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https://github.com/kassambara/ggpubr
'ggplot2' Based Publication Ready Plots
https://github.com/kassambara/ggpubr
Last synced: 3 months ago
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'ggplot2' Based Publication Ready Plots
- Host: GitHub
- URL: https://github.com/kassambara/ggpubr
- Owner: kassambara
- Created: 2016-07-19T19:35:48.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-08-24T09:40:01.000Z (10 months ago)
- Last Synced: 2024-01-20T15:43:21.917Z (5 months ago)
- Language: R
- Homepage: https://rpkgs.datanovia.com/ggpubr/
- Size: 83.7 MB
- Stars: 1,064
- Watchers: 33
- Forks: 162
- Open Issues: 213
-
Metadata Files:
- Readme: README.Rmd
Lists
- awesome-r-dataviz - ggpubr - 'ggplot2' Based Publication Ready Plots. (ggplot / Miscellaneous)
- awesome-stars - kassambara/ggpubr - 'ggplot2' Based Publication Ready Plots (R)
- awesome-stars - ggpubr - 'ggplot2' Based Publication Ready Plots (R)
- awesome-stars - kassambara/ggpubr - 'ggplot2' Based Publication Ready Plots (R)
README
---
output:
md_document:
variant: markdown_github
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "tools/README-",
warning = FALSE
)
```
[](https://github.com/kassambara/ggpubr/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=ggpubr)
[](https://cran.r-project.org/package=ggpubr)
[](https://cran.r-project.org/package=ggpubr)
# ggpubr: 'ggplot2' Based Publication Ready Plots
[ggplot2, by Hadley Wickham, ](https://ggplot2.tidyverse.org/) is an excellent and flexible package for elegant data visualization in R. However the default generated plots requires some formatting before we can send them for publication. Furthermore, to customize a ggplot, the syntax is opaque and this raises the level of difficulty for researchers with no advanced R programming skills.
The 'ggpubr' package provides some easy-to-use functions for creating and customizing 'ggplot2'- based publication ready plots.
Find out more at https://rpkgs.datanovia.com/ggpubr/.
## Installation and loading
- Install from [CRAN](https://cran.r-project.org/package=ggpubr) as follow:
```{r, eval = FALSE}
install.packages("ggpubr")
```
- Or, install the latest version from [GitHub](https://github.com/kassambara/ggpubr) as follow:
```{r, eval = FALSE}
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
```
## Distribution
```{r ggpubr, fig.width=5.3, fig.height=5, warning=FALSE}
library(ggpubr)
# Create some data format
# :::::::::::::::::::::::::::::::::::::::::::::::::::
set.seed(1234)
wdata = data.frame(
sex = factor(rep(c("F", "M"), each=200)),
weight = c(rnorm(200, 55), rnorm(200, 58)))
head(wdata, 4)
# Density plot with mean lines and marginal rug
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom palette
ggdensity(wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", fill = "sex",
palette = c("#00AFBB", "#E7B800"))
# Histogram plot with mean lines and marginal rug
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline and fill colors by groups ("sex")
# Use custom color palette
gghistogram(wdata, x = "weight",
add = "mean", rug = TRUE,
color = "sex", fill = "sex",
palette = c("#00AFBB", "#E7B800"))
```
## Box plots and violin plots
```{r ggpubr-box-plot-dot-plots-strip-charts, fig.width=5.3, fig.height=5,}
# Load data
data("ToothGrowth")
df <- ToothGrowth
head(df, 4)
# Box plots with jittered points
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change outline colors by groups: dose
# Use custom color palette
# Add jitter points and change the shape by groups
p <- ggboxplot(df, x = "dose", y = "len",
color = "dose", palette =c("#00AFBB", "#E7B800", "#FC4E07"),
add = "jitter", shape = "dose")
p
# Add p-values comparing groups
# Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
p + stat_compare_means(comparisons = my_comparisons)+ # Add pairwise comparisons p-value
stat_compare_means(label.y = 50) # Add global p-value
# Violin plots with box plots inside
# :::::::::::::::::::::::::::::::::::::::::::::::::::
# Change fill color by groups: dose
# add boxplot with white fill color
ggviolin(df, x = "dose", y = "len", fill = "dose",
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
add = "boxplot", add.params = list(fill = "white"))+
stat_compare_means(comparisons = my_comparisons, label = "p.signif")+ # Add significance levels
stat_compare_means(label.y = 50) # Add global the p-value
```
## Bar plots
### Demo data set
Load and prepare data:
```{r}
# 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")])
```
### Ordered bar plots
Change the fill color by the grouping variable "cyl". Sorting will be done globally, but not by groups.
```{r ordered-bar-plots, fig.width=6, fig.height=5}
ggbarplot(dfm, x = "name", y = "mpg",
fill = "cyl", # change fill color by cyl
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 90 # Rotate vertically x axis texts
)
```
Sort bars inside each group. Use the argument **sort.by.groups = TRUE**.
```{r ordered-bar-plots-by-groups, fig.width=6, fig.height=6}
ggbarplot(dfm, x = "name", y = "mpg",
fill = "cyl", # change fill color by cyl
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "asc", # Sort the value in dscending order
sort.by.groups = TRUE, # Sort inside each group
x.text.angle = 90 # Rotate vertically x axis texts
)
```
### Deviation graphs
The deviation graph shows the deviation of quantitatives values to a reference value. In the R code below, we'll plot the mpg z-score from the mtcars dataset.
Calculate the z-score of the mpg data:
```{r}
# 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")])
```
Create an ordered barplot, colored according to the level of mpg:
```{r deviation-graphs, fig.width=6, fig.height=5}
ggbarplot(dfm, x = "name", y = "mpg_z",
fill = "mpg_grp", # change fill color by mpg_level
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "asc", # Sort the value in ascending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 90, # Rotate vertically x axis texts
ylab = "MPG z-score",
xlab = FALSE,
legend.title = "MPG Group"
)
```
Rotate the plot: use rotate = TRUE and sort.val = "desc"
```{r deviation-graphs-horizontal, fig.width=6.5, fig.height=6}
ggbarplot(dfm, x = "name", y = "mpg_z",
fill = "mpg_grp", # change fill color by mpg_level
color = "white", # Set bar border colors to white
palette = "jco", # jco journal color palett. see ?ggpar
sort.val = "desc", # Sort the value in descending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 90, # Rotate vertically x axis texts
ylab = "MPG z-score",
legend.title = "MPG Group",
rotate = TRUE,
ggtheme = theme_minimal()
)
```
## Dot charts
### Lollipop chart
Lollipop chart is an alternative to bar plots, when you have a large set of values to visualize.
Lollipop chart colored by the grouping variable "cyl":
```{r lollipop-chart, fig.width=7.5, fig.height=5}
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "ascending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
ggtheme = theme_pubr() # ggplot2 theme
)
```
- Sort in decending order. **sorting = "descending"**.
- Rotate the plot vertically, using **rotate = TRUE**.
- Sort the mpg value inside each group by using **group = "cyl"**.
- Set **dot.size** to 6.
- Add mpg values as label. **label = "mpg"** or **label = round(dfm$mpg)**.
```{r lollipop-chart-rotate, fig.width=5, fig.height=7.5}
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = TRUE, # Rotate vertically
group = "cyl", # Order by groups
dot.size = 6, # Large dot size
label = round(dfm$mpg), # Add mpg values as dot labels
font.label = list(color = "white", size = 9,
vjust = 0.5), # Adjust label parameters
ggtheme = theme_pubr() # ggplot2 theme
)
```
Deviation graph:
- Use y = "mpg_z"
- Change segment color and size: add.params = list(color = "lightgray", size = 2)
```{r lollipop-chart-deviation, fig.width=7.5, fig.height=5}
ggdotchart(dfm, x = "name", y = "mpg_z",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
add.params = list(color = "lightgray", size = 2), # Change segment color and size
group = "cyl", # Order by groups
dot.size = 6, # Large dot size
label = round(dfm$mpg_z,1), # Add mpg values as dot labels
font.label = list(color = "white", size = 9,
vjust = 0.5), # Adjust label parameters
ggtheme = theme_pubr() # ggplot2 theme
)+
geom_hline(yintercept = 0, linetype = 2, color = "lightgray")
```
### Cleveland's dot plot
Color y text by groups. Use y.text.col = TRUE.
```{r cleveland-dot-plots, fig.width=5, fig.height=7.5}
ggdotchart(dfm, x = "name", y = "mpg",
color = "cyl", # Color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"), # Custom color palette
sorting = "descending", # Sort value in descending order
rotate = TRUE, # Rotate vertically
dot.size = 2, # Large dot size
y.text.col = TRUE, # Color y text by groups
ggtheme = theme_pubr() # ggplot2 theme
)+
theme_cleveland() # Add dashed grids
```
## More
Find out more at https://rpkgs.datanovia.com/ggpubr/.
## Blog posts
- A. Kassambara. [ggpubr R Package: ggplot2-Based Publication Ready Plots](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/)