combine = TRUE,                  # Combine the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE                       # Add marginal rug
)

Exploratory Data visualization: Gene Expression Data

# Change color and fill by dataset
gghistogram(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..density..",
       combine = TRUE,                  # Combine the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE,                      # Add marginal rug
       color = "dataset", 
       fill = "dataset",
       palette = "jco"
)

Exploratory Data visualization: Gene Expression Data

# Merge the 3 plots
# and use y = "..count.." instead of "..density.."
gghistogram(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..count..",
       merge = TRUE,                    # Merge the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE ,                     # Add marginal rug
       palette = "jco"                  # Change color palette
)

Exploratory Data visualization: Gene Expression Data

# color and fill by x variables
gghistogram(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..count..",
       color = ".x.", fill = ".x.",     # color and fill by x variables
       merge = TRUE,                    # Merge the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE ,                     # Add marginal rug
       palette = "jco"                  # Change color palette
)

Exploratory Data visualization: Gene Expression Data

# Facet by "dataset"
gghistogram(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       y = "..count..",
       color = ".x.", fill = ".x.", 
       facet.by = "dataset",            # Split by "dataset" into multi-panel
       merge = TRUE,                    # Merge the 3 plots
       xlab = "Expression", 
       add = "median",                  # Add median line. 
       rug = TRUE ,                     # Add marginal rug
       palette = "jco"                  # Change color palette
)

Exploratory Data visualization: Gene Expression Data

Empirical cumulative density function

(ggplot2 way of creating ECDF plots)

# Basic ECDF plot 
ggecdf(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       combine = TRUE,                 
       xlab = "Expression", ylab = "F(expression)"
)

Exploratory Data visualization: Gene Expression Data

# Change color  by dataset
ggecdf(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       combine = TRUE,                 
       xlab = "Expression", ylab = "F(expression)",
       color = "dataset", palette = "jco"
)

Exploratory Data visualization: Gene Expression Data

# Merge the 3 plots and color by x variables
ggecdf(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       merge = TRUE,                 
       xlab = "Expression", ylab = "F(expression)",
       color = ".x.", palette = "jco"
)

Exploratory Data visualization: Gene Expression Data

# Merge the 3 plots and color by x variables
# facet by "dataset" into multi-panel
ggecdf(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       merge = TRUE,                 
       xlab = "Expression", ylab = "F(expression)",
       color = ".x.", palette = "jco",
       facet.by = "dataset"
)

Exploratory Data visualization: Gene Expression Data

Quantile - Quantile plot

(ggplot2 way of creating QQ plots)

# Basic ECDF plot 
ggqqplot(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       combine = TRUE, size = 0.5
)

Exploratory Data visualization: Gene Expression Data

# Change color  by dataset
ggqqplot(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       combine = TRUE, color = "dataset", palette = "jco",
       size = 0.5
)

Exploratory Data visualization: Gene Expression Data

# Merge the 3 plots and color by x variables
ggqqplot(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       merge = TRUE,  
       color = ".x.", palette = "jco"
)

Exploratory Data visualization: Gene Expression Data

# Merge the 3 plots and color by x variables
# facet by "dataset" into multi-panel
ggqqplot(expr,
       x = c("GATA3", "PTEN",  "XBP1"),
       merge = TRUE, size = 0.5,
       color = ".x.", palette = "jco",
       facet.by = "dataset"
)

Exploratory Data visualization: Gene Expression Data

Infos

This analysis has been performed using R software (ver. 3.3.2) and ggpubr (ver. 0.1.3).

Add P-values and Significance Levels to ggplots

Thu, 08 Jun 2017 11:48:52 +0200

In this article, we’ll describe how to easily i) compare means of two or multiple groups; ii) and to automatically add p-values and significance levels to a ggplot (such as box plots, dot plots, bar plots and line plots …).

Contents:

Prerequisites
Methods for comparing means
R functions to add p-values
Compare two independent groups
Compare two paired samples
Compare more than two groups
Multiple grouping variables
Other plot types
Infos

Prerequisites

Install and load required R packages

Required R package: ggpubr (version >= 0.1.3), for ggplot2-based publication ready plots.

Install from CRAN as follow:

install.packages("ggpubr")

Or, install the latest developmental version from GitHub as follow:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")

Load ggpubr:

library(ggpubr)

Official documentation of ggpubr is available at: https://www.sthda.com/english/rpkgs/ggpubr

Demo data sets

Data: ToothGrowth data sets.

data("ToothGrowth")
head(ToothGrowth)

   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
5  6.4   VC  0.5
6 10.0   VC  0.5

Methods for comparing means

The standard methods to compare the means of two or more groups in R, have been largely described at: comparing means in R.

The most common methods for comparing means include:

Methods	R function	Description
T-test	t.test()	Compare two groups (parametric)
Wilcoxon test	wilcox.test()	Compare two groups (non-parametric)
ANOVA	aov() or anova()	Compare multiple groups (parametric)
Kruskal-Wallis	kruskal.test()	Compare multiple groups (non-parametric)

A practical guide to compute and interpret the results of each of these methods are provided at the following links:

Comparing one-sample mean to a standard known mean:
- One-Sample T-test (parametric)
- One-Sample Wilcoxon Test (non-parametric)
Comparing the means of two independent groups:
- Unpaired Two Samples T-test (parametric)
- Unpaired Two-Samples Wilcoxon Test (non-parametric)
Comparing the means of paired samples:
- Paired Samples T-test (parametric)
- Paired Samples Wilcoxon Test (non-parametric)
Comparing the means of more than two groups
- Analysis of variance (ANOVA, parametric):
  - One-Way ANOVA Test in R
  - Two-Way ANOVA Test in R
- Kruskal-Wallis Test in R (non parametric alternative to one-way ANOVA)

R functions to add p-values

Here we present two new R functions in the ggpubr package:

compare_means(): easy to use solution to performs one and multiple mean comparisons.
stat_compare_means(): easy to use solution to automatically add p-values and significance levels to a ggplot.

compare_means()

As we’ll show in the next sections, it has multiple useful options compared to the standard R functions.

The simplified format is as follow:

compare_means(formula, data, method = "wilcox.test", paired = FALSE,
  group.by = NULL, ref.group = NULL, ...)

formula: a formula of the form x ~ group, where x is a numeric variable and group is a factor with one or multiple levels. For example, formula = TP53 ~ cancer_group. It’s also possible to perform the test for multiple response variables at the same time. For example, formula = c(TP53, PTEN) ~ cancer_group.
data: a data.frame containing the variables in the formula.
method: the type of test. Default is “wilcox.test”. Allowed values include:
- “t.test” (parametric) and “wilcox.test”" (non-parametric). Perform comparison between two groups of samples. If the grouping variable contains more than two levels, then a pairwise comparison is performed.
- “anova” (parametric) and “kruskal.test” (non-parametric). Perform one-way ANOVA test comparing multiple groups.
paired: a logical indicating whether you want a paired test. Used only in t.test and in wilcox.test.
group.by: variables used to group the data set before applying the test. When specified the mean comparisons will be performed in each subset of the data formed by the different levels of the group.by variables.
ref.group: a character string specifying the reference group. If specified, for a given grouping variable, each of the group levels will be compared to the reference group (i.e. control group). ref.group can be also “.all.”. In this case, each of the grouping variable levels is compared to all (i.e. base-mean).

stat_compare_means()

This function extends ggplot2 for adding mean comparison p-values to a ggplot, such as box blots, dot plots, bar plots and line plots.

The simplified format is as follow:

stat_compare_means(mapping = NULL, comparisons = NULL hide.ns = FALSE,
                   label = NULL,  label.x = NULL, label.y = NULL,  ...)

mapping: Set of aesthetic mappings created by aes().
comparisons: A list of length-2 vectors. The entries in the vector are either the names of 2 values on the x-axis or the 2 integers that correspond to the index of the groups of interest, to be compared.
hide.ns: logical value. If TRUE, hide ns symbol when displaying significance levels.
label: character string specifying label type. Allowed values include “p.signif” (shows the significance levels), “p.format” (shows the formatted p value).
label.x,label.y: numeric values. coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.
…: other arguments passed to the function compare_means() such as method, paired, ref.group.

Compare two independent groups

Perform the test:

compare_means(len ~ supp, data = ToothGrowth)

# A tibble: 1 x 8
    .y. group1 group2          p      p.adj p.format p.signif   method
                              
1   len     OJ     VC 0.06449067 0.06449067    0.064       ns Wilcoxon

By default method = “wilcox.test” (non-parametric test). You can also specify method = “t.test” for a parametric t-test.

Returned value is a data frame with the following columns:

.y.: the y variable used in the test.
p: the p-value
p.adj: the adjusted p-value. Default value for p.adjust.method = “holm”
p.format: the formatted p-value
p.signif: the significance level.
method: the statistical test used to compare groups.

Create a box plot with p-values:

p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "jco",
          add = "jitter")
#  Add p-value
p + stat_compare_means()

# Change method
p + stat_compare_means(method = "t.test")

Add p-values and significance levels to ggplots

Note that, the p-value label position can be adjusted using the arguments: label.x, label.y, hjust and vjust.

The default p-value label displayed is obtained by concatenating the method and the p columns of the returned data frame by the function compare_means(). You can specify other combinations using the aes() function.

For example,

aes(label = ..p.format..) or aes(label = paste0(“p =”, ..p.format..)): display only the formatted p-value (without the method name)
aes(label = ..p.signif..): display only the significance level.
aes(label = paste0(..method.., “\n”, “p =”, ..p.format..)): Use line break (“\n”) between the method name and the p-value.

As an illustration, type this:

p + stat_compare_means( aes(label = ..p.signif..), 
                        label.x = 1.5, label.y = 40)

Add p-values and significance levels to ggplots

If you prefer, it’s also possible to specify the argument label as a character vector:

p + stat_compare_means( label = "p.signif", label.x = 1.5, label.y = 40)

Compare two paired samples

Perform the test:

compare_means(len ~ supp, data = ToothGrowth, paired = TRUE)

# A tibble: 1 x 8
    .y. group1 group2           p       p.adj p.format p.signif   method
                                
1   len     OJ     VC 0.004312554 0.004312554   0.0043       ** Wilcoxon

Visualize paired data using the ggpaired() function:

ggpaired(ToothGrowth, x = "supp", y = "len",
         color = "supp", line.color = "gray", line.size = 0.4,
         palette = "jco")+
  stat_compare_means(paired = TRUE)

Add p-values and significance levels to ggplots

Compare more than two groups

Global test:

# Global test
compare_means(len ~ dose,  data = ToothGrowth, method = "anova")

# A tibble: 1 x 6
    .y.            p        p.adj p.format p.signif method
                            
1   len 9.532727e-16 9.532727e-16  9.5e-16     ****  Anova

Plot with global p-value:

# Default method = "kruskal.test" for multiple groups
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means()

# Change method to anova
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means(method = "anova")

Add p-values and significance levels to ggplots

Pairwise comparisons. If the grouping variable contains more than two levels, then pairwise tests will be performed automatically. The default method is “wilcox.test”. You can change this to “t.test”.

# Perorm pairwise comparisons
compare_means(len ~ dose,  data = ToothGrowth)

# A tibble: 3 x 8
    .y. group1 group2            p        p.adj p.format p.signif   method
                                  
1   len    0.5      1 7.020855e-06 1.404171e-05  7.0e-06     **** Wilcoxon
2   len    0.5      2 8.406447e-08 2.521934e-07  8.4e-08     **** Wilcoxon
3   len      1      2 1.772382e-04 1.772382e-04  0.00018      *** Wilcoxon

# Visualize: Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+ 
  stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
  stat_compare_means(label.y = 50)     # Add global p-value

Add p-values and significance levels to ggplots

If you want to specify the precise y location of bars, use the argument label.y:

ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+ 
  stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+
  stat_compare_means(label.y = 45)

Add p-values and significance levels to ggplots

(Adding bars, connecting compared groups, has been facilitated by the ggsignif R package )

Multiple pairwise tests against a reference group:

# Pairwise comparison against reference
compare_means(len ~ dose,  data = ToothGrowth, ref.group = "0.5",
              method = "t.test")

# A tibble: 2 x 8
    .y. group1 group2            p        p.adj p.format p.signif method
                                
1   len    0.5      1 6.697250e-09 6.697250e-09  6.7e-09     **** T-test
2   len    0.5      2 1.469534e-16 2.939068e-16  < 2e-16     **** T-test

# Visualize
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means(method = "anova", label.y = 40)+      # Add global p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = "0.5")                    # Pairwise comparison against reference

Add p-values and significance levels to ggplots

Multiple pairwise tests against all (base-mean):

# Comparison of each group against base-mean
compare_means(len ~ dose,  data = ToothGrowth, ref.group = ".all.",
              method = "t.test")

# A tibble: 3 x 8
    .y. group1 group2            p        p.adj p.format p.signif method
                                
1   len  .all.    0.5 1.244626e-06 3.733877e-06  1.2e-06     **** T-test
2   len  .all.      1 5.667266e-01 5.667266e-01     0.57       ns T-test
3   len  .all.      2 1.371704e-05 2.743408e-05  1.4e-05     **** T-test

# Visualize
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+
  stat_compare_means(method = "anova", label.y = 40)+      # Add global p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = ".all.")                  # Pairwise comparison against all

Add p-values and significance levels to ggplots

A typical situation, where pairwise comparisons against “all” can be useful, is illustrated here using the myeloma data set from the survminer package.

We’ll plot the expression profile of the DEPDC1 gene according to the patients’ molecular groups. We want to know if there is any difference between groups. If yes, where the difference is?

To answer to this question, you can perform a pairwise comparison between all the 7 groups. This will lead to a lot of comparisons between all possible combinations. If you have many groups, as here, it might be difficult to interpret.

Another easy solution is to compare each of the seven groups against “all” (i.e. base-mean). When the test is significant, then you can conclude that DEPDC1 is significantly overexpressed or downexpressed in a group xxx compared to all.

# Load myeloma data from survminer package
if(!require(survminer)) install.packages("survminer")
data("myeloma", package = "survminer")

# Perform the test
compare_means(DEPDC1 ~ molecular_group,  data = myeloma,
              ref.group = ".all.", method = "t.test")

# A tibble: 7 x 8
     .y. group1           group2            p        p.adj p.format p.signif method
                                           
1 DEPDC1  .all.       Cyclin D-1 1.496896e-01 4.490687e-01  0.14969       ns T-test
2 DEPDC1  .all.       Cyclin D-2 5.231428e-01 1.000000e+00  0.52314       ns T-test
3 DEPDC1  .all.     Hyperdiploid 2.815333e-04 1.689200e-03  0.00028      *** T-test
4 DEPDC1  .all. Low bone disease 5.083985e-03 2.541992e-02  0.00508       ** T-test
5 DEPDC1  .all.              MAF 8.610664e-02 3.444265e-01  0.08611       ns T-test
6 DEPDC1  .all.            MMSET 5.762908e-01 1.000000e+00  0.57629       ns T-test
7 DEPDC1  .all.    Proliferation 1.241416e-09 8.689910e-09  1.2e-09     **** T-test

# Visualize the expression profile
ggboxplot(myeloma, x = "molecular_group", y = "DEPDC1", color = "molecular_group", 
          add = "jitter", legend = "none") +
  rotate_x_text(angle = 45)+
  geom_hline(yintercept = mean(myeloma$DEPDC1), linetype = 2)+ # Add horizontal line at base mean
  stat_compare_means(method = "anova", label.y = 1600)+        # Add global annova p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = ".all.")                      # Pairwise comparison against all

Add p-values and significance levels to ggplots

From the plot above, we can conclude that DEPDC1 is significantly overexpressed in proliferation group and, it’s significantly downexpressed in Hyperdiploid and Low bone disease compared to all.

Note that, if you want to hide the ns symbol, specify the argument hide.ns = TRUE.

# Visualize the expression profile
ggboxplot(myeloma, x = "molecular_group", y = "DEPDC1", color = "molecular_group", 
          add = "jitter", legend = "none") +
  rotate_x_text(angle = 45)+
  geom_hline(yintercept = mean(myeloma$DEPDC1), linetype = 2)+ # Add horizontal line at base mean
  stat_compare_means(method = "anova", label.y = 1600)+        # Add global annova p-value
  stat_compare_means(label = "p.signif", method = "t.test",
                     ref.group = ".all.", hide.ns = TRUE)      # Pairwise comparison against all

Add p-values and significance levels to ggplots

Multiple grouping variables

Two independent sample comparisons after grouping the data by another variable:

Perform the test:

compare_means(len ~ supp, data = ToothGrowth, 
              group.by = "dose")

# A tibble: 3 x 9
   dose   .y. group1 group2           p      p.adj p.format p.signif   method
                                
1   0.5   len     OJ     VC 0.023186427 0.04637285    0.023        * Wilcoxon
2   1.0   len     OJ     VC 0.004030367 0.01209110    0.004       ** Wilcoxon
3   2.0   len     OJ     VC 1.000000000 1.00000000    1.000       ns Wilcoxon

In the example above, for each level of the variable “dose”, we compare the means of the variable “len” in the different groups formed by the grouping variable “supp”.

Visualize (1/2). Create a multi-panel box plots facetted by group (here, “dose”):

# Box plot facetted by "dose"
p <- ggboxplot(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "jco",
          add = "jitter",
          facet.by = "dose", short.panel.labs = FALSE)
# Use only p.format as label. Remove method name.
p + stat_compare_means(label = "p.format")

Add p-values and significance levels to ggplots

# Or use significance symbol as label
p + stat_compare_means(label =  "p.signif", label.x = 1.5)

Add p-values and significance levels to ggplots

To hide the ‘ns’ symbol, use the argument hide.ns = TRUE.

Visualize (2/2). Create one single panel with all box plots. Plot y = “len” by x = “dose” and color by “supp”:

p <- ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "supp", palette = "jco",
          add = "jitter")
p + stat_compare_means(aes(group = supp))

Add p-values and significance levels to ggplots

# Show only p-value
p + stat_compare_means(aes(group = supp), label = "p.format")

Add p-values and significance levels to ggplots

# Use significance symbol as label
p + stat_compare_means(aes(group = supp), label = "p.signif")

Add p-values and significance levels to ggplots

Paired sample comparisons after grouping the data by another variable:

Perform the test:

compare_means(len ~ supp, data = ToothGrowth, 
              group.by = "dose", paired = TRUE)

# A tibble: 3 x 9
   dose   .y. group1 group2          p      p.adj p.format p.signif   method
                               
1   0.5   len     OJ     VC 0.03296938 0.06593876    0.033        * Wilcoxon
2   1.0   len     OJ     VC 0.01905889 0.05717667    0.019        * Wilcoxon
3   2.0   len     OJ     VC 1.00000000 1.00000000    1.000       ns Wilcoxon

Visualize. Create a multi-panel box plots facetted by group (here, “dose”):

# Box plot facetted by "dose"
p <- ggpaired(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "jco", 
          line.color = "gray", line.size = 0.4,
          facet.by = "dose", short.panel.labs = FALSE)
# Use only p.format as label. Remove method name.
p + stat_compare_means(label = "p.format", paired = TRUE)

Add p-values and significance levels to ggplots

Other plot types

Bar and line plots (one grouping variable):

# Bar plot of mean +/-se
ggbarplot(ToothGrowth, x = "dose", y = "len", add = "mean_se")+
  stat_compare_means() +                                         # Global p-value
  stat_compare_means(ref.group = "0.5", label = "p.signif",
                     label.y = c(22, 29))                   # compare to ref.group

# Line plot of mean +/-se
ggline(ToothGrowth, x = "dose", y = "len", add = "mean_se")+
  stat_compare_means() +                                         # Global p-value
  stat_compare_means(ref.group = "0.5", label = "p.signif",
                     label.y = c(22, 29))

Add p-values and significance levels to ggplots

Bar and line plots (two grouping variables):

ggbarplot(ToothGrowth, x = "dose", y = "len", add = "mean_se",
          color = "supp", palette = "jco", 
          position = position_dodge(0.8))+
  stat_compare_means(aes(group = supp), label = "p.signif", label.y = 29)

ggline(ToothGrowth, x = "dose", y = "len", add = "mean_se",
          color = "supp", palette = "jco")+
  stat_compare_means(aes(group = supp), label = "p.signif", 
                     label.y = c(16, 25, 29))

Add p-values and significance levels to ggplots

Infos

This analysis has been performed using R software (ver. 3.3.2) and ggpubr (ver. 0.1.3).

R packages

Tue, 13 Dec 2016 00:24:14 +0100

In this section, you’ll find R packages developed by STHDA for easy data analyses.

factoextra

factoextra let you extract and create ggplot2-based elegant visualizations of multivariate data analyse results, including PCA, CA, MCA, MFA, HMFA and clustering methods.

Overview >>
factoextra Site Link >>

survminer

survminer provides functions for facilitating survival analysis and visualization.

Overview >>
survminer Site Link >>

Releases: v0.2.4 |

ggpubr

The default plots generated by ggplot2 requires some formatting before we can send them for publication. 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.

Overview >>
ggpubr Site Link >>

Infos

This analysis has been performed using R software (ver. 3.3.2)

Bar plot of Group Means with Individual Observations

Thu, 27 Oct 2016 16:55:39 +0200

Example data sets
Install ggpubr
Bar plot of group means with individual informations

ggpubr is great for data visualization and very easy to use for non-“R programmer”. It makes easy to simply produce an elegant ggplot2-based graphs. Read more about ggpubr: ggpubr .

Here we demonstrate how to plot easily a barplot of group means +/- standard error with individual observations.

Example data sets

d <- as.data.frame(mtcars[, c("am", "hp")])
d$rowname <- rownames(d)
head(d)

##                   am  hp           rowname
## Mazda RX4          1 110         Mazda RX4
## Mazda RX4 Wag      1 110     Mazda RX4 Wag
## Datsun 710         1  93        Datsun 710
## Hornet 4 Drive     0 110    Hornet 4 Drive
## Hornet Sportabout  0 175 Hornet Sportabout
## Valiant            0 105           Valiant

Install ggpubr

The latest version of ggpubr can be installed as follow:

# Install ggpubr
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")

Bar plot of group means with individual informations

Plot y = “hp” by groups x = “am”
Add mean +/- standard error and individual points: add = c(“mean_se”, “point”). Allowed values are one or the combination of: “none”, “dotplot”, “jitter”, “boxplot”, “point”, “mean”, “mean_se”, “mean_sd”, “mean_ci”, “mean_range”, “median”, “median_iqr”, “median_mad”, “median_range”.
Color and fill by groups: color = “am”, fill = “am”
Add row names as labels.

library(ggpubr)
# Bar plot of group means + points
ggbarplot(d, x = "am", y = "hp",
          add = c("mean_se", "point"),
          color = "am", fill = "am", alpha = 0.5)+ 
  ggrepel::geom_text_repel(aes(label = rowname))

Bar plot of Group Means with Individual Observations

ggpubr R Package: ggplot2-Based Publication Ready Plots

Sun, 24 Jul 2016 16:44:26 +0200

Why ggpubr?

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.

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")

Load ggpubr as follow:

library(ggpubr)

Geting started

See the online documentation (https://www.sthda.com/english/rpkgs/ggpubr) for a complete list.

Density and histogram plots

Create some data

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"))

Note that:

the argument palette is used for coloring or filling by groups. Allowed values include:
- “grey” for grey color palettes;
- brewer palettes e.g. “RdBu”, “Blues”, …; click here to see all brewer palettes.
- or custom color palettes e.g. c(“blue”, “red”) or c(“#00AFBB”, “#E7B800”);
- and scientific journal palettes from ggsci R package, e.g.: “npg”, “aaas”, “lancet”, “jco”, “ucscgb”, “uchicago”, “simpsons” and “rickandmorty”.
the argument add can be used to add mean or median lines to density and to histogram plots. Allowed values are: “mean” and “median”.

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"))

If you want to create the above histogram with the standard ggplot2 functions, the syntax is extremely complex for beginners (see the R script below). The ggpubr package is a wrapper around ggplot2 functions to make your life easier and to produce quickly a publication ready plot.

# ggplot2 standard syntax for creating histogram
# +++++++++++++++++++++++++++++++++++++
# Compute group mean
library("dplyr")
mu <- wdata %>%
group_by(sex) %>%
summarise(grp.mean = mean(weight))
# Plot
ggplot(data = wdata, aes(weight)) +
  geom_histogram(aes(color = sex, fill = sex),
                 position = "identity", alpha = 0.5)+
  geom_vline(data = mu, aes(xintercept=grp.mean, color = sex),
             linetype="dashed", size=1) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  theme_classic()+
  theme(
    axis.text.x = element_text(size = 12, colour = "black",face = "bold"),
    axis.text.y = element_text(size = 12, colour = "black",face = "bold"),
    axis.line.x = element_line(colour = "black", size = 1),
    axis.line.y = element_line(colour = "black", size = 1),
    legend.position = "bottom"
    )

Box plots, violin plots, dot plots and strip charts

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
 ggboxplot(df, x = "dose", y = "len",
    color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
    add = "jitter", shape = "dose")

Note that, when using ggpubr functions for drawing box plots, violin plots, dot plots, strip charts, bar plots, line plots or error plots, the argument add can be used for adding another plot element (e.g.: dot plot or error bars).

In this case, allowed values for the argument add are one or the combination of: “none”, “dotplot”, “jitter”, “boxplot”, “mean”, “mean_se”, “mean_sd”, “mean_ci”, “mean_range”, “median”, “median_iqr”, “median_mad”, “median_range”; see ?desc_statby for more details.

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"))

Dot plots with summary statistics

# Change outline and fill colors by groups: dose
# Add mean + sd
ggdotplot(df, x = "dose", y = "len", color = "dose", fill = "dose", 
          palette = c("#00AFBB", "#E7B800", "#FC4E07"),
          add = "mean_sd", add.params = list(color = "gray"))

Recall that, possible summary statistics include “boxplot”, “mean”, “mean_se”, “mean_sd”, “mean_ci”, “mean_range”, “median”, “median_iqr”, “median_mad”, “median_range”; see ?desc_statby for more details.

Strip chart with summary statistics

# Change points size
# Change point colors and shapes by groups: dose
# Use custom color palette
 ggstripchart(df, "dose", "len",  size = 2, shape = "dose",
   color = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
   add = "mean_sd")

Bar plots

Basic plot with labels outsite

# Data
df2 <- data.frame(dose=c("D0.5", "D1", "D2"),
   len=c(4.2, 10, 29.5))
print(df2)

##   dose  len
## 1 D0.5  4.2
## 2   D1 10.0
## 3   D2 29.5

# Change ouline and fill colors by groups: dose
# Use custom color palette
# Add labels
 ggbarplot(df2, x = "dose", y = "len",
   fill = "dose", color = "dose",
   palette = c("#00AFBB", "#E7B800", "#FC4E07"),
   label = TRUE)

Use lab.pos = “in”, to put labels inside bars
Use lab.col, to change label colors

Bar plot with multiple groups

# Create some data
df3 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
   dose=rep(c("D0.5", "D1", "D2"),2),
   len=c(6.8, 15, 33, 4.2, 10, 29.5))
print(df3)

##   supp dose  len
## 1   VC D0.5  6.8
## 2   VC   D1 15.0
## 3   VC   D2 33.0
## 4   OJ D0.5  4.2
## 5   OJ   D1 10.0
## 6   OJ   D2 29.5

# Plot "len" by "dose" and change color by a second group: "supp"
# Add labels inside bars
ggbarplot(df3, x = "dose", y = "len",
  fill = "supp", color = "supp", palette = c("#00AFBB", "#E7B800"),
  label = TRUE, lab.col = "white", lab.pos = "in")

Bar plot visualizing the mean of each group with error bars

# Data: ToothGrowth data set we'll be used.
df <- ToothGrowth
head(df, 10)

##     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
## 5   6.4   VC  0.5
## 6  10.0   VC  0.5
## 7  11.2   VC  0.5
## 8  11.2   VC  0.5
## 9   5.2   VC  0.5
## 10  7.0   VC  0.5

# Visualize the mean of each group
# Change point and outline colors by groups: dose
# Add jitter points and errors (mean_se)
ggbarplot(df, x = "dose", y = "len", color = "dose",
          palette = c("#00AFBB", "#E7B800", "#FC4E07"),
          add = c("mean_se", "jitter"))

Line plots

Line plots with multiple groups

# Plot "len" by "dose" and
# Change line types and point shapes by a second groups: "supp"
# Change color by groups "supp"
ggline(df3, x = "dose", y = "len",
  linetype = "supp", shape = "supp",
  color = "supp",  palette = c("#00AFBB", "#E7B800"))

Line plot visualizing the mean of each group with error bars

# Visualize the mean of each group: dose
# Change colors by a second groups: supp
# Add jitter points and errors (mean_se)
ggline(df, x = "dose", y = "len", 
       color = "supp", 
       palette = c("#00AFBB", "#E7B800", "#FC4E07"),
       add = c("mean_se", "jitter"))

Pie chart

Create some data

df4 <- data.frame(
  group = c("Male", "Female", "Child"),
  value = c(25, 25, 50))
head(df4)

##    group value
## 1   Male    25
## 2 Female    25
## 3  Child    50

Pie chart

# Change fill color by group
# set outline line color to white
# Use custom color palette
# Show group names and value as labels
labs <- paste0(df4$group, " (", df4$value, "%)")
ggpie(df4, x = "value", fill = "group", color = "white",
   palette = c("#00AFBB", "#E7B800", "#FC4E07"),
   label = labs, lab.pos = "in", lab.font = "white")

Scatter plots

Load and prepare data

data("mtcars")
df5 <- mtcars
df5$cyl <- as.factor(df5$cyl) # grouping variable
df5$name = rownames(df5) # for point labels
head(df5[, c("wt", "mpg", "cyl")], 3)

##                  wt  mpg cyl
## Mazda RX4     2.620 21.0   6
## Mazda RX4 Wag 2.875 21.0   6
## Datsun 710    2.320 22.8   4

Scatter plots with regression line and confidence interval

ggscatter(df5, x = "wt", y = "mpg",
   color = "black", shape = 21, size = 4, # Points color, shape and size
   add = "reg.line",  # Add regressin line
   add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
   conf.int = TRUE, # Add confidence interval
   cor.coef = TRUE # Add correlation coefficient
   )

Note that, when using ggpubr functions for drawing scatter plots, allowed values for the argument add are one of “none”, “reg.line” (for adding linear regression line) or “loess” (for adding local regression fitting).

Scatter plot with concentration ellipses and labels

# Change point colors and shapes by groups: cyl
# Use custom palette
# Add concentration ellipses with mean points (barycenters)
# Add marginal rug
# Add label and use repel = TRUE to avoid label overplotting
ggscatter(df5, x = "wt", y = "mpg",
   color = "cyl", shape = "cyl",
   palette = c("#00AFBB", "#E7B800", "#FC4E07"),
   ellipse = TRUE, mean.point = TRUE,
   rug = TRUE, label = "name", font.label = 10, repel = TRUE)

Note that, it’s possible to change the ellipse type by using the argument ellipse.type. Possible values are ‘convex’, ‘confidence’ or types supported by ggplot2::stat_ellipse() including one of c(“t”, “norm”, “euclid”).

Cleveland’s dot plots

# Change colors by  group cyl
ggdotchart(df5, x = "mpg", label = "name",
   group = "cyl", color = "cyl",
   palette = c("#00AFBB", "#E7B800", "#FC4E07") )

ggpar(): customize ggplot easily

The function ggpar() [in ggpubr] can be used to simply and easily customize any ggplot2-based graphs. The graphical parameters that can be changed using ggpar() include:

Main titles, axis labels and legend titles
Legend position and appearance
colors
Axis limits
Axis transformations: log and sqrt
Axis ticks
Themes
Rotate a plot

Note that all the arguments accepted by the function ggpar() can be also directly passed to the plotting functions in ggpubr package.

We start by creating a basic box plot colored by groups as follow:

df <- ToothGrowth
p <- ggboxplot(df, x = "dose", y = "len",
               color = "dose")
print(p)

Main titles, axis labels and legend titles

# Change title texts and fonts
ggpar(p, main = "Plot of length \n by dose",
      xlab ="Dose (mg)", ylab = "Teeth length",
      legend.title = "Dose (mg)",
      font.main = c(14,"bold.italic", "red"),
      font.x = c(14, "bold", "#2E9FDF"),
      font.y = c(14, "bold", "#E7B800"))

# Hide titles
ggpar(p, xlab = FALSE, ylab = FALSE)

Note that,

font.main, font.x, font.y are vectors of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: “plain”, “bold”, “italic”, “bold.italic”) and the color (e.g.: “red”) of main title, xlab and ylab, respectively. For example font.x = c(14, “bold”, “red”). Use font.x = 14, to change only font size; or use font.x = “bold”, to change only font face.
you can use \n, to split long title into multiple lines.

Legend position and appearance

ggpar(p,
 legend = "right", legend.title = "Dose (mg)",
 font.legend = c(10, "bold", "red"))

Note that, the legend argument is a character vector specifying legend position. Allowed values are one of c(“top”, “bottom”, “left”, “right”, “none”). Default is “bottom” side position. to remove the legend use legend = “none”. Legend position can be also specified using a numeric vector c(x, y). Their values should be between 0 and 1. c(0,0) corresponds to the “bottom left” and c(1,1) corresponds to the “top right” position.

Color palettes

As mentioned above, the argument palette is used to change group color palettes. Allowed values include:

Custom color palettes e.g. c(“blue”, “red”) or c(“#00AFBB”, “#E7B800”);
“grey” for grey color palettes;
brewer palettes e.g. “RdBu”, “Blues”, …; click here to see all brewer palettes.
and scientific journal palettes from ggsci R package, e.g.: “npg”, “aaas”, “lancet”, “jco”, “ucscgb”, “uchicago”, “simpsons” and “rickandmorty”.

# Use custom color palette
ggpar(p, palette = c("#00AFBB", "#E7B800", "#FC4E07"))

# Use brewer palette
ggpar(p, palette = "Dark2" )

# Use grey palette
ggpar(p, palette = "grey")
   

# Use scientific journal palette from ggsci package
# Allowed values: "npg", "aaas", "lancet", "jco", 
#   "ucscgb", "uchicago", "simpsons" and "rickandmorty".
ggpar(p, palette = "npg") # nature

Axis limits and scales

The following arguments can be used:

xlim, ylim: a numeric vector of length 2, specifying x and y axis limits (minimum and maximum values), respectively. e.g.: ylim = c(0, 50).
xscale, yscale: x and y axis scale, respectively. Allowed values are one of c(“none”, “log2”, “log10”, “sqrt”); e.g.: yscale=“log2”.
format.scale: logical value. If TRUE, axis tick mark labels will be formatted when xscale or yscale = “log2” or “log10”.

# Change y axis limits
ggpar(p, ylim = c(0, 50))

# Change y axis scale to log2
ggpar(p, yscale = "log2")

# Format axis scale
ggpar(p, yscale = "log2", format.scale = TRUE)

Axis ticks: customize tick marks and labels

The following arguments can be used:

ticks: logical value. Default is TRUE. If FALSE, hide axis tick marks.
tickslab: logical value. Default is TRUE. If FALSE, hide axis tick labels.
font.tickslab: Font style (size, face, color) for tick labels, e.g.: c(14, “bold”, “red”).
xtickslab.rt, ytickslab.rt: Rotation angle of x and y axis tick labels, respectively. Default value is 0.
xticks.by, yticks.by: numeric value controlling x and y axis breaks, respectively. For example, if yticks.by = 5, a tick mark is shown on every 5. Default value is NULL.

# Axis tick labels style: "plain", "italic", "bold" or "bold.italic"
# Rotation angle = 45
ggpar(p, font.tickslab = c(12, "bold", "#2E9FDF"),
      xtickslab.rt = 45, ytickslab.rt = 45)

# Hide ticks and tickslab
ggpar(p, ticks = FALSE, tickslab = FALSE)

Themes

The R package ggpubr contains two main functions for changing the default ggplot theme to a publication ready theme:

theme_pubr(): change the theme to a publication ready theme
labs_pubr(): Format only plot labels to a publication ready style

theme_pubr() will produce plots with bold axis labels, bold tick mark labels and legend at the bottom leaving extra space for the plotting area.

The argument ggtheme can be used in any ggpubr plotting functions to change the plot theme. Default value is theme_pubr() for publication ready theme. Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), etc. It’s also possible to use the function “+” to add a theme.

# Gray theme
p + theme_gray()

# Minimal theme
p + theme_minimal()

# Format only plot labels to a publication ready style
# by using the function labs_pubr()
p + theme_minimal() + labs_pubr(base_size = 16)

Rotate a plot

Create some data

set.seed(1234)
wdata = data.frame(
   sex = factor(rep(c("F", "M"), each=200)),
   weight = c(rnorm(200, 55), rnorm(200, 58)))

Create a density plot and change plot orientation

# Basic density plot
p <- ggdensity(wdata, x = "weight") + theme_gray()
p

# Horizontal plot
ggpar(p, orientation = "horizontal" ) + theme_gray()

# y axis reversed
ggpar(p, orientation = "reverse" ) + theme_gray()

See the online documentation (https://www.sthda.com/english/rpkgs/ggpubr) for a complete list.

Infos

This analysis has been performed using R software (ver. 3.2.4) and ggpubr (ver. 0.1.0.999)