Jupyter Notebook: Python or R—Or Both?

import warnings
warnings.filterwarnings("ignore")

print("\n\n")

import sys

functdir = "c:/data/jupyter/notebooks/functions"
sys.path.append(functdir)

print("\n\n")

from myfuncs import *

blanks(2)

import os
import pandas as pd
import numpy as np

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owd = os.getcwd()

nwd = "c:/data/russell/potus"
os.chdir(nwd)

print(os.getcwd())

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urllst = [
"http://www.ftse.com/products/russell-index-values/home/getfile?id=valuesytd_US3000.csv",
"http://www.ftse.com/products/russell-index-values/home/getfile?id=valueshist_US3000.csv"
]

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ytd = pd.read_csv(urllst[0])
ytd.columns = ["portfolio","date","levwodiv","levwdiv"]
ytd['portfolio'] = "r3000"
ytd['date'] = pd.DatetimeIndex(ytd.date)

ytd = ytd.sort_values(['date']).iloc[1:,]

ytd.reset_index(drop=True,inplace=True)

prarr(ytd)

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hist = pd.read_csv(urllst[1])
hist.columns = ["portfolio","date","levwodiv","levwdiv"]
hist['portfolio'] = "r3000"
hist['date'] = pd.DatetimeIndex(hist.date)

hist.sort_values(['date'],inplace=True)

hist.reset_index(drop=True,inplace=True)

prarr(hist)

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combine = pd.concat([ytd,hist])
combine.sort_values(['date'],inplace=True)

print(combine.shape,"\n\n")
prarr(combine)

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combine.drop_duplicates(subset=['levwodiv','levwdiv'], inplace=True)

print(combine.shape,"\n\n")
prarr(combine)

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combine['pctwodiv'] = combine.levwodiv.pct_change()
combine['pctwdiv'] = combine.levwdiv.pct_change()

print(combine.shape,"\n\n")
prarr(combine)

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out = "r3000pd.csv"
combine.to_csv(out,index=None, sep=',')

print(round((1+combine.pctwodiv).prod(),2))
print(round((1+combine.pctwdiv).prod(),2))

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%load_ext rpy2.ipython

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import rpy2                    
import rpy2.robjects.numpy2ri  
import rpy2.robjects as robjects

robjects.pandas2ri.activate()

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r3000 = robjects.pandas2ri.py2ri(combine[['date','pctwdiv']])

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%R -i r3000 tail(r3000)

%R require(tidyverse); require(data.table); require(RColorBrewer); require(R.utils); require(lubridate)

%%R  -w700 -h700 -i r3000 

r3000 <- data.table(r3000)

mdte <- max(r3000[['date']])
dte <- substr(mdte,6,11)

tdates <- lubridate::date(paste(c("2019-","2011-","2015-"),dte,sep=""))
fdates <- lubridate::date(c("2017-01-20","2009-01-20","2013-01-21"))

#############################################
# function to build the data.table for ggplot.
#############################################

nmkreturn <- function(to,from,dt)
{
    
    rbind(
    data.table(potus='Trump',
           date=dt[date>=fdates[1] & date<=tdates[1]]$date,
           returnpct=dt[date>=fdates[1] & date<=tdates[1],cumprod(1+pctwdiv)]
          ),  
    data.table(potus='Obama 1',
           date=dt[date>=fdates[2] & date<=tdates[2]]$date,
           returnpct=dt[date>=fdates[2] & date<=tdates[2],cumprod(1+pctwdiv)]
          ),
    data.table(potus='Obama 2',
            date=dt[date>=fdates[3] & date<=tdates[3]]$date,
            returnpct=dt[date>=fdates[3] & date<=tdates[3],cumprod(1+pctwdiv)]
          )  
    )     

}

#########################################
# nwork is the final graphics data.table.
#########################################

work <- nmkreturn(tdates,fdates,r3000)
nwork <- data.table(left_join(work,work[,.(legend=paste(potus,date[1], date[.N], "\n",sep="\n")),.(potus)],by=c("potus"="potus")))



X <- nwork[,.(date[.N],returnpct=round(100*(returnpct[.N])-100)),.(potus)]$V1
Y <- nwork[,.(date[.N],returnpct=round(100*(returnpct[.N])-100)),.(potus)]$returnpct

###############################
# save data to an rds data set.
###############################

ofile = "r3000.rds"
save(r3000,X,work,nwork,file=ofile)
 
###########################################
# set parm vars and execute the ggplot code.
###########################################

titstr <- paste("Russell 3000 Returns", " thru ", mdte,sep="")
stitstr <- "Trump vs Obama\n"
xstr <- "\nYear"
ystr <- "Growth %\n"
cstr <- "Administration\n"

pal <- brewer.pal(9,"Blues")

g <- ggplot(nwork,aes(x=date,y=100*(returnpct-1), col=legend)) +
      geom_line(size=.7) +
      theme(legend.position = "right", plot.background = element_rect(fill = pal[2]), 
      panel.background = element_rect(fill = pal[2])) +
      theme(axis.text.x = element_text(size=10, angle = 45)) +
      labs(title=titstr,subtitle=stitstr,x=xstr,y=ystr,col=cstr) + 
      annotate("text", x = X+100*24*60*60, y = Y, label = Y, size=4) 
g