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R

Python

Arithmetic operators

x + y
x - y
x * y
x / y
x ^ y
x + y
x - y
x * y
x / y
x ** y

The only difference is that R uses ^ for exponentiation, while Python uses **.

Comparison operators

# R and Python
x == y
x != y
x > y
x < y
x >= y
x <= y

No differences here.

Logical operators


x && y
x || y
!x
v1 & v2
v1 | v2
import numpy as np

x and y
x or y
not x
np.logical_and(v1, v2)
np.logical_or(v1, v2)

Python doesn’t have built-in logical operations for vectors.

numpy is a popular library for this purpose, but it’s not as clean as R’s syntax.

Assignment operator

name <- "John Doe"

name = "John Doe"

R does accept the = syntax for assignment. However, it’s non-idiomatic and can lead to unexpected errors.

For example, 

my_function <- function(y = 3, x = 5) {
  print(paste(y, x))
}

my_function(x = 10)   # Passes 10 to the x parameter, and outputs "3 10"

my_function(x <- 20)  # Assigns 20 to the global x variable, which it passes to the y parameter. Outputs "20 5"

To avoid confusion, always use <- for assignment, and use = for passing arguments within function calls.

Ranges

1:10
seq(1, 20, 2)
print(1:10)
range(1, 11)
range(1, 21, 2)
print(list(range(1, 11)))

Ouch. Python’s code is not only longer, but it excludes the final number.
range(1, 11) creates a range object in Python, which represents the numbers 1 through 10.

list(range(1, 11)) creates a list object in Python, which represents [1, 2, 3, … 10]
Both function similarly to the 1:10 vector in R. However, you need to change a range to a list in order to output it.

Conditional Statements

if (x > 5) {
  print("x is greater than 5")
} else if (x >= 2) {
  print("x is between 2 and 5")
} else {
  print("x is less than 2")
}
if x > 5:
    print("x is greater than 5")
elif x >= 2:
    print("x is between 2 and 5")
else:
    print("x is less than 2")

R requires the condition to be in parentheses. That would also work in Python, but it’s not required.

R begins a block statement with an opening curly brace ({), and it ends it with a closing curly brace (}).

Python is the oddball here, using a colon (:) to begin a block statement. It then uses indentation for the block’s statements. To end a block, you stop indenting.

This can take some getting used to, because very few computer languages have similar syntax.

Note that the recommended indentation for Python is 4 spaces, but consistency is all that matters.

For Loops

for (i in 1:5) {
  print(i)
}
for i in range(1, 6):
    print(i)

Note the similarity to the if statement comparisons above – particularly the absence of parentheses in the iteration expression, and the block syntax.

Functions

my_function <- function(x, y) {
  return(x + y)
}
def my_function(x, y):
    return x + y

In Python, the return statement does not require parentheses, unlike R.

Indexing

my_list[1]
my_list[0]

R uses 1-based indexing.

Python uses 0-based indexing.

R is actually the oddball here. Most modern languages use 0-based indexing.

Slicing

my_list <- c(1, 2, 3, 4, 5)
print(my_list[2:4])  # Outputs 2 3 4
my_list = [1, 2, 3, 4, 5]
print(my_list[1:4])  # Outputs [2, 3, 4]

Because Python uses 0-based indexing, the second element is at position 1 instead of 2.

Python supports similar start:end syntax to R. However, the end number is non-inclusive, meaning the slice stops just before the end index

Comments

# This is a comment

No difference.

Libraries

library(dplyr)
library(terra)
library(sf)
library(exactextractr)
import pandas as pd
import rasterio
import geopandas as gpd
from rasterstats import zonal_stats

For data manipulation and geospatial analysis, R and Python rely on external libraries.

You can alias libraries in Python using as, which simply saves typing when calling them in your program.

You can selectively import functions from a Python library using from. To do the same in R you need to use ::, such as dplyr::select().

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