| R | Python |
|---|
Arithmetic operators | | Code Block |
|---|
x + y
x - y
x * y
x / y
x ^ y |
| | Code Block |
|---|
x + y
x - y
x * y
x / y
x ** y |
|
The only difference is that R uses ^ for exponentiation, while Python uses **. |
Comparison operators | | Code Block |
|---|
# R and Python
x == y
x != y
x > y
x < y
x >= y
x <= y |
|
No differences here. |
Logical operators | | Code Block |
|---|
x && y
x || y
!x
v1 & v2
v1 | v2 |
| | Code Block |
|---|
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, | Code Block |
|---|
| 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 | | Code Block |
|---|
| 1:10
seq(1, 20, 2)
print(1:10) |
| | Code Block |
|---|
| 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 | | Code Block |
|---|
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")
} |
| | Code Block |
|---|
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 | | Code Block |
|---|
for (i in 1:5) {
print(i)
} |
| | Code Block |
|---|
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 | | Code Block |
|---|
my_function <- function(x, y) {
return(x + y)
} |
| | Code Block |
|---|
def my_function(x, y):
return x + y
|
|
|
Indexing | | |
R uses 1-based indexing. Python uses 0-based indexing. R is actually the oddball here. Most modern languages use 0-based indexing. |
Slicing | | Code Block |
|---|
my_list <- c(1, 2, 3, 4, 5)
print(my_list[2:4]) # Outputs 2 3 4 |
| | Code Block |
|---|
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 | | Code Block |
|---|
# This is a comment |
|
No difference. |
Libraries | | Code Block |
|---|
| library(dplyr)
library(terra)
library(sf)
library(exactextractr) |
| | Code Block |
|---|
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(). |