Unfortunately, this is going to be difficult because the Python -> R transformation is better than it used to be, but isn't perfect, and is still hard on Windows currently, which it looks like you're using.
This is a bit of a hack, but as a work-around you might try setting the name and time variables while you are assigning the pd.DataFrame before you convert the DataFrame into R.
Once it's in R, you'll need to use R functions to operate on the data frame, rather than your python functions---even your getter and setter will need to be passed into the R environment in a way that looks more like this:
myfunct = robjects.r('''
f <- function(r, verbose=FALSE) {
if (verbose) {
cat("I am calling f().\n")
}
2 * pi * r
}
f(3)
''')
from here.
But just to check that your DataFrame is being converted appropriately in the first place, you might start your debugging by running this:
import pandas as pd
import numpy as np
import pandas.rpy.common as com
from datetime import datetime
n = 10
df = pd.DataFrame({
"timestamp": [datetime.now() for t in range(n)],
"value": np.random.uniform(-1, 1, n)
})
r_dataframe = com.convert_to_r_dataframe(df)
print(r_dataframe)
Is that producing something that looks like an R print statement of a dataframe, like so
>>> timestamp value
0 2014-06-03 15:02:20 -0.36672....
1 2014-06-03 15:02:20 -0.89136....
2 2014-06-03 15:02:20 0.509215....
3 2014-06-03 15:02:20 0.862909....
4 2014-06-03 15:02:20 0.389879....
5 2014-06-03 15:02:20 -0.80607....
6 2014-06-03 15:02:20 -0.97116....
7 2014-06-03 15:02:20 0.376419....
8 2014-06-03 15:02:20 0.848243....
9 2014-06-03 15:02:20 0.446798....