rpy2不会转换回熊猫

问题描述

我有一个R对象，该对象不会转换为Pandas，奇怪的是它不会引发错误。

已更新了我正在使用的代码，很抱歉不要提前提供该代码-并会在2周内错过请求！

调用R脚本的Python代码

import pandas as pd
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
import datetime
from rpy2.robjects.conversion import localconverter


def serial_date_to_string(srl_no):
    new_date = datetime.datetime(1970,1,0) + datetime.timedelta(srl_no - 1)
    return new_date.strftime("%Y-%m-%d")

jurisdiction='TX'
r=ro.r
r_df=r['source']('farrington.R')

with localconverter(ro.default_converter + pandas2ri.converter):
    pd_from_r_df = ro.conversion.rpy2py(r_df)

问题是pd_from_r_df返回R对象而不是Pandas数据帧：

>>> pd_from_r_df
R object with classes: ('list',) mapped to:
[ListSexpVector,BoolSexpVector]
  value: <class 'rpy2.rinterface.ListSexpVector'>
  <rpy2.rinterface.ListSexpVector object at 0x7faa4c4eff08> [RTYPES.VECSXP]
  visible: <class 'rpy2.rinterface.BoolSexpVector'>
  <rpy2.rinterface.BoolSexpVector object at 0x7faa4c4e7948> [RTYPES.LGLSXP]

这是R脚本“ farrington.R”，该脚本返回一个监视时间序列，ro.conversion.rpy2py并未（如上所述）转换为熊猫数据帧

library('surveillance')
library(readr)
library(tidyr)
library(dplyr)
w<-1
b<-3
nfreq<-52
steps_back<- 28
alpha<-0.05

counts <- read_csv("Weekly_counts_of_death_by_jurisdiction_and_cause_of_death.csv")
counts<-counts[,!colnames(counts) %in% c('Cause Subgroup','Time Period','Suppress','Note','Average Number of Deaths in Time Period','Difference from 2015-2019 to 2020','Percent Difference from 2015-2019 to 2020')]
wide_counts_by_cause<-pivot_wider(counts,names_from='Cause Group',values_from='Number of Deaths',values_fn=(`Cause Group`=sum))
wide_state <- filter(wide_counts_by_cause,`State Abbreviation`==jurisdiction)
wide_state <- filter(wide_state,Type=='Unweighted')
wide_state[is.na(wide_state)] <-0
important_columns=c('Alzheimer disease and dementia','Cerebrovascular diseases','Heart failure','Hypertensive dieases','Ischemic heart disease','Other diseases of the circulatory system','Malignant neoplasms','Diabetes','Renal failure','Sepsis','Chronic lower respiratory disease','Influenza and pneumonia','Other diseases of the respiratory system','Residual (all other natural causes)')

all_columns <- append(c('Year','Week'),important_columns)

selected_wide_state<-wide_state[,names(wide_state) %in% all_columns]
start<-c(as.numeric(min(selected_wide_state[,'Year'])),as.numeric(min(selected_wide_state[,'Week'])))
freq<-as.numeric(max(selected_wide_state[,'Week']))

sts <- new("sts",epoch=1:nrow(numeric_wide_state),start=start,freq=freq,observed=numeric_wide_state)
sts_4 <- aggregate(sts[,important_columns],nfreq=nfreq)
start_idx=end_idx-steps_back

cntrlFar <- list(range=start_idx:end_idx,w==w,b==b,alpha==alpha)
surveil_ts_4_far <- farrington(sts_4,control=cntrlFar)
far_df<-tidy.sts(surveil_ts_4_far)
far_df

（使用[几个月前] https://data.cdc.gov/NCHS/Weekly-counts-of-death-by-jurisdiction-and-cause-o/u6jv-9ijr/处的NCHS数据）

解决方法

在R中，默认情况下在没有命名函数的脚本上调用source()时，返回的对象是两个命名组件$value和$visible的列表，其中：

• $value是最后显示或定义的对象，在您的情况下是far_df数据框（在R data.frame中是扩展了list类型的类对象）；
• $visible是一个布尔向量，指示是否显示了上一个对象（在您的情况下为TRUE）。如果您在FALSE处结束脚本，则应该是far_df <- tidy.sts(surveil_ts_4_far)。

实际上，您的Python错误确认此输出指示了[ListSexpVector,BoolSexpVector]的列表。

因此，由于只需要第一项，因此请按编号或名称为第一项建立索引。

r_raw = ro.r['source']('farrington.R')        # IN R: r_raw <- source('farrington.R')
r_df  = r_raw[0]                              # IN R: r_df  <- r_raw[1]
r_df  = r_raw[r_raw.names.index('value')]     # IN R: r_df  <- r_raw$value

with localconverter(ro.default_converter + pandas2ri.converter):
    pd_from_r_df = ro.conversion.rpy2py(r_df)