diff --git a/README.md b/README.md
index 2234191..f57d923 100644
--- a/README.md
+++ b/README.md
@@ -7,19 +7,20 @@
[](https://cran.r-project.org/package=ggVennDiagram)
[](https://cran.r-project.org/package=ggVennDiagram)
-[](https://cran.r-project.org/package=ggVennDiagram)
+[](https://doi.org/10.3389/fgene.2021.706907)
[](https://cran.r-project.org/package=ggVennDiagram)
-[](https://github.com/gaospecial/ggVennDiagram)
+[](https://github.com/gaospecial/ggVennDiagram)
[](https://codecov.io/gh/gaospecial/ggVennDiagram)
‘`ggVennDiagram`’ enables fancy Venn plot with 2-7 sets and generates
-publication quality figure.
+publication quality figure. It also support upset plot with unlimited
+number of sets from version 1.4.4.
## Installation
You can install the released version of ggVennDiagram from
-[CRAN](https://CRAN.R-project.org) with (under evaluation in CRAN):
+[CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("ggVennDiagram")
@@ -74,10 +75,10 @@ ggVennDiagram(x) + scale_color_brewer(palette = "Paired")
-`ggVennDiagram` now support 2-7 dimension Venn plot. The generated
-figure is generally ready for publish. The main function
-`ggVennDiagram()` will check how many items in the first parameter and
-call corresponding function automatically.
+`ggVennDiagram` support 2-7 dimension Venn plot. The generated figure is
+generally ready for publish. The main function `ggVennDiagram()` will
+check how many items in the first parameter and call corresponding
+function automatically.
The parameter `category.names` is set names. And the parameter `label`
can label how many items are included in each parts.
@@ -95,7 +96,7 @@ ggVennDiagram(x,category.names = c("Stage 1","Stage 2","Stage 3", "Stage4"), lab
-Set `label_alpha = 0` to remove label background
+Set `label_alpha = 0` to remove label background.
``` r
ggVennDiagram(x, label_alpha=0)
@@ -154,22 +155,16 @@ In web browser or RStudio, you will get:
# Customizing your plot
There are three components in a Venn plot: 1) the set labels; 2) the
-edge of sets; and 3) the filling regions of each parts. We separately
-stored these data in a structured S4 class `VennPlotData` object, in
-which labels, edges and regions are stored as simple features.
-
-Simple features or simple feature access refers to a formal standard
-(ISO 19125-1:2004) that describes how objects in the real world can be
-represented in computers, with emphasis on the spatial geometry of these
-objects. But here we employed this to describe the coordinates of Venn
-components.
+edge of sets; and 3) the filling regions and labels (optional) of each
+parts. We separately stored these data in a structured `VennPlotData`
+object, in which labels, edges and regions are stored as data frames.
In general, `ggVennDiagram()` plot a Venn in three steps:
- get the coordinates of a applicable shape from internal `shapes`
datasets.
- calculate sub regions of sets, including both the shape regions and
- sets members, and return a `PlotDataVenn` object that includes all
+ sets members, and return a `VennPlotData` object that includes all
necessary definitions. We implement a number of set operations
functions to do this job.
- plot using `ggplot2` functions.
@@ -260,13 +255,6 @@ upset_plot(venn, nintersects = 30, relative_height = 2, relative_width = 0.3)
# Futher information
-## Venn Diagram cookbook in R
-
-I have released a online book to introduce the development of
-`ggVennDiagram`, it contains a chapter that compare many different R
-packages in plotting a Venn. Refer to [Venn Diagram cookbook in
-R](https://venn.bio-spring.top).
-
## 公众号文章
在 *@GuangchuangYu* 的公众号下面，我投稿了两篇文章，介绍了
diff --git a/README.rmd b/README.rmd
index f373043..06e02e2 100644
--- a/README.rmd
+++ b/README.rmd
@@ -19,17 +19,17 @@ library("badger")
`r badge_cran_download("ggVennDiagram", type = "grand-total")`
`r badge_cran_download("ggVennDiagram", type = "last-month")`
-`r badge_cran_download("ggVennDiagram", type = "last-week")`
+`r badge_doi("10.3389/fgene.2021.706907", "blue")`
`r badge_cran_release("ggVennDiagram","green")`
`r badge_devel(color = "green")`
`r badge_codecov(token = "c365345d-e34c-40f6-b2c0-881b5b2841e7")`
-'`ggVennDiagram`' enables fancy Venn plot with 2-7 sets and generates publication quality figure.
+'`ggVennDiagram`' enables fancy Venn plot with 2-7 sets and generates publication quality figure. It also support upset plot with unlimited number of sets from version 1.4.4.
## Installation
-You can install the released version of ggVennDiagram from [CRAN](https://CRAN.R-project.org) with (under evaluation in CRAN):
+You can install the released version of ggVennDiagram from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("ggVennDiagram")
@@ -66,8 +66,6 @@ x <- list(A=sample(genes,300),
```
-
-
`ggVennDiagram` return a `ggplot` object, the fill/edge colors can be further modified with `ggplot` functions.
```{r}
@@ -77,7 +75,7 @@ ggVennDiagram(x) + scale_fill_gradient(low="blue",high = "red")
ggVennDiagram(x) + scale_color_brewer(palette = "Paired")
```
-`ggVennDiagram` now support 2-7 dimension Venn plot. The generated figure is generally ready for publish. The main function `ggVennDiagram()` will check how many items in the first parameter and call corresponding function automatically.
+`ggVennDiagram` support 2-7 dimension Venn plot. The generated figure is generally ready for publish. The main function `ggVennDiagram()` will check how many items in the first parameter and call corresponding function automatically.
The parameter `category.names` is set names. And the parameter `label` can label how many items are included in each parts.
@@ -87,7 +85,7 @@ ggVennDiagram(x,category.names = c("Stage 1","Stage 2","Stage 3", "Stage4"))
ggVennDiagram(x,category.names = c("Stage 1","Stage 2","Stage 3", "Stage4"), label = "none")
```
-Set `label_alpha = 0` to remove label background
+Set `label_alpha = 0` to remove label background.
```{r}
ggVennDiagram(x, label_alpha=0)
@@ -117,8 +115,6 @@ If only several items were included, intersections may also be viewed interactiv
ggVennDiagram(y, show_intersect = TRUE)
```
-
-
In web browser or RStudio, you will get:
```{r echo=FALSE}
@@ -128,17 +124,13 @@ knitr::include_graphics("https://vnote-1251564393.cos.ap-chengdu.myqcloud.com/ty
# Customizing your plot
There are three components in a Venn plot: 1) the set labels; 2) the edge of sets;
-and 3) the filling regions of each parts. We separately stored these data in a structured S4 class
-`VennPlotData` object, in which labels, edges and regions are stored as simple features.
-
-Simple features or simple feature access refers to a formal standard (ISO 19125-1:2004) that describes how objects in the real world can be represented in computers, with emphasis on the spatial geometry of these objects. But here we employed this to describe the coordinates of Venn
-components.
+and 3) the filling regions and labels (optional) of each parts. We separately stored these data in a structured `VennPlotData` object, in which labels, edges and regions are stored as data frames.
In general, `ggVennDiagram()` plot a Venn in three steps:
- get the coordinates of a applicable shape from internal `shapes` datasets.
- calculate sub regions of sets, including both the shape regions and sets members,
- and return a `PlotDataVenn` object that includes all necessary definitions.
+ and return a `VennPlotData` object that includes all necessary definitions.
We implement a number of set operations functions to do this job.
- plot using `ggplot2` functions.
@@ -229,12 +221,6 @@ upset_plot(venn, nintersects = 30, relative_height = 2, relative_width = 0.3)
# Futher information
-## Venn Diagram cookbook in R
-
-I have released a online book to introduce the development of `ggVennDiagram`,
-it contains a chapter that compare many different R packages in plotting a Venn.
-Refer to [Venn Diagram cookbook in R](https://venn.bio-spring.top).
-
## 公众号文章
在 *@GuangchuangYu* 的公众号下面，我投稿了两篇文章，介绍了 "`ggVennDiagram`" 包开发的始末。