reticulate: R对python的接口包

介绍1

reticulate2 是用于Python和R之间协同操作的全套工具,在RRstudio中均可使用;要求Rstudio必须在1.2版本以上;

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install.packages("reticulate")
library(reticulate)

特性

  • reticulate 在R中支持多种方式调用python;
  • 实现R和python对象之间的转换;
  • 随意切换不同版本的python;
  • R内使用$调用python对象;
  • python内使用.调用R对象;
  • 使用import函数导入python模块import("os");
  • source_python()获取任何Python脚本;
  • 使用repl_python()交互使用python;

范例

Rpython对同一数据进行可视化,可视化图形包括scatterplotboxplotbarplotheatmap

散点图

R代码

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library(dplyr)
library(ggplot2)

iris %>% mutate(Species=factor(Species, levels = c("setosa", "versicolor", "virginica"))) %>%
ggplot(aes(x=Sepal.Width, y=Petal.Width, color=Species))+
geom_point()+
guides(color=guide_legend("", keywidth = .5, keyheight = .5))+
labs(title = 'Scatter plot')+
theme_bw()+
scale_color_manual(values = c("red", "green", "blue"))+
theme(plot.title = element_text(size = 10, color = "black", face = "bold", hjust = 0.5),
axis.title = element_text(size = 10, color = "black", face = "bold"),
axis.text = element_text(size = 9, color = "black"),
text = element_text(size = 8, color = "black"),
strip.text = element_text(size = 9, color = "black", face = "bold"),
panel.grid = element_blank(),
legend.position = c(1, 1),
legend.justification = c(1, 1),
legend.background = element_rect(fill="white", color = "black"))

Python代码

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dat = r.iris  # Python调用R内嵌数据使用r.data
species_map = {'setosa':1, 'versicolor':2, 'virginica':3}
dat['Species'] = dat['Species'].map(species_map)

import numpy as np
import matplotlib.pyplot as plt
# plt.scatter(dat['Sepal.Width'], dat['Petal.Width'], c=dat['Species'],
# alpha=0.8, edgecolors='none', s=30, label=["1", "2", "3"])
# plt.title('Scatter plot in iris')
# plt.xlabel('Sepal.Width (cm)')
# plt.ylabel('Petal.Width (cm)')
# plt.legend(loc=1)
# plt.show()

dat1 = (np.array(dat[dat.Species==1]['Sepal.Width']),
np.array(dat[dat.Species==1]['Petal.Width']))
dat2 = (np.array(dat[dat.Species==2]['Sepal.Width']),
np.array(dat[dat.Species==2]['Petal.Width']))
dat3 = (np.array(dat[dat.Species==3]['Sepal.Width']),
np.array(dat[dat.Species==3]['Petal.Width']))

mdat = (dat1, dat2, dat3)
colors = ("red", "green", "blue")
groups = ("setosa", "versicolor", "virginica")

# step1 build figure background
fig = plt.figure()

# step2 build axis
ax = fig.add_subplot(1, 1, 1, facecolor='1.0')

# step3 build figure
for data, color, group in zip(mdat, colors, groups):
x, y = data
ax.scatter(x, y, alpha=0.8, c=color,
edgecolors='none', s=30, label=group)

plt.title('Scatter plot')
plt.legend(loc=1)

# step4 show figure in the screen
plt.show()

箱形图

R代码

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library(dplyr)
library(ggplot2)

iris %>% mutate(Species=factor(Species, levels = c("setosa", "versicolor", "virginica"))) %>%
ggplot(aes(x=Species, y=Sepal.Width, fill=Species))+
stat_boxplot(geom = "errorbar", width = .12)+
geom_boxplot(width = .3, outlier.shape = 3, outlier.size = 1)+
guides(fill=guide_legend(NULL, keywidth = .5, keyheight = .5))+
xlab("")+
theme_bw()+
scale_fill_manual(values = c("red", "green", "blue"))+
theme(plot.title = element_text(size = 10, color = "black", face = "bold", hjust = 0.5),
axis.title = element_text(size = 10, color = "black", face = "bold"),
axis.text = element_text(size = 9, color = "black"),
text = element_text(size = 8, color = "black"),
strip.text = element_text(size = 9, color = "black", face = "bold"),
panel.grid = element_blank(),
legend.position = c(1, 1),
legend.justification = c(1, 1),
legend.background = element_rect(fill="white", color = "black"))

Python代码

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dat = r.iris  # Python调用R内嵌数据使用r.data
species_map = {'setosa':1, 'versicolor':2, 'virginica':3}
dat['Species'] = dat['Species'].map(species_map)

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

dat11 = (np.array(dat[dat.Species==1]['Sepal.Width']))
dat21 = (np.array(dat[dat.Species==2]['Sepal.Width']))
dat31 = (np.array(dat[dat.Species==3]['Sepal.Width']))

mdat2 = (dat11, dat21, dat31)
colors = ("red", "green", "blue")
groups = ("setosa", "versicolor", "virginica")

fig = plt.figure()
axes = fig.add_subplot(facecolor='1.0')
bplot = axes.boxplot(mdat2, patch_artist=True, notch=0, sym='+', vert=1, whis=1.5,
whiskerprops = dict(linestyle='--',linewidth=1.2, color='black'))

# color
for patch, color in zip(bplot['boxes'], colors):
patch.set_facecolor(color)

# axes labels
plt.setp(axes, xticks=[1,2,3],
xticklabels=["setosa", "versicolor", "virginica"])

red_patch = mpatches.Patch(color='red', label='setosa')
green_patch = mpatches.Patch(color='green', label='versicolor')
blue_patch = mpatches.Patch(color='blue', label='virginica')

plt.legend(handles=[red_patch, green_patch, blue_patch], loc=1)

plt.show()

条形图

R代码

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library(dplyr)
library(ggplot2)

iris %>% mutate(Species=factor(Species, levels = c("setosa", "versicolor", "virginica"))) %>%
select(Species, Sepal.Width) %>% group_by(Species) %>%
summarize(avg=mean(Sepal.Width), n=n(), sd=sd(Sepal.Width), se=sd/sqrt(n)) %>%
ungroup() %>%
ggplot(aes(x=Species, y=avg, fill=Species))+
geom_bar(stat="identity", width=.4, color="black")+
geom_errorbar(aes(ymin=avg-sd, ymax=avg+sd), width=.15,
position=position_dodge(.9), size=1)+
guides(fill=guide_legend(NULL, keywidth = .5, keyheight = .5))+
xlab("")+
ylab("Sepal.Width")+
scale_y_continuous(breaks=seq(0, 3.5,0.5), limits=c(0, 4.4),expand = c(0,0))+
theme_bw()+
scale_fill_manual(values = c("red", "green", "blue"))+
theme(axis.title = element_text(size = 10, color = "black", face = "bold"),
axis.text = element_text(size = 9, color = "black"),
text = element_text(size = 8, color = "black"),
strip.text = element_text(size = 9, color = "black", face = "bold"),
panel.grid = element_blank(),
legend.position = c(1, 1),
legend.justification = c(1, 1),
legend.background = element_rect(fill="white", color = "black"))

Python代码

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dat = r.iris  # Python调用R内嵌数据使用r.data
species_map = {'setosa':1, 'versicolor':2, 'virginica':3}
dat['Species'] = dat['Species'].map(species_map)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

mean = list(dat['Sepal.Width'].groupby(dat['Species']).mean())
sd = list(dat.groupby('Species').agg(np.std, ddof=0)['Sepal.Width'])

df = pd.DataFrame({'mean':mean}, index=["setosa", "versicolor", "virginica"])
df.plot(kind='bar', alpha=0.75, rot=0, edgecolor='black',
yerr=sd, align='center', ecolor='black', capsize=5,
color=("red", "green", "blue"),
ylim=(0.0, 4.4),
yticks=list(np.arange(0, 4.0, 0.5)))

# xlabel
plt.xlabel('')
plt.ylabel('Sepal.Width')

# legend
red_patch = mpatches.Patch(color='red', label='setosa')
green_patch = mpatches.Patch(color='green', label='versicolor')
blue_patch = mpatches.Patch(color='blue', label='virginica')
plt.legend(handles=[red_patch, green_patch, blue_patch], # color and group
loc=1, # location
prop={'size': 8}) # size
plt.show()

热图

R代码

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library(dplyr)
library(ggplot2)

get_upper_tri <- function(x){
x[upper.tri(x)] <- NA
return(x)
}

round(cor(mtcars[, c(1:7)], method = "spearman"), 2) %>%
get_upper_tri() %>% reshape2::melt(na.rm = TRUE) %>%

ggplot(aes(x=Var1, y=Var2, fill=value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab", name="Spearman\nCorrelation")+
theme_minimal()+
guides(fill = guide_colorbar(barwidth = 7, barheight = 1,
title.position = "top", title.hjust = 0.5))+
coord_fixed()+
geom_text(aes(label = value), color = "black", size = 4)+
theme(axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, size = 12, hjust = 1),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(0.6, 0.7),
legend.direction = "horizontal")

Python代码

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import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

corr = r.mtcars.corr()
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask)] = True

f, ax = plt.subplots(figsize=(6, 5))
heatmap = sns.heatmap(corr, vmin=-1, vmax=1, mask=mask, center=0,
# , orientation='horizontal'
cbar_kws=dict(shrink=.4, label='Spearman\nCorrelation', ticks=[-.8, -.4, 0, .4, .8]),
annot_kws={'size': 8, 'color': 'white'},
#cbar_kws = dict(use_gridspec=False,location="right"),
linewidths=.2, cmap = 'seismic', square=True, annot=True,
xticklabels=corr.columns.values,
yticklabels=corr.columns.values)

#add the column names as labels
ax.set_xticklabels(corr.columns, rotation = 45)
ax.set_yticklabels(corr.columns)
sns.set_style({'xtick.bottom': True}, {'ytick.left': True})

#heatmap.get_figure().savefig("heatmap.pdf", bbox_inches='tight')

plt.show()

心得

初次使用reticulate的感觉还不错,可以比较完美串联R和Python,尤其是在Rmarkdown文件内使用R和Python代码,但缺点也很明显:

  • 运行Python cell没有详细报错信息;
  • 粗略的报错提示行信息不以Rmd文件整体行作为开始;
  • 无法兼容带有汉字的注释信息;
  • 无法像R一样查看python环境下变量;
  • 出错后有时无任何报错信息

根据visual studio code的最新python插件公布情况看,以后vsc可以完美兼容Jupyter notebook格式文件,因此如果想单独使用python但无较好交互编辑器,可以使用vsc的python插件读取ipynb文件3

引用

1. https://zhuanlan.zhihu.com/p/35049732
2. https://github.com/rstudio/reticulate
3. https://code.visualstudio.com/docs/python/jupyter-support

参考文章如引起任何侵权问题,可以与我联系,谢谢。


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