# Install packages
if (!requireNamespace("data.table", quietly = TRUE)) {
install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
install.packages("jsonlite")
}
if (!requireNamespace("patchwork", quietly = TRUE)) {
install.packages("patchwork")
}
if (!requireNamespace("ggplotify", quietly = TRUE)) {
install.packages("ggplotify")
}
if (!requireNamespace("cowplot", quietly = TRUE)) {
install.packages("cowplot")
}
# Load packages
library(data.table)
library(jsonlite)
library(patchwork)
library(ggplotify)
library(cowplot)Pie Group
Note
Hiplot website
This page is the tutorial for source code version of the Hiplot Pie Group plugin. You can also use the Hiplot website to achieve no code ploting. For more information please see the following link:
Setup
System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages:
data.table;jsonlite;patchwork;ggplotify;cowplot
sessioninfo::session_info("attached")β Session info βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
setting value
version R version 4.5.2 (2025-10-31)
os Ubuntu 24.04.3 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate C.UTF-8
ctype C.UTF-8
tz UTC
date 2026-01-27
pandoc 3.1.3 @ /usr/bin/ (via rmarkdown)
quarto 1.8.27 @ /usr/local/bin/quarto
β Packages βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
package * version date (UTC) lib source
cowplot * 1.2.0 2025-07-07 [1] RSPM
data.table * 1.18.0 2025-12-24 [1] RSPM
ggplotify * 0.1.3 2025-09-20 [1] RSPM
jsonlite * 2.0.0 2025-03-27 [1] RSPM
patchwork * 1.3.2 2025-08-25 [1] RSPM
[1] /home/runner/work/_temp/Library
[2] /opt/R/4.5.2/lib/R/site-library
[3] /opt/R/4.5.2/lib/R/library
* ββ Packages attached to the search path.
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Data Preparation
# Load data
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/pie-group/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)
# Convert data structure
data[,"genre"] <- factor(data[,"genre"], levels = unique(data[,"genre"]))
data[,"mpaa"] <- factor(data[,"mpaa"], levels = unique(data[,"mpaa"]))
# View data
head(data) title year length budget rating
1 Shawshank Redemption, The 1994 142 25 9.1
2 Lord of the Rings: The Return of the King, The 2003 251 94 9.0
3 Lord of the Rings: The Fellowship of the Ring, The 2001 208 93 8.8
4 Lord of the Rings: The Two Towers, The 2002 223 94 8.8
5 Pulp Fiction 1994 168 8 8.8
6 Schindler's List 1993 195 25 8.8
votes mpaa genre
1 149494 R Drama
2 103631 PG-13 Action
3 157608 PG-13 Action
4 114797 PG-13 Action
5 132745 R Drama
6 97667 R Drama
Visualization
# Pie Group
col <- c("#E64B35FF","#4DBBD5FF","#00A087FF","#3C5488FF","#F39B7FFF","#8491B4FF",
"#91D1C2FF","#DC0000FF","#7E6148FF","#B09C85FF")
plist <- list()
for (i in 1:length(unique(data[,"mpaa"]))) {
data_tmp <- data[data[,"mpaa"] == unique(data[,"mpaa"])[i],]
x <- table(data_tmp[,"genre"])
ptmp <- as.ggplot(function(){
par(oma=c(0,0,0,0))
pie(x,
labels = sprintf("%s\n(n=%s, %s%%)", names(x), x,
round(x / sum(x) * 100, 0)),
col = col,
main = paste0("mpaa", ":", unique(data[,"mpaa"])[i]),
edges = 200,
radius = 0.8,
clockwise = F
)
})
plist[[i]] <- ptmp
}
plot_grid(plotlist = plist, ncol = 2)
