Thanks to reticulate is it possible embedding a Python session within an R session. The Earth Engine Python API and rgee share the same modules, classes, functions, and methods. In other words, the logic of the syntax is the same and just as fast (just change . by a $). Notwithstanding, differences in the language design of R and Python might cause some problems in specific scenarios. We identify four bug-potential cases. Each of them is explained in-depth below.

1. The map message error:

This issue happens when the map method is used under the next two cases: (1) users employing a reticulate version lower than < 1.14 (please update it!); and (2) if you are leading with ee$List objects. For instance:

mylist = ee$List$sequence(10)
mylist$map(function(x) ee$Number(x)$add(1))
#> Error in py_call_impl(callable, dots$args, dots$keywords): RuntimeError: Evaluation error: argument "x" is missing, with no default.
#> Detailed traceback: 
#>   File "/home/aybarpc01/.virtualenvs/r-reticulate/lib/python3.7/site-packages/ee/", line 205, in <lambda>
#>     return lambda *args, **kwargs:*args, **kwargs)  # pylint: disable=unnecessary-lambda
#>   File "/home/aybarpc01/.virtualenvs/r-reticulate/lib/python3.7/site-packages/ee/", line 67, in call
#>     return self.apply(self.nameArgs(args, kwargs))
#>   File "/home/aybarpc01/.virtualenvs/r-reticulate/lib/python3.7/site-packages/ee/", line 80, in apply
#>     result = computedobject.ComputedObject(self, self.promoteArgs(named_args))
#>   File "/home/aybarpc01/.virtualenvs/r-reticulate/lib/python3.7/site-packages/ee/", line 107, in promoteArgs
#>     promoted_args[name] = Function._promoter(args[name], spec['type'])
#>   File "/home/aybarpc01/.virtualenvs/r-reticulate/lib/python3.7/site-packages/ee/", line 242, in _Promote
#>     return CustomFunction.create(arg, 'Object', ['Object'] * args_count)
#>   File "/home/aybarpc01/.virtualenvs/r-reticulate/lib/python3.7/site-packages/ee/", line 121, in create
#>     return CustomFunction(signature, func)
#>   File "/home/aybarpc01/.virtualenvs/r-reticulate/lib/python3.7/site-packages/ee/", line 47, in __init__
#>     self._body = body(*variables)
#>   File "/home/aybarpc01/R/x86_64-pc-linux-gnu-library/3.6/reticulate/python/rpytools/", line 21, in python_function
#>     raise RuntimeError(res[kErrorKey])

The code before is perfectly valid but rgee will produce an error. This problem should be easily solved by adding the function ee_utils_pyfunc. It will permit to wrap R functions before to send it to reticulate. Let’s see:

mylist = ee$List$sequence(0,10)
mynewlist = mylist$map(
    function(x) ee$Number(x)$add(1)   
#>  [1]  1  2  3  4  5  6  7  8  9 10 11

2. Do not forget the L

By default, when you define a number in R it will produce a double precision value. This does not happen in Python because, by default it will create a int value.


#> <class 'int'>


#> [1] "numeric"

But why does this matter? Let’s explain it with an example:


and_bitwise = ee.Number(32).bitwiseAnd(100)
#> 32


and_bitwise = ee$Number(32)$bitwiseAnd(100) #caution: silent error
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/aybarpc01/.local/lib/python3.7/site-packages/ee/", line 95, in getInfo
    return data.computeValue(self)
  File "/home/aybarpc01/.local/lib/python3.7/site-packages/ee/", line 490, in computeValue
    return send_('/value', ({'json': obj.serialize(), 'json_format': 'v2'}))
  File "/home/aybarpc01/.local/lib/python3.7/site-packages/ee/", line 1186, in send_
    raise ee_exception.EEException(json_content['error']['message'])
ee.ee_exception.EEException: Number.bitwiseAnd: Bitwise operands must be integer only.

Users need to take into consideration that most of the arguments of the Earth Engine methods are strict to admit only integer values. The creation of integers in R is quite simple, you just need to add the letter L to the end of the specific number or employ the function as.integer. The correct code in R would be:

and_bitwise = ee$Number(32L)$bitwiseAnd(100L)
#> [1] 32

3. Be careful with ee$Date

This problem also appears due to differences between the design of R and Python as programming languages. Currently, R only support integer data type of 32 bits. Such integers can only count up to about 2 billion. Unfortunately, this range is extremely insufficient to deal with