rpy2 does not convert back to pandas - r

Question

I have an R object that will not convert to Pandas, and the strange part is that it doesn't throw an error.
Updated with the code I'm using, sorry not to supply that up front -- and to miss the request for 2 weeks!
Python code that calls an R script
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(1970,1,1,0,0) + 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)
The issue is that pd_from_r_df returns an R object rather than a Pandas dataframe:
>>> 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]
Here's the R script "farrington.R", which returns a surveillance time series, which ro.conversion.rpy2py isn't (as used above) converting to a pandas dataframe
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
(using the NCHS data here [from a couple months back] https://data.cdc.gov/NCHS/Weekly-counts-of-death-by-jurisdiction-and-cause-o/u6jv-9ijr/ )

Answer 1

In R, when calling source() by default on a script without named functions, the returned object is a list of two named components, $value and $visible, where:
$value is the last displayed or defined object which in your case is the far_df data frame (which in R data.frame is a class object extending list type);
$visible is a boolean vector indicating if last object was displayed or not which in your case is TRUE. This would be FALSE had you ended script at far_df <- tidy.sts(surveil_ts_4_far).
In fact, your Python error confirms this output indicatating a list of [ListSexpVector, BoolSexpVector].
Therefore, since you only want the first item, index for first item accordingly by number or name.
r_raw = ro.r['source']('farrington.R') # IN R: r_raw <- source('farrington.R')
r_df = r_raw[0] # IN R: r_df <- r_raw[1]
r_df = r_raw[r_raw.names.index('value')] # IN R: r_df <- r_raw$value
with localconverter(ro.default_converter + pandas2ri.converter):
pd_from_r_df = ro.conversion.rpy2py(r_df)