Python中如何绘制R的科研级可视化图

rpy2# pip install rpy2   我的R语言版本为 4.1.3 ，需要rpy2 3.5.X版本去对应

确保你电脑已安装 R，并在 R 里装好相关包：# install.packages(c("ggplot2", "ggstatsplot", "patchwork","ggpmisc"))

install.packages("https://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_3.4.2.tar.gz",
                 repos = NULL, type = "source")

options(repos = c(CRAN = "https://cran.microsoft.com/snapshot/2022-08-15/"))

install.packages("ggstatsplot")

options(repos = c(CRAN = "https://packagemanager.rstudio.com/cran/__linux__/focal/2022-08-15"))

options(repos = c(CRAN = "https://packagemanager.posit.co/cran/2022-08-15"))

ro.r('Sys.setenv(LANGUAGE="en")')

import pandas as pd
import rpy2.robjects as ro
ro.r('Sys.setenv(LANGUAGE="en")')
from rpy2.robjects import pandas2ri
from rpy2.robjects.packages import importr
pandas2ri.activate()
# 生成一些三组数据
df = pd.DataFrame({
    'score': [85, 90, 88, 92, 87, 70, 75, 72, 68, 71, 55, 60, 58, 62, 59],
    'method': ['A']*5 + ['B']*5 + ['C']*5
})
ro.globalenv['df'] = df
ro.r('''
library(ggplot2)
library(ggstatsplot)
p <- ggbetweenstats(
  data = df,
  x = method,
  y = score,
  type = "parametric",
  title = "Comparison of Teaching Methods",
  caption = "Auto: F-value, p-value, eta^2"
)
ggsave("statsplot.png", p, width=8, height=5, dpi=300)
''')
# 用你的默认图片查看器打开
import os
os.startfile("statsplot.png")

import os
import pandas as pd
import rpy2.robjects as ro
ro.r('Sys.setenv(LANGUAGE="en")')
from rpy2.robjects import pandas2ri
pandas2ri.activate()
# 直接拿 ggplot2 里的 mpg 数据
ro.globalenv['mpg'] = ro.r('ggplot2::mpg')
ro.r('''
library(ggplot2)
library(patchwork)
p1 <- ggplot(mpg, aes(displ, hwy)) + 
        geom_point(aes(color = class)) + 
        geom_smooth(se = FALSE) + 
        theme_minimal()
p2 <- ggplot(mpg, aes(class)) + 
        geom_bar(fill = "steelblue") + 
        coord_flip() + 
        theme_minimal()
p3 <- ggplot(mpg, aes(cty, hwy)) + 
        geom_point(alpha = 0.3) + 
        facet_wrap(~ year) + 
        theme_minimal()
combined <- (p1 / p2) | p3 + 
  plot_annotation(title = "汽车数据多维度展示", tag_levels = 'A')
ggsave("patchwork_demo.png", combined, width=12, height=7, dpi=300)
''')
os.startfile("patchwork_demo.png")

import os
import pandas as pd
import rpy2.robjects as ro
ro.r('Sys.setenv(LANGUAGE="en")')
from rpy2.robjects import pandas2ri
pandas2ri.activate()
# 生成模拟数据
df = pd.DataFrame({
    'x': [1,2,3,4,5]*3,
    'y': [2,4,6,8,10, 1,4,7,10,13, 3,5,7,9,11],
    'group': ['线性A']*5 + ['线性B']*5 + ['线性C']*5
})
ro.globalenv['df'] = df
ro.r('''
library(ggplot2)
library(ggpmisc)  # 提供 geom_text_npc 等标注函数
p <- ggplot(df, aes(x, y, color = group)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = TRUE, alpha = 0.2) +
  facet_wrap(~ group, scales = "free") +
  stat_poly_eq(
    aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~")),
    formula = y ~ x, 
    parse = TRUE
  ) +
  theme_minimal(base_size = 14) +
  labs(title = "分面自动标注回归方程和 R²")
ggsave("facet_annotation.png", p, width=10, height=4, dpi=300)
''')
os.startfile("facet_annotation.png")

mport os
import pandas as pd
import Rplot
# 生成模拟数据
df = pd.DataFrame({
    'x': [1,2,3,4,5]*3,
    'y': [2,4,6,8,10, 1,4,7,10,13, 3,5,7,9,11],
    'group': ['线性A']*5 + ['线性B']*5 + ['线性C']*5
})
Rtext='''
library(ggplot2)
library(ggpmisc)  # 提供 geom_text_npc 等标注函数
p <- ggplot(Data, aes(x, y, color = group)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm", se = TRUE, alpha = 0.2) +
  facet_wrap(~ group, scales = "free") +
  stat_poly_eq(
    aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~")),
    formula = y ~ x, 
    parse = TRUE
  ) +
  theme_minimal(base_size = 14) +
  labs(title = "分面自动标注回归方程和 R²")
ggsave("Output.png", p, width=10, height=4, dpi=300)
'''
Rplot.PyRplot(Data=df,Rtext=Rtext)
os.startfile("Output.png")

import pandas as pd
import rpy2.robjects as ro
ro.r('Sys.setenv(LANGUAGE="en")')
from rpy2.robjects import pandas2ri
pandas2ri.activate()
ro.globalenv['mpg'] = ro.r('ggplot2::mpg')
def PyRplot(Data,Rtext):
    if not isinstance(Data, pd.DataFrame):
        data = pd.DataFrame(Data)
    ro.globalenv['Data'] = Data
    ro.r(Rtext)
    ro.r('rm(Data)')  # 用后即删，保持环境干净