Prerequisites

install.packages("ggpubr")

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

library(ggpubr)

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:

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

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

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

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

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

# 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

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

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).

Prerequisites

install.packages("ggpubr")

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

library(ggpubr)

# 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")

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

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

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

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

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
          )

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

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).

Prerequisites

install.packages("ggpubr")

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

Prerequisites

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

install.packages("ggpubr")

library(ggpubr)

# ToothGrowth
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

# mtcars 
data("mtcars")
mtcars$name <- rownames(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
head(mtcars[, 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 some plots

Here, we’ll use ggplot2-based plotting functions available in ggpubr. You can use any ggplot2 functions to create the plots that you want for arranging them later.

We’ll start by creating 4 different plots:

• Box plots and dot plots using the ToothGrowth data set
• Bar plots and scatter plots using the mtcars data set

You’ll learn how to combine these plots in the next sections using specific functions.

• Create a box plot and a dot plot:

# Box plot (bp)
bxp <- ggboxplot(ToothGrowth, x = "dose", y = "len",
                 color = "dose", palette = "jco")
bxp
# Dot plot (dp)
dp <- ggdotplot(ToothGrowth, x = "dose", y = "len",
                 color = "dose", palette = "jco", binwidth = 1)
dp

Arrange multiple ggplots on the same page

• Create an ordered bar plot and a scatter plot:

Create ordered bar plots. Change the fill color by the grouping variable “cyl”. Sorting will be done globally, but not by groups.

# Bar plot (bp)
bp <- ggbarplot(mtcars, 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 ascending order
          sort.by.groups = TRUE,      # Sort inside each group
          x.text.angle = 90           # Rotate vertically x axis texts
          )
bp + font("x.text", size = 8)
# Scatter plots (sp)
sp <- ggscatter(mtcars, x = "wt", y = "mpg",
                add = "reg.line",               # Add regression line
                conf.int = TRUE,                # Add confidence interval
                color = "cyl", palette = "jco", # Color by groups "cyl"
                shape = "cyl"                   # Change point shape by groups "cyl"
                )+
  stat_cor(aes(color = cyl), label.x = 3)       # Add correlation coefficient
sp

Arrange multiple ggplots on the same page

Arrange on one page

To arrange multiple ggplots on one single page, we’ll use the function ggarrange()[in ggpubr], which is a wrapper around the function plot_grid() [in cowplot package]. Compared to the standard function plot_grid(), ggarange() can arrange multiple ggplots over multiple pages.

ggarrange(bxp, dp, bp + rremove("x.text"), 
          labels = c("A", "B", "C"),
          ncol = 2, nrow = 2)

Arrange multiple ggplots on the same page

Alternatively, you can also use the function plot_grid() [in cowplot]:

library("cowplot")
plot_grid(bxp, dp, bp + rremove("x.text"), 
          labels = c("A", "B", "C"),
          ncol = 2, nrow = 2)

or, the function grid.arrange() [in gridExtra]:

library("gridExtra")
grid.arrange(bxp, dp, bp + rremove("x.text"), 
             ncol = 2, nrow = 2)

Annotate the arranged figure

R function: annotate_figure() [in ggpubr].

figure <- ggarrange(sp, bp + font("x.text", size = 10),
                    ncol = 1, nrow = 2)
annotate_figure(figure,
                top = text_grob("Visualizing mpg", color = "red", face = "bold", size = 14),
                bottom = text_grob("Data source: \n mtcars data set", color = "blue",
                                   hjust = 1, x = 1, face = "italic", size = 10),
                left = text_grob("Figure arranged using ggpubr", color = "green", rot = 90),
                right = "I'm done, thanks :-)!",
                fig.lab = "Figure 1", fig.lab.face = "bold"
                )

Arrange multiple ggplots on the same page

Align plot panels

A real use case is, for example, when plotting survival curves with the risk table placed under the main plot.

To illustrate this case, we’ll use the survminer package. First, install it using install.packages(“survminer”), then type this:

# Fit survival curves
library(survival)
fit <- survfit( Surv(time, status) ~ adhere, data = colon )
# Plot survival curves
library(survminer)
ggsurv <- ggsurvplot(fit, data = colon, 
                     palette = "jco",                              # jco palette
                     pval = TRUE, pval.coord = c(500, 0.4),        # Add p-value
                     risk.table = TRUE                            # Add risk table
                     )
names(ggsurv)

[1] "plot""table""data.survplot""data.survtable"

ggsurv is a list including the components:

• plot: survival curves
• table: the risk table plot

You can arrange the survival plot and the risk table as follow:

ggarrange(ggsurv$plot, ggsurv$table, heights = c(2, 0.7),
          ncol = 1, nrow = 2)

Arrange multiple ggplots on the same page

ggarrange(ggsurv$plot, ggsurv$table, heights = c(2, 0.7),
          ncol = 1, nrow = 2, align = "v")

Arrange multiple ggplots on the same page

Change column/row span of a plot

Use ggpubr R package

We’ll use nested ggarrange() functions to change column/row span of plots.

For example, using the R code below:

• the scatter plot (sp) will live in the first row and spans over two columns
• the box plot (bxp) and the dot plot (dp) will be first arranged and will live in the second row with two different columns

ggarrange(sp,                                                 # First row with scatter plot
          ggarrange(bxp, dp, ncol = 2, labels = c("B", "C")), # Second row with box and dot plots
          nrow = 2, 
          labels = "A"                                        # Labels of the scatter plot
          ) 

Arrange multiple ggplots on the same page

Use cowplot R package

The combination of the functions ggdraw() + draw_plot() + draw_plot_label() [in cowplot] can be used to place graphs at particular locations with a particular size.

ggdraw(). Initialize an empty drawing canvas:

ggdraw()

Note that, by default, coordinates run from 0 to 1, and the point (0, 0) is in the lower left corner of the canvas (see the figure below).

draw_plot(). Places a plot somewhere onto the drawing canvas:

draw_plot(plot, x = 0, y = 0, width = 1, height = 1)

• plot: the plot to place (ggplot2 or a gtable)
• x, y: The x/y location of the lower left corner of the plot.
• width, height: the width and the height of the plot

draw_plot_label(). Adds a plot label to the upper left corner of a graph. It can handle vectors of labels with associated coordinates.

draw_plot_label(label, x = 0, y = 1, size = 16, ...)

• label: a vector of labels to be drawn
• x, y: Vector containing the x and y position of the labels, respectively.
• size: Font size of the label to be drawn

For example, you can combine multiple plots, with particular locations and different sizes, as follow:

library("cowplot")
ggdraw() +
  draw_plot(bxp, x = 0, y = .5, width = .5, height = .5) +
  draw_plot(dp, x = .5, y = .5, width = .5, height = .5) +
  draw_plot(bp, x = 0, y = 0, width = 1, height = 0.5) +
  draw_plot_label(label = c("A", "B", "C"), size = 15,
                  x = c(0, 0.5, 0), y = c(1, 1, 0.5))

Arrange multiple ggplots on the same page

Use gridExtra R package

The function arrangeGrop() [in gridExtra] helps to change the row/column span of a plot.

For example, using the R code below:

• the scatter plot (sp) will live in the first row and spans over two columns
• the box plot (bxp) and the dot plot (dp) will live in the second row with two plots in two different columns

library("gridExtra")
grid.arrange(sp,                             # First row with one plot spaning over 2 columns
             arrangeGrob(bxp, dp, ncol = 2), # Second row with 2 plots in 2 different columns
             nrow = 2)                       # Number of rows

Arrange multiple ggplots on the same page

It’s also possible to use the argument layout_matrix in the grid.arrange() function, to create a complex layout.

In the R code below layout_matrix is a 2x2 matrix (2 columns and 2 rows). The first row is all 1s, that’s where the first plot lives, spanning the two columns; the second row contains plots 2 and 3 each occupying one column.

grid.arrange(bp,                                    # bar plot spaning two columns
             bxp, sp,                               # box plot and scatter plot
             ncol = 2, nrow = 2, 
             layout_matrix = rbind(c(1,1), c(2,3)))

Arrange multiple ggplots on the same page

Note that, it’s also possible to annotate the output of the grid.arrange() function using the helper function draw_plot_label() [in cowplot].

To easily annotate the grid.arrange() / arrangeGrob() output (a gtable), you should first transform it to a ggplot using the function as_ggplot() [in ggpubr ]. Next you can annotate it using the function draw_plot_label() [in cowplot].

library("gridExtra")
library("cowplot")
# Arrange plots using arrangeGrob
# returns a gtable (gt)
gt <- arrangeGrob(bp,                               # bar plot spaning two columns
             bxp, sp,                               # box plot and scatter plot
             ncol = 2, nrow = 2, 
             layout_matrix = rbind(c(1,1), c(2,3)))
# Add labels to the arranged plots
p <- as_ggplot(gt) +                                # transform to a ggplot
  draw_plot_label(label = c("A", "B", "C"), size = 15,
                  x = c(0, 0, 0.5), y = c(1, 0.5, 0.5)) # Add labels
p

Arrange multiple ggplots on the same page

In the above R code, we used arrangeGrob() instead of grid.arrange().

Note that, the main difference between these two functions is that, grid.arrange() draw automatically the output of the arranged plots.

As we want to annotate the arranged plots before drawing it, the function arrangeGrob() is preferred in this case.

Use grid R package

The grid R package can be used to create a complex layout with the help of the function grid.layout(). It provides also the helper function viewport() to define a region or a viewport on the layout. The function print() is used to place plots in a specified region.

The different steps can be summarized as follow :

1. Create plots : p1, p2, p3, ….
2. Move to a new page on a grid device using the function grid.newpage()
3. Create a layout 2X2 - number of columns = 2; number of rows = 2
4. Define a grid viewport : a rectangular region on a graphics device
5. Print a plot into the viewport

library(grid)
# Move to a new page
grid.newpage()
# Create layout : nrow = 3, ncol = 2
pushViewport(viewport(layout = grid.layout(nrow = 3, ncol = 2)))
# A helper function to define a region on the layout
define_region <- function(row, col){
  viewport(layout.pos.row = row, layout.pos.col = col)
} 
# Arrange the plots
print(sp, vp = define_region(row = 1, col = 1:2))   # Span over two columns
print(bxp, vp = define_region(row = 2, col = 1))
print(dp, vp = define_region(row = 2, col = 2))
print(bp + rremove("x.text"), vp = define_region(row = 3, col = 1:2))

Arrange multiple ggplots on the same page

Use common legend for combined ggplots

To place a common unique legend in the margin of the arranged plots, the function ggarrange() [in ggpubr] can be used with the following arguments:

• common.legend = TRUE: place a common legend in a margin
• legend: specify the legend position. Allowed values include one of c(“top”, “bottom”, “left”, “right”)

ggarrange(bxp, dp, labels = c("A", "B"),
          common.legend = TRUE, legend = "bottom")

Arrange multiple ggplots on the same page

Scatter plot with marginal density plots

# Scatter plot colored by groups ("Species")
sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width",
                color = "Species", palette = "jco",
                size = 3, alpha = 0.6)+
  border()                                         
# Marginal density plot of x (top panel) and y (right panel)
xplot <- ggdensity(iris, "Sepal.Length", fill = "Species",
                   palette = "jco")
yplot <- ggdensity(iris, "Sepal.Width", fill = "Species", 
                   palette = "jco")+
  rotate()
# Cleaning the plots
yplot <- yplot + clean_theme() 
xplot <- xplot + clean_theme()
# Arranging the plot
ggarrange(xplot, NULL, sp, yplot, 
          ncol = 2, nrow = 2,  align = "hv", 
          widths = c(2, 1), heights = c(1, 2),
          common.legend = TRUE)

Arrange multiple ggplots on the same page

Mix table, text and ggplot2 graphs

In this section, we’ll show how to plot a table and text alongside a chart. The iris data set will be used.

We start by creating the following plots:

1. a density plot of the variable “Sepal.Length”. R function: ggdensity() [in ggpubr]
2. a plot of the summary table containing the descriptive statistics (mean, sd, … ) of Sepal.Length.
   • R function for computing descriptive statistics: desc_statby() [in ggpubr].
   • R function to draw a textual table: ggtexttable() [in ggpubr].
3. a plot of a text paragraph. R function: ggparagraph() [in ggpubr].

We finish by arranging/combining the three plots using the function ggarrange() [in ggpubr]

# Density plot of "Sepal.Length"
#::::::::::::::::::::::::::::::::::::::
density.p <- ggdensity(iris, x = "Sepal.Length", 
                       fill = "Species", palette = "jco")
# Draw the summary table of Sepal.Length
#::::::::::::::::::::::::::::::::::::::
# Compute descriptive statistics by groups
stable <- desc_statby(iris, measure.var = "Sepal.Length",
                      grps = "Species")
stable <- stable[, c("Species", "length", "mean", "sd")]
# Summary table plot, medium orange theme
stable.p <- ggtexttable(stable, rows = NULL, 
                        theme = ttheme("mOrange"))
# Draw text
#::::::::::::::::::::::::::::::::::::::
text <- paste("iris data set gives the measurements in cm",
              "of the variables sepal length and width",
              "and petal length and width, respectively,",
              "for 50 flowers from each of 3 species of iris.",
             "The species are Iris setosa, versicolor, and virginica.", sep = "")
text.p <- ggparagraph(text = text, face = "italic", size = 11, color = "black")
# Arrange the plots on the same page
ggarrange(density.p, stable.p, text.p, 
          ncol = 1, nrow = 3,
          heights = c(1, 0.5, 0.3))

Arrange multiple ggplots on the same page

Insert a graphical element inside a ggplot

The function annotation_custom() [in ggplot2] can be used for adding tables, plots or other grid-based elements within the plotting area of a ggplot. The simplified format is :

annotation_custom(grob, xmin, xmax, ymin, ymax)

Place a table within a ggplot

We’ll use the plots - density.p and stable.p - created in the previous section (@ref(mix-table-text-and-ggplot)).

density.p + annotation_custom(ggplotGrob(stable.p),
                              xmin = 5.5, ymin = 0.7,
                              xmax = 8)

Arrange multiple ggplots on the same page

Place a box plot within a ggplot

1. Create a scatter plot of y = “Sepal.Width” by x = “Sepal.Length” using the iris data set. R function ggscatter() [ggpubr]
2. Create separately the box plot of x and y variables with transparent background. R function: ggboxplot() [ggpubr].
3. Transform the box plots into graphical objects called a “grop” in Grid terminology. R function ggplotGrob() [ggplot2].
4. Place the box plot grobs inside the scatter plot. R function: annotation_custom() [ggplot2].

As the inset box plot overlaps with some points, a transparent background is used for the box plots.

# Scatter plot colored by groups ("Species")
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width",
                color = "Species", palette = "jco",
                size = 3, alpha = 0.6)
# Create box plots of x/y variables
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Box plot of the x variable
xbp <- ggboxplot(iris$Sepal.Length, width = 0.3, fill = "lightgray") +
  rotate() +
  theme_transparent()
# Box plot of the y variable
ybp <- ggboxplot(iris$Sepal.Width, width = 0.3, fill = "lightgray") +
  theme_transparent()
# Create the external graphical objects
# called a "grop" in Grid terminology
xbp_grob <- ggplotGrob(xbp)
ybp_grob <- ggplotGrob(ybp)
# Place box plots inside the scatter plot
#::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
xmin <- min(iris$Sepal.Length); xmax <- max(iris$Sepal.Length)
ymin <- min(iris$Sepal.Width); ymax <- max(iris$Sepal.Width)
yoffset <- (1/15)*ymax; xoffset <- (1/15)*xmax
# Insert xbp_grob inside the scatter plot
sp + annotation_custom(grob = xbp_grob, xmin = xmin, xmax = xmax, 
                       ymin = ymin-yoffset, ymax = ymin+yoffset) +
  # Insert ybp_grob inside the scatter plot
  annotation_custom(grob = ybp_grob,
                       xmin = xmin-xoffset, xmax = xmin+xoffset, 
                       ymin = ymin, ymax = ymax)

Arrange multiple ggplots on the same page

Add background image to ggplot2 graphs

Import the background image. Use either the function readJPEG() [in jpeg package] or the function readPNG() [in png package] depending on the format of the background image.

To test the example below, make sure that the png package is installed. You can install it using install.packages(“png”) R command.

# Import the image
img.file <- system.file(file.path("images", "background-image.png"),
                        package = "ggpubr")
img <- png::readPNG(img.file)

Combine a ggplot with the background image. R function: background_image() [in ggpubr].

library(ggplot2)
library(ggpubr)
ggplot(iris, aes(Species, Sepal.Length))+
  background_image(img)+
  geom_boxplot(aes(fill = Species), color = "white")+
  fill_palette("jco")

Arrange multiple ggplots on the same page

Change box plot fill color transparency by specifying the argument alpha. Value should be in [0, 1], where 0 is full transparency and 1 is no transparency.

library(ggplot2)
library(ggpubr)
ggplot(iris, aes(Species, Sepal.Length))+
  background_image(img)+
  geom_boxplot(aes(fill = Species), color = "white", alpha = 0.5)+
  fill_palette("jco")

Arrange multiple ggplots on the same page

Another example, overlaying the France map and a ggplot2:

mypngfile <- download.file("https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/France_Flag_Map.svg/612px-France_Flag_Map.svg.png", 
                           destfile = "france.png", mode = 'wb') 
img <- png::readPNG('france.png') 
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  background_image(img)+
  geom_point(aes(color = Species), alpha = 0.6, size = 5)+
  color_palette("jco")+
  theme(legend.position = "top")

Arrange multiple ggplots on the same page

Arrange over multiple pages

If you have a long list of ggplots, say n = 20 plots, you may want to arrange the plots and to place them on multiple pages. With 4 plots per page, you need 5 pages to hold the 20 plots.

The function ggarrange() [in ggpubr] provides a convenient solution to arrange multiple ggplots over multiple pages. After specifying the arguments nrow and ncol, the function ggarrange() computes automatically the number of pages required to hold the list of the plots. It returns a list of arranged ggplots.

For example the following R code,

multi.page <- ggarrange(bxp, dp, bp, sp,
                        nrow = 1, ncol = 2)

returns a list of two pages with two plots per page. You can visualize each page as follow:

multi.page[[1]] # Visualize page 1
multi.page[[2]] # Visualize page 2

You can also export the arranged plots to a pdf file using the function ggexport() [in ggpubr]:

ggexport(multi.page, filename = "multi.page.ggplot2.pdf")

PDF file: Multi.page.ggplot2

library(gridExtra)
res <- marrangeGrob(list(bxp, dp, bp, sp), nrow = 1, ncol = 2)
# Export to a pdf file
ggexport(res, filename = "multi.page.ggplot2.pdf")
# Visualize interactively
res

Nested layout with ggarrange()

We’ll arrange the plot created in section (@ref(mix-table-text-and-ggplot)) and (@ref(create-some-plots)).

p1 <- ggarrange(sp, bp + font("x.text", size = 9),
                ncol = 1, nrow = 2)
p2 <- ggarrange(density.p, stable.p, text.p, 
                ncol = 1, nrow = 3,
                heights = c(1, 0.5, 0.3))
ggarrange(p1, p2, ncol = 2, nrow = 1)

Arrange multiple ggplots on the same page

Export plots

R function: ggexport() [in ggpubr].

First, create a list of 4 ggplots corresponding to the variables Sepal.Length, Sepal.Width, Petal.Length and Petal.Width in the iris data set.

plots <- ggboxplot(iris, x = "Species",
                   y = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
                   color = "Species", palette = "jco"
                   )
plots[[1]]  # Print the first plot
plots[[2]]  # Print the second plots and so on...

Next, you can export individual plots to a file (pdf, eps or png) (one plot per page). It’s also possible to arrange the plots (2 plot per page) when exporting them.

Export individual plots to a pdf file (one plot per page):

ggexport(plotlist = plots, filename = "test.pdf")

Arrange and export. Specify nrow and ncol to display multiple plots on the same page:

ggexport(plotlist = plots, filename = "test.pdf",
         nrow = 2, ncol = 1)

Acknoweledgment

We sincerely thank all developers for their efforts behind the packages that ggpubr depends on, namely:

• Baptiste Auguie (2016). gridExtra: Miscellaneous Functions for “Grid” Graphics. R package version 2.2.1. https://CRAN.R-project.org/package=gridExtra
• Claus O. Wilke (2016). cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’. R package version 0.7.0. https://CRAN.R-project.org/package=cowplot
• H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.

Infos

This analysis has been performed using R software (ver. 3.3.2) and ggpubr (ver. 0.1.4.999).

When to you use F-test?

Comparing two variances is useful in several cases, including:

• When you want to perform a two samples t-test to check the equality of the variances of the two samples

• When you want to compare the variability of a new measurement method to an old one. Does the new method reduce the variability of the measure?

Research questions and statistical hypotheses

Typical research questions are:

In statistics, we can define the corresponding null hypothesis (\(H_0\)) as follow:

1. \(H_0: \sigma^2_A = \sigma^2_B\)
2. \(H_0: \sigma^2_A \leq \sigma^2_B\)
3. \(H_0: \sigma^2_A \geq \sigma^2_B\)

The corresponding alternative hypotheses (\(H_a\)) are as follow:

1. \(H_a: \sigma^2_A \ne \sigma^2_B\) (different)
2. \(H_a: \sigma^2_A > \sigma^2_B\) (greater)
3. \(H_a: \sigma^2_A < \sigma^2_B\) (less)

Formula of F-test

The test statistic can be obtained by computing the ratio of the two variances \(S_A^2\) and \(S_B^2\).

\[F = \frac{S_A^2}{S_B^2}\]

The degrees of freedom are \(n_A - 1\) (for the numerator) and \(n_B - 1\) (for the denominator).

Note that, the more this ratio deviates from 1, the stronger the evidence for unequal population variances.

Note that, the F-test requires the two samples to be normally distributed.

Compute F-test in R

# Method 1
var.test(values ~ groups, data, 
         alternative = "two.sided")
# or Method 2
var.test(x, y, alternative = "two.sided")

# If .txt tab file, use this
my_data <- read.delim(file.choose())
# Or, if .csv file, use this
my_data <- read.csv(file.choose())

# Store the data in the variable my_data
my_data <- ToothGrowth

library("dplyr")
sample_n(my_data, 10)

    len supp dose
43 23.6   OJ  1.0
28 21.5   VC  2.0
25 26.4   VC  2.0
56 30.9   OJ  2.0
46 25.2   OJ  1.0
7  11.2   VC  0.5
16 17.3   VC  1.0
4   5.8   VC  0.5
48 21.2   OJ  1.0
37  8.2   OJ  0.5

# F-test
res.ftest <- var.test(len ~ supp, data = my_data)
res.ftest

    F test to compare two variances
data:  len by supp
F = 0.6386, num df = 29, denom df = 29, p-value = 0.2331
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
 0.3039488 1.3416857
sample estimates:
ratio of variances 
         0.6385951 

# ratio of variances
res.ftest$estimate

ratio of variances 
         0.6385951 

# p-value of the test
res.ftest$p.value

[1] 0.2331433

Infos

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

Create a ggplot with semi-transparent color

To illustrate this, we start by creating ggplot2-based survival curves using the function ggsurvplot() in the survminer package. The ggsurvplot() function creates survival curves with the 95% confidence bands in a semi-transparent color.

First install (if needed) survminer as follow:

install.packages("survminer")

Then type, this:

# Fit survival curves
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)

# Visualize
library("survminer")
p <- ggsurvplot(fit, data = lung,
         surv.median.line = "hv", # Add medians survival
         pval = TRUE,             # Add p-value and tervals
        
         conf.int = TRUE,        # Add the 95% confidence band
         risk.table = TRUE,      # Add risk table
         tables.height = 0.2,
         tables.theme = theme_cleantable(),
         palette = "jco",
         ggtheme = theme_bw()
    )
print(p)

If you try to export the picture as vector file (EPS or SVG, …), the 95% confidence interval will disappear and the saved plot looks as follow:

The problem is that EPS in R does not support transparency.

Save ggplots with semi-transparent colors

ggsave(filename = "survival-curves.eps",
       plot = print(p),
       device = cairo_eps)

cairo_ps(filename = "survival-curves.eps",
         width = 7, height = 7, pointsize = 12,
         fallback_resolution = 300)

print(p)

dev.off()

library('ReporteRs')

# Create a new powerpoint document
doc <- pptx()

# Add a new slide into the ppt document 
doc <- addSlide(doc, slide.layout = "Two Content"  )

# Add a slide title
doc <- addTitle(doc, "Survival Curves: Editable Vector Graphics" )

# Print the survival curves in the powerpoint
doc <- addPlot(doc, function() print(p, newpage = FALSE), 
               vector.graphic = TRUE  # Make it editable
               )

# write the document to a file
writeDoc(doc, file = "editable-survival-curves.pptx")

Infos

Correlation matrix analysis

The following R code computes a correlation matrix using mtcars data. Click here to read more.

mcor<-round(cor(mtcars),2)
mcor

       mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
mpg   1.00 -0.85 -0.85 -0.78  0.68 -0.87  0.42  0.66  0.60  0.48 -0.55
cyl  -0.85  1.00  0.90  0.83 -0.70  0.78 -0.59 -0.81 -0.52 -0.49  0.53
disp -0.85  0.90  1.00  0.79 -0.71  0.89 -0.43 -0.71 -0.59 -0.56  0.39
hp   -0.78  0.83  0.79  1.00 -0.45  0.66 -0.71 -0.72 -0.24 -0.13  0.75
drat  0.68 -0.70 -0.71 -0.45  1.00 -0.71  0.09  0.44  0.71  0.70 -0.09
wt   -0.87  0.78  0.89  0.66 -0.71  1.00 -0.17 -0.55 -0.69 -0.58  0.43
qsec  0.42 -0.59 -0.43 -0.71  0.09 -0.17  1.00  0.74 -0.23 -0.21 -0.66
vs    0.66 -0.81 -0.71 -0.72  0.44 -0.55  0.74  1.00  0.17  0.21 -0.57
am    0.60 -0.52 -0.59 -0.24  0.71 -0.69 -0.23  0.17  1.00  0.79  0.06
gear  0.48 -0.49 -0.56 -0.13  0.70 -0.58 -0.21  0.21  0.79  1.00  0.27
carb -0.55  0.53  0.39  0.75 -0.09  0.43 -0.66 -0.57  0.06  0.27  1.00

The result is a table of correlation coefficients between all possible pairs of variables.

Lower and upper triangular part of a correlation matrix

To get the lower or the upper part of a correlation matrix, the R function lower.tri() or upper.tri() can be used. The formats of the functions are :

lower.tri(x, diag = FALSE)
upper.tri(x, diag = FALSE)

- x : is the correlation matrix - diag : if TRUE the diagonal are not included in the result.

The two functions above, return a matrix of logicals which has the same size of a the correlation matrix. The entries is TRUE in the lower or upper triangle :

upper.tri(mcor)

       [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11]
 [1,] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [2,] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [3,] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [4,] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [5,] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
 [6,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
 [7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
 [8,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
[10,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
[11,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

# Hide upper triangle
upper<-mcor
upper[upper.tri(mcor)]<-""
upper<-as.data.frame(upper)
upper

       mpg   cyl  disp    hp  drat    wt  qsec    vs   am gear carb
mpg      1                                                         
cyl  -0.85     1                                                   
disp -0.85   0.9     1                                             
hp   -0.78  0.83  0.79     1                                       
drat  0.68  -0.7 -0.71 -0.45     1                                 
wt   -0.87  0.78  0.89  0.66 -0.71     1                           
qsec  0.42 -0.59 -0.43 -0.71  0.09 -0.17     1                     
vs    0.66 -0.81 -0.71 -0.72  0.44 -0.55  0.74     1               
am     0.6 -0.52 -0.59 -0.24  0.71 -0.69 -0.23  0.17    1          
gear  0.48 -0.49 -0.56 -0.13   0.7 -0.58 -0.21  0.21 0.79    1     
carb -0.55  0.53  0.39  0.75 -0.09  0.43 -0.66 -0.57 0.06 0.27    1

#Hide lower triangle
lower<-mcor
lower[lower.tri(mcor, diag=TRUE)]<-""
lower<-as.data.frame(lower)
lower

     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
mpg      -0.85 -0.85 -0.78  0.68 -0.87  0.42  0.66   0.6  0.48 -0.55
cyl              0.9  0.83  -0.7  0.78 -0.59 -0.81 -0.52 -0.49  0.53
disp                  0.79 -0.71  0.89 -0.43 -0.71 -0.59 -0.56  0.39
hp                         -0.45  0.66 -0.71 -0.72 -0.24 -0.13  0.75
drat                             -0.71  0.09  0.44  0.71   0.7 -0.09
wt                                     -0.17 -0.55 -0.69 -0.58  0.43
qsec                                          0.74 -0.23 -0.21 -0.66
vs                                                  0.17  0.21 -0.57
am                                                        0.79  0.06
gear                                                            0.27
carb                                                                

Use xtable R package to display nice correlation table in html format

library(xtable)
print(xtable(upper), type="html")

Combine matrix of correlation coefficients and significance levels

Custom function corstars() is used to combine the correlation coefficients and the level of significance. The R code of the function is provided at the end of this article. It requires 2 packages :

• The Hmisc R package to compute the matrix of correlation coefficients and the corresponding p-values.
• The xtable R package for displaying in HTML or Latex format.

corstars(mtcars[,1:7], result="html")

p < .0001 ‘****’; p < .001 ‘***’, p < .01 ‘**’, p < .05 ‘*’

The code of corstars function (The code is adapted from the one posted on this forum and on this blog ):

# x is a matrix containing the data
# method : correlation method. "pearson"" or "spearman"" is supported
# removeTriangle : remove upper or lower triangle
# results :  if "html" or "latex"
  # the results will be displayed in html or latex format
corstars <-function(x, method=c("pearson", "spearman"), removeTriangle=c("upper", "lower"),
                     result=c("none", "html", "latex")){
    #Compute correlation matrix
    require(Hmisc)
    x <- as.matrix(x)
    correlation_matrix<-rcorr(x, type=method[1])
    R <- correlation_matrix$r # Matrix of correlation coeficients
    p <- correlation_matrix$P # Matrix of p-value 
    
    ## Define notions for significance levels; spacing is important.
    mystars <- ifelse(p < .0001, "****", ifelse(p < .001, "*** ", ifelse(p < .01, "**  ", ifelse(p < .05, "*   ", "    "))))
    
    ## trunctuate the correlation matrix to two decimal
    R <- format(round(cbind(rep(-1.11, ncol(x)), R), 2))[,-1]
    
    ## build a new matrix that includes the correlations with their apropriate stars
    Rnew <- matrix(paste(R, mystars, sep=""), ncol=ncol(x))
    diag(Rnew) <- paste(diag(R), " ", sep="")
    rownames(Rnew) <- colnames(x)
    colnames(Rnew) <- paste(colnames(x), "", sep="")
    
    ## remove upper triangle of correlation matrix
    if(removeTriangle[1]=="upper"){
      Rnew <- as.matrix(Rnew)
      Rnew[upper.tri(Rnew, diag = TRUE)] <- ""
      Rnew <- as.data.frame(Rnew)
    }
    
    ## remove lower triangle of correlation matrix
    else if(removeTriangle[1]=="lower"){
      Rnew <- as.matrix(Rnew)
      Rnew[lower.tri(Rnew, diag = TRUE)] <- ""
      Rnew <- as.data.frame(Rnew)
    }
    
    ## remove last column and return the correlation matrix
    Rnew <- cbind(Rnew[1:length(Rnew)-1])
    if (result[1]=="none") return(Rnew)
    else{
      if(result[1]=="html") print(xtable(Rnew), type="html")
      else print(xtable(Rnew), type="latex") 
    }
} 

Conclusions

Infos

This analysis was performed using R (ver. 3.3.2).

Introduction

Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced.

This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R.

Where to find the book:

• Download the PDF through payhip
• Read the ebook on google play
• Order a physical copy from amazon
• (Download the book preview)

The following figure illustrates the type of analysis to be performed depending on the type of variables contained in the data set.

There are a number of R packages implementing principal component methods. These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition.

However, the result is presented differently depending on the used package.

To help in the interpretation and in the visualization of multivariate analysis - such as cluster analysis and principal component methods - we developed an easy-to-use R package named factoextra (official online documentation: http://www.sthda.com/english/rpkgs/factoextra).

Methods, which outputs can be visualized using the factoextra package are shown in the figure below:

In this book, we’ll use mainly:

How this book is organized

This book contains 4 parts.

Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra.

Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include:

• Principal Component Analysis (PCA, for continuous variables),
• Simple correspondence analysis (CA, for large contingency tables formed by two categorical variables)
• Multiple correspondence analysis (MCA, for a data set with more than 2 categorical variables).

In Part III, you’ll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups:

• Factor Analysis of Mixed Data (FAMD) and,
• Multiple Factor Analysis (MFA).

Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables

Key features of this book

This book presents the basic principles of the different methods and provide many examples in R. This book offers solid guidance in data mining for students and researchers.

Key features:

• Covers principal component methods and implementation in R
• Highlights the most important information in your data set using ggplot2-based elegant visualization
• Short, self-contained chapters with tested examples that allow for flexibility in designing a course and for easy reference

Examples of plots

Some examples of plots generated in this book are shown hereafter. You’ll learn how to create, customize and interpret these plots.

1. Eigenvalues/variances of principal components. Proportion of information retained by each principal component.

2. PCA - Graph of variables:

• Control variable colors using their contributions to the principal components.

• Highlight the most contributing variables to each principal dimension:

3. PCA - Graph of individuals:

• Control automatically the color of individuals using the cos2 (the quality of the individuals on the factor map)

• Change the point size according to the cos2 of the corresponding individuals:

4. PCA - Biplot of individuals and variables

5. Correspondence analysis. Association between categorical variables.

6. FAMD/MFA - Analyzing mixed and structured data

7. Clustering on principal components

Book preview

Download the preview of the book at: Principal Component Methods in R (Book preview)

Order now

About the author

Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization (read more: http://www.alboukadel.com/).

He has work experiences in statistical and computational methods to identify prognostic and predictive biomarker signatures through integrative analysis of large-scale genomic and clinical data sets.

He created a bioinformatics web-tool named GenomicScape (www.genomicscape.com) which is an easy-to-use web tool for gene expression data analysis and visualization.

He developed also a training website on data science, named STHDA (Statistical Tools for High-throughput Data Analysis, www.sthda.com/english), which contains many tutorials on data analysis and visualization using R software and packages.

He is the author of many popular R packages for:

• multivariate data analysis (factoextra, http://www.sthda.com/english/rpkgs/factoextra),
• survival analysis (survminer, http://www.sthda.com/english/rpkgs/survminer/),
• correlation analysis (ggcorrplot, http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2),
• creating publication ready plots in R (ggpubr, http://www.sthda.com/english/rpkgs/ggpubr).

Recently, he published three books on data analysis and visualization:

1. Practical Guide to Cluster Analysis in R (https://goo.gl/DmJ5y5)
2. Guide to Create Beautiful Graphics in R (https://goo.gl/vJ0OYb).
3. Complete Guide to 3D Plots in R (https://goo.gl/v5gwl0).

Install and load ggpubr

• 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:

library("ggpubr")

Related articles

Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data

Description: Plot one or a list of variables at once.

Contents:

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

Add P-values and Significance Levels to ggplots

Description: Compute and add automatically p-values and significance levels to ggplots.

Contents:

• Methods for comparing means
• R functions to add p-values
  • compare_means()
  • stat_compare_means()
• Compare two independent groups
• Compare two paired samples
• Compare more than two groups
• Multiple grouping variables

Perfect Scatter Plots with Correlation and Marginal Histograms

Description: Create beautiful scatter plots with correlation coefficients and marginal histograms/density.

Contents:

• Basic plots
• Color by groups
• Add concentration ellipses
• Add point labels
• Bubble chart
• Color by a continuous variable
• Add marginal plots
• Add 2d density estimation
• Application to gene expression data

Plot Means/Medians and Error Bars

Description: Plot easily means or medians with error bars.

Contents:

• Error plots
• Line plots
• Bar plots
• Add labels
• Application to gene expression data

Bar Plots and Modern Alternatives

Description: Create easily basic and ordered bar plots, as well as, some modern alternatives to bar plots, including lollipop charts and cleveland’s dot plots.

Contents:

• Basic bar plots
• Multiple grouping variables
• Ordered bar plots
• Deviation graphs
• Alternatives to bar plots
  • Lollipop chart
  • Cleveland’s dot plot

Add Text Labels to Histogram and Density Plots

Description: Create histograms/density plots and highlight some key elements on the plot.

ggplot2 - Easy Way to Mix Multiple Graphs on The Same Page

Description: Step by step guide to combine multiple ggplots on the same page, as well as, over multiple pages.

Contents:

• Create some plots
• Arrange on one page
• Annotate the arranged figure
• Align plot panels
• Change column/row span of a plot
• Use common legend for combined ggplots
• Scatter plot with marginal density plots
• Mix table, text and ggplot2 graphs
• Insert a graphical element inside a ggplot
  • Place a table within a ggplot
  • Place a box plot within a ggplot
  • Add background image to ggplot2 graphs
• Arrange over multiple pages
• Nested layout with ggarrange()
• Export plots

ggplot2 - Easy Way to Change Graphical Parameters

Description: Describe the function ggpar() [in ggpubr], which can be used to simply and easily customize any ggplot2-based graphs.

Contents:

• Change titles and axis labels
• Change legend position & appearance
• Change color palettes
  • Group colors
  • Gradient colors
• Change axis limits and scales
• Customize axis text and ticks
• Rotate a plot
• Change themes
• Remove ggplot components

Create and Customize Multi-panel ggplots: Easy Guide to Facet

Description: split up your data by one or more variables and to visualize the subsets of the data together.

Contents:

• Facet by one grouping variables
• Facet by two grouping variables
• Modifying panel label appearance

Basic barplots

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

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

library(ggplot2)
# Basic barplot
p<-ggplot(data=df, aes(x=dose, y=len)) +
  geom_bar(stat="identity")
p
   
# Horizontal bar plot
p + coord_flip()

# Change the width of bars
ggplot(data=df, aes(x=dose, y=len)) +
  geom_bar(stat="identity", width=0.5)
# Change colors
ggplot(data=df, aes(x=dose, y=len)) +
  geom_bar(stat="identity", color="blue", fill="white")
# Minimal theme + blue fill color
p<-ggplot(data=df, aes(x=dose, y=len)) +
  geom_bar(stat="identity", fill="steelblue")+
  theme_minimal()
p

p + scale_x_discrete(limits=c("D0.5", "D2"))

# Outside bars
ggplot(data=df, aes(x=dose, y=len)) +
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=len), vjust=-0.3, size=3.5)+
  theme_minimal()
# Inside bars
ggplot(data=df, aes(x=dose, y=len)) +
  geom_bar(stat="identity", fill="steelblue")+
  geom_text(aes(label=len), vjust=1.6, color="white", size=3.5)+
  theme_minimal()

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

# Don't map a variable to y
ggplot(mtcars, aes(x=factor(cyl)))+
  geom_bar(stat="bin", width=0.7, fill="steelblue")+
  theme_minimal()

Change barplot colors by groups

# Change barplot line colors by groups
p<-ggplot(df, aes(x=dose, y=len, color=dose)) +
  geom_bar(stat="identity", fill="white")
p

# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey() + theme_classic()

# Change barplot fill colors by groups
p<-ggplot(df, aes(x=dose, y=len, fill=dose)) +
  geom_bar(stat="identity")+theme_minimal()
p

# Use custom color palettes
p+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# use brewer color palettes
p+scale_fill_brewer(palette="Dark2")
# Use grey scale
p + scale_fill_grey()

ggplot(df, aes(x=dose, y=len, fill=dose))+
geom_bar(stat="identity", color="black")+
scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
  theme_minimal()

Change the legend position

# Change bar fill colors to blues
p <- p+scale_fill_brewer(palette="Blues")
p + theme(legend.position="top")
p + theme(legend.position="bottom")
# Remove legend
p + theme(legend.position="none")

The allowed values for the arguments legend.position are : “left”,“top”, “right”, “bottom”.

Read more on ggplot legend : ggplot2 legend

Change the order of items in the legend

The function scale_x_discrete can be used to change the order of items to “2”, “0.5”, “1” :

p + scale_x_discrete(limits=c("D2", "D0.5", "D1"))

Barplot with multiple groups

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))
head(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

# Stacked barplot with multiple groups
ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity")
# Use position=position_dodge()
ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
geom_bar(stat="identity", position=position_dodge())

# Change the colors manually
p <- ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
geom_bar(stat="identity", color="black", position=position_dodge())+
  theme_minimal()
# Use custom colors
p + scale_fill_manual(values=c('#999999','#E69F00'))
# Use brewer color palettes
p + scale_fill_brewer(palette="Blues")

ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity", position=position_dodge())+
  geom_text(aes(label=len), vjust=1.6, color="white",
            position = position_dodge(0.9), size=3.5)+
  scale_fill_brewer(palette="Paired")+
  theme_minimal()

library(plyr)
# Sort by dose and supp
df_sorted <- arrange(df2, dose, supp) 
head(df_sorted)

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

# Calculate the cumulative sum of len for each dose
df_cumsum <- ddply(df_sorted, "dose",
                   transform, label_ypos=cumsum(len))
head(df_cumsum)

##   supp dose  len label_ypos
## 1   OJ D0.5  4.2        4.2
## 2   VC D0.5  6.8       11.0
## 3   OJ   D1 10.0       10.0
## 4   VC   D1 15.0       25.0
## 5   OJ   D2 29.5       29.5
## 6   VC   D2 33.0       62.5

# Create the barplot
ggplot(data=df_cumsum, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity")+
  geom_text(aes(y=label_ypos, label=len), vjust=1.6, 
            color="white", size=3.5)+
  scale_fill_brewer(palette="Paired")+
  theme_minimal()

df_cumsum <- ddply(df_sorted, "dose",
                   transform, 
                   label_ypos=cumsum(len) - 0.5*len)
# Create the barplot
ggplot(data=df_cumsum, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity")+
  geom_text(aes(y=label_ypos, label=len), vjust=1.6, 
            color="white", size=3.5)+
  scale_fill_brewer(palette="Paired")+
  theme_minimal()

Barplot with a numeric x-axis

If the variable on x-axis is numeric, it can be useful to treat it as a continuous or a factor variable depending on what you want to do :

# Create some data
df2 <- data.frame(supp=rep(c("VC", "OJ"), each=3),
                dose=rep(c("0.5", "1", "2"),2),
                len=c(6.8, 15, 33, 4.2, 10, 29.5))
head(df2)

##   supp dose  len
## 1   VC  0.5  6.8
## 2   VC    1 15.0
## 3   VC    2 33.0
## 4   OJ  0.5  4.2
## 5   OJ    1 10.0
## 6   OJ    2 29.5

# x axis treated as continuous variable
df2$dose <- as.numeric(as.vector(df2$dose))
ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity", position=position_dodge())+
  scale_fill_brewer(palette="Paired")+
  theme_minimal()
# Axis treated as discrete variable
df2$dose<-as.factor(df2$dose)
ggplot(data=df2, aes(x=dose, y=len, fill=supp)) +
  geom_bar(stat="identity", position=position_dodge())+
  scale_fill_brewer(palette="Paired")+
  theme_minimal()

Barplot with error bars

The helper function below will be used to calculate the mean and the standard deviation, for the variable of interest, in each group :

#+++++++++++++++++++++++++
# Function to calculate the mean and the standard deviation
  # for each group
#+++++++++++++++++++++++++
# data : a data frame
# varname : the name of a column containing the variable
  #to be summariezed
# groupnames : vector of column names to be used as
  # grouping variables
data_summary <- function(data, varname, groupnames){
  require(plyr)
  summary_func <- function(x, col){
    c(mean = mean(x[[col]], na.rm=TRUE),
      sd = sd(x[[col]], na.rm=TRUE))
  }
  data_sum<-ddply(data, groupnames, .fun=summary_func,
                  varname)
  data_sum <- rename(data_sum, c("mean" = varname))
 return(data_sum)
}

Summarize the data :

df3 <- data_summary(ToothGrowth, varname="len", 
                    groupnames=c("supp", "dose"))
# Convert dose to a factor variable
df3$dose=as.factor(df3$dose)
head(df3)

##   supp dose   len       sd
## 1   OJ  0.5 13.23 4.459709
## 2   OJ    1 22.70 3.910953
## 3   OJ    2 26.06 2.655058
## 4   VC  0.5  7.98 2.746634
## 5   VC    1 16.77 2.515309
## 6   VC    2 26.14 4.797731

The function geom_errorbar() can be used to produce a bar graph with error bars :

# Standard deviation of the mean as error bar
p <- ggplot(df3, aes(x=dose, y=len, fill=supp)) + 
   geom_bar(stat="identity", position=position_dodge()) +
  geom_errorbar(aes(ymin=len-sd, ymax=len+sd), width=.2,
                 position=position_dodge(.9))
  
p + scale_fill_brewer(palette="Paired") + theme_minimal()

Customized barplots

# Change color by groups
# Add error bars
p + labs(title="Plot of length  per dose", 
         x="Dose (mg)", y = "Length")+
   scale_fill_manual(values=c('black','lightgray'))+
   theme_classic()

Change fill colors manually :

# Greens
p + scale_fill_brewer(palette="Greens") + theme_minimal()
# Reds
p + scale_fill_brewer(palette="Reds") + theme_minimal()

Infos

This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. 1.0.0)

Prepare the data

ToothGrowth data is used in the following examples.

# convert dose column from a numeric to a factor variable
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
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

Make sure that the variable dose is converted as a factor using the above R script.

Example of plot

library(ggplot2)
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot()
p

Change the main title and axis labels

Change plot titles by using the functions ggtitle(), xlab() and ylab() :

p + ggtitle("Plot of length \n by dose") +
  xlab("Dose (mg)") + ylab("Teeth length")

Note that, you can use \n to split long title into multiple lines.

Change plot titles using the function labs() as follow :

p +labs(title="Plot of length \n by dose",
        x ="Dose (mg)", y = "Teeth length")

It is also possible to change legend titles using the function labs():

# Default plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose))+
  geom_boxplot()
p
# Modify legend titles
p + labs(fill = "Dose (mg)")

Change the appearance of the main title and axis labels

Main title and, x and y axis labels can be customized using the functions theme() and element_text() as follow :

# main title
p + theme(plot.title = element_text(family, face, colour, size))
# x axis title 
p + theme(axis.title.x = element_text(family, face, colour, size))
# y axis title
p + theme(axis.title.y = element_text(family, face, colour, size))

The arguments below can be used for the function element_text() to change the appearance of the text :

# Default plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot() +
  ggtitle("Plot of length \n by dose") +
  xlab("Dose (mg)") + ylab("Teeth length")
p
# Change the color, the size and the face of
# the main title, x and y axis labels
p + theme(
plot.title = element_text(color="red", size=14, face="bold.italic"),
axis.title.x = element_text(color="blue", size=14, face="bold"),
axis.title.y = element_text(color="#993333", size=14, face="bold")
)

Remove x and y axis labels

It’s possible to hide the main title and axis labels using the function element_blank() as follow :

# Hide the main title and axis titles
p + theme(
  plot.title = element_blank(),
  axis.title.x = element_blank(),
  axis.title.y = element_blank())

Infos

This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. )

Prepare the data

ToothGrowth and mtcars data sets are used in the examples below.

# Convert dose and cyl columns from numeric to factor variables
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
mtcars$cyl <- as.factor(mtcars$cyl)
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

head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Make sure that the columns dose and cyl are converted as factor variables using the R script above.

Simple plots

library(ggplot2)
# Box plot
ggplot(ToothGrowth, aes(x=dose, y=len)) +geom_boxplot()
# scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()

Use a single color

# box plot
ggplot(ToothGrowth, aes(x=dose, y=len)) +
  geom_boxplot(fill='#A4A4A4', color="darkred")
# scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(color='darkblue')

Change colors by groups

# Box plot
bp<-ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose)) +
  geom_boxplot()
bp
# Scatter plot
sp<-ggplot(mtcars, aes(x=wt, y=mpg, color=cyl)) + geom_point()
sp

# Box plot
bp + scale_fill_hue(l=40, c=35)
# Scatter plot
sp + scale_color_hue(l=40, c=35)

# Box plot
bp + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Scatter plot
sp + scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))

# Box plot
bp + scale_fill_manual(breaks = c("2", "1", "0.5"), 
                       values=c("red", "blue", "green"))
# Scatter plot
sp + scale_color_manual(breaks = c("8", "6", "4"),
                        values=c("red", "blue", "green"))

# Box plot
bp + scale_fill_brewer(palette="Dark2")
# Scatter plot
sp + scale_color_brewer(palette="Dark2")

# Install
install.packages("wesanderson")
# Load
library(wesanderson)

library(wesanderson)
# Box plot
bp+scale_fill_manual(values=wes_palette(n=3, name="GrandBudapest"))
# Scatter plot
sp+scale_color_manual(values=wes_palette(n=3, name="GrandBudapest"))

Use gray colors

The functions to use are :

• scale_colour_grey() for points, lines, etc
• scale_fill_grey() for box plot, bar plot, violin plot, etc

# Box plot
bp + scale_fill_grey() + theme_classic()
# Scatter plot
sp + scale_color_grey() + theme_classic()

Change the gray value at the low and the high ends of the palette :

# Box plot
bp + scale_fill_grey(start=0.8, end=0.2) + theme_classic()
# Scatter plot
sp + scale_color_grey(start=0.8, end=0.2) + theme_classic()

Note that, the default value for the arguments start and end are : start = 0.2, end = 0.8

Continuous colors

The graph can be colored according to the values of a continuous variable using the functions :

• scale_color_gradient(), scale_fill_gradient() for sequential gradients between two colors
• scale_color_gradient2(), scale_fill_gradient2() for diverging gradients
• scale_color_gradientn(), scale_fill_gradientn() for gradient between n colors

# Color by qsec values
sp2<-ggplot(mtcars, aes(x=wt, y=mpg, color=qsec)) + geom_point()
sp2
# Change the low and high colors
# Sequential color scheme
sp2+scale_color_gradient(low="blue", high="red")
# Diverging color scheme
mid<-mean(mtcars$qsec)
sp2+scale_color_gradient2(midpoint=mid, low="blue", mid="white",
                     high="red", space ="Lab" )

set.seed(1234)
x <- rnorm(200)
# Histogram
hp<-qplot(x =x, fill=..count.., geom="histogram") 
hp
# Sequential color scheme
hp+scale_fill_gradient(low="blue", high="red")

# Scatter plot
# Color points by the mpg variable
sp3<-ggplot(mtcars, aes(x=wt, y=mpg, color=mpg)) + geom_point()
sp3
# Gradient between n colors
sp3+scale_color_gradientn(colours = rainbow(5))

Infos

This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. 1.0.0)

Data

ToothGrowth data is used in the examples hereafter.

# Convert dose column from numeric to factor variable
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
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

Make sure that dose column are converted as a factor using the above R script.

Example of plots

library(ggplot2)
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + geom_boxplot()
p

Change the appearance of the axis tick mark labels

The color, the font size and the font face of axis tick mark labels can be changed using the functions theme() and element_text() as follow :

# x axis tick mark labels
p + theme(axis.text.x= element_text(family, face, colour, size))
# y axis tick mark labels
p + theme(axis.text.y = element_text(family, face, colour, size))

The following arguments can be used for the function element_text() to change the appearance of the text :

# Change the appearance and the orientation angle
# of axis tick labels
p + theme(axis.text.x = element_text(face="bold", color="#993333", 
                           size=14, angle=45),
          axis.text.y = element_text(face="bold", color="#993333", 
                           size=14, angle=45))

Hide x and y axis tick mark labels

axis ticks and tick mark labels can be removed using the function element_blank() as follow :

# Hide x an y axis tick mark labels
p + theme(
  axis.text.x = element_blank(),
  axis.text.y = element_blank())
# Remove axis ticks and tick mark labels
p + theme(
  axis.text.x = element_blank(),
  axis.text.y = element_blank(),
  axis.ticks = element_blank())

Change axis lines

Axis lines can be changed using the function element_line() as follow :

p + theme(axis.line = element_line(colour, size, linetype,
                                   lineend, color))

The arguments of element_line() are :

# Change the line type and color of axis lines
p + theme( axis.line = element_line(colour = "darkblue", 
                      size = 1, linetype = "solid"))

Set axis ticks for discrete and continuous axes

x or y axis can be discrete or continuous. In each of these two cases, the functions to be used for setting axis ticks are different.

scale_x_discrete(name, breaks, labels, limits)
scale_y_discrete(name, breaks, labels, limits)

# default plot
p
# Change the order of items
# Change the x axis name
p + scale_x_discrete(name ="Dose (mg)", 
                    limits=c("2","1","0.5"))

# Solution 1
p + scale_x_discrete(breaks=c("0.5","1","2"),
        labels=c("Dose 0.5", "Dose 1", "Dose 2"))
# Solution 2 : same plot as solution 1
p + scale_x_discrete(labels=c("0.5" = "Dose 0.5", "1" = "Dose 1",
                              "2" = "Dose 2"))

# Solution 1
p + scale_x_discrete(limits=c("0.5", "2"))
# Solution 2 : same result as solution 1
p + xlim("0.5", "2")

scale_x_continuous(name, breaks, labels, limits, trans)
scale_y_continuous(name, breaks, labels, limits, trans)

# scatter plot
sp<-ggplot(cars, aes(x = speed, y = dist)) + geom_point()
sp
# Change x and y axis labels, and limits
sp + scale_x_continuous(name="Speed of cars", limits=c(0, 30)) +
  scale_y_continuous(name="Stopping distance", limits=c(0, 150))

# Set tick marks on y axis
# a tick mark is shown on every 5
p + scale_y_continuous(breaks=seq(0,40,5))
# Tick marks can be spaced randomly
p + scale_y_continuous(breaks=c(5,7.5, 20, 25))
                     
# Remove tick mark labels and gridlines
p + scale_y_continuous(breaks=NULL)

library(scales)
# Format labels as percents
p + scale_y_continuous(labels = percent)
# Format labels as scientific
p + scale_y_continuous(labels = scientific)

Infos

This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. )

Prepare the data

ToothGrowth data sets are used :

# Convert the variable dose from a numeric to a factor variable
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
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

Make sure that the variable dose is converted as a factor variable using the above R script.

Basic box plots

library(ggplot2)
# Basic box plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_boxplot()
p
# Rotate the box plot
p + coord_flip()
# Notched box plot
ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_boxplot(notch=TRUE)
# Change outlier, color, shape and size
ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_boxplot(outlier.colour="red", outlier.shape=8,
                outlier.size=4)

The function stat_summary() can be used to add mean points to a box plot :

# Box plot with mean points
p + stat_summary(fun.y=mean, geom="point", shape=23, size=4)

Choose which items to display :

p + scale_x_discrete(limits=c("0.5", "2"))

Box plot with dots

Dots (or points) can be added to a box plot using the functions geom_dotplot() or geom_jitter() :

# Box plot with dot plot
p + geom_dotplot(binaxis='y', stackdir='center', dotsize=1)
# Box plot with jittered points
# 0.2 : degree of jitter in x direction
p + geom_jitter(shape=16, position=position_jitter(0.2))

Change box plot colors by groups

# Change box plot line colors by groups
p<-ggplot(ToothGrowth, aes(x=dose, y=len, color=dose)) +
  geom_boxplot()
p

# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey() + theme_classic()

# Use single color
ggplot(ToothGrowth, aes(x=dose, y=len)) +
  geom_boxplot(fill='#A4A4A4', color="black")+
  theme_classic()
# Change box plot colors by groups
p<-ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose)) +
  geom_boxplot()
p

# Use custom color palettes
p+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# use brewer color palettes
p+scale_fill_brewer(palette="Dark2")
# Use grey scale
p + scale_fill_grey() + theme_classic()

Change the legend position

p + theme(legend.position="top")
p + theme(legend.position="bottom")
p + theme(legend.position="none") # Remove legend

The allowed values for the arguments legend.position are : “left”,“top”, “right”, “bottom”.

Read more on ggplot legend : ggplot2 legend

Change the order of items in the legend

The function scale_x_discrete can be used to change the order of items to “2”, “0.5”, “1” :

p + scale_x_discrete(limits=c("2", "0.5", "1"))

Box plot with multiple groups

# Change box plot colors by groups
ggplot(ToothGrowth, aes(x=dose, y=len, fill=supp)) +
  geom_boxplot()
# Change the position
p<-ggplot(ToothGrowth, aes(x=dose, y=len, fill=supp)) +
  geom_boxplot(position=position_dodge(1))
p

Change box plot colors and add dots :

# Add dots
p + geom_dotplot(binaxis='y', stackdir='center',
                 position=position_dodge(1))
# Change colors
p+scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))

Customized box plots

# Basic box plot
ggplot(ToothGrowth, aes(x=dose, y=len)) + 
  geom_boxplot(fill="gray")+
  labs(title="Plot of length per dose",x="Dose (mg)", y = "Length")+
  theme_classic()
# Change  automatically color by groups
bp <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose)) + 
  geom_boxplot()+
  labs(title="Plot of length  per dose",x="Dose (mg)", y = "Length")
bp + theme_classic()

Change fill colors manually :

# Continuous colors
bp + scale_fill_brewer(palette="Blues") + theme_classic()
# Discrete colors
bp + scale_fill_brewer(palette="Dark2") + theme_minimal()
# Gradient colors
bp + scale_fill_brewer(palette="RdBu") + theme_minimal()

Read more on ggplot2 colors here : ggplot2 colors

Infos

This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. 1.0.0)

Data

ToothGrowth data is used in the examples below :

# Convert the variable dose from numeric to factor variable
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
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

Make sure that the variable dose is converted as a factor variable using the above R script.

Example of plot

library(ggplot2)
p <- ggplot(ToothGrowth, aes(x=dose, y=len, fill=dose)) + 
  geom_boxplot()
p

Change the legend position

The position of the legend can be changed using the function theme() as follow :

p + theme(legend.position="top")
p + theme(legend.position="bottom")

The allowed values for the arguments legend.position are : “left”,“top”, “right”, “bottom”.

Note that, the argument legend.position can be also a numeric vector c(x,y). In this case it is possible to position the legend inside the plotting area. x and y are the coordinates of the legend box. 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.

p + theme(legend.position = c(0.8, 0.2))

Change the legend title and text font styles

# legend title
p + theme(legend.title = element_text(colour="blue", size=10, 
                                      face="bold"))
# legend labels
p + theme(legend.text = element_text(colour="blue", size=10, 
                                     face="bold"))

Change the background color of the legend box

# legend box background color
p + theme(legend.background = element_rect(fill="lightblue", 
                                  size=0.5, linetype="solid"))
p + theme(legend.background = element_rect(fill="lightblue",
                                  size=0.5, linetype="solid", 
                                  colour ="darkblue"))

Change the order of legend items

To change the order of items to “2”, “0.5”, “1” :

p + scale_x_discrete(limits=c("2", "0.5", "1"))

Remove the plot legend

# Remove only the legend title
p + theme(legend.title = element_blank())
# Remove the plot legend
p + theme(legend.position='none')

Remove slashes in the legend of a bar plot

# Default plot
ggplot(data=ToothGrowth, aes(x=dose, fill=dose)) + geom_bar()
# Change bar plot border color, 
# but slashes are added in the legend
ggplot(data=ToothGrowth, aes(x=dose, fill=dose)) +
  geom_bar(colour="black")
# Hide the slashes: 
  #1. plot the bars with no border color,
  #2. plot the bars again with border color, but with a blank legend.
ggplot(data=ToothGrowth, aes(x=dose, fill=dose))+ 
  geom_bar() + 
  geom_bar(colour="black", show_guide=FALSE)

guides() : set or remove the legend for a specific aesthetic

It’s possible to use the function guides() to set or remove the legend of a particular aesthetic(fill, color, size, shape, etc).

mtcars data sets are used :

# Prepare the data : convert cyl and gear to factor variables
mtcars$cyl<-as.factor(mtcars$cyl)
mtcars$gear <- as.factor(mtcars$gear)
head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

p <- ggplot(data = mtcars, 
    aes(x=mpg, y=wt, color=cyl, size=qsec, shape=gear))+
    geom_point()
# Print the plot without guide specification
p

# Change the legend position
p +theme(legend.position="bottom")

# Horizontal legend box
p +theme(legend.position="bottom", legend.box = "horizontal")

p+guides(color = guide_legend(order=1),
         size = guide_legend(order=2),
         shape = guide_legend(order=3))

qplot(data = mpg, x = displ, y = cty, size = hwy,
      colour = cyl, shape = drv) +
  guides(colour = guide_colourbar(order = 1),
         alpha = guide_legend(order = 2),
         size = guide_legend(order = 3))

p+guides(color = FALSE, size = FALSE)

# Remove legend for the point shape
p+scale_shape(guide=FALSE)
# Remove legend for size
p +scale_size(guide=FALSE)
# Remove legend for color
p + scale_color_manual(values=c('#999999','#E69F00','#56B4E9'),
                       guide=FALSE)

Infos

This analysis has been performed using R software (ver. 3.1.0) and ggplot2 (ver. 1.0.0)

Prepare the data

The data below will be used :

set.seed(1234)
df <- data.frame(
  sex=factor(rep(c("F", "M"), each=200)),
  weight=round(c(rnorm(200, mean=55, sd=5), rnorm(200, mean=65, sd=5)))
  )
head(df)

##   sex weight
## 1   F     49
## 2   F     56
## 3   F     60
## 4   F     43
## 5   F     57
## 6   F     58

Basic histogram plots

library(ggplot2)
# Basic histogram
ggplot(df, aes(x=weight)) + geom_histogram()
# Change the width of bins
ggplot(df, aes(x=weight)) + 
  geom_histogram(binwidth=1)
# Change colors
p<-ggplot(df, aes(x=weight)) + 
  geom_histogram(color="black", fill="white")
p

Add mean line and density plot on the histogram

• The histogram is plotted with density instead of count on y-axis
• Overlay with transparent density plot. The value of alpha controls the level of transparency

# Add mean line
p+ geom_vline(aes(xintercept=mean(weight)),
            color="blue", linetype="dashed", size=1)
# Histogram with density plot
ggplot(df, aes(x=weight)) + 
 geom_histogram(aes(y=..density..), colour="black", fill="white")+
 geom_density(alpha=.2, fill="#FF6666") 

Read more on ggplot2 line types : ggplot2 line types

Change histogram plot line types and colors

# Change line color and fill color
ggplot(df, aes(x=weight))+
  geom_histogram(color="darkblue", fill="lightblue")
# Change line type
ggplot(df, aes(x=weight))+
  geom_histogram(color="black", fill="lightblue",
                 linetype="dashed")

Change histogram plot colors by groups

library(plyr)
mu <- ddply(df, "sex", summarise, grp.mean=mean(weight))
head(mu)

##   sex grp.mean
## 1   F    54.70
## 2   M    65.36

# Change histogram plot line colors by groups
ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white")
# Overlaid histograms
ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white", alpha=0.5, position="identity")

# Interleaved histograms
ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white", position="dodge")+
  theme(legend.position="top")
# Add mean lines
p<-ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white", position="dodge")+
  geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed")+
  theme(legend.position="top")
p

# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey() + theme_classic() +
  theme(legend.position="top")

# Change histogram plot fill colors by groups
ggplot(df, aes(x=weight, fill=sex, color=sex)) +
  geom_histogram(position="identity")
# Use semi-transparent fill
p<-ggplot(df, aes(x=weight, fill=sex, color=sex)) +
  geom_histogram(position="identity", alpha=0.5)
p
# Add mean lines
p+geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed")

# Use custom color palettes
p+scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
# use brewer color palettes
p+scale_color_brewer(palette="Dark2")+
  scale_fill_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey()+scale_fill_grey() +
  theme_classic()

Change the legend position

p + theme(legend.position="top")
p + theme(legend.position="bottom")
# Remove legend
p + theme(legend.position="none")

The allowed values for the arguments legend.position are : “left”,“top”, “right”, “bottom”.

Read more on ggplot legends : ggplot2 legends

Use facets

Split the plot into multiple panels :

p<-ggplot(df, aes(x=weight))+
  geom_histogram(color="black", fill="white")+
  facet_grid(sex ~ .)
p
# Add mean lines
p+geom_vline(data=mu, aes(xintercept=grp.mean, color="red"),
             linetype="dashed")

Read more on facets : ggplot2 facets

Customized histogram plots

# Basic histogram
ggplot(df, aes(x=weight, fill=sex)) +
  geom_histogram(fill="white", color="black")+
  geom_vline(aes(xintercept=mean(weight)), color="blue",
             linetype="dashed")+
  labs(title="Weight histogram plot",x="Weight(kg)", y = "Count")+
  theme_classic()
# Change line colors by groups
ggplot(df, aes(x=weight, color=sex, fill=sex)) +
  geom_histogram(position="identity", alpha=0.5)+
  geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed")+
  scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
  labs(title="Weight histogram plot",x="Weight(kg)", y = "Count")+
  theme_classic()

Combine histogram and density plots :

# Change line colors by groups
ggplot(df, aes(x=weight, color=sex, fill=sex)) +
geom_histogram(aes(y=..density..), position="identity", alpha=0.5)+
geom_density(alpha=0.6)+
geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
           linetype="dashed")+
scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))+
labs(title="Weight histogram plot",x="Weight(kg)", y = "Density")+
theme_classic()

Change line colors manually :

p<-ggplot(df, aes(x=weight, color=sex)) +
  geom_histogram(fill="white", position="dodge")+
  geom_vline(data=mu, aes(xintercept=grp.mean, color=sex),
             linetype="dashed")
# Continuous colors
p + scale_color_brewer(palette="Paired") + 
  theme_classic()+theme(legend.position="top")
# Discrete colors
p + scale_color_brewer(palette="Dark2") +
  theme_minimal()+theme_classic()+theme(legend.position="top")
# Gradient colors
p + scale_color_brewer(palette="Accent") + 
  theme_minimal()+theme(legend.position="top")

Read more on ggplot2 colors here : ggplot2 colors

Infos

This analysis has been performed using R software (ver. 3.1.2) and ggplot2 (ver. 1.0.0)

Prepare the data

mtcars data sets are used in the examples below.

# Convert cyl column from a numeric to a factor variable
mtcars$cyl <- as.factor(mtcars$cyl)
head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Basic scatter plots

Simple scatter plots are created using the R code below. The color, the size and the shape of points can be changed using the function geom_point() as follow :

geom_point(size, color, shape)

library(ggplot2)
# Basic scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()
# Change the point size, and shape
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point(size=2, shape=23)

Note that, the size of the points can be controlled by the values of a continuous variable as in the example below.

# Change the point size
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(aes(size=qsec))

Read more on point shapes : ggplot2 point shapes

Label points in the scatter plot

The function geom_text() can be used :

ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point() + 
  geom_text(label=rownames(mtcars))

Read more on text annotations : ggplot2 - add texts to a plot

geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95)

# Add the regression line
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth(method=lm)
# Remove the confidence interval
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE)
# Loess method
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth()

# Change the point colors and shapes
# Change the line type and color
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(shape=18, color="blue")+
  geom_smooth(method=lm, se=FALSE, linetype="dashed",
             color="darkred")
# Change the confidence interval fill color
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(shape=18, color="blue")+
  geom_smooth(method=lm,  linetype="dashed",
             color="darkred", fill="blue")

Scatter plots with multiple groups

This section describes how to change point colors and shapes automatically and manually.

# Change point shapes by the levels of cyl
ggplot(mtcars, aes(x=wt, y=mpg, shape=cyl)) +
  geom_point()
# Change point shapes and colors
ggplot(mtcars, aes(x=wt, y=mpg, shape=cyl, color=cyl)) +
  geom_point()
# Change point shapes, colors and sizes
ggplot(mtcars, aes(x=wt, y=mpg, shape=cyl, color=cyl, size=cyl)) +
  geom_point()

# Add regression lines
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm)
# Remove confidence intervals
# Extend the regression lines
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)

ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, aes(fill=cyl))

# Change point shapes and colors manually
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  scale_shape_manual(values=c(3, 16, 17))+ 
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
  theme(legend.position="top")
  
# Change the point sizes manually
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl))+
  geom_point(aes(size=cyl)) + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  scale_shape_manual(values=c(3, 16, 17))+ 
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
  scale_size_manual(values=c(2,3,4))+
  theme(legend.position="top")

p <- ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  theme_classic()
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey()

Add marginal rugs to a scatter plot

The function geom_rug() can be used :

geom_rug(sides ="bl")

sides : a string that controls which sides of the plot the rugs appear on. Allowed value is a string containing any of “trbl”, for top, right, bottom, and left.

# Add marginal rugs
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point() + geom_rug()
# Change colors
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl)) +
  geom_point() + geom_rug()
# Add marginal rugs using faithful data
ggplot(faithful, aes(x=eruptions, y=waiting)) +
  geom_point() + geom_rug()

Scatter plots with the 2d density estimation

The functions geom_density_2d() or stat_density_2d() can be used :

# Scatter plot with the 2d density estimation
sp <- ggplot(faithful, aes(x=eruptions, y=waiting)) +
  geom_point()
sp + geom_density_2d()
# Gradient color
sp + stat_density_2d(aes(fill = ..level..), geom="polygon")
# Change the gradient color
sp + stat_density_2d(aes(fill = ..level..), geom="polygon")+
  scale_fill_gradient(low="blue", high="red")

Read more on ggplot2 colors here : ggplot2 colors

Scatter plots with ellipses

The function stat_ellipse() can be used as follow:

# One ellipse arround all points
ggplot(faithful, aes(waiting, eruptions))+
  geom_point()+
  stat_ellipse()
# Ellipse by groups
p <- ggplot(faithful, aes(waiting, eruptions, color = eruptions > 3))+
  geom_point()
p + stat_ellipse()
# Change the type of ellipses: possible values are "t", "norm", "euclid"
p + stat_ellipse(type = "norm")

Scatter plots with rectangular bins

The number of observations is counted in each bins and displayed using any of the functions below :

• geom_bin2d() for adding a heatmap of 2d bin counts
• stat_bin_2d() for counting the number of observation in rectangular bins
• stat_summary_2d() to apply function for 2D rectangular bins

The simplified formats of these functions are :

plot + geom_bin2d(...)
plot+stat_bin_2d(geom=NULL, bins=30)
plot + stat_summary_2d(geom = NULL, bins = 30, fun = mean)

• geom : geometrical object to display the data
• bins : Number of bins in both vertical and horizontal directions. The default value is 30
• fun : function for summary

The data sets diamonds from ggplot2 package is used :

head(diamonds)

##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48

# Plot
p <- ggplot(diamonds, aes(carat, price))
p + geom_bin2d()

Change the number of bins :

# Change the number of bins
p + geom_bin2d(bins=10)

Or specify the width of bins :

# Or specify the width of bins
p + geom_bin2d(binwidth=c(1, 1000))

Scatter plot with marginal density distribution plot

Step 1/3. Create some data :

set.seed(1234)
x <- c(rnorm(500, mean = -1), rnorm(500, mean = 1.5))
y <- c(rnorm(500, mean = 1), rnorm(500, mean = 1.7))
group <- as.factor(rep(c(1,2), each=500))
df <- data.frame(x, y, group)
head(df)

##             x          y group
## 1 -2.20706575 -0.2053334     1
## 2 -0.72257076  1.3014667     1
## 3  0.08444118 -0.5391452     1
## 4 -3.34569770  1.6353707     1
## 5 -0.57087531  1.7029518     1
## 6 -0.49394411 -0.9058829     1

Step 2/3. Create the plots :

# scatter plot of x and y variables
# color by groups
scatterPlot <- ggplot(df,aes(x, y, color=group)) + 
  geom_point() + 
  scale_color_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position=c(0,1), legend.justification=c(0,1))
scatterPlot
# Marginal density plot of x (top panel)
xdensity <- ggplot(df, aes(x, fill=group)) + 
  geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position = "none")
xdensity
# Marginal density plot of y (right panel)
ydensity <- ggplot(df, aes(y, fill=group)) + 
  geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position = "none")
ydensity

Create a blank placeholder plot :

blankPlot <- ggplot()+geom_blank(aes(1,1))+
  theme(plot.background = element_blank(), 
   panel.grid.major = element_blank(),
   panel.grid.minor = element_blank(), 
   panel.border = element_blank(),
   panel.background = element_blank(),
   axis.title.x = element_blank(),
   axis.title.y = element_blank(),
   axis.text.x = element_blank(), 
   axis.text.y = element_blank(),
   axis.ticks = element_blank()
     )

Step 3/3. Put the plots together:

To put multiple plots on the same page, the package gridExtra can be used. Install the package as follow :

install.packages("gridExtra")

Arrange ggplot2 with adapted height and width for each row and column :

library("gridExtra")
grid.arrange(xdensity, blankPlot, scatterPlot, ydensity, 
        ncol=2, nrow=2, widths=c(4, 1.4), heights=c(1.4, 4))

Read more on how to arrange multiple ggplots in one page : ggplot2 - Easy way to mix multiple graphs on the same page

Customized scatter plots

# Basic scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth(method=lm, color="black")+
  labs(title="Miles per gallon \n according to the weight",
       x="Weight (lb/1000)", y = "Miles/(US) gallon")+
  theme_classic()  
# Change color/shape by groups
# Remove confidence bands
p <- ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  labs(title="Miles per gallon \n according to the weight",
       x="Weight (lb/1000)", y = "Miles/(US) gallon")
p + theme_classic()  

Change colors manually :

# Continuous colors
p + scale_color_brewer(palette="Paired") + theme_classic()
# Discrete colors
p + scale_color_brewer(palette="Dark2") + theme_minimal()
# Gradient colors
p + scale_color_brewer(palette="Accent") + theme_minimal()

Read more on ggplot2 colors here : ggplot2 colors

Infos

This analysis has been performed using R software (ver. 3.2.4) and ggplot2 (ver. 2.1.0)