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This article describes how to create easily basic and ordered bar plots using ggplot2 based helper functions available in the ggpubr R package. We’ll also present some modern alternatives to bar plots, including lollipop charts and cleveland’s dot plots. Note that, the approach to build a bar plot, using ggplot2 standard verbs, has been described in our previous article available at: ggplot2 barplots : Quick start guide. You might be also interested by the following articles: Add P-values and Significance Levels to ggplots Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data Bar plots and modern alternatives Contents: Prerequisites Basic bar plots Multiple grouping variables Ordered bar plots Deviation graphs Alternatives to bar plots Infos Prerequisites Required R package You need to install the R package ggpubr (version >= 0.1.3), to easily create ggplot2-based publication ready plots. Install from CRAN: install.packages("ggpubr") Or, install the latest developmental version from GitHub as follow: if(!require(devtools)) install.packages("devtools") devtools::install_github("kassambara/ggpubr") Load ggpubr: library(ggpubr) Basic bar plots Create a demo data set: df dose len 1 D0.5 4.2 2 D1 10.0 3 D2 29.5 Basic bar plots: # Basic bar plots with label p 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 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 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 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).